Optimization Approach to Hybrid Electric Propulsion System Design∗

Assanis, Dennis N.; Delagrammatikas, George J.; Fellini, Ryan; Filipi, Zoran; Liedtke, J.; Michelena, Nestor; Papalambros, Panos Y.; Reyes, David; Rosenbaum, D.; Sales, A.; Sasena, Michael J.

doi:10.1080/08905459908915705

Cited by 82 publications

(34 citation statements)

References 11 publications

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“…It is, however, feasible that other patterns of optimal torque distribution could arise for motors with different efficiency characteristics to the motor of this study. The offline optimisation could be used without modification for these cases, and a more complex function such as a neural network [13] or a look-up table [14] could be used for online approximation of the optimisation results for real time wheel torque allocation.…”

Section: Discussionmentioning

confidence: 99%

Reducing the motor power losses of a four-wheel drive, fully electric vehicle via wheel torque allocation

Pennycott

Novellis

Sabbatini

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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Individually-controlled electric motors provide opportunities for enhancing the handling characteristics and energy efficiency of fully electric vehicles. Online power loss minimisation schemes based on electric motor efficiency data may, however, be impractical for real-time implementation due to the heavy computational demand. In this paper, the optimal wheel torque distribution for minimal power losses in the electric motor drives is evaluated in an offline optimisation procedure and then approximated using a simple function for online control allocation. The wheel torque allocation scheme is evaluated via a simulation approach incorporating constant speed driving at constant speed, ramp and step steer manoeuvres. The energy efficient wheel torque allocation scheme provides motor power loss reductions and yields savings in the total power utilisation compared to a simpler method in which the torques are evenly distributed across the four wheels. The method does not rely on complex online optimisation and can be applied on real electric vehicles in order to improve efficiency and thus reduce power consumption during different manoeuvres.

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Section: Discussionmentioning

confidence: 99%

Reducing the motor power losses of a four-wheel drive, fully electric vehicle via wheel torque allocation

Pennycott

Novellis

Sabbatini

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…For the hybrid truck, the original V8 7.3 L diesel engine is downsized by reducing the number of cylinders from 8 to 6, and hence the displacement to 5.5 L. The turbo-machinery maps are scaled down to match the smaller engine, following the methodology described in (Assanis, et al 1999). The whole procedure for generating torque look-up tables based on predictions of a validated high fidelity engine system code is illustrated in Figure 3.…”

Section: Enginementioning

confidence: 99%

Modelling and control of a medium-duty hybrid electric truck

Lin¹,

Filipi²,

Louca³

et al. 2004

IJHVS

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Abstract:The main contributions of this paper are the development of a forward-looking hybrid vehicle simulation tool, and its application to the design of a power management control algorithm. The hybrid electric vehicle simulation tool (HE-VESIM) was developed at the Automotive Research Center of the University of Michigan to study the potential fuel economy and emission benefits of the parallel hybrid propulsion system for a medium truck. The fundamental architecture of the feed-forward simulation tool and the dynamic equations of its sub-system modules are first described. A power management control algorithm is then designed and evaluated, which is based on mimicking the behaviour of a dynamic-programming optimisation scheme. Simulation results over an urban driving cycle demonstrate that the hybrid control algorithm is able to improve vehicle fuel economy significantly, compared with the original vehicle, powered only by a diesel engine.

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“…Optimal parameter value of component sizes and control strategy for HEVs have been studied previously [1][2][3]. The parametric optimization based on rule-based control was widely used in early studies whereas control concepts based on optimal theories such as Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) [4,5] is more current.…”

Section: Introductionmentioning

confidence: 99%

Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles

2015

Int J Swarm Intel Evol Comput

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A Pheromone-Based Bees Algorithm (PBA) is employed to optimize the key component sizes and control strategy for parallel Hybrid Electric Vehicles (parallel HEVs) presented. The Basic Bees Algorithm (BBA) is an intelligent optimization tool mimicking the food foraging behavior of honey bees. In this research, however, a new version of BBA which uses pheromones, chemical substances secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species, is applied. The PBA employs the pheromone to attract bees to explore the promising regions of the search space, and the parallel HEV configuration and an Electric Assist Control Strategy are used to formulate the research. The value of the key component size and control strategy parameters is adjusted according to PBA to obtain the minimization of weighted sum of Fuel Consumption (FC) and emissions while vehicle performance that satisfy the PNGV constraints. In this research, ADVISOR software has been used as the simulation tool, and driving cycles, FTP, ECE-EUDC and UDDS, are employed to evaluate FC, emissions and dynamic performances. Following a description of the algorithm, the paper shows the results obtained for the simultaneous optimization of key component sizes and control strategy for parallel Hybrid Electric Vehicles. The results prove that PBA is a strong algorithm for determining the optimal parameters of component sizes and control strategy resulting in improvement of FC and emissions without sacrificing vehicle performance. Compared to BBA, the new version, PBA, showed an improvement of about 25% in convergence speed with the nearly same results of optimization targets.

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Optimization Approach to Hybrid Electric Propulsion System Design∗

Cited by 82 publications

References 11 publications

Reducing the motor power losses of a four-wheel drive, fully electric vehicle via wheel torque allocation

Reducing the motor power losses of a four-wheel drive, fully electric vehicle via wheel torque allocation

Modelling and control of a medium-duty hybrid electric truck

Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles

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