An integrated approach towards increased energy efficiency of vehicle subsystems

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P

OLITECNICO DI

M

ILANO

D

EPARTMENT OF

M

ECHANICAL

E

NGINEERING

D

OCTORAL

P

ROGRAMME IN

M

ECHANICAL

E

NGINEERING

AN INTEGRATED APPROACH

TOWARDS INCREASED ENERGY

EFFICIENCY OF VEHICLE

SUBSYSTEMS

Doctoral Dissertation of:

Nikola Holjevac

Supervisor:

Prof. Massimiliano Gobbi Prof. Federico Cheli

Tutor:

Prof. Michele Monno

The Chair of the Doctoral Program:

Prof. Daniele Rocchi

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A

bstract

This thesis presents a methodology to assess vehicle performances from the very early concept phase and it is specifically addressed to the study of current and future powertrains. The powertrain development plays a crucial role in the car design and although standard technologies are widely adopted, rapid and abrupt changes are affecting future design solutions. Diesel scandal, stringent regulations and public awareness for more environmental friendly vehicles are stressing automotive manufacturers to provide more efficient cars and requiring therefore strong investments in the vehicle development. The work tackles these needs by strongly focusing on the analysis of the energy flows occurring in the powertrain and it faces the complexity of addressing the improvement of energy efficiency within a broader scenario concerning the vehicle development and the fulfilment of several requirements.

The development and adoption of simulation methods is extensively pursued to describe the vehicle’s system and to assess its performances. Specific approaches and models are chosen according to the desired accuracy and computational cost at each vehicle’s system level. At the subsystem level advanced techniques are used, the internal combustion engine model uses computational fluid dynamics while the electric motor relies on electromagnetic finite element method. The results are used at the vehicle level to define the corresponding lumped parameters models that are realised in order to face a multi-physic domain considering mechanical, thermal and electrical behaviour. Further lumped parameters models are used to define battery, inverter and transmission and are based on accurate mathematical formulations and data available from manufacturers and research centres. The components are coupled together and provide the complete vehicle model that is then used to estimate energy consumption and dynamic performances.

The analysis is restricted to conventional combustion engine, hybrid electric and battery electric vehicles that appear to be the most adopted solutions considering current and future market. The simulation procedure provides an effective way to evaluate the influence on energy consumption and dynamic performances of the wheel drive, transmission and installed electric motors layout, combustion engine and battery sizing, and also operating aspects concerning gear shifting strategy and power management in hybrid vehicles.

The optimisation problem requires the definition of design variables, constraints and objectives. The design variables set considers the most relevant design parameters of the powertrain components while constraints and objectives are computed through specific models. Besides energy consumption and longitudinal dynamics, the vehicle study is extended to capture other relevant requirements in order to meet more effectively the vehicle optimisation procedure. The energy consumption simulation model gives also range and emissions, the lateral dynamics model provides a broader outlook on the dynamic performances and the costs estimation allows to consider another relevant aspect. Besides these objectives, a “consumer” objective is also introduced to provide a simplified indicator of the overall vehicle behaviour and it is further used as a constraint to restrict the optimisation search toward solutions that provide acceptable values for all the objectives. Further constraints are introduced by considering pull away manoeuvre on uphill road, handling behaviour at low lateral acceleration and fulfilment of the driving tests. The design variables define the vehicle and are crucial to identify the most promising design solutions, however in order to improve the

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vehicle’s energy consumption it is also necessary to optimally manage the vehicle system in driving conditions. The use of efficiency maps to describe the vehicle components allows to define an optimisation procedure from the power source to the wheels to minimise the energy losses. This procedure applies to gear shifting scheduling, to power split between front and rear electric motors and between the combustion engine and the electric motor in hybrid electric vehicles. The multi-objective problem defined is solved by using the Archive-based Micro Genetic Algorithm (AMGA) which benefits from an archive to keep track of the search history and to store the best solutions allowing a faster convergence and a small population size.

The optimisation results are then analysed through various techniques to identify the most suitable solutions and highlight the impact of the design variables on the vehicle behaviour. This task is extremely relevant since multi-objective optimisation can provide a large set of solutions providing the best solution for a specific objective or trade-off solutions among different objectives. The provided approach and the techniques used allow to effectively compare and critically analyse the various powertrain layouts and the components design variables.

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List of Figures

FIGURE 1.EMISSIONS BY SECTOR AND TRANSPORTATION MODE IN EUROPE, ADAPTED FROM [2] ... 2

FIGURE 2.PASSENGER CARS AVERAGE CO2 EMISSION TREND BY BRAND FROM [7] ... 2

FIGURE 3.2016CO2 PERFORMANCE OF KEY EUROPEAN PASSENGER CAR MANUFACTURERS [7] ... 3

FIGURE 4.DIESEL SHARE OF NEW CAR REGISTRATIONS (IN %)[7] ... 3

FIGURE 5.ADOPTION OF SIMULATION-BASED (A) AND INTEGRATION OF CONTROLS (B) IN THE PRODUCT DEVELOPMENT [9] ... 4

FIGURE 6.SCHEMATIC REPRESENTATION OF THE INITIAL PHASES OF THE PDP[11] ... 8

FIGURE 7.NEW PASSENGER CAR SALES IN 2016[7] ... 9

FIGURE 8.2016CAR MARKET SHARE IN EUROPE BY TECHNOLOGY, COUNTRY OF REGISTRATION, AND BRAND [7] ... 10

FIGURE 9.NEW ELECTRIC CAR SALES IN 2016[6] ... 10

FIGURE 10.EVOLUTION OF THE GLOBAL ELECTRIC CAR STOCK,2010-16[19] ... 11

FIGURE 11.CO2 EMISSIONS OF NEW PASSENGER CARS [7] ... 11

FIGURE 12.PAN-AREA (LENGTH × WIDTH)(A) AND MASS (B) TREND BY SEGMENT [7] ... 12

FIGURE 13.ENGINE POWER (A) AND DISPLACEMENT (B) TREND BY SEGMENT [7] ... 13

FIGURE 14.ENGINE SPECIFIC TORQUE VS DISPLACEMENT (A) AND CONSUMPTION VS ACCELERATION (B) ... 13

FIGURE 15.NUMBER OF CYLINDERS ADOPTED ACCORDING TO ENGINE DISPLACEMENT (A) AND VEHICLE PERFORMANCES (B) ... 14

FIGURE 16.CONSUMPTION VS ACCELERATION BY TRANSMISSION TYPE ... 14

FIGURE 17.NUMBER OF GEARS TREND [7] ... 15

FIGURE 18.2016 ELECTRIC VEHICLE SALES SHARE AND PUBLIC CHARGE POINTS PER MILLION POPULATION IN MAJOR NATIONAL MARKETS [21] ... 16

FIGURE 19.PHEV ANALYSIS: PLUG-IN DESIGN EFFECTIVENESS (A) AND BATTERY INFLUENCE (B) ON FUEL CONSUMPTION ... 17

FIGURE 20.EV ANALYSIS: RANGE VS BATTERY RATE (A) AND CONSUMPTION VS ACCELERATION (B) ... 18

FIGURE 21.PASSENGER CAR AERODYNAMIC DRAG COEFFICIENT TREND FOR DIFFERENT CARS, DATA FROM [25] ... 19

FIGURE 22.CROSS-SECTION AREA BY SEGMENT ... 19

FIGURE 23.TIRE ROLLING RESISTANCE TREND [33] ... 20

FIGURE 24.VEHICLE'S MASS BREAKDOWN [36] ... 21

FIGURE 25.FORESIGHT OF FUTURE ADOPTION OF LIGHTWEIGHT MATERIALS [34]... 21

FIGURE 26.FROM CONCEPT TO SERIES PRODUCTION:BMWGINA(2008),BMWVISION EFFICIENT DYNAMICS (IAA2009) AND BMW I8 (2014) ... 22

