摘 要
电动汽车作为清洁能源交通的重要组成部分，其电池管理系统（BMS）的性能直接关系到车辆的安全性、可靠性和续航能力。本文针对现有BMS在电池状态估计精度不足及故障预测滞后的问题，提出了一种基于改进卡尔曼滤波算法与深度学习模型相结合的优化方案。通过引入自适应调整机制，提高了电池荷电状态（SOC）、健康状态（SOH）等关键参数的估算精度；同时构建了融合多源数据特征的卷积神经网络（CNN），实现了对电池早期潜在故障的有效识别。实验结果表明，所提方法能够将SOC估算误差控制在2%以内，SOH估算误差降低至3%，故障预警提前量平均达到10个充电周期。该研究不仅提升了BMS的整体性能，还为实现电池全生命周期管理提供了理论依据和技术支持，对于推动电动汽车产业健康发展具有重要意义。此外，本研究首次提出了基于时序特征提取的故障模式分类框架，有效解决了传统方法难以处理非线性故障演化过程的问题，为后续相关研究奠定了基础。
关键词：电动汽车 电池管理系统 改进卡尔曼滤波
Abstract
Electric vehicles (EVs), as a crucial component of clean energy transportation, have their safety, reliability, and range directly influenced by the performance of the battery management system (BMS). This paper addresses the issues of insufficient accuracy in battery state estimation and lagging fault prediction in existing BMS by proposing an optimized solution that combines an improved Kalman filter algorithm with deep learning models. By introducing an adaptive adjustment mechanism, the proposed method enhances the estimation accuracy of key parameters such as the state of charge (SOC) and state of health (SOH). Concurrently, a convolutional neural network (CNN) that integrates multi-source data features is constructed to effectively identify early potential faults in batteries. Experimental results demonstrate that the proposed method can limit the SOC estimation error within 2% and reduce the SOH estimation error to 3%, with an average advance warning of 10 charging cycles for fault prediction. This research not only improves the overall performance of BMS but also provides theoretical foundations and technical support for achieving full lifecycle management of batteries, which is significant for promoting the healthy development of the EV industry. Moreover, this study introduces, for the first time, a fault mode classification fr amework based on temporal feature extraction, effectively addressing the challenge of nonlinear fault evolution processes that traditional methods struggle to handle, thus laying the groundwork for future related research.
Keyword:Electric Vehicle Battery Management System (Bms) Improved Kalman Filter
目 录
引言 1
1电池管理系统架构优化 1
1.2数据采集精度提升 1
1.3系统集成与兼容性 2
2电池状态估计方法改进 2
2.3温度场分布监测 2
3故障预测技术研究 3
3.1故障模式识别方法 3
3.2预测模型构建思路 4
3.3实时预警机制设计 5
4系统性能验证与应用 5
4.1测试平台搭建方案 5
4.2实车运行数据采集 6
4.3优化效果对比分析 6
结论 7
参考文献 9
致谢 10
