State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM

Zhaosheng Zhang, Shuo Wang, Ni Lin (), Zhenpo Wang and Peng Liu
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Zhaosheng Zhang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Shuo Wang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Ni Lin: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Zhenpo Wang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Peng Liu: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

Sustainability, 2023, vol. 15, issue 3, 1-20

Abstract: Battery state of health (SOH) estimation is a prerequisite for battery health management and is vital for second-life utilization. Existing techniques implemented in well-controlled experimental conditions fail to reflect complex working conditions during actual vehicular operation. In this article, a novel SOH estimation method for battery systems in real-world electric vehicles (EVs) is presented by combing results of regional capacity calculation and a light gradient boosting machine (LGBM) model. The LGBM model is used to capture the relationship between battery degeneration and influential factors based on datasets from real-world EVs. The regional capacity, which is calculated through incremental capacity analysis with a Gaussian smoothing filter, is utilized to reflect the battery degradation level while ensuring high flexibility and applicability. Accumulated mileage, average charging current, average charging temperature, and start and end of SOC values are chosen as influential factors for model establishment. The effectiveness, complexity, superiority, and robustness of the proposed method are verified using data from real-world EVs. Results indicate accurate SOH estimation can be achieved with an average absolute error of only 0.89 Ah, where the MAPE and RMSE of the test vehicles are 2.049% and 1.153%, respectively.

Keywords: electric vehicles (EVs); lithium-ion battery; state of health (SOH); regional capacity; light gradient boosting machine (LGBM); real-world data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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