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Research overview : design specifications for hybrid vehicles

Citation for published version (APA):

Hofman, T., & Druten, van, R. M. (2004). Research overview : design specifications for hybrid vehicles. In European ELE-DRIVE Transportation (pp. 6-).

Document status and date: Published: 01/01/2004

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DESIGN SPECIFICATIONS FOR HYBRID VEHICLES

THEO HOFMAN AND RO¨ELL VAN DRUTEN

Abstract. In this paper a method is proposed for determination of the de-sign specifications regarding the energy exchange systems for different charge-sustaining hybrid vehicles of different vehicle classes. Hybrid drivetrains for vehicles combine multiple power sources in order to increase the driving func-tions. The function can enhance the fuel consumption, emissions, comfort, driving performance and safety. In this paper the focus is on fuel consump-tion reducconsump-tion. The optimal energy management strategy is determined by using dynamic programming. Initially, the efficiencies of the energy exchange between the engine, vehicle road load and additional energy exchange sys-tem are assumed to be constant and independent of engine torque and speed. Therefore, the simulation results will be independent of the component tech-nology and topology. The outcome of the simulations will be the required constant system component efficiencies, sizes and power specifications in order to achieve the required fuel consumption reduction, maintain state-of-charge and accomplish any power demand over a defined drive cycle. These design specifications will be used to chose system component technology. Finally, the chosen system component technologies will be used to determine optimal topologies and optimal energy management system control.

1. Introduction

A hybrid vehicle uses at least two different power sources to propel the vehicle of which one of them can generate power for energy storage in an accumulator, in order to increase the driving functions of the vehicle propulsion system. The driving functions can enhance the:

• Fuel consumption; • Emissions;

• Comfort;

• Driveability (performance); • Safety.

A secondary power source which consists of a bi-directional energy accumulator with energy conversion components (EC) is further mentioned in this paper as an energy exchange system (EES). Examples of EESs are battery combined with an electromotor or a flywheel combined with a continuously variable transmission and planet gear set. For example the fuel consumption and emissions can be reduced, because a smaller primary power source (engine or fuel cell) can be coupled with an EES during brief periods of high power demand. At other times, the primary power source operation is kept in high-efficiency region by using the EES to manage the

Key words and phrases. hybrid strategy, energy consumption, energy recovery, energy storage, energy source, state of charge, vehicle technology.

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2 THEO HOFMAN AND RO ¨ELL VAN DRUTEN

vehicle load. More fuel consumption and emission reduction with the application of an EES can be achieved by shutting off the primary power source at idle eliminating fuel waste when primary power source is not needed. Or by regenerative braking, which stores energy for later use. For example the comfort and driveability can be increased due to torque transients absorption of the engine and driveline by the EES. Or an EES masks deficiencies of conventional drivetrains such as pauses during shifts. Also an EES consisting of an electromotor provides high torque at low speed, which gives satisfying launch feel. The safety can be enhanced by the application of advanced electric braking systems or torque traction systems (all-wheel-drive confidence). In this case, a safety function could also be combined with the fuel consumption function regarding brake energy recovery due to the application of an EES.

1.1. Research Programme. The NWO1 research programme called “Impulse

Drive” focusses on the design of a hybrid vehicle with significant reduction of fuel consumption (50% - 75%) and CO2emissions on a representative drive cycle. The

design process is complex and therefore a design path is made. A schematic overview of the NWO design path is shown in figure 1.

Type of vehicle class Climate- & Geographical influences Electrical accessory loads Type of engine Driving behavior Functional constraints on EES EES Technology Hybrid drivetrain Topology Fuel consumption Energy management Strategy Driveability Comfort Emissions Safety DRIVING FUNCTIONS Constant EC & -Storage efficiencies System component sizes System component power specifications ENERGY MANAGEMENT ALGORITHM DESIGN SPECs

1.

2.

3.

4.

Figure 1. Design path

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1.2. Design specifications EES. First, the required design specifications of EESs will be determined with help of an optimization tool, which consists of an energy management algorithm. In figure 1, this is depicted by an arrow with the label 1. Typically, the EES component specification requirements are

• storage power; • energy storage level;

• energy storage - and energy converter efficiency.

Furthermore, the required design specifications of an EES depend on,

• the type of vehicle class; • the type of engine; • driving behavior;

• climate - and geographical influences; • additional electrical (accessory) loads; • functional constraints on the EES.

