DDPG-guided Adaptive Equivalent Consumption Minimization Strategy for a Range-Extended Electric Vehicle

Xu Wang, Ying Huang, Jian Wang, Chengliang Luo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To improve the optimality without significantly increasing the computational burden, this paper proposes a DDPG-guided adaptive equivalent consumption minimization strategy (A-ECMS) for a range-extended electric vehicle. DDPG agent is trained to find the optimal equivalent factor sequence by trial and error and ECMS is used to achieve power distribution based on numerical optimization. In real-time applications, the actor network outputs the equivalent factors based on the vehicle states. This architecture can combine the exploration capability of DDPG with the stable and efficient optimization capability of ECMS. Simulation results show that the equivalent fuel consumption of DDPG-guided A-ECMS is reduced by 7.1% compared with that of PI-based A-ECMS.

Original languageEnglish
Title of host publication2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665453745
DOIs
Publication statusPublished - 2022
Event6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022 - Nanjing, China
Duration: 28 Oct 202230 Oct 2022

Publication series

Name2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022

Conference

Conference6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
Country/TerritoryChina
CityNanjing
Period28/10/2230/10/22

Keywords

  • A-ECMS
  • DDPG
  • energy management
  • range-extended electric vehicle

Fingerprint

Dive into the research topics of 'DDPG-guided Adaptive Equivalent Consumption Minimization Strategy for a Range-Extended Electric Vehicle'. Together they form a unique fingerprint.

Cite this