{"title":"Core temperature estimation of lithium-ion battery based on numerical model fusion deep learning","authors":"","doi":"10.1016/j.est.2024.114148","DOIUrl":null,"url":null,"abstract":"<div><div>Temperature has a critical impact on the lifespan and safety of lithium batteries. This paper proposes a battery core temperature estimation method based on numerical model fused with long short-term memory (LSTM) neural network. The proposed technique extracts features from the numerical model, estimates the volume-averaged temperature by electrochemical impedance spectroscopy (EIS), uses an LSTM neural network to learn thermodynamic parameters and complex calculations, which takes advantage of the strengths of each method, and achieves accurate core temperature estimation. The effects of state of charge (SOC) and temperature on EIS are explored, impedance properties are selected on the criteria of robustness and rapidity, and the estimation of the volume-averaged temperature is achieved using the imaginary part of the impedance. The proposed method can achieve root mean squared error (RMSE) of less than 0.28 °C and mean absolute error (MAE) of less than 0.23 °C. The proposed method has advantages of high estimation accuracy and does not require an electrothermal model. It also considers the effect of ambient temperature and has a good generalization capability.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":null,"pages":null},"PeriodicalIF":8.9000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of energy storage","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352152X24037344","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Temperature has a critical impact on the lifespan and safety of lithium batteries. This paper proposes a battery core temperature estimation method based on numerical model fused with long short-term memory (LSTM) neural network. The proposed technique extracts features from the numerical model, estimates the volume-averaged temperature by electrochemical impedance spectroscopy (EIS), uses an LSTM neural network to learn thermodynamic parameters and complex calculations, which takes advantage of the strengths of each method, and achieves accurate core temperature estimation. The effects of state of charge (SOC) and temperature on EIS are explored, impedance properties are selected on the criteria of robustness and rapidity, and the estimation of the volume-averaged temperature is achieved using the imaginary part of the impedance. The proposed method can achieve root mean squared error (RMSE) of less than 0.28 °C and mean absolute error (MAE) of less than 0.23 °C. The proposed method has advantages of high estimation accuracy and does not require an electrothermal model. It also considers the effect of ambient temperature and has a good generalization capability.
期刊介绍:
Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.