FIGURE 27.SIMULATION INTEGRATION IN VEHICLE DEVELOPMENT, ADAPTED FROM [39] ... 24

FIGURE 28.VISIO.M, ELECTRIC VEHICLE CONCEPT BY TUM(2014) ... 25

FIGURE 29.DEEP ORANGE 7,BMWMINI CONCEPT BY CLEMSON (2017) ... 26

FIGURE 30.INTERNAL COMBUSTION ENGINE MODEL IN DYMOLA® ... 29

FIGURE 31.ENGINE MODELING WORKFLOW ... 30

FIGURE 32.COMBUSTION IGNITION TIMING OPTIMISATION CONSIDERING MAXIMUM PRESSURE VS EFFICIENCY, Η,(A) AND MAP DEFINITION WITH CRANKANGLE DEFINED ACCORDING TO TOP DEAD CENTER (TDC)(B) ... 32

FIGURE 33.COMBUSTION FRACTION, XB,(A), RATE, DXB/DΑ,(B) AND CYLINDER PRESSURE, P, RESPECTIVELY AS FUNCTION OF CRANKANGLE, Α, (C) AND NORMALISED VOLUME, V/VMAX(D) FOR VARIOUS ENGINE SPEEDS ... 32

FIGURE 34.ENGINE MODEL IN RICARDO WAVE®_{... 34}

FIGURE 35.ENGINE FRICTION LOSSES DEPENDING ON SPEED (A) AND LOAD (B) ... 35

FIGURE 36.ENGINE OIL TEMPERATURE (A) AND INFLUENCE ON CONSUMPTION (B) FOR A 2.0L ENGINE DURING WLTP ... 36

FIGURE 37.VWEA888GEN 3ENGINE BSFC COMPARISON: REAL DATA (A) VS SIMULATION RESULTS (B) ... 37

FIGURE 38.ELECTRIC TRACTION SYSTEM MODEL IN DYMOLA® ... 39

FIGURE 39.BATTERY SOC(A) AND RATIO BETWEEN VDC AND OCV(B) DURING WLTP ... 41

FIGURE 40.ELECTRIC MOTOR GEOMETRY DEFINITION IN MOTOR-CAD®_{... 42}

FIGURE 41.CALCULATION OF ELECTROMAGNETIC QUANTITIES THROUGH FEM IN MOTOR-CAD® ... 42

FIGURE 42.EM EFFICIENCY MAP:PMSM(A) AND AIM(B) ... 43

FIGURE 43.PMSM ENERGY LOSS DISTRIBUTION: COPPER (A), IRON (B), MAGNETS (C) AND MECHANICAL (D) ... 43

FIGURE 44.AIM ENERGY LOSS DISTRIBUTION: COPPER (A), IRON (B), STRAY LOAD (C) AND MECHANICAL (D) ... 44

FIGURE 45.PMSM PARAMETERS: VOLTAGE (A), CURRENT (B) AND POWER FACTOR (C) ... 44

FIGURE 46.AIM PARAMETERS: VOLTAGE (A), CURRENT (B) AND POWER FACTOR (C) ... 45

FIGURE 47.EM CONTROLLERS:PMSM CURRENT PHASE ADVANCE (A) AND AIM SLIP (B) ... 45

FIGURE 48.BMW I3 EFFICIENCY MAPS: DOWN SCALING (A), NOMINAL (B), UP SCALING (C) ... 46

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FIGURE 50.EM ANALYSIS: EFFICIENCY VS POWER (A) AND VS SPECIFIC POWER (B) FOR VARIOUS DESIGN SOLUTIONS ... 47

FIGURE 51.GEARBOX (RATIO_G) AND DIFFERENTIAL (RATIO_D) GEAR RATIO FOR ICE VEHICLES (A) AND BEVS (B) ... 49

FIGURE 52.TRANSMISSION MODEL IN DYMOLA® ... 50

FIGURE 53.DATA FITTING OF VEHICLE MASS (A) AND DRAG COEFFICIENT (C) VS ENGINE DISPLACEMENT, ESTIMATED PARAMETERS OF VEHICLE’S FRONTAL AREA (B) AND TIRE’S ROLLING RESISTANCE AND FRICTION COEFFICIENT (D) VS ENGINE DISPLACEMENT ... 52

FIGURE 54.SIMULATION RESULTS OF THE NUMBER OF GEARSHIFTS FOR ICE VEHICLES (SEE TABLE 12) IN WLTP DRIVING CYCLE ... 53

FIGURE 55.GEAR SHIFTING STRATEGY IN WLTP: GEARS TIME HISTORY (A), ENGINE OPERATING CONDITIONS (B) AND SHIFTING SCHEDULING ACCORDING TO VEHICLE DYNAMICS (C)(FUEL CUT-OFF OPERATING CONDITIONS REMOVED FROM ENGINE EFFICIENCY) ... 53

FIGURE 56.BATTERY TEMPERATURE INFLUENCE ON VEHICLE ACCELERATION PERFORMANCE ... 54

FIGURE 57.REGENERATIVE BRAKING DISTRIBUTION IN WLTP AND PERCENTAGE OF BRAKING ENERGY RECOVERED ... 54

FIGURE 58.VEHICLE SPEED (A) AND ERROR (B) FOR ICE VEHICLE AND BEV SIMULATIONS ... 55

FIGURE 59.WORKFLOW DEVELOPED IN ISIGHT®TO EVALUATE THE VEHICLE ... 56

FIGURE 60.ICE VEHICLE (SEE TABLE 12) RESULTS: FUEL CONSUMPTION, TOP SPEED AND ACCELERATION ... 57

FIGURE 61.ICE VEHICLES ENGINE AND GEARBOX OIL MEAN TEMPERATURE ... 58

FIGURE 62.ICE VEHICLES ENERGY SHARE: VEHICLE (A) AND VEHICLE WITHOUT ENGINE (B) ... 58

FIGURE 63.ICE VEHICLE ENERGY LOSSES SHARE DURING WLTP(A) AND FOR THE SPECIFIC WLTP SECTIONS W/O THE ENGINE CONTRIBUTIONS (B) ... 59

FIGURE 64.BEV(SEE TABLE 13) RESULTS: ENERGY CONSUMPTION, TOP SPEED AND ACCELERATION ... 60

FIGURE 65.BEV(SEE TABLE 13) REGRESSION RESULTS: ENERGY CONSUMPTION, TOP SPEED AND ACCELERATION ... 60

FIGURE 66.BEVS ENERGY SHARE: COMPONENTS (A) AND SUBSYSTEMS (B) ... 61

FIGURE 67.BEV ENERGY LOSSES SHARE DURING WLTP(A) AND FOR THE SPECIFIC WLTP SECTIONS CONTRIBUTIONS (B) ... 61

FIGURE 68.BEVS RANGE BASED ON WLTP DRIVING CYCLE ... 62

FIGURE 69.POWER CONTROLLER AND STATE OF CHARGE VARIATION IN WLTP FOR FIXED LOH ... 63

FIGURE 70.POWER CONTROLLER AND STATE OF CHARGE VARIATION IN WLTP FOR VARIOUS LOH ... 63

FIGURE 71.COMPARISON BETWEEN ICEVS,BEVS AND HEVS FOR DIFFERENT LOH ... 64

FIGURE 72.COMPARISON BETWEEN ICEVS,BEVS AND HEVS BY VARYING THE ALLOWED BATTERY DISCHARGE ... 64

FIGURE 73.ICE VEHICLES CORRELATION GRAPHS: SIMULATION RESULTS (BLACK) AND POLYNOMIAL REGRESSION (GREEN) ... 65

FIGURE 74.ICE VEHICLES CORRELATION TABLE ... 66

FIGURE 75.BEVS WITH SINGLE EM CORRELATION GRAPHS: SIMULATION RESULTS (BLACK) AND POLYNOMIAL REGRESSION (GREEN) ... 66

FIGURE 76.CORRELATION TABLE FOR BEVS... 67

FIGURE 77.ICE VEHICLES LIGHTWEIGHT SENSITIVITY ANALYSIS ... 67

FIGURE 78.BEV LIGHTWEIGHT SENSITIVITY ANALYSIS ... 68

FIGURE 79.ENGINE BSFC COMPARISON: REAL DATA (A) VS SIMULATION RESULTS (B) ... 73

FIGURE 80.ICE SCALABLE MODEL: PARAMETER IDENTIFICATION ... 74

FIGURE 81.VEHICLE LAYOUTS:ICEV(A),HEVP (B),HEVS (C) AND BEV(D)(RED: COMBUSTION ENGINE, YELLOW: ELECTRIC MOTOR, ORANGE: GEAR COMPONENTS)... 76

