Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology

International Journal of Energy ResearchVolume 44, Issue 13 p. 10307-10319 RESEARCH ARTICLE Neural network-based learning and estimation of battery state-of-charge: A comparison study between direct and indirect methodology Wen Sun, Wen Sun Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, ChinaSearch for more papers by this authorYicheng Qiu, Yicheng Qiu Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, ChinaSearch for more papers by this authorLi Sun, Corresponding Author Li Sun sunli12@seu.edu.cn orcid.org/0000-0001-8960-8773 Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, China Correspondence Li Sun, Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China. Email: sunli12@seu.edu.cn Qingsong Hua, College of Nuclear Science and Technology, Beijing Normal University, 100875, Beijing, China. Email: 11112019039@bnu.edu.cnSearch for more papers by this authorQingsong Hua, Corresponding Author Qingsong Hua 11112019039@bnu.edu.cn Key Laboratory of Beam Technology of Ministry of Education, College of Nuclear Science and Technology, College of Nuclear Science and Technology, Beijing Normal University, Beijing, China Correspondence Li Sun, Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China. Email: sunli12@seu.edu.cn Qingsong Hua, College of Nuclear Science and Technology, Beijing Normal University, 100875, Beijing, China. Email: 11112019039@bnu.edu.cnSearch for more papers by this author Wen Sun, Wen Sun Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, ChinaSearch for more papers by this authorYicheng Qiu, Yicheng Qiu Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, ChinaSearch for more papers by this authorLi Sun, Corresponding Author Li Sun sunli12@seu.edu.cn orcid.org/0000-0001-8960-8773 Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, China Correspondence Li Sun, Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China. Email: sunli12@seu.edu.cn Qingsong Hua, College of Nuclear Science and Technology, Beijing Normal University, 100875, Beijing, China. Email: 11112019039@bnu.edu.cnSearch for more papers by this authorQingsong Hua, Corresponding Author Qingsong Hua 11112019039@bnu.edu.cn Key Laboratory of Beam Technology of Ministry of Education, College of Nuclear Science and Technology, College of Nuclear Science and Technology, Beijing Normal University, Beijing, China Correspondence Li Sun, Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China. Email: sunli12@seu.edu.cn Qingsong Hua, College of Nuclear Science and Technology, Beijing Normal University, 100875, Beijing, China. Email: 11112019039@bnu.edu.cnSearch for more papers by this author First published: 16 July 2020 https://doi.org/10.1002/er.5654Citations: 14 Funding information: National Natural Science Foundation of China, Grant/Award Numbers: 51806034, 51936003; Natural Science Foundation of Jiangsu Province, China, Grant/Award Number: BK20170686 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Summary Faced with the ever-increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium-ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state-of-charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)-based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX-NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)-based NN with unscented Kalman filter (RBFNN-UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN-UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV-BMS application. Citing Literature Supporting Information Filename Description er5654-sup-0001-SuppInfo.zipZip archive, 3.6 MB Appendix S1: The dataset and programming codes Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume44, Issue1325 October 2020Pages 10307-10319 RelatedInformation