Multi-objective optimisation of a hybrid

Multi-objective Optimisation of a Hybrid Electric Vehicle: Drive Train and Driving Strategy
Robert Cook1 , Arturo Molina-Cristobal2, Geoff Parks1, Cuitlahuac Osornio Correa3, and P. John Clarkson1
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Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK 2 Electrical Machines and Drives Group, Department of Electronic andElectrical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK 3 Department of Engineering, Iberoamericana University, Prolongacion Paseo de la Reforma 880, Lomas de Santa Fe, C.P. 01210, Mexico City, Mexico

Abstract. The design of a Hybrid Electric Vehicle (HEV) system is an energy management strategy problem between two sources of power. Traditionally, the drive train has beendesigned first, and then a driving strategy chosen and sometimes optimised. This paper considers the simultaneous optimisation of both drive train and driving strategy variables of the HEV system through use of a multi-objective evolutionary optimiser. The drive train is well understood. However, the optimal driving strategy to determine efficient and opportune use of each prime mover is subject to thedriving cycle (the type of dynamic environment, e.g. urban, highway), and has been shown to depend on the correct selection of the drive train parameters (gear ratios) as well as driving strategy heuristic parameters. In this paper, it is proposed that the overall optimal design problem has to consider multiple objectives, such as fuel consumption, reduction in electrical energy stored, and the‘driveability’ of the vehicle. Numerical results shows improvement when considering multiple objectives and simultaneous optimisation of both drive train and driving strategy.

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Introduction

A current environmental issue is the reduction of the total energy consumption of a passenger car. Despite their higher manufacturing cost, HEVs have been shown to be an effective way to substantiallyreduce fuel consumption [1]. Combining an electric motor and internal combustion engine to propel a vehicle results in an energy management problem. The fundamental issue of seeking for an effective and optimal strategy to split the power between thermal and electrical paths is addressed in this paper. Guzzella and Sciarretta [2] have classified the optimisation of a HEV system in three layers, asfollows: 1) Structural optimisation, where the objective is to find the best possible structure (arrangement of power train and prime movers);
S. Obayashi et al. (Eds.): EMO 2007, LNCS 4403, pp. 330–345, 2007. c Springer-Verlag Berlin Heidelberg 2007

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2) Parametric optimisation, where the objective is to find the best possibleparameters for a fixed power train structure; and 3) Control system optimisation, where the objective is to find the best possible supervisory control algorithm and best parameters thereof. Guzzella and Sciarretta identify that these stages are not independent. However, due to the limitations of conventional optimisation techniques (nonlinear programming, dynamic programming) they have yet to beconsidered simultaneously. In this paper, the parametric optimisation, via an Evolutionary Algorithm (EA), for a fixed power train structure and for a supervisory control algorithm is simultaneously considered. This problem could be seen as an optimisation problem in Dynamic Environments (DEs). Using Branke’s criteria [3], it clearly has the characteristics of a DE: the change in optimum value (optimaldistribution of the power between the thermal and electrical paths depends on time and energy usage of the vehicle), frequency of the change and severity of the change depend on the driving cycle. There is some degree of predictability of change: there are three main types of driving cycle. In the United States, the Urban Dynamometer Driving Schedule (UDDS – also known as the US federal test...