Multi-objective Optimisation of a Hybrid Electric Vehicle: Drive Train and Driving Strategy

Cook, Robert P.; Molina-Cristóbal, Arturo; Parks, GT; Correa, Cuitláhuac Osornio; Clarkson, PJ

doi:10.1007/978-3-540-70928-2_27

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…This coincides with results shown by [15] but the straight line is determined in a different process as in the cited paper. Also in [9] a linear behaviour can be observed. It can be explained with averaging effects of the load points in the MVEG cycle.…”

Section: Optimization Resultsmentioning

confidence: 86%

“…Therefore, in contrast to the previous mentioned works, a multi-criterion optimization is done by using a genetic algorithm (GA). This type of algorithm is also used in [8] and [9] but with different objectives like emissions or electrical energy consumption as additional criteria. A further difference of the proposed approach is the use of an object oriented feedforward dynamic model, built-up in Dymola/Modelica.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Buerger

Lohmann

Merz

et al. 2010

2010 IEEE Vehicle Power and Propulsion Conference

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In this paper a new concept for the layout of hybridelectric-powertrains is developed that includes optimization of the component-sizes as well as control strategies. In contrast to most existing publications, the approach explicitly considers the conflicting goals of low fuel consumption and high vehicle longitudinal dynamics and the trade-off is quantified. Two multiobjective optimization subproblems are solved for one example with a parallelized genetic algorithm (NSGA-II) using the Condor software framework. The analysis of the solutions (Pareto front) shows that combinations exist which improve the fuel consumption with only a slight deterioration of the dynamic performance. So the designers are supported in their decision for a configuration which is attractive for the customers.

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Section: Optimization Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Buerger

Lohmann

Merz

et al. 2010

2010 IEEE Vehicle Power and Propulsion Conference

View full text Add to dashboard Cite

show abstract

“…However, such a target leads to multi-objective optimization schemes which are, in general, more difficult to solve. An evolutionary algorithm (EA), in form of a genetic algorithm, is often applied in numerical optimization practice to find simultaneously the best parameters for powertrain design and energy management [2], [11]. This is mainly motivated by the fact that EAs can cope naturally with multiobjective, discontinuous and non-differentiable problems.…”

Section: Two-layer Optimization Strategymentioning

confidence: 99%

Multi-objective optimal powertrain design of parallel hybrid vehicles with respect to fuel consumption and driving performance

Boehme

Frank

Schori

et al. 2014

2014 European Control Conference (ECC)

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In the past decade, Hybrid Electric Vehicles have been demonstrated to significantly reduce the fuel consumption and emissions. However, this capability strongly depends on the sizing of the components and on the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space to find the optimal component sizings and control trajectories. A novel two-layer optimization strategy based on a multi-objective problem formulation is proposed. The first layer consists of a multi-objective genetic algorithm for determining the best system design parameters with respect to fuel consumption and driving performance. The second layer solves a deterministic hybrid optimal control problem (HOCP) to find for each individual of the population pool the optimal continuous and discrete control trajectories for the energy management. The proposed optimization strategy is benchmarked to a one-layer genetic algorithm approach on a parallel hybrid design study.

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“…Buerger, S [7] used this technique for the optimization of hybrid vehicles component-sizes as well as control strategies, he also quantified the trade-off between solutions. Robert Cook et al [8] optimized drivetrain and driving strategies as per multiple design objectives. But sometimes Paretooptimal set is too large for the designers to consider and choose one solution from them.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of in-wheel motor powertrain and validation using vehicle simulator

Ramakrishnan

Gobbi

Mastinu

2015

2015 Tenth International Conference on Ecological Vehicles and Renewable Energies (EVER)

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A method based on multi-objective optimization (MOO) is employed for the concept design of electric powertrain with in-wheel motor (IWM). The inherent challenges of IWM vehicles are powertrain packaging, increased unsprung mass, and motor cooling. Elimination of hub-reduction gearbox and adaption of high torque and low power electric motor can mitigate these challenges, but it degrades high speed gradeability. Simultaneous optimization of motor power, battery size and drivability parameters to size the powertrain components (electric motor and gearbox) is important for the effective design of powertrain layout. In this study, motor torque, base speed, and gear-ratio are taken as design variables to minimize the motor peak power and the battery size, while maximizing the drivability performance. AC induction machines and Li-ion batteries are considered for the discussion, but the same approach can be applied for other motor and battery types. The results are validated virtually using a highfidelity vehicle simulator, which confirms the findings of MOO. The optimal powertrain layout includes a high torque (600Nm) and low speed motor without gearbox.

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Multi-objective Optimisation of a Hybrid Electric Vehicle: Drive Train and Driving Strategy

Cited by 9 publications

References 9 publications

Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Multi-objective optimal powertrain design of parallel hybrid vehicles with respect to fuel consumption and driving performance

Multi-objective optimization of in-wheel motor powertrain and validation using vehicle simulator

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