Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic fra...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 69; no. 3; pp. 2588 - 2598
Main Authors: Wei, Zhongbao, Quan, Zhongyi, Wu, Jingda, Li, Yang, Pou, Josep, Zhong, Hao
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Batteries
Battery health
Charging
Computational modeling
Constraints
deep deterministic policy gradient
deep deterministic policy gradient (DDPG)
Degradation
Electric vehicles
Fast charging
Heating systems
Integrated circuit modeling
Life extension
Lithium
Lithium-ion batteries
lithium-ion battery
lithium-ion battery (LIB)
Machine learning
Multiple objective analysis
Observers
Optimization
Predictive control
Product safety
Rechargeable batteries
Safety
State observers
thermal safety
Trajectory
ISSN:0278-0046, 1557-9948, 1557-9948
Online Access:Get full text
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Summary:Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.
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ISSN:0278-0046
1557-9948
1557-9948
DOI:10.1109/TIE.2021.3070514