阻抗不确定情况下锂离子电池健康状态估计的特征选择策略优化

IF 13.1 1区 化学 Q1 Energy Journal of Energy Chemistry Pub Date : 2024-09-27 DOI:10.1016/j.jechem.2024.09.032
Xinghao Du , Jinhao Meng , Yassine Amirat , Fei Gao , Mohamed Benbouzid
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引用次数: 0

摘要

电池健康评估和管理对于电动汽车中锂离子电池的长期可靠性和最佳性能至关重要。电化学阻抗光谱(EIS)为电池降解分析和建模提供了宝贵的见解。然而,以往的研究并未充分考虑阻抗的不确定性,尤其是在电池工作条件下,这会严重影响健康状况(SOH)估算的稳健性和准确性。受此启发,本文提出了一种将阻抗有效性评估与相关性分析相结合的综合特征优化方案。通过利用阻抗残差和相关系数等指标,本文提出的方法能有效过滤无效和不重要的阻抗数据,从而提高输入特征的可靠性。随后,构建了极端梯度提升(XGBoost)建模框架,用于估计电池退化轨迹。XGBoost 模型包含多种超参数,并通过遗传算法进行了优化,以提高其适应性和泛化性能。实验验证证实了所提出的特征优化方案的有效性,表明与四种基准技术相比,所提出的方法具有更优越的估算性能。
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Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties
Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles. Electrochemical impedance spectroscopy (EIS) offers valuable insights into battery degradation analysis and modeling. However, previous studies have not adequately addressed the impedance uncertainties, particularly during battery operating conditions, which can substantially impact the robustness and accuracy of state of health (SOH) estimation. Motivated by this, this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis. By utilizing metrics such as impedance residuals and correlation coefficients, the proposed method effectively filters out invalid and insignificant impedance data, thereby enhancing the reliability of the input features. Subsequently, the extreme gradient boosting (XGBoost) modeling framework is constructed for estimating the battery degradation trajectories. The XGBoost model incorporates a diverse range of hyperparameters, optimized by a genetic algorithm to improve its adaptability and generalization performance. Experimental validation confirms the effectiveness of the proposed feature optimization scheme, demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.
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来源期刊
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
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