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基于集成高斯过程回归的锂离子电池健康状态预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
锂离子电池性能会随着时间推移而逐渐下降，实时预测电池的健康状态(State of health，SOH)至关重要。本文提出一种融合间接健康指标和集成高斯过程回归(Ensemble Gaussian process regression，EGPR)的锂离子电池SOH预测模型。首先，通过分析放电过程中电压和温度的变化提炼出能够反应电池退化过程的6个间接健康因子。其次，利用塘鹅优化算法(Gannet optimization algorithm，GOA)对EGPR模型中的参数进行优化并估计SOH。最后，该预测模型在多种实验场景下进行了测试和比较。针对NASA锂电池数据集的仿真实验表明，该方法具有较高的预测精度和泛化能力，均方根误差保持在0。20％以内，平均绝对误差低于0。16％。
State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression
The performance of lithium-ion batteries(LIBs) gradually declines over time,making it critical to predict the battery's state of health(SOH) in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression (EGPR) to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs) derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration (NASA) LIB.The root mean square error(RMSE) is maintained within 0.20％,and the mean absolute error(MAE) is below 0.16％,illustrating the proposed approach's excellent predictive accuracy and wide applicability.

lithium-ion batteryies (LIBs)ensemble Gaussian process regression(EGPR)state of health(SOH)health indicators (HIs)gannet optimization algorithm(GOA)

惠周利、王瑞洁、冯娜娜、杨明

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中北大学数学学院,山西太原 030051

锂离子电池 集成高斯过程回归 健康状态 健康因子 塘鹅优化算法

Fundamental Research Program of Shanxi ProvinceShanxi Provincial Natural Science Foundation

202203021211088202204021301049

2024

10.62756/jmsi.1674-8042.2024041

测试科学与仪器
中北大学

测试科学与仪器

影响因子：0.111
ISSN：1674-8042
年,卷(期)：2024.15(3)