For example a multi-purpose vehicle (MPV) is a much bigger and heavier vehicle than a mini-compact vehicle. Between these type of vehicle classes is a significant difference in power request during vehicle launch or energy recovery gain during braking.

The function of a Diesel or a gasoline engine is the same i.e. by combustion of fuel achieve kinetic energy. The two types of engine differ in the type of combustion, compression ratios and energy density. Generally, the higher compression ratio results in a higher efficiency for the Diesel engine. However, as a drawback the Diesel engine produces, also depending on the energy management strategy to some extent, relative high NOx emissions and particulates [5].

The benefits or the obtained increase in driving functions of hybrid vehicles vary with the usage. For example the benefit may vary with driving behavior, climate and geography. The driving behavior, which is represented by the certified driving cycles (e.g. NEDC, HYZEM), show for example large differences in power requests. Regarding climate and geography at relative high environmental temperatures, the ‘Airco’ or ‘Climate Control’ may cancel some hybrid functions or fuel savings. ‘Cli-mate Control’ may even cancel the Stop-and-Go function. However, benefits of regenerative braking (BER) and operating optimization (E-line) remain.

For example additional electrical loads, which have affect on the specification of the EES are due to electric power assist steering or electric water pumps.

The functions of the EES are accumulate energy from the engine, the vehicle (i.e. BER) and supply energy to the vehicle. If one or two of these functions are not applied, this will lead to different specifications of the components and therefore also different system component technologies and topologies [4].

1.3. EES Technology and Topology. Initially, the efficiencies of the energy exchange between the engine, vehicle road load and EES are assumed to be constant and independent of engine torque and speed. Therefore, simulation results will be independent of the component technology and topology. The outcome of the simulations with the optimization tool will be the energy management strategy in accordance with the required constant system component efficiencies, sizes and power specifications. These design specifications will be a good benchmark in order to chose system component technology, which fulfill the required vehicle driving

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4 THEO HOFMAN AND RO ¨ELL VAN DRUTEN

functions (depicted by arrow with the label 2.). Finally, the optimization tool will be used to determine optimal and explore alternative vehicle topologies with optimal energy management control for different hybrid vehicles of different vehicle classes with the chosen EES technologies (depicted by the arrows with the label 3. and 4.). Then, the power dependant efficiency of the system components and kinematical constraints of the hybrid drivetrain will be incorporated during these simulations.

2. Simulation Model and Method

A generic energy flow scheme for a hybrid vehicle is shown in figure 2. The energy sources (accumulators) are Ef, El and Ea. The energy flow paths are

depicted by the arrows. Efrepresents the primary or chemical energy source, which

could be for example fossil fuel or hydrogen fuel. El represents the vehicle load

which accumulates and dissipates energy over a certain drive cycle. Ea represents

an energy accumulator, which is able to store energy, but also to supply energy (e.g. battery or flywheel). EC1, EC2 and EC3represent energy converters, which

E

f

E

l

EC

3

E

a

EC

1

EC

2

Figure 2. Energy flow scheme for a hybrid vehicle

convert chemical, electrical, mechanical or hydraulic energy.

2.1. Optimization criteria. The problem is to optimize the energy flow of the energy converters over a defined drive cycle in order to

• Minimize the fuel consumption and emissions.

• Maintain state-of-charge of the accumulator within a certain range. • Accomplish any power drive demand.

The energy converters EC2, EC3 and energy accumulator Ea are part of the

EES. If the efficiencies of the energy converters and energy accumulator are 100%, the engine operates at its ‘sweet spot’2 and delivers exactly the energy demand

by driving the vehicle over the cycle. If the efficiencies are less than 100%, the

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energy delivered by the engine is higher than the required vehicle drive energy El

and depends on engine operating power over the cycle. This is determined by the energy management strategy.

2.2. Energy management algorithms. An approach called dynamic program-ming (DP) will be used to solve this problem [1]. With DP it is possible to find solutions for optimal energy flow control from the energy sources to achieve the overall control objectives. However, the control objectives or drive functions such as fuel consumption minimization or driveability are subjected to an integral con-straint i.e. maintaining state-of-charge. This concon-straint requires for the DP solver to have foreknowledge of the drive cycle. So for real-time implementation other types of algorithms (quadratic programming (QP) and rule-based (RB) algorithm) will also be investigated [3], [6]. The optimization problem can be approximated by a QP problem, which has much less computational requirements. The RB en-ergy management strategy is mainly based on engineering intuition and analysis of efficiency specifications of system components, while the DP algorithm is based to compute the optimal control strategy. Because with the DP technique it is possible to handle difficult constraint sets such as integer or discrete sets, it has a wider scope of applicability. Furthermore, DP leads to a globally optimal solution and is therefore useful as a benchmark for other optimization algorithms [2]. For example DP can be used to construct improved rules.

