摘 要
电动汽车作为绿色交通的重要组成部分，其电池健康状态评估与寿命预测对保障车辆安全运行、优化电池管理及延长使用寿命具有重要意义。本文聚焦于锂离子动力电池，针对现有评估方法精度不足、预测模型适应性差等问题，提出基于多源数据融合的电池健康状态评估框架，通过采集电压、电流、温度等运行参数，结合机器学习算法实现对电池老化特征的精准提取。研究建立了考虑环境因素影响的非线性寿命预测模型，采用长短期记忆网络进行训练，有效提升了预测准确性与时效性。实验结果表明，所提方法在不同工况下均能准确反映电池衰退趋势，预测误差控制在5%以内，较传统方法降低约30%。该研究为电动汽车电池管理系统提供了理论依据和技术支持，创新性地将多源数据与智能算法相结合，实现了从静态评估到动态预测的转变，为电池全生命周期管理奠定了坚实基础，有助于推动新能源汽车产业健康发展。
关键词：电动汽车电池健康状态评估 多源数据融合 非线性寿命预测模型
Abstract
Electric vehicles, as a crucial component of green transportation, play an essential role in ensuring vehicle safety, optimizing battery management, and extending battery life through accurate assessment of battery health state and lifespan prediction. This paper focuses on lithium-ion power batteries and addresses the issues of insufficient accuracy in existing evaluation methods and poor adaptability of predictive models by proposing a battery health state assessment fr amework based on multi-source data fusion. By collecting operational parameters such as voltage, current, and temperature, and integrating machine learning algorithms, this study achieves precise extraction of battery aging characteristics. A nonlinear lifespan prediction model considering environmental factors is established, and it is trained using long short-term memory networks, thereby significantly enhancing prediction accuracy and timeliness. Experimental results demonstrate that the proposed method accurately reflects battery degradation trends under various conditions with a prediction error controlled within 5%, approximately 30% lower than traditional methods. This research provides theoretical foundations and technical support for electric vehicle battery management systems, innovatively combining multi-source data with intelligent algorithms to achieve a transition from static assessment to dynamic prediction, thus laying a solid foundation for the entire lifecycle management of batteries and promoting the healthy development of the new energy vehicle industry.
Keyword:Electric Vehicle Battery Health State Evaluation Multi-Source Data Fusion Nonlinear Lifetime Prediction Model
目 录
引言 1
1电池健康状态评估基础 1
1.1电动汽车电池系统概述 1
1.2健康状态评估指标体系 2
1.3数据采集与预处理方法 2
2关键参数对健康状态的影响 3
2.1温度对电池性能的影响 3
2.2充放电速率的影响分析 3
2.3循环次数与容量衰减关系 4
3健康状态评估模型构建 4
3.1基于数据驱动的评估模型 4
3.2物理模型与混合模型应用 5
3.3模型准确性验证方法 5
4寿命预测技术及应用 6
4.1剩余使用寿命预测方法 6
4.2预测模型优化策略研究 6
4.3实际工况下的寿命预测 7
结论 7
参考文献 9
致谢 10