FIGURE 82.GEAR SHIFTING CONTROL STRATEGY FOR 6-SPEED ICE GEARBOX ... 76

FIGURE 83.GEAR SHIFTING CONTROL STRATEGY FOR 2-SPEED EM GEARBOX ... 77

FIGURE 84.POWER SPLIT BETWEEN FRONT (110 KW) AND REAR (140 KW) ELECTRIC MOTOR IN WLTP DRIVING CYCLE ... 77

FIGURE 85.HEV RECHARGING MODE STRATEGY:HEVP COMPARISON OF ICE EFFICIENCY MAPS,ICEV VS HEVP, AND HEVS MATCHING BETWEEN ICE(A) AND GEN(B) ... 78

FIGURE 86.HEVP OPERATING MODE UTILISATION ... 79

FIGURE 87.WLTP DRIVE CYCLE STATISTICAL ANALYSIS ... 80

FIGURE 88.OBJECTIVES FRONTIERS: COST-CONSUMPTION (A), DYNAMICS-CONSUMPTION (B), DYNAMICS-COST (C) AND CONSUMER-COST (D) ... 85

FIGURE 89.SPECIFIC VEHICLE PERFORMANCES:CO2 EMISSION VS VEHICLE MASS (A) AND RANGE VS VEHICLE POWER (B) ... 86

FIGURE 90.CO2 EMISSIONS FOR ICEV,HEVP AND HEVS CALCULATED BY USING THE UNECER101 FORMULA ... 86

FIGURE 91.SPIDER GRAPH OF CLOSEST UTOPIA POINTS ... 87

FIGURE 92.MAIN DESIGN VARIABLES PROBABILITY DISTRIBUTION:ICE DISPLACEMENT (A), LEVEL OF HYBRIDISATION (B), BATTERY NOMINAL POWER RATE (C) AND POWER SPLIT BETWEEN ELECTRIC MOTORS (D) ... 88

FIGURE 93.LEVEL OF HYBRIDISATION, POWER SPLIT AND VEHICLE MAXIMUM POWER INFLUENCE OF MAXIMUM ACHIEVABLE CONSUMPTION (A, B, C) AND DYNAMICS (D, E, F) OBJECTIVES ... 89

FIGURE 94.ICEV FUEL CONSUMPTION VS 0-100 KM/H ACCELERATION WITH RESPECT TO ICE DISPLACEMENT (A), NUMBER OF CYLINDERS (B) AND GEARBOX (C) ... 89

FIGURE 95.BEV ENERGY CONSUMPTION VS 0-100 KM/H ACCELERATION WITH RESPECT TO NUMBER OF MOTORS (A), MOTOR TYPE (B) AND GEARBOX (C) ... 90

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FIGURE 97.BATTERY MAXIMUM CURRENT VS MAXIMUM VOLTAGE FOR HEVP,HEVS AND BEV ACCORDING TO BATTERY NOMINAL POWER

LEVELS ... 91

FIGURE 98.REGION OF POWER COVERED (A) AND OPTIMAL CONSUMPTION REACHABLE (B) VS LOH IN THE OPTIMISATION FOR HEVP AND HEVS ... 92

FIGURE 99.INFLUENCE OF LOH ON FUEL CONSUMPTION AND ACCELERATION PERFORMANCE IN CS DRIVING MODE ... 93

FIGURE 100.FUEL CONSUMPTION IN CS DRIVING MODE DEPENDENCY ON LOH ... 93

FIGURE 101.ICEV DESIGN VARIABLES REQUIREMENTS’ FULFILLMENT LEVEL ... 95

FIGURE 102.HEVP DESIGN VARIABLES REQUIREMENTS’ FULFILLMENT LEVEL ... 95

FIGURE 103.HEVS DESIGN VARIABLES REQUIREMENTS’ FULFILLMENT LEVEL ... 96

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List of Tables

TABLE 1.INTERNAL COMBUSTION ENGINE MODEL ... 29

TABLE 2.ENGINE DATA STATISTICS (VALUES RANGE) ... 31

TABLE 3.IGNITION TIMING MAP ... 31

TABLE 4.ENGINE 1D-CFD COMPONENTS SIZING ... 33

TABLE 5.MECHANICAL FRICTION MODEL ... 35

TABLE 6.VOLKSWAGEN 2.0L MAIN ENGINE INPUT DATA ... 36

TABLE 7.ELECTRIC TRACTION SYSTEM MODEL ... 37

TABLE 8.ELECTRIC MOTORS SCALING PROCEDURE ... 46

TABLE 9.TRANSMISSION LAYOUTS ... 48

TABLE 10.GEAR RATIOS DESIGN METHODS ... 49

TABLE 11.LIST OF POWER LOSS MODELS FOR TRANSMISSION COMPONENTS ... 51

TABLE 12.ICE VEHICLE LAYOUTS FOR DOE ANALYSIS ... 57

TABLE 13.BEV LAYOUTS FOR DOE ANALYSIS ... 59

TABLE 14.LIST OF HEVS SIMULATED ... 62

TABLE 15.ENGINE DATA STATISTICS (VALUES RANGE) ... 73

TABLE 16.ELECTRIC MOTORS SCALING COEFFICIENTS ... 75

TABLE 17.VEHICLE DYNAMICS MODELS ... 79

TABLE 18.MODEL INITIALISATION DESIGN INPUTS ... 81

TABLE 19.OBJECTIVES AND CONSTRAINTS ... 82

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1. Introduction

Energy demand growth, environmental issues and future fuel availability are strongly influencing the global scenario and leading to deep changes in shaping future society. Transportation and in particular road mobility of passengers and goods play a major role in energy share frame, representing therefore a crucial field for important achievements towards energy savings. International committees and public agencies enacted several standards and regulations forcing automotive manufacturers to provide more energy efficient vehicles in fulfilment of pollutant and emission restrictions. The outbreak of the diesel scandal has put the automotive manufacturers even more on pressure requesting to promptly bring the energetic issue as a central topic in the vehicle’s development phase and if necessary to even rethink the vehicle’s architecture towards environmental friendly energy sources and therefore new technologies.

In this context, the early concept design of a vehicle is becoming increasingly crucial to determine the success of a car. Broadening market competition, more stringent regulations and fast technological changes require a prompt response from carmakers and Computer-Aided-Engineering (CAE) has emerged in recent years as the promising way to provide more efficient and cost-effective design procedures by also cutting development time and costs. Simulation models are increasingly adopted in the entire vehicle development and analysis flow, specific methods and tools are used according to the required accuracy, level of detail and computational cost.

