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 

机构地区：[1]College of Electrical Engineering,Sichuan University,Chengdu,China [2]Faculty of Computer Science and Engineering,Shanxi Key Laboratory for Network Computing and Security Technology,Xi’an University of Technology,Xi’an,China [3]Smart Grid Research Institute,Nanjing Institute of Technology,Nanjing,China [4]Department of Automation,University of Science and Technology of China,Hefei,China [5]Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin,China [6]Department of Energy Technology,Aalborg University,9220 Aalborg,Denmark

出　　处：《Automotive Innovation》2022年第2期134-145,共12页汽车创新工程（英文）

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.

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 system.This 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 features.The 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 GPR.As for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency.In 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 efficiency.Advantages 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.

关 键 词：Li-ion battery State of health Gaussian process regression Kernel function Feature optimization 

分 类 号：TM912[电气工程—电力电子与电力传动]