Abstract
Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
