TY  - JOUR
AU  - Li, Weihan
AU  - Cui, Han
AU  - Nemeth, Thomas
AU  - Jansen, Jonathan
AU  - Ünlübayir, Cem
AU  - Wei, Zhongbao
AU  - Zhang, Lei
AU  - Wang, Zhenpo
AU  - Ruan, Jiageng
AU  - Dai, Haifeng
AU  - Wei, Xuezhe
AU  - Sauer, Dirk Uwe
TI  - Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles
JO  - Journal of energy storage
VL  - 36
SN  - 2352-152X
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - FZJ-2021-01867
SP  - 102355 -
PY  - 2021
AB  - In this paper, we propose an energy management strategy based on deep reinforcement learning for a hybrid battery system in electric vehicles consisting of a high-energy and a high-power battery pack. The energy management strategy of the hybrid battery system was developed based on the electrical and thermal characterization of the battery cells, aiming at minimizing the energy loss and increasing both the electrical and thermal safety level of the whole system. Primarily, we designed a novel reward term to explore the optimal operating range of the high-power pack without imposing a rigid constraint of state of charge. Furthermore, various load profiles were randomly combined to train the deep Q-learning model, which avoided the overfitting problem. The training and validation results showed both the effectiveness and reliability of the proposed strategy in loss reduction and safety enhancement. The proposed energy management strategy has demonstrated its superiority over the reinforcement learning-based methods in both computation time and energy loss reduction of the hybrid battery system, highlighting the use of such an approach in future energy management systems.
LB  - PUB:(DE-HGF)16
C6  - WOS:000635503100004
UR  - <Go to ISI:>//WOS:000635503100004
DO  - DOI:10.1016/j.est.2021.102355
UR  - https://juser.fz-juelich.de/record/891995
ER  -