The possibility to rapidly assess the vehicle behavior through simulation has promoted the use of optimisation techniques in vehicle design. The increasing system’s complexity, especially with the introduction of new technologies, can be successfully tackled with optimisation methods to rapidly drive the design process to the best solutions. However, fulfilling targets and requirements without sacrificing vehicle’s performances leads to extremely challenging tasks for the vehicle’s designers and even the identification of a suitable workflow to streamline the process in terms of models choice and integration can be challenging.

1.1 Motivation

Road transportation still represents the major mean of mobility of people and goods and furthermore, in general transportation it plays a leading role regarding the energy share. As shown in Figure 1, the transportation sector causes 20% of Europe emissions, less only than the industry sector, within which road transportation has the major contribution with almost 72%. This scenario has led to important changes in policies and regulations during the last decades, local agencies and international committees provided mandatory standards towards CO2 reduction [1].

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Figure 1. Emissions by sector and transportation mode in Europe, adapted from [2]

From automotive industry perspective, mandatory regulations definitely boosted research activities investments towards energy efficient vehicles providing a sharp reduction in emission levels. Improvements are provided by both current technology advance and new technologies introduction allowing a continuous reduction in vehicle energy demand and hence complying with current regulation requirements [3]. However, many critics arise due to the testing conditions and especially the driving cycle adopted, the New European Driving Cycle (NEDC), is considered not representative of the real-world driving conditions. The study in [4] shows that the reduction of CO2 emissions in

real-world driving conditions during the last years has been almost insignificant and pointing so to strong critics toward NEDC based results which, as shown in Figure 2, would suggest on the opposite an extremely positive trend in recent years. The introduction of the World Harmonised Light Vehicles Test Procedure (WLTP) from 1st September 2018 [5] is expected to provide more realistic values and therefore to effectively force automotive manufacturers to look out for more sensitive improvements. Furthermore, the 2020 CO2 emission target reduction to 95 g/km [6] require an even greater effort.

The 2015 target line has been successfully achieved as provided in Figure 3, but fulfilling the 2020 target could be extremely challenging with strong investments.

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Figure 3. 2016 CO2 performance of key European passenger car manufacturers [7]

Nevertheless, the “Dieselgate” break in 2015 has strongly prompted to accelerate the introduction of more severe methods and procedures to evaluate also real-world driving emissions and is strongly affecting both automotive manufacturers and consumers with a sensitive decline in diesel vehicles share, as shown in Figure 4. The “Dieselgate” and the stringent emission regulations are not the only factors shaping future mobility, further trigger factors involve a different way of seeing the mobility and the car ownership besides autonomous driving and connectivity. These abrupt, continuously evolving changes might require to sharply reduce vehicle’s time to market in order to keep the pace with consumers’ demand.

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In this context virtual prototyping found a successful role and has been increasingly adopted and improved to meet the needs of automotive designers. CAE spread over several individual applications and focused on specific task showing to be extremely cost-effective, however to achieve significant time savings, productivity gain and strategic benefit, it is necessary to promote a deeper integration among CAE application and reach a higher level of automation. In [8] the advantages of CAE methods are shown regarding crashworthiness, strength and durability performance, noise vibration harshness (NVH) response, and driveability. The analysis further concentrate on the role of CAE in the development of iterative procedures and the major breakthrough in CAE adoption is related to the possibility to hierarchically model, simulate and optimise the entire vehicle. The work in [9] discusses the evolution in extending CAE tools toward multi-physics simulation and the integration of control functions already in the design phase. Examples of proposed workflows for simulation-based product development and control integration are presented in Figure 5. A crucial aspect concerns the handling of complexity, data and models management to streamline the workflow. In [10] the technologies needed to support the application of simulation-based design are discussed with emphasis on the technical components that must be added to existing CAE tools to enable a successful application of simulation-based design like simulation model managers, simulation data managers, adaptive control tools and simulation model generators.

Figure 5. Adoption of simulation-based (a) and integration of controls (b) in the product development [9]

1.2 Work Structure

The work focuses on the vehicle simulation and optimisation with a close look on the current market and the most promising technologies currently adopted. Chapter 2 provides the state of the art in vehicle concept design and product development by stressing the major driving factors and particularly the analysis of the current automotive market. The market scenario is proposed through a top-down description moving from the vehicle system to the individual subsystems and components showing trends in recent years, main current solutions and performances. The comprehensive vehicle system’s simulation and optimisation is a very challenging and complex task that has mainly been faced in industry environments, however rapid changes shaping the future mobility and particularly the introduction of innovative powertrains have attracted huge interest also in the academic research. In this context, simulation driven approaches are extensively pursued to speed up the product development. Chapter 3 focuses on the description of the realised workflow to streamline the vehicle performances assessment by a bottom-up procedure involving modelling and simulation from the individual components to the entire vehicle system. The study focuses on the powertrain and therefore conventional combustion engine, electric hybrid and battery electric propelled vehicles are considered through a detailed analysis of the most relevant components like combustion engine, electric traction unit and transmission. The model-based approach is fully exploited at both component and system

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level by showing the capability to guarantee accuracy and reliability through the adoption of specific models. The results provided in this chapter allow for a sensitivity analysis of vehicle’s consumption and dynamic performances with respect to powertrain technology, topology and components design. Chapter 4 tackles more effectively the aspects concerning the optimisation procedure in terms of problem definition and results analysis. Strong attention is given to the development of suitable models and the selection of the design variables and objectives. The results analysis faces a multi-objectives optimisation for which even effectively visualise the results can be a challenging task and this is pursued through several mathematical and data analysis techniques. The study allows to compare the various powertrain solutions (combustion engine, electric hybrid and battery electric) and to identify the most relevant design variables by showing their impact on comprehensive vehicle performances. Chapter 5 briefly discusses and critically reviews the developed work while Chapter 6 is mainly dedicated to summarise the work procedure and the obtained results.

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2. State of the Art

This chapter provides the current state of the art in car concept development. It is given importance to highlight the role and the relevance of the concept design in the entire car development chain. A product development is generally driven by many factors, vehicles’ market scenario and key technical parameters are analysed to understand recent years trends and therefore, to foresee which are the most impacting factors that define investments in automotive industry and shape the future mobility. The vehicle concept design is then directly tackled and in particular the increasing adoption of virtual prototyping is discussed. The success of virtual methods rely on the capability to effectively approach the problem through a sequence of activities allowing to finally identify the most suitable design. Finally, a collection of current ongoing research projects and developed methodologies both from industry and academia perspective is provided.

2.1 Product Development

In a competitive marketplace, a company must achieve a satisfactory level of profitability. Increasing profitability involves determining which aspects are crucial, how to improve them and eventually when to tackle necessary changes with planned and targeted investments. Understanding the key factors assists managers in developing an effective profitable strategy for their company concerning sales, pricing, expenses and cost of staying in business. Eventually, the core aspects deal with the product sold by the company and therefore with a proper definition and management of the product development process (PDP).

The PDP is a living construct continuously reshaping on the basis of knowledge and new challenges. The strong market orientation of automobile manufacturers and, due to rapid changes, the request to shorten development times are essential more than ever. Individualisation of the product, quality requirements, internal and external networking of resources between companies, partners and supplier must be properly scheduled and organised. The successful PDP eventually targets to synchronise the several processes and activities involved in the realisation of the product. Porsche Engineering has realised an interesting schematic representation of the PDP in [11] and it is partially provided in Figure 6 reporting only the first phases of the PDP. Fundamentally, the overall PDP process is divided into main phases with milestones known as quality gates between them. As synchronisation points, these quality gates are used to check the status of predefined criteria which, once fulfilled, trigger the approval of the preceding phase and the continuation of the project. The PDP contains interfaces between development, project management, quality, procurement, production, and sales. Thus, it forms a detailed procedural model for mapping the simultaneous engineering processes and, in terms of methodology, represents the ideal workflow for creating a vehicle.

