{"title":"Battery health state estimation method based on data feature mining","authors":"Geng Chamin, Zhang Tianhai, Chen Bo, Zhou Qingfu","doi":"10.1587/elex.20.20230370","DOIUrl":null,"url":null,"abstract":"The health status estimation of lithium-ion battery is a challenging through measurement. To establish a connection between battery health status and data features, a battery State of Health (SOH) estimation method based on data feature mining is proposed. Four features are extracted from the battery charging curve, and the grey correlation analysis is used to determine the high correlation between features and health status. The method combines a Backpropagation (BP) neural network with Genetic Algorithm (GA) for feature training and learning, enabling the estimation of battery SOH. The feasibility of the proposed method is validated using the NASA battery dataset. The results show that the battery SOH estimation method proposed in this paper outperforms the traditional BP neural network method achieving accurate estimation.","PeriodicalId":50387,"journal":{"name":"Ieice Electronics Express","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ieice Electronics Express","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1587/elex.20.20230370","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The health status estimation of lithium-ion battery is a challenging through measurement. To establish a connection between battery health status and data features, a battery State of Health (SOH) estimation method based on data feature mining is proposed. Four features are extracted from the battery charging curve, and the grey correlation analysis is used to determine the high correlation between features and health status. The method combines a Backpropagation (BP) neural network with Genetic Algorithm (GA) for feature training and learning, enabling the estimation of battery SOH. The feasibility of the proposed method is validated using the NASA battery dataset. The results show that the battery SOH estimation method proposed in this paper outperforms the traditional BP neural network method achieving accurate estimation.
期刊介绍：
An aim of ELEX is rapid publication of original, peer-reviewed short papers that treat the field of modern electronics and electrical engineering. The boundaries of acceptable fields are not strictly delimited and they are flexibly varied to reflect trends of the fields. The scope of ELEX has mainly been focused on device and circuit technologies. Current appropriate topics include:
- Integrated optoelectronics (lasers and optoelectronic devices, silicon photonics, planar lightwave circuits, polymer optical circuits, etc.)
- Optical hardware (fiber optics, microwave photonics, optical interconnects, photonic signal processing, photonic integration and modules, optical sensing, etc.)
- Electromagnetic theory
- Microwave and millimeter-wave devices, circuits, and modules
- THz devices, circuits and modules
- Electron devices, circuits and modules (silicon, compound semiconductor, organic and novel materials)
- Integrated circuits (memory, logic, analog, RF, sensor)
- Power devices and circuits
- Micro- or nano-electromechanical systems
- Circuits and modules for storage
- Superconducting electronics
- Energy harvesting devices, circuits and modules
- Circuits and modules for electronic displays
- Circuits and modules for electronic instrumentation
- Devices, circuits and modules for IoT and biomedical applications
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