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@ARTICLE{Li:891995,
      author       = {Li, Weihan and Cui, Han and Nemeth, Thomas and Jansen,
                      Jonathan and Ünlübayir, Cem and Wei, Zhongbao and Zhang,
                      Lei and Wang, Zhenpo and Ruan, Jiageng and Dai, Haifeng and
                      Wei, Xuezhe and Sauer, Dirk Uwe},
      title        = {{D}eep reinforcement learning-based energy management of
                      hybrid battery systems in electric vehicles},
      journal      = {Journal of energy storage},
      volume       = {36},
      issn         = {2352-152X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-01867},
      pages        = {102355 -},
      year         = {2021},
      abstract     = {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.},
      cin          = {IEK-12},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {122 - Elektrochemische Energiespeicherung (POF4-122)},
      pid          = {G:(DE-HGF)POF4-122},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {WOS:000635503100004},
      UT           = {WOS:000635503100004},
      doi          = {10.1016/j.est.2021.102355},
      url          = {https://juser.fz-juelich.de/record/891995},
}