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Figure 6. Schematic representation of the initial phases of the PDP [11]

Obviously the biggest challenge is to successfully define the PDP since several approaches can be pursued and the resulting organisational structure is generally very complex. It involves many resources and requires to handle the various interactions internally to the company itself and externally with suppliers. A literature review is provided in [12] focusing on decision making that occurs at various phases of the PDP: concept development, supply-chain design, product design and production ramp-up and launch. In [13] more insight is given to the importance of environmental and contextual variables such as market growth rate, competitive environment, or the level of top-management support. Nevertheless, when it comes to the success of a new product, the consumer is still the ultimate judge and in [14] methods and functional departments are addressed to the adoption of PDP.

This work focuses on the conceptual design of new passenger cars and therefore it deals with the product concept design. A deep insight to the topic is provided in [15] in terms of processes, user information and strategies looking particularly to the automotive industry. The concepting process has drastically change during the last century, moving from being driven by technological capabilities to systematic research of the market and anticipation of forthcoming needs that strongly encouraged the concepting of the product itself.

The following sections are dedicated to describe the current market scenario, in particular concerning the powertrain technologies adopted and the key parameters defining the vehicle. Emissions regulations are strongly influencing the introduction of innovation in the powertrain technology while the vehicle key parameters are affected by a broad combination of factors meeting eventually the consumers’ requirements like comfort, dynamic performances, consumption and cost.

2.2 Passenger car market

The passenger car market has constantly increasing in recent years recovering from the economic crisis and is reaching a similar amount of sales as at the beginning of 21th century with almost 16 million of new registrations per year in Europe [7]. Germany is the largest market with a 23% share and together with France and UK covers nearly 60% of the European market. A steep increase in sales has been also registered in lately EU joined countries where new registrations has increased up to 40%. The automotive market is mainly concentrated in Europe, North America and Asia, as shown

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in Figure 7, and worldwide it is still expected an average growth in sales of 1.5% to 3% in the next years with high expectations from emerging markets [16]. Despite an expected future stability with a continuous growth of the market, there are also many rising aspects that should be carefully considered. McKinsey’s analysis in [17] warns against a period of wide-ranging and transformative changes as sales continue to shift and environmental regulations tighten. A more recent study in [18] stresses how the convergence of disruptive technology-driven trends could transform the automotive industry. A different interpretation of the mobility, forced by city policies and new business models gaining momentum, could shift away from car ownership to shared mobility. Autonomous driving and electrification demand for rapid changes and require relevant investments. Nevertheless, the competitive landscape forces established car companies to compete on multiple fronts facing mobility providers (car sharing, etc.), emerging companies (Tesla, etc.) and tech giants (Apple, Google, etc.). The report provided in [7] allows a deep analysis over the passenger car market share and further considerations depending on country, brand, car technology and key parameters. Passenger cars represent the majority of the vehicle transportation sector and in Europe are covering a share of 88% [7] with VW Golf being the most popular car (4%). A strong increase, over 6 times compared to 15 years ago, took place in sales of the sport utility vehicle (SUV) segment with market share of almost 30% while the remaining 60% is almost entirely covered by small to medium cars, meaning A-Class to D-Class.

Figure 7. New passenger car sales in 2016 [7]

2.2.1 Technology

Todays automotive market is still characterised by standard technology that gained consumer’s trust and for which established automotive manufacturers have developed expertise. As provided in Figure 8, diesel and gasoline engine propelled vehicles correspond to 96% of the share in the European market with electric vehicles, including also hybrid vehicles, selling less than 3%. A more general overview of the electric vehicles market penetration considering the world market, provided in Figure 9, points out slightly more successful scenarios in U.S. and in China.

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Figure 8. 2016 Car market share in Europe by technology, country of registration, and brand [7]

Figure 9. New electric car sales in 2016 [6]

Electric vehicles are still experiencing hard times due to battery technology and costs, charging stations availability and driver’s range anxiety. However, emission regulations are forcing automotive manufacturers to strongly invest towards cleaner vehicles and electrification has so far emerged as the most promising solution. In fact, very interesting are the consideration over the electric vehicles market when looking at the trend in recent years. The study in [19] collected data from 2010 up to

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2016 showing, as provided in Figure 10, an actual exponential increase in electric vehicles. Besides regulations, research is the key towards electric vehicle breakthrough and particularly regarding battery cost which is expected to drop by 50% in the next few years. Nevertheless, financial incentives directed at electric car customers and users are currently essential for reducing the purchase cost and the total cost of ownership (TCO) gap between electric and conventional cars. They are adopted not only for private customers but also for company cars and fleets, both in the public and private sectors.

Figure 10. Evolution of the global electric car stock, 2010-16 [19]

Figure 11. CO2 emissions of new passenger cars [7]

The increasing share of alternative powertrain driven vehicles is strongly encouraged by the future emission targets leaving less and less room to standard fossil fuel driven vehicle. Figure 11 allows to

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compare several new passenger cars according to various powertrains. Conventional vehicles, gasoline and diesel, are struggling to achieve 2020 emissions targets and therefore, automotive manufacturers need to increase the share of alternative fuelled vehicles in their fleets. Plug-in hybrid vehicles (PHEV) and electric vehicles (EV) are mainly adopted to achieve this goal and models adopting fuel cell system are also available on the market.

2.2.2 Key technical parameters

The technology adopted and specifically the powertrain expresses the need of cleaner and environmental-friendly future passenger cars. Conventional vehicles still represent the majority of the market share and thus it is interesting to analyse the development over time of some of the main technical parameters in order to gain an outlook towards future trends. According to data collected in [7], vehicle’s size and mass have significantly increased in last decades, as provided in Figure 12.

Figure 12. Pan-area (length × width) (a) and mass (b) trend by segment [7]

The vehicle’s size increase represents the need of more comfortable and spacious vehicles but still constraints apply to guarantee a feasible track occupation and proper room for parking. The weight increase is mainly associated with improved vehicle’s safety but also the entertainment equipment has played an important role. Lightweight design is currently driving innovation in vehicle’s body and components design by searching for lighter materials without loss in strength and durability. Another trend with relevant impact on the vehicle mass is the increasing installed power. In order to satisfy such requirement without escalating consumption, engine downsizing has proven as a successful approach. The engine displacement has been constantly reduced and the requested power has been achieved through turbocharging. The trend of power and displacement for various vehicle classes can be seen in Figure 13.

In order to analyse more in detail the engine key parameters, data from new passenger cars has been collected so that it would be possible to deeply understand the most used design solutions and their impact on the engine and vehicle performances. The set of vehicles considers several models introduced into the market in 2017 from Audi, BMW, Citroen, FCA, Ford, Ferrari, Honda, Infinity, Jaguar, Mercedes, Mitsubishi and Opel [20]. Considering the uncertainty over diesel engines, the following analysis restricts to gasoline spark ignition (SI) engines.

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Figure 13. Engine power (a) and displacement (b) trend by segment [7]

Firstly the effect of turbocharging is considered by showing the effect on the engine for which the specific torque can be magnified and the subsequent impact on the vehicle in terms of consumption and acceleration. Figure 14 shows how for a naturally aspirated (NA) engine the maximum specific torque is generally a constant value of 100 Nm/l while by introducing turbocharging (TC) it is even possible to double the performance. At the vehicle level, the adoption of a TC engine allows to push the front in the consumption-acceleration graph with respect to NA solutions. Nevertheless, a more comprehensive analysis should also consider the impact of turbocharging on cost, control complexity and components reliability.

