Safety and Longevity-Enhanced Energy Management of Fuel Cell Hybrid Electric Vehicle With Machine Learning Approach

Abstract

The safety, life expectancy and operating cost of fuel cell hybrid electric vehicle (FCHEV) are highly dependent on the power allocation among the onboard power sources. Motivated by this, this article proposes a machine learning-based multi-physical-constrained energy management strategy to improve the driving economy, thermal safety, and durability of FCHEV. In particular, the fully-continues deep deterministic policy gradient (DDPG) algorithm is exploited to optimize the power distribution of FCHEV in a real-time fashion. Within the proposed framework, the thermal and aging behaviors of the hybrid power sources are scrutinized and optimized, for the first time, to enhance the safety and life performance of FCHEV. The proposed strategy is tested under typical road missions for validation. The unexpected temperature build-up of lithium-ion battery (LIB) and the degradation of hybrid system can be well suppressed to enhance the thermal safety and life performance. Moreover, comparative results suggest that the proposed strategy can optimize the hybrid sources split collaborated with the improvement of operating performance, economy performance and operating time.

Key findings

• ▸The proposed strategy can suppress the unexpected temperature build-up of lithium-ion batteries and degradation of hybrid systems.
• ▸The strategy can optimize the hybrid sources split and improve operating performance, economy performance, and operating time.
• ▸The safety and life expectancy of fuel cell hybrid electric vehicles can be enhanced using the proposed machine learning-based approach.

Keywords

Identifiers

DOI

10.1109/TTE.2023.3295433

Journal

IEEE Transactions on Transportation Electrification

Year

2024