摘 要
随着全球能源危机和环境污染问题的日益加剧，电动汽车作为可持续交通的重要解决方案受到广泛关注，而电池管理系统（BMS）作为电动汽车核心组件之一，对提升电池性能、延长使用寿命及保障安全性具有关键作用。本研究旨在设计并优化一种高效、可靠的电动汽车电池管理系统，通过引入先进的算法与硬件架构，解决传统BMS在精度、效率及适应性方面的不足。研究采用模块化设计方法，结合高精度状态估计算法（如扩展卡尔曼滤波）与自适应均衡策略，实现了电池荷电状态（SOC）、健康状态（SOH）的精确评估，并显著提升了电池组的能量利用效率。此外，本研究提出了一种基于机器学习的故障诊断模型，能够实时监测电池运行状态并预测潜在故障，从而进一步提高系统的可靠性和安全性。实验结果表明，所设计的BMS在复杂工况下表现出优异的稳定性和准确性，SOC估算误差低于3%，能量回收效率提升约10%。该研究不仅为电动汽车BMS的设计提供了新思路，还为未来智能化电池管理技术的发展奠定了基础，具有重要的理论价值和实际应用前景。
关键词：电池管理系统；电动汽车；状态估算；机器学习；故障诊断
Abstract
With the escalating global energy crisis and environmental pollution, electric vehicles (EVs) have garnered significant attention as a crucial solution for sustainable transportation. As one of the core components of EVs, the battery management system (BMS) plays a pivotal role in enhancing battery performance, extending service life, and ensuring safety. This study aims to design and optimize an efficient and reliable BMS for electric vehicles by incorporating advanced algorithms and hardware architectures to address the limitations of traditional BMS systems in terms of accuracy, efficiency, and adaptability. A modular design approach is adopted, integrating high-precision state estimation algorithms such as the extended Kalman filter with adaptive balancing strategies, thereby achieving precise evaluations of the battery's state of charge (SOC) and state of health (SOH), while significantly improving the energy utilization efficiency of the battery pack. Furthermore, this research proposes a machine-learning-based fault diagnosis model capable of real-time monitoring of battery operation and predicting potential failures, thus further enhancing the reliability and safety of the system. Experimental results demonstrate that the designed BMS exhibits excellent stability and accuracy under complex operating conditions, with an SOC estimation error below 3% and an approximately 10% improvement in energy recovery efficiency. This study not only provides new insights into the design of EV BMS but also lays a foundation for the development of future intelligent battery management technologies, offering significant theoretical value and practical application prospects.
Keywords: Battery Management System；Electric Vehicle；State Estimation；Machine Learning；Fault Diagnosis
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电动汽车电池管理系统研究背景与意义 1
（二）电池管理系统设计与优化的研究现状 1
（三）本文研究方法与技术路线 2
二、电池管理系统的功能需求分析 2
（一）电动汽车电池系统的基本特性 2
（二）电池管理系统的功能模块划分 3
（三）关键功能需求的识别与定义 3
三、电池管理系统的设计方案 4
（一）系统架构设计与硬件选型 4
（二）软件算法设计与实现路径 4
（三）设计方案的可行性验证 5
四、电池管理系统的优化策略研究 5
（一）电池状态估计的优化方法 5
（二）热管理性能的提升策略 6
（三）系统效率与可靠性的综合优化 6
结 论 7
致 谢 8
参考文献 9