Another interesting design aspect relates to the choice of the number of cylinders according to the desired displacement. Figure 15 shows that generally some overlapping occurs when choosing between 3 and 4 cylinders within 1.2 to 1.4 litre engines. The individual cylinder displacement lies mostly in between 0.3 to 0.7 with 3 cylinders configurations mainly in the lower range, 6 and 8 cylinders in the upper range while the 4 cylinders cover a broader range.

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Figure 15. Number of cylinders adopted according to engine displacement (a) and vehicle performances (b)

Figure 16. Consumption vs acceleration by transmission type

The transmission provides the power flow from the engine to the tires. Different solutions are adopted in current vehicles according to the level of automation of the gear shifting device, the complexity of the gearbox and eventually on the adopted drive. The most popular transmission systems adopted are manual (MT), automatic (AT) and dual-clutch (DCT) transmissions. MT are the most adopted solution in European cars, the advantage lies on lower cost, simple design and higher efficiency. AT are widely used and are the standard technology in most of U.S. cars: the gear shifting system is performed automatically and thus no effort is required from the driver but it generally suffers from worst efficiency. DCT is mainly adopted in sport cars or, in general, in cases where acceleration performance has to be enhanced since with a double clutch connecting the engine to the transmission it’s possible to reduce shifting time. Considering complexity, efficiency, cost and other relevant

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aspects it provides a trade-off solution between MT and AT. Figure 16 shows the design selection of the different transmission according to the desired performance. MT covers a broad range and in particular it is used for low consumption vehicles, DCT is limited to vehicles with strong requirements on acceleration performance while AT locates in the middle without enabling improvement of either consumption or acceleration.

A further relevant aspect is the number of gears used, since increasing the number of gears leads to more complex and expensive transmissions, still efficiency and acceleration performances can gain relevant advantage. During the last decade 4 gears transmission have definitely disappeared, 5 and 6 gears solutions still dominate the market with the first being the standard solution for smaller cars while the latter representing the mostly adopted configuration up to medium cars. The 7 and 8 gears transmissions have also increased their share in the market, especially in upper segments.

Figure 17. Number of gears trend [7]

The increasing introduction of innovative powertrains, in particular those equipped with battery pack, allows to provide an insight towards the solutions so far adopted by the major automotive manufacturers. Although conventional vehicles already use a battery pack to power auxiliaries (lightening system, engine auxiliary, etc. ) specific packs are to be introduced in electric driven vehicles with proper materials, design and size. The most relevant parameter defining the battery is its rate, respectively the nominal power that can be delivered.

Hybrid electric vehicles (HEV) represent the transition step towards electric driven vehicles. Fuel savings and lower emissions without sacrificing vehicle’s range are among the key factors of the success even if cost still represents a major barrier. The electric driving mode is used to avoid the combustion engine to operate in poor efficiency conditions and, by optimising the power flow management, it is possible to efficiently recharge the battery through the combustion engine. However, fuel consumption and emission reduction are quite limited and remarkable improvements

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can be achieved only through plug-in models (PHEV) since in this case the car can operate much more like a full electric vehicle with utilisation of the combustion engine in recharging mode for a shorter time. However, range anxiety and especially the availability of charging station are major concerns toward purely electric drive since the current infrastructure scenario doesn’t appear to be prepared for a massive introduction of electric driven vehicles, as shown in Figure 18.

Figure 18. 2016 electric vehicle sales share and public charge points per million population in major national markets [21]

Focusing on the impact of plug-in hybrid vehicles, fuel consumption and savings are provided in Figure 19. The fuel consumption is reported for several vehicles [22] and, by using the stated electric energy consumption and range, the equivalent fuel consumption for driving in purely HEV mode is calculated. This operating mode expresses the actual driving condition that a HEV without plug-in recharging capability would experience, meaning a model where the battery charge sustainability would completely rely on the combustion engine. It can be noticed that such scenario would return a fuel consumption very close to conventional vehicles, especially in case of smaller models. An example is clearly provided by the VW Golf 1.4 TSI, considering the conventional model a consumption of 5.2 l/100km is stated by the manufacturer while the hybrid model, the GTE 1.4, reaches a value of only 1.5 l/100 km. However, when calculating the equivalent consumption without the plug-in capability the consumption drops at only 4.5 l/100km and thus allows a fuel saving of less than 15%. Looking closer to the battery rate, it’s possible to highlight its impact on fuel savings. Most of models use batteries of 5-10 kWh gaining approximately up to 70% of fuel saving while for batteries above 20 kWh values of even 90% can be achieved providing vehicles behaving similarly to full electric vehicles. Nevertheless, it is important to observe that the emission calculation is based on two testing procedures [23]. In both consumption is measured, the charge-depleting (CD) test also provides the electric range while the charge-sustaining (CS) test mainly assesses the fuel consumption when the battery state of charge has to reach back the starting level.

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Figure 19. PHEV analysis: plug-in design effectiveness (a) and battery influence (b) on fuel consumption

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Figure 20. EV analysis: range vs battery rate (a) and consumption vs acceleration (b)

The full electric vehicles (EV) have a simpler powertrain design compared to HEV since the conventional combustion engine and transmission are removed. The major issues are related to cost and driver’s range anxiety, with the latter strongly felt for smaller cars where smaller battery packs allow reducing consumption and costs but, due to lower stored energy, suffer from poor range. The battery rate dependency is clearly shown in Figure 20 with values from a set of commonly market EV [24]. The range increases quite linearly for low rate batteries up to 40 kWh while a less sensitivity gain occurs with high rate batteries for which the increased amount of energy capacity is counterposed by the increasing size and mass of the vehicle. Besides the range, also the consumption-acceleration graph is provided and it reveals how the market offer is already quite wide, ranging from low consumption models to sportscars. Sportiness is boosted by the capability of electric motors to provide high torque already at low speed while the high efficiency allows for vehicles with very low energy demand.

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Besides the powertrain, other crucial aspects defining the vehicle and leading its design are the vehicle’s body and chassis.

The vehicle’s body shape mainly defines the aerodynamic drag, generally expressed in terms of a coefficient and proportional to the cross-section area. The aerodynamic drag coefficient, Cd, is a measure of the ability of the vehicle to flow through the surrounding air. A lot of development has been devoted in this area, as can be seen in the history trend provided in Figure 21. The body shape evolution has historically strongly impacted on this factor [25,26] while todays further improvements are mainly achieved by optimizing the individual components geometry and through active systems [27,28,29].

Figure 21. Passenger car aerodynamic drag coefficient trend for different cars, data from [25] Nevertheless, when considering the aerodynamic resistance it is important to keep in mind that the overall resistive force is also proportional to the cross-section area and therefore, besides the quality of the aerodynamic profile given by Cd, vehicle’s size play an important role. Figure 22 provides common values of cross-section area for several vehicle segments [20]. The value is generally roughly 85-90% [30] of the product of vehicle’s width and height, where height and width are generally proportional to the vehicle’s length and only height shows significant variation in case of sport cars, where it is about 20% lower, and SUV and vans, for which it is about 5% higher.

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The chassis consists of all the components defining the framework intended for sustaining the vehicle’s weight (body, engine, passengers, interiors, etc.) and guarantee a proper drive. Many aspects are of course relevant, a major one is the tire, which identifies the interface element between the car and the ground. It sustains the vehicle weight and provides the necessary traction, braking and cornering capabilities. Many relevant aspects determine the tire selection: dry and wet friction, stiffness, noise, etc., and among these, the rolling resistance coefficient (RRC) has become crucial to reduce energy consumption. Its reduction has been constantly pursued by tire manufacturers as demonstrated in Figure 23. Moreover recent regulations [31,32] imposing tire labelling, regarding also tire wet braking performances and noise levels, demand even more efforts towards fuel efficient tires development. Nevertheless, operating conditions strongly affect tire behaviour and in particular inflation pressure is relevant especially for safety issues. For this purpose monitoring systems has been introduced in many vehicles and have become even mandatory in many countries for new cars [33].

