Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion BatteriesSource: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003::page 31009-1Author:Lin, Chunsong
,
Tuo, Xianguo
,
Wu, Longxing
,
Zhang, Guiyu
,
Lyu, Zhiqiang
,
Zeng, Xiangling
DOI: 10.1115/1.4066636Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.
|
Show full item record
contributor author | Lin, Chunsong | |
contributor author | Tuo, Xianguo | |
contributor author | Wu, Longxing | |
contributor author | Zhang, Guiyu | |
contributor author | Lyu, Zhiqiang | |
contributor author | Zeng, Xiangling | |
date accessioned | 2025-04-21T10:09:05Z | |
date available | 2025-04-21T10:09:05Z | |
date copyright | 10/16/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2381-6872 | |
identifier other | jeecs_22_3_031009.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4305599 | |
description abstract | With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries | |
type | Journal Paper | |
journal volume | 22 | |
journal issue | 3 | |
journal title | Journal of Electrochemical Energy Conversion and Storage | |
identifier doi | 10.1115/1.4066636 | |
journal fristpage | 31009-1 | |
journal lastpage | 31009-17 | |
page | 17 | |
tree | Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003 | |
contenttype | Fulltext |