{"title":"Toward a BMS2 Design Framework: Adaptive Data-Driven State-of-Health Estimation for Second-Life Batteries With BIBO Stability Guarantees","authors":"Xiaofan Cui;Muhammad Aadil Khan;Surinder Singh;Ratnesh Sharma;Simona Onori","doi":"10.1109/TTE.2025.3530498","DOIUrl":null,"url":null,"abstract":"A key challenge that is currently hindering the widespread use of retired electric vehicle (EV) batteries for second-life (SL) applications is the ability to accurately estimate and monitor their state of health (SOH). SL battery systems can be sourced from different battery packs with a lack of knowledge of their historical usage. Accurate SOH estimation is critical because it enables reliable performance, safety, and optimal utilization of SL batteries, ensuring they meet the requirements of various applications including grid energy storage and backup power. In this work, for in-the-field use of SL batteries, we introduce an online adaptive health estimation approach with the guarantees of bounded-input, bounded-output (BIBO) stability. This method relies exclusively on operational data that can be accessed in real-time from SL batteries. The effectiveness of the proposed approach is shown on a laboratory-aged experimental dataset of retired EV batteries.","PeriodicalId":56269,"journal":{"name":"IEEE Transactions on Transportation Electrification","volume":"11 3","pages":"7684-7696"},"PeriodicalIF":8.3000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Transportation Electrification","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10843798/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
A key challenge that is currently hindering the widespread use of retired electric vehicle (EV) batteries for second-life (SL) applications is the ability to accurately estimate and monitor their state of health (SOH). SL battery systems can be sourced from different battery packs with a lack of knowledge of their historical usage. Accurate SOH estimation is critical because it enables reliable performance, safety, and optimal utilization of SL batteries, ensuring they meet the requirements of various applications including grid energy storage and backup power. In this work, for in-the-field use of SL batteries, we introduce an online adaptive health estimation approach with the guarantees of bounded-input, bounded-output (BIBO) stability. This method relies exclusively on operational data that can be accessed in real-time from SL batteries. The effectiveness of the proposed approach is shown on a laboratory-aged experimental dataset of retired EV batteries.
期刊介绍:
IEEE Transactions on Transportation Electrification is focused on components, sub-systems, systems, standards, and grid interface technologies related to power and energy conversion, propulsion, and actuation for all types of electrified vehicles including on-road, off-road, off-highway, and rail vehicles, airplanes, and ships.