摘 要
随着新能源汽车的快速发展，动力电池作为其核心部件，其管理系统的性能优化成为研究热点。本研究旨在通过改进电池管理系统（BMS）的设计，提升其在状态估计、均衡控制及热管理方面的综合性能。为此，提出了一种基于多源数据融合的状态估计算法，结合卡尔曼滤波与机器学习模型，显著提高了电池荷电状态（SOC）和健康状态（SOH）的估算精度。同时，设计了一种自适应均衡策略，可根据电池组的实际工况动态调整均衡电流，有效延长了电池寿命。此外，开发了一种新型相变材料辅助的热管理系统，实现了对电池温度的精准调控，确保其在极端环境下的稳定运行。实验结果表明，所提出的优化方案可将SOC估算误差降低至2%以内，均衡效率提升15%，并使电池温差控制在3℃范围内。本研究为动力电池管理系统的性能提升提供了创新思路和技术支撑，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；状态估计算法；自适应均衡策略
Abstract
With the rapid development of new energy vehicles, the performance optimization of battery management systems (BMS), as a core component, has become a research hotspot. This study aims to enhance the overall performance of BMS in state estimation, balancing control, and thermal management through improved design. To achieve this, a multi-source data fusion-based state estimation algorithm was proposed, integrating Kalman filtering with machine learning models, which significantly improved the estimation accuracy of the battery's state of charge (SOC) and state of health (SOH). Simultaneously, an adaptive balancing strategy was designed to dynamically adjust the balancing current according to the actual operating conditions of the battery pack, effectively extending the battery's lifespan. Furthermore, a novel phase-change material-assisted thermal management system was developed, enabling precise temperature regulation and ensuring stable operation under extreme environmental conditions. Experimental results demonstrate that the proposed optimization approach reduces SOC estimation error to within 2%, increases balancing efficiency by 15%, and maintains battery temperature differences within 3°C. This research provides innovative ideas and technical support for enhancing the performance of battery management systems, offering significant theoretical value and practical application potential.
Keywords: Battery Management System；State Estimation Algorithm；Adaptive Balancing Strategy
目 录
一、绪论 1
（一）动力电池管理系统的背景与意义 1
（二）国内外研究现状分析 1
二、动力电池管理系统优化设计基础 1
（一）动力电池管理系统的功能需求 2
（二）关键技术分析与选择 2
（三）系统架构设计优化 3
三、数据采集与状态估计性能提升 3
（一）数据采集精度优化策略 3
（二）电池状态估计算法改进 4
（三）实时性能评估与验证 4
四、控制策略与均衡管理优化 5
（一）先进控制策略设计 5
（二）电池均衡技术优化方案 5
（三）系统稳定性与效率提升 6
结 论 6
致 谢 7
参考文献 8
