Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering
2022; Wiley; Volume: 46; Issue: 13 Linguagem: Inglês
10.1002/er.8436
ISSN1099-114X
AutoresGaoqi Lian, Min Ye, Qiao Wang, Meng Wei, Xinxin Xu,
Tópico(s)Advanced Battery Materials and Technologies
ResumoInternational Journal of Energy ResearchVolume 46, Issue 13 p. 18192-18211 RESEARCH ARTICLE Considering the temperature influence state-of-charge estimation for lithium-ion batteries based on a back propagation neural network and improved unscented Kalman filtering Gaoqi Lian, Gaoqi Lian orcid.org/0000-0003-2673-0661 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this authorMin Ye, Corresponding Author Min Ye mingye@chd.edu.cn orcid.org/0000-0002-8301-5843 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, China Correspondence Min Ye, National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, Shaanxi 710064, China. Email: mingye@chd.edu.cnSearch for more papers by this authorQiao Wang, Qiao Wang orcid.org/0000-0001-7331-0016 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this authorMeng Wei, Meng Wei orcid.org/0000-0001-9027-9436 Department of Mechanical Engineering, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorXinxin Xu, Xinxin Xu National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this author Gaoqi Lian, Gaoqi Lian orcid.org/0000-0003-2673-0661 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this authorMin Ye, Corresponding Author Min Ye mingye@chd.edu.cn orcid.org/0000-0002-8301-5843 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, China Correspondence Min Ye, National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, Shaanxi 710064, China. Email: mingye@chd.edu.cnSearch for more papers by this authorQiao Wang, Qiao Wang orcid.org/0000-0001-7331-0016 National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this authorMeng Wei, Meng Wei orcid.org/0000-0001-9027-9436 Department of Mechanical Engineering, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorXinxin Xu, Xinxin Xu National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an, ChinaSearch for more papers by this author First published: 25 July 2022 https://doi.org/10.1002/er.8436 Funding information: the Henan Province Outstanding Foreign Scientist Workshop, Grant/Award Number: GZS2022004; the Innovative Talents Promotion Project of Shannxi Province of China, Grant/Award Number: 2020KJXX-044; the Science and Technology Innovation Team of Shaan'xi Provincial, Grant/Award Number: 2020TD0012 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 onFacebookTwitterLinked InRedditWechat Summary Obtaining an accurate mapping relationship between the state-of-charge (SOC) and open-circuit voltage (OCV) of lithium-ion batteries at different ambient temperatures is of great significance for realizing accurate lithium-ion battery SOC estimation considering the ambient temperature influence. However, the desired OCV-SOC relationship is highly nonlinear, and the conventional polynomial fitting method is likely to result in relatively large fitting errors. To solve this problem, a method based on a backpropagation neural network (BPNN) to improve the OCV-SOC fitting accuracy is proposed, and the SOC estimation of lithium-ion batteries considering ambient temperature influence is completed. First, the relationship between the SOC and OCV of a lithium-ion battery at different ambient temperatures is obtained by establishing a BPNN fitting model. Second, by optimizing the covariance decomposition process, a diagonalization of matrix unscented Kalman filtering (UKF) is proposed, which improves the accuracy and stability of the filtering algorithm. Then, the forgetting factor recursive least squares algorithm is combined to accomplish the online update of battery model parameters. Finally, under three working conditions, the effectiveness and robustness of the proposed method are verified. The simulation results show that the proposed method can obtain the most accurate SOC estimation results at each temperature, and the root mean square error (RMSE) and mean absolute error (MAE) under all three working conditions are less than 1.1%. Even if there is a certain error in the initial SOC, the proposed method can ensure that the RMSE and MAE of the SOC estimation results at each temperature do not exceed 1.5%. Volume46, Issue1325 October 2022Pages 18192-18211 RelatedInformation
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