An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

作     者：Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu 

作者机构：College of Electrical EngineeringSichuan UniversityChengduChina Faculty of Computer Science and EngineeringShanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research InstituteNanjing Institute of TechnologyNanjingChina Department of AutomationUniversity of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of EducationTianjin UniversityTianjinChina Department of Energy TechnologyAalborg University9220 AalborgDenmark 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2022年第5卷第2期

页      面：134-145页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：This work is financially supported by the Natural Science Foundation of China under Grant 52107229 the Fundamental Research Funds for the Sichuan Science and Technology Program under Grant 2021YJ0063 the China Postdoctoral Science Foundation under Grant 2020M673218 Hunan High-tech Industry Science and Technology Innovation Plan under Grant 2020GK2081 the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province under Grant 20KFKT02 

主　　题：Li-ion battery State of health Gaussian process regression Kernel function Feature optimization 

摘      要：In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.