Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model
State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries acc...
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doaj-8f15d629ffb1459bb4c8379215e1fdf32021-03-30T01:58:14ZengIEEEIEEE Access2169-35362020-01-018953339534410.1109/ACCESS.2020.29958999097168Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming ModelHang Yao0https://orcid.org/0000-0003-4579-8602Xiang Jia1Qian Zhao2https://orcid.org/0000-0003-4000-1473Zhi-Jun Cheng3https://orcid.org/0000-0002-6854-697XBo Guo4College of Systems Engineering, National University of Defense Technology, Changsha, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha, ChinaCollege of Information and Communication, National University of Defense Technology, Xi’an, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha, ChinaState-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate.https://ieeexplore.ieee.org/document/9097168/Genetic programmingLi-ion batterystate-of-health (SOH)prognostic and health management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hang Yao Xiang Jia Qian Zhao Zhi-Jun Cheng Bo Guo |
spellingShingle |
Hang Yao Xiang Jia Qian Zhao Zhi-Jun Cheng Bo Guo Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model IEEE Access Genetic programming Li-ion battery state-of-health (SOH) prognostic and health management |
author_facet |
Hang Yao Xiang Jia Qian Zhao Zhi-Jun Cheng Bo Guo |
author_sort |
Hang Yao |
title |
Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model |
title_short |
Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model |
title_full |
Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model |
title_fullStr |
Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model |
title_full_unstemmed |
Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model |
title_sort |
novel lithium-ion battery state-of-health estimation method using a genetic programming model |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate. |
topic |
Genetic programming Li-ion battery state-of-health (SOH) prognostic and health management |
url |
https://ieeexplore.ieee.org/document/9097168/ |
work_keys_str_mv |
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