摘 要
电动汽车的广泛应用对电池健康状态(SOH)评估与剩余寿命(RUL)预测提出了更高要求，准确评估SOH和预测RUL是保障车辆安全运行、优化电池管理的关键。为此，本文提出一种融合多源数据特征提取与深度学习模型的电池健康状态评估与剩余寿命预测方法。该方法首先通过采集电池充放电过程中的电压、电流、温度等多源数据，利用小波变换进行特征提取，获取反映电池内部退化机制的有效特征；然后构建基于长短期记忆网络(LSTM)的预测模型，将提取的特征作为输入，训练得到能够准确评估SOH并预测RUL的模型。实验结果表明，所提方法在不同工况下均能实现高精度的SOH评估和RUL预测，相比传统方法具有更高的准确性和鲁棒性。本研究创新性地将多源数据融合与深度学习相结合，为电动汽车电池健康管理提供了新思路，对提升电池使用效率、延长使用寿命具有重要意义。
关键词：电池健康状态评估；剩余寿命预测；多源数据融合
Abstract
The widespread application of electric vehicles has imposed higher demands on the assessment of battery State of Health (SOH) and prediction of Remaining Useful Life (RUL). Accurate SOH assessment and RUL prediction are critical for ensuring safe vehicle operation and optimizing battery management. To address these challenges, this paper proposes a method that integrates multi-source data feature extraction with deep learning models for battery health state evaluation and remaining life prediction. This method first collects multi-source data including voltage, current, and temperature during the battery’s charging and discharging processes, and employs wavelet transform for feature extraction to obtain effective features reflecting the internal degradation mechanisms of the battery. Subsequently, a prediction model based on Long Short-Term Memory (LSTM) networks is constructed, using the extracted features as inputs to train a model capable of accurately assessing SOH and predicting RUL. Experimental results demonstrate that the proposed method achieves high-precision SOH assessment and RUL prediction under various operating conditions, exhibiting higher accuracy and robustness compared to traditional methods. Innovatively combining multi-source data fusion with deep learning, this study provides new insights into electric vehicle battery health management, significantly contributing to improved battery utilization efficiency and extended service life.
Keywords: Battery Health State Evaluation；Remaining Useful Life Prediction；Multi-Source Data Fusion
目 录
引言 1
一、电池健康状态评估基础 1
（一）电池老化机理分析 1
（二）健康状态关键参数 1
（三）数据采集与预处理 2
二、状态评估模型构建 2
（一）特征参数选取原则 2
（二）模型算法选择依据 3
（三）模型训练与验证 3
三、剩余寿命预测方法 4
（一）预测模型框架设计 4
（二）关键影响因素分析 4
（三）预测精度优化策略 5
四、实验验证与应用 5
（一）测试平台搭建 5
（二）实验结果分析 6
（三）应用案例研究 6
结 论 7
致 谢 8
参考文献 9
电动汽车的广泛应用对电池健康状态(SOH)评估与剩余寿命(RUL)预测提出了更高要求，准确评估SOH和预测RUL是保障车辆安全运行、优化电池管理的关键。为此，本文提出一种融合多源数据特征提取与深度学习模型的电池健康状态评估与剩余寿命预测方法。该方法首先通过采集电池充放电过程中的电压、电流、温度等多源数据，利用小波变换进行特征提取，获取反映电池内部退化机制的有效特征；然后构建基于长短期记忆网络(LSTM)的预测模型，将提取的特征作为输入，训练得到能够准确评估SOH并预测RUL的模型。实验结果表明，所提方法在不同工况下均能实现高精度的SOH评估和RUL预测，相比传统方法具有更高的准确性和鲁棒性。本研究创新性地将多源数据融合与深度学习相结合，为电动汽车电池健康管理提供了新思路，对提升电池使用效率、延长使用寿命具有重要意义。
关键词：电池健康状态评估；剩余寿命预测；多源数据融合
Abstract
The widespread application of electric vehicles has imposed higher demands on the assessment of battery State of Health (SOH) and prediction of Remaining Useful Life (RUL). Accurate SOH assessment and RUL prediction are critical for ensuring safe vehicle operation and optimizing battery management. To address these challenges, this paper proposes a method that integrates multi-source data feature extraction with deep learning models for battery health state evaluation and remaining life prediction. This method first collects multi-source data including voltage, current, and temperature during the battery’s charging and discharging processes, and employs wavelet transform for feature extraction to obtain effective features reflecting the internal degradation mechanisms of the battery. Subsequently, a prediction model based on Long Short-Term Memory (LSTM) networks is constructed, using the extracted features as inputs to train a model capable of accurately assessing SOH and predicting RUL. Experimental results demonstrate that the proposed method achieves high-precision SOH assessment and RUL prediction under various operating conditions, exhibiting higher accuracy and robustness compared to traditional methods. Innovatively combining multi-source data fusion with deep learning, this study provides new insights into electric vehicle battery health management, significantly contributing to improved battery utilization efficiency and extended service life.
Keywords: Battery Health State Evaluation；Remaining Useful Life Prediction；Multi-Source Data Fusion
目 录
引言 1
一、电池健康状态评估基础 1
（一）电池老化机理分析 1
（二）健康状态关键参数 1
（三）数据采集与预处理 2
二、状态评估模型构建 2
（一）特征参数选取原则 2
（二）模型算法选择依据 3
（三）模型训练与验证 3
三、剩余寿命预测方法 4
（一）预测模型框架设计 4
（二）关键影响因素分析 4
（三）预测精度优化策略 5
四、实验验证与应用 5
（一）测试平台搭建 5
（二）实验结果分析 6
（三）应用案例研究 6
结 论 7
致 谢 8
参考文献 9
