摘 要
随着电动汽车产业的快速发展，电池作为核心部件，其性能和寿命直接影响车辆的整体表现。为解决传统电池管理系统在监控与维护方面存在的局限性，本文提出一种基于智能算法的电动汽车电池管理系统的监控与维护策略。研究旨在通过引入机器学习算法和大数据分析技术，实现对电池状态的实时精准监测，并根据监测数据制定科学合理的维护方案。采用实验验证与仿真模拟相结合的方法，构建了包含电池充放电特性、温度变化等多因素在内的综合评价模型。结果表明，该智能监控系统能够有效提高电池使用效率，延长使用寿命，降低故障率。与传统方法相比，本研究创新性地将人工智能技术应用于电池管理领域，实现了从被动维护到主动预防的转变，为提升电动汽车整体性能提供了新的思路和技术支持，具有重要的理论意义和应用价值。
关键词：电动汽车电池管理；智能算法；机器学习
Abstract
With the rapid development of the electric vehicle (EV) industry, batteries as core components play a crucial role in determining the overall performance of vehicles. To address the limitations of traditional battery management systems (BMS) in monitoring and maintenance, this study proposes an intelligent algorithm-based BMS monitoring and maintenance strategy for electric vehicles. The research aims to introduce machine learning algorithms and big data analytics to achieve real-time and precise monitoring of battery states, and to develop scientifically sound maintenance plans based on the monitoring data. By integrating experimental validation with simulation modeling, a comprehensive evaluation model incorporating multiple factors such as battery charge-discharge characteristics and temperature variations has been constructed. The results indicate that the intelligent monitoring system can effectively enhance battery usage efficiency, extend service life, and reduce failure rates. Compared with traditional methods, this study innovatively applies artificial intelligence technology to the field of battery management, achieving a shift from reactive maintenance to proactive prevention. This provides new insights and technical support for improving the overall performance of electric vehicles, demonstrating significant theoretical implications and practical value.
Keywords: Electric Vehicle Battery Management；Intelligent Algorithm；Machine Learning
目 录
引言 1
一、电池管理系统架构分析 1
（一）系统架构设计原则 1
（二）关键组件功能解析 2
（三）架构优化策略研究 2
二、智能监控技术应用 2
（一）数据采集与处理 3
（二）实时状态监测方法 3
（三）异常预警机制建立 3
三、维护策略体系构建 4
（一）预防性维护措施 4
（二）故障诊断流程优化 4
（三）寿命预测模型建立 5
四、系统性能评估方法 5
（一）性能指标体系构建 5
（二）测试验证方案设计 6
（三）改进措施与建议 6
结 论 7
致 谢 8
参考文献 9
随着电动汽车产业的快速发展，电池作为核心部件，其性能和寿命直接影响车辆的整体表现。为解决传统电池管理系统在监控与维护方面存在的局限性，本文提出一种基于智能算法的电动汽车电池管理系统的监控与维护策略。研究旨在通过引入机器学习算法和大数据分析技术，实现对电池状态的实时精准监测，并根据监测数据制定科学合理的维护方案。采用实验验证与仿真模拟相结合的方法，构建了包含电池充放电特性、温度变化等多因素在内的综合评价模型。结果表明，该智能监控系统能够有效提高电池使用效率，延长使用寿命，降低故障率。与传统方法相比，本研究创新性地将人工智能技术应用于电池管理领域，实现了从被动维护到主动预防的转变，为提升电动汽车整体性能提供了新的思路和技术支持，具有重要的理论意义和应用价值。
关键词：电动汽车电池管理；智能算法；机器学习
Abstract
With the rapid development of the electric vehicle (EV) industry, batteries as core components play a crucial role in determining the overall performance of vehicles. To address the limitations of traditional battery management systems (BMS) in monitoring and maintenance, this study proposes an intelligent algorithm-based BMS monitoring and maintenance strategy for electric vehicles. The research aims to introduce machine learning algorithms and big data analytics to achieve real-time and precise monitoring of battery states, and to develop scientifically sound maintenance plans based on the monitoring data. By integrating experimental validation with simulation modeling, a comprehensive evaluation model incorporating multiple factors such as battery charge-discharge characteristics and temperature variations has been constructed. The results indicate that the intelligent monitoring system can effectively enhance battery usage efficiency, extend service life, and reduce failure rates. Compared with traditional methods, this study innovatively applies artificial intelligence technology to the field of battery management, achieving a shift from reactive maintenance to proactive prevention. This provides new insights and technical support for improving the overall performance of electric vehicles, demonstrating significant theoretical implications and practical value.
Keywords: Electric Vehicle Battery Management；Intelligent Algorithm；Machine Learning
目 录
引言 1
一、电池管理系统架构分析 1
（一）系统架构设计原则 1
（二）关键组件功能解析 2
（三）架构优化策略研究 2
二、智能监控技术应用 2
（一）数据采集与处理 3
（二）实时状态监测方法 3
（三）异常预警机制建立 3
三、维护策略体系构建 4
（一）预防性维护措施 4
（二）故障诊断流程优化 4
（三）寿命预测模型建立 5
四、系统性能评估方法 5
（一）性能指标体系构建 5
（二）测试验证方案设计 6
（三）改进措施与建议 6
结 论 7
致 谢 8
参考文献 9
