Specialized deep neural networks for battery health prognostics:Opportunities and challenges  

作　　者：Jingyuan Zhao Xuebing Han Minggao Ouyang Andrew F.Burke 

机构地区：[1]Institute of Transportation Studies,University of California,Davis,CA 95616,USA [2]State Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,China

出　　处：《Journal of Energy Chemistry》2023年第12期416-438,I0011,共24页能源化学（英文版）

摘　　要：Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.

关 键 词：Lithium-ion batteries State of health LIFETIME Deep learning Transformer Transfer learning Physics-informed learning Generative adversarial networks Reinforcement learning Open data 

分 类 号：TM912[电气工程—电力电子与电力传动] TP183[自动化与计算机技术—控制理论与控制工程]