Figure 23. Tire rolling resistance trend [33]

The last key parameter considered in this analysis is the lightweight design that has raised as an additional promising approach to reduce vehicle’s emissions. The attempt to reduce vehicle’s weight has been always pursued, however new materials and improvements in technological production methods and processes have drawn the attention of the designers. Furthermore, computer technology and CAE allow for further improvements through optimal design of the individual components by identification of the critically loaded areas and by fine shaping the geometry. The effectiveness of lightweight design relies on the capability to efficiently operate across the several car components. The approximate vehicle weight distribution is provided in Figure 24 showing that body, powertrain and chassis account for roughly 75% of the entire mass. The weight reduction generally results from a trade-off mainly due to material costs, depending on the raw material itself or on the technology to process it. A commonly adopted approach is therefore to reach the desired weight reduction by accurately choosing the correct balance between lightweight materials according to weight, strength and cost. Several studies [34,35] show potential future trends over lightweight material selection,

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adoption and costs, and several scenarios are exploited with an example is given in Figure 25. The crucial role of lightweight design is furthermore expected considering the powertrain electrification and the use of heavy battery packs causing up to 20-30% increase in vehicle’s mass.

Figure 24. Vehicle's mass breakdown [36]

Figure 25. Foresight of future adoption of lightweight materials [34]

2.3 Vehicle concept design

The concept design represents the first step in PDP expressing the set of activities leading to the realisation of a new product. It embraces the perception of either need or possibility to improve

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current products or even to open up towards new solutions. Market, policies, competitors, environmental and society changes can be a strong promoter shaping company’s intentions and ways of relating to future investments. In the automotive industry, it is quite common to refer to concept car when it comes to foresee the future of the mobility, whether it concerns a design restyling, a powertrain breakthrough or a driving experience, and there are many examples of vehicles in production today that originated from previously presented concept vehicles. An example of the process from the concept idea, through a prototype and finally the series production can be seen in Figure 26 for the BMW i8.

Figure 26. From concept to series production: BMW GINA (2008), BMW Vision Efficient Dynamics (IAA 2009) and BMW i8 (2014)

The work in [37] focuses on decision-making methods in engineering design for the automotive industry, it provides a review over different approaches that can be adopted across the entire vehicle development and among others also the conceptual design. The conceptual design is described as a set of activities to solve a problem driven by few keywords. The main activities include identifying the essential problems of the task, establishing the functional structures, searching for solution principles, combining these principles with concept variations, and evaluating the obtained concepts through technical and economic criteria. Among the most relevant problems, there are the correct description of the decision-maker preferences, the generation of design alternatives and their selection while the governing keywords concern the solution to these problems, respectively meet the decision-maker preferences, generate satisfying design alternatives and select the proper design.

The concept design of a product deals with many aspects: technical issues, production fitness, consumers interest, stakeholders profitability, etc. Considering a passenger car, engineering play a crucial role and faces several tasks. In [38] an extended description of the design procedures, challenges and methods in modern vehicles is provided, various topics are discussed in detailed giving a comprehensive outlook. Vehicle design faces a complex system combining a large number of very different components whose realisation typically requires a deep understanding of all disciplines in engineering science. The materials selection deals with trade-off considerations over strength, cost, weight and processing. Metals and alloys are mainly used and considering nowadays demanding requirements high strength steels (HSS) and aluminium are increasingly adopted, composite materials appear as the forthcoming evolution step even if cost and processing are currently slowing down their spread. Nevertheless plastics, polymers, ceramics and glasses find large application in automotive industry and therefore choosing the proper material requires a scrupulous analysis. Body design is strongly influenced by its aerodynamic implications on vehicle’s dynamics, fuel consumption and noise, so that each exterior component of the car undergoes deep considerations and analysis. The chassis structural design deals with the study of the loads undertaken by the framework and focuses mainly on strength, stiffness and vibrational issues. In particular, crashworthiness shapes many aspects regarding not only the chassis but also the body and the interiors in order to fulfil the requested tests and standards and therefore guaranteeing the passengers’ safety. The vibrational issues are also related to the various vehicle components and the importance is hugely raised. The positioning of the various elements is another relevant factor impacting on the room available for passenger and luggage

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transportation, accompanied with the interior design, it eventually defines the ergonomics. Occupant accommodation, seats design and comfort have become crucial considering everyday travelling and the time spent by the consumers in the vehicle. The dynamic performances of a vehicle are generally associated with the engine size, still an important role is given by a proper design of the suspension system which contributes to ride comfort, handling performances, good response with respect to traction and braking forces from the tires, and isolation from vibration caused from road unevenness, tire-ground excitation and shocks from obstacles on the road. The modern vehicles have evolved as mechatronic systems, more and more electronics is adopted to ensure engine management, steering and braking supervision, drivability performances, passenger safety and comfort. The control system has to be necessary included in the design process and the advantages of optimal controllers accompanied with reliable sensor can actually provide a breakthrough towards a new interpretation of mobility in terms of advanced driver assistance systems (ADAS). The passenger safety is of course a crucial aspect and considering normal driving conditions, the element of major interest is the braking system allowing the vehicle to slow down, in this context another major aspect that eventually applies to the entire vehicle is the ability to provide reliable design in terms of endurance and durability, and in particular, failure prevention and fail-safe design are extremely relevant design features. Last but not least the powertrain, the system that could be considered the core of the vehicle providing the traction source and, from the very beginning of cars spreading, the distinguishing feature of prestige in a passenger car. The powertrain has a central role in defining fuel consumption, driving performances, NVH factors, vehicle’s weight and, due to components allocation and therefore available room, also ergonomics.

The following sections provide a description over virtual prototyping and the way it shapes the vehicle development process. Importance is given to the definition of the workflow showing the major steps to be followed and pointing out some common issues. The literature review finally provides an overview on actual research activities and works in the field of vehicle concept development both considering realised concept cars and developed methodologies.

2.3.1 Virtual prototyping

The complexity of vehicle design together with the increasingly stringent requirements and the pressure to reduce development time and costs require to change the way designer approach the vehicle design already at the concept phase with respect to the past. Recent years has seen an exponential rise in adopting computer aided engineering (CAE) tools to tackle design tasks and the concept of virtual prototyping has become an everyday expression when it comes to vehicle development. A detailed description of the role of virtual prototyping in the automotive industry is provided in [39]. The adoption of simulation methods should not only reduce the development time but also provide an effective solution to handle product alternatives and variations, reduce modifications and corrections in the subsequent production phases, realise prototypes closer to the final product and lower costs and needs of experimental testing. Another relevant aspect is the anticipation of the synchronisation among the various development phases. In the traditional development process the vehicle systems and components are realised individually and then set up together which leads to a late synchronisation.

Through virtual prototyping is possible to integrate the components at different milestones and therefore verify how they fit together and, if necessary, anticipate the iteration process to remove mistakes and to bring improvements. In order to guarantee the effectiveness of the virtual prototyping, process information management and up-to-date integration tools have to be developed and delivered efficiently to the designers. It requires the generation and maintenance of a database containing the relevant data necessary to develop models and to perform simulations and should also define the targets to be achieved. Logistic should be adopted to provide an effective integration among the different departments, guarantee the correct input-output flow and allow the detection of possible synergies. The integration in the actual development of the product is eventually provided through

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simulations. To achieve this goal is however necessary to have at disposal several simulation tools and models allowing to describe the vehicle at different level of detail and therefore accompanying the development of respectively the vehicle, the modules and the components simultaneously, as schematically depicted in Figure 27.

