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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Authors :
Liu, Tong
Tan, Kaige
Zhu, Wenyao
Feng, Lei
Liu, Tong
Tan, Kaige
Zhu, Wenyao
Feng, Lei

Abstract

Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin’s minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and then has higher fuel efficiency than a non-adaptive dynamic programming (DP)controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.<br />QC 20230503

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1400069249
Document Type :
Electronic Resource