3. Conclusions and Future work

The proposed method for determination of the design specifications of the EES for different types of hybrid vehicles of different vehicle classes is discussed. The optimal energy management strategy is determined by using DP. The influence of the energy exchange efficiencies between the engine, vehicle load and the EES on the driving functions can be investigated. In order to determine the design speci-fications of the EES, these efficiencies are initially kept constant and independent of the engine torque en speed during the simulations. Therefore, the outcome of the simulations are independent of the topology and system component technology. As with DP, other optimizing algorithms QP and RB can be used to determine the performance of candidate power system combinations in order to determine the optimal topology. During this process the power dependant efficiency of the system components and kinematical constraints of the hybrid drivetrain will be incorporated.

Acknowledgment. This study is part of “Impulse Drive” which is a research project at the Technische Universiteit Eindhoven in The Netherlands within the section Control Systems Technology of the Department of Mechanical Engineering. The project is subsidized by the NWO Technology Foundation within the Innova-tional Research Incentives Scheme 2000/2001.

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6 THEO HOFMAN AND RO ¨ELL VAN DRUTEN

Authors

Theo Hofman, Ph.D.-Student

Theo Hofman received the M.Sc. degree in Mechanical Engineering from Eindhoven University of Technology, The Netherlands (1999). From 1999 to 2003 he worked as a development engineer at Thales Cryogenics B.V. in Eindhoven. Since 2003 he is engaged as a Ph.D.-Student in the development of design tools for hybrid vehicle power trains at Eindhoven University of Technology, Department of Mechanical Engineering.

Dept. of Mechanical Engineering, (MMP 1.15), Section Control Systems Technol-ogy, Eindhoven University of TechnolTechnol-ogy, P.O. Box 513, NL 5600 MB Eindhoven, The Netherlands, Tel: +31 40 247 2947 Fax: +31 40 246 1418 URL: www.wtb.tue.nl E-mail address: T.Hofman@tue.nl

Ro¨ell van Druten, Assistent Professor

Ro¨ell van Druten was born in 1969 in the Netherlands. He finished his Masters in June 1996 and his Ph.D. in October 2001, both at the Eindhoven University of Technology. In 2001 he, Alex Serrarens and Bas Vroemen started Drivetrain Innovations (DTI) a com-pany that supports the automotive industry with innovative ideas and fundamental knowl-edge about hybrid powertrains.

Drivetrain Innovations: MMP 1.42, Horsten 1, 5612 AX Eindhoven , The Netherlands, Tel: +31 40 247 5812 Fax: +31 40 247 5904 URL: www.dtinnovations.nl

E-mail address: druten@dtinnovations.nl

Dept. of Mechanical Engineering (WH 0.140), Section Control Systems Technology, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Nether-lands, Tel: +31 40 247 4828 Fax: +31 40 246 1418 URL: www.wtb.tue.nl

E-mail address: r.m.v.druten@tue.nl

References

1. Dimitri P. Bertsekas, Dynamic programming and optimal control, Athena Scientific, Mas-sachusetts, 1995.

2. Yongshen Wang Loucas Louca Huei Peng Dennis Assanis Jeffrey Stein Chan-Chiao Lin, Zo-ran Filipi, Integrated, feed-forward hybrid electric vehicle simulation in simulink and its use for power management studies, SAE 2001 World Congress, Detroit, Michigan (2001), no. 2001-01-1334.

3. Hyunsoo Kim Dookhwan Choi, Moonhyuk Im, An operation algorithm with state of charge recovery for a parallel-type hybrid vehicle, Proc. Inst. Mechn. Engrs. 217 (2003), 801–807. 4. Scott Fish, Simulaton-based optimal sizing of hybrid vehicle components for specific combat

missions, IEEE Transactions on Magnetics 37 (2001), no. 1, 485–488.

5. Karin Jonasson, Comperative study of petrol- and diesel hybrid topologies vs directly diesel driven vehicle, Electrical Vehicle Symposium 20th, LA, CA (2003).

6. Takeshi Matsuo Masaaki Hayashi Masahiko Amano, Ram V. Gopal, Practical optimization of energy management for paralell hybrid electric vehicles, Electrical Vehicle Symposium 20th, LA, CA (2003).

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