Figure 27. Simulation integration in vehicle development, adapted from [39]

2.3.2 Problem definition and solving methods

The vehicle development process through virtual prototyping requires the definition of a specific workflow providing a clear path from the problem definition to the selection of the final design solution. The major steps involve:

• definition of the objectives, preferably accompanied with reference target values or range of acceptable values;

• definition of the set of design variables and their range of validity or allowed values;

• realisation of simulation models, according to the design variables and objectives proper models should be used and therefore proper tools must be adopted to obtain the desired level of detail, accuracy and computational cost;

• application of optimisation algorithms driving the search towards the most suitable design solutions;

• use of post-processing methods and tools to analyse the solution space and adoption of techniques allowing decision-making and selection of the final design.

The definition of the objectives, at least in terms of the main aspects of interest in the study, allows an initial thin out of the huge set of possible parameters defining the vehicle. Especially when dealing with introduction of innovative aspects, it is common to limit the analysis to what are considered to be the outcomes of main interest to assess a possible future investment. As an example, the work in [40] provides a literature review over innovative powertrain technologies compared to conventional solutions and the impact is assessed only in terms of energy consumption and costs. It could be assumed that those objectives are the most relevant, given the alternative powertrains topic and therefore, the whole concept design could be focused only into these aspects allowing at one side to reduce the number of objectives to be computed, design variables to be analysed and simulation models to realise and on the other hand to dedicate much more effort in those simulation models describing the system in a very detailed and accurate manner.

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The choice of the optimisation algorithm is crucial to effectively tackle the problem solving. Many algorithms are available in the literature [41]. The selection of the algorithm generally follows many aspects, however the most adopted solutions are in case of multi-objective optimisation the evolutionary algorithms (EA) where design solutions search evolves according to specific criteria and methods in order to properly analyse a broad portion of the design space and at the same time identifies the most suitable design solutions according to the objectives defined [42]. Among these, genetic algorithms have raised as a very successful approach especially when dealing with non-continuous design variable and objective values [43].

The process of defining and selecting a priori the design variables and objectives is generally based on experience and available knowledge, therefore the analysis of the optimisation results is also crucial to identify the most relevant contributions and to prove the effectiveness of the ranges adopted. In complex problems with several design variables and multiple objectives, even identifying those relationship can be challenging. It is therefore useful to use proper data analysis tools and methods to efficiently assess and visualise the obtained results. The Pareto frontier is a well-known technique to identify the conflicts among several objectives and the most interesting solutions [44]. The approach is well suited when comparing two objectives, further methods like spider graphs [45] or parallel coordinates graphs [46] can be adopted to simultaneously compare several objectives. Nevertheless, correlation ranking and clustering [47] and self-organizing maps [48] can be additional approaches to highlight relationship among objectives and design variables.

2.3.3 Research activities and projects

The environmental issues and stringent regulations are shaping the future mobility. Automotive manufacturers are increasingly moving to electrified powertrain vehicles to meet emissions regulations, new companies raised in the electric vehicles market rush, and yet universities and research centres are claiming a relevant role in this scenario.

In 2014 the Visio.M was presented, Figure 28, as the result of a project collaboration between Technische Universität München (TUM) and several industry partners (BMW, Continental, Daimler, Siemens, etc.). The concept car is driven by an electric motor on the rear axle, torque-vectoring is achieved through an additional smaller electric motor introduced in the differential case allowing to split torque between the left and right tire, and high efficiency is achieved through the body design with the use of lightweight materials and a low drag resistance shape.

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Figure 29. Deep Orange 7, BMW MINI concept by Clemson (2017)

The Clemson university with the collaboration of various automotive manufacturers is running the “Deep Orange” project. From 2010 to today nine concept cars have been proposed, covering several topics: powertrain electrification, human machine interface, cost-efficient manufacturing and autonomous mobility. In 2017 the “Deep Orange 7”, Figure 29, was unveiled, it was intended to reinvent the BMW MINI and several powertrain models were realised ranging from conventional combustion engine, plug-in hybrid to battery electric. A major task also involved the reshaping of the body to maximise the interior space.

Besides the production of concept cars, many research activity has been addressed to the development of simulation-based methods to assess and optimise passenger cars, especially concerning future powertrains. The TUM itself put strong effort on simulation and optimisation, particularly regarding electric vehicles, based on components model scalability [49] and focused on consumption, cost and vehicle dynamics. The TU Wien work focused on hybrid powertrain and developed a multi-stage optimisation scheme [50] targeting the powertrain’s efficiency. Further examples of powertrain optimisation can be found for hybrid electric vehicles in [51,52,53,54,55] and for electric vehicles in [56,57]. Other studies generally involve software companies providing consulting to automotive manufacturers like GT [58], Ricardo [59], AVL [60], Siemens [61], and Dassault Systemes [62].

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3. Model-Based Approach for Vehicle Concept Design

3.1 Introduction

The automotive industry is facing new and pressing challenges from increasing market competition, more stringent environmental regulations and new technologies that contribute to change drastically the current scenario. Modern vehicles must comply with several standards and policies, fulfil customers’ expectations, and nevertheless be profitable for carmakers. In this context, decision-making becomes crucial already at the very early phase of the vehicle design. Nowadays, this aim is pursued more and more extensively through Computer-Aided-Engineering (CAE). It allows cutting development time and expensive prototypes production by reducing the need for experimental procedures and, from the very beginning of the vehicle development, provides a broad analysis over different vehicle solutions.

The most common approach for complex systems design is based on the decomposition of the system into its subsystems and furthermore to its components, in a cascade manner up to the desired level of detail. Applications in automotive field using this approach are proposed by Ford [63], however further references can be found in aerospace industry as shown by Airbus [64]. At each stage, mathematical models are defined to properly evaluate the quantities of interest. A wide range of models can be adopted, from simplest empirical formulations or data collection to more complex dedicated software packages depending on trade-off choices regarding computational cost, available data, expertise and desired results’ accuracy. The integration of the various models up to the vehicle system level gives the opportunity to assess vehicle performances and furthermore, to highlight which design variables have greater impact on fulfilling vehicle requirements. Through optimisation techniques, it is possible to rapidly explore the design variables space and thus the various vehicle design solutions. Areas for possible improvements and most effective parameters can be detected, providing guidelines towards better and more efficient design solutions. The introduction of optimisation techniques in the early phase of the vehicle design clearly helps to gain a better, deeper and earlier understanding of design limitations and potential problems allowing for anticipated and more effective modifications. The automotive industry employs these techniques over the comprehensive product development process (PDP), particularly in the car body design many studies are proposed by automotive manufacturers like Daimler [65], BMW [66] and Ford [67].

The work presented in this chapter shows how CAE can be adopted during the early phase of a vehicle design to analyse various vehicle layouts and to identify the most significant design variables and their mutual interaction. Considering the growing importance of emission standards, fuel economy is a primary aspect in this study. Nevertheless, speed and acceleration performances are at the foundation of a car success, especially when moving from city cars to the more luxury and sport segment. Therefore, fuel consumption and longitudinal dynamic performances are chosen as key vehicle requirements. The proposed methodology is based on a bottom-up approach moving from the individual powertrain subsystems to the entire vehicle. Each subsystem is analysed through appropriate modelling techniques and optimisation algorithms are applied for the components design. The use of specific tools to evaluate the complex behaviour of the different subsystems provides more

An integrated approach towards increased energy efficiency of vehicle subsystems

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TOWARDS INCREASED ENERGY

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Introduction

1.1 Motivation

1.2 Work Structure

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2.1 Product Development

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Model-Based Approach for Vehicle Concept Design

3.1 Introduction