Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Mohammadali Kargar, Chen Zhang, Xingyong Song

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations

Abstract

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.
Original languageAmerican English
Pages (from-to)11147-11155
Number of pages9
JournalIEEE Transactions on Vehicular Technology
Volume72
Issue number9
DOIs
StatePublished - 2023

NREL Publication Number

  • NREL/JA-5400-86592

Keywords

  • autonomous vehicles
  • energy management
  • hybrid electric vehicles

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