Abstract
Knowing the batteries’ health state in electric vehicles (EVs) accurately and effectively would directly enhance the system’s reliability and safety. Accordingly, this article proposes a state of health (SOH) estimation method based on the collaboration of feature selection and machine learning methods. Specifically, actual EV data from more than 1200 charging processes are analyzed to designate the constrained voltage range for data processing. Features are then derived, extracted, and selected from the capacity-based curves to depict the battery degradation process and estimate the SOH. Afterward, to find the relevant SOH estimation features, a recursive approach is utilized for pruning the unimportant features cooperated with a linear regression (LR) model. SOH estimation is therefore realized based on the above-obtained features and a low computation cost linear regressor. Six lithium-ion batteries are used to verify the proposed method. The maximum estimation error can be quantitatively limited in the range of −1%–1% with the synergy of the model and features. Furthermore, comparative experiments demonstrate that the LR with optimized features could achieve a similar SOH estimation accuracy as the complicated nonlinear model but using less operating time.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Journal of Emerging and Selected Topics in Power Electronics
Disclaimer: 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.