Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery
Artikel i vetenskaplig tidskrift, 2021

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This paper proposes a knowledge-based, multi-physics-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 multi-objective 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 trade-off 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.

Fast charging

battery health

deep deterministic policy gradient

thermal safety

lithium-ion battery


Zhongbao Wei

Beijing Institute of Technology

Zhongyi Quan

University of Alberta

Jingda Wu

Beijing Institute of Technology

Yang Li

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Josep Pou

Nanyang Technological University

Hao Zhong

Beijing Institute of Technology

IEEE Transactions on Industrial Electronics

0278-0046 (ISSN)

Vol. In Press




Datavetenskap (datalogi)



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