摘要
随着全球能源危机和环境污染问题日益严峻，新能源汽车作为可持续交通的重要发展方向，其核心部件电池管理系统（BMS）的优化设计成为研究热点。本研究旨在提升BMS在能量管理、安全性和寿命预测方面的性能，以满足新能源汽车对高效、可靠运行的需求。为此，提出了一种基于多目标优化算法的BMS设计框架，结合实时状态估计与动态均衡策略，显著改善了电池组的一致性和能量利用效率。研究通过引入深度学习模型对电池老化特性进行精准建模，并开发了自适应控制算法以应对复杂工况下的性能波动。实验结果表明，所提出的优化方法能够将电池组的能量利用率提高约8%，同时延长循环寿命达15%以上。此外，该系统在极端温度条件下的稳定性也得到了有效验证。本研究的主要创新点在于将人工智能技术与传统BMS架构深度融合，实现了更高精度的状态估计和更优的资源分配策略，为未来新能源汽车电池管理系统的智能化发展提供了重要参考。
关键词：电池管理系统；多目标优化算法；深度学习模型
Abstract
With the increasing severity of global energy crises and environmental pollution, new energy vehicles (NEVs) have become a crucial direction for sustainable transportation development, and the optimization design of their core component, the battery management system (BMS), has emerged as a research hotspot. This study aims to enhance the performance of BMS in energy management, safety, and lifespan prediction to meet the demands for efficient and reliable operation of NEVs. To this end, a BMS design fr amework based on multi-ob jective optimization algorithms is proposed, integrating real-time state estimation with dynamic balancing strategies, which significantly improves the consistency of battery packs and energy utilization efficiency. By introducing deep learning models for precise modeling of battery aging characteristics and developing adaptive control algorithms to address performance fluctuations under complex operating conditions, the study achieves notable improvements. Experimental results demonstrate that the proposed optimization method can increase the energy utilization rate of battery packs by approximately 8% while extending the cycle life by over 15%. Additionally, the stability of the system under extreme temperature conditions has been effectively validated. The primary innovation of this research lies in the deep integration of artificial intelligence technologies with traditional BMS architectures, achieving higher-precision state estimation and superior resource allocation strategies, thereby providing significant reference for the intelligent development of future NEV battery management systems.
Keywords：Battery Management System; Multi-ob jective Optimization Algorithm; Deep Learning Model
目 录
摘要 I
Abstract II
一、绪论 1
(一) 新能源汽车电池管理系统研究背景 1
(二) 电池管理系统优化设计的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与内容安排 2
二、电池管理系统关键问题分析 2
(一) 电池状态监测技术研究 2
(二) 电池均衡策略分析 3
(三) 系统热管理需求评估 3
三、电池管理系统优化设计方法 4
(一) 数据采集与处理方案设计 4
(二) 控制算法优化策略 4
(三) 硬件架构改进方向 5
四、优化设计的验证与应用 6
(一) 实验平台搭建与测试方法 6
(二) 性能评估与结果分析 6
(三) 实际应用场景探讨 7
结 论 8
参考文献 9
随着全球能源危机和环境污染问题日益严峻，新能源汽车作为可持续交通的重要发展方向，其核心部件电池管理系统（BMS）的优化设计成为研究热点。本研究旨在提升BMS在能量管理、安全性和寿命预测方面的性能，以满足新能源汽车对高效、可靠运行的需求。为此，提出了一种基于多目标优化算法的BMS设计框架，结合实时状态估计与动态均衡策略，显著改善了电池组的一致性和能量利用效率。研究通过引入深度学习模型对电池老化特性进行精准建模，并开发了自适应控制算法以应对复杂工况下的性能波动。实验结果表明，所提出的优化方法能够将电池组的能量利用率提高约8%，同时延长循环寿命达15%以上。此外，该系统在极端温度条件下的稳定性也得到了有效验证。本研究的主要创新点在于将人工智能技术与传统BMS架构深度融合，实现了更高精度的状态估计和更优的资源分配策略，为未来新能源汽车电池管理系统的智能化发展提供了重要参考。
关键词：电池管理系统；多目标优化算法；深度学习模型
Abstract
With the increasing severity of global energy crises and environmental pollution, new energy vehicles (NEVs) have become a crucial direction for sustainable transportation development, and the optimization design of their core component, the battery management system (BMS), has emerged as a research hotspot. This study aims to enhance the performance of BMS in energy management, safety, and lifespan prediction to meet the demands for efficient and reliable operation of NEVs. To this end, a BMS design fr amework based on multi-ob jective optimization algorithms is proposed, integrating real-time state estimation with dynamic balancing strategies, which significantly improves the consistency of battery packs and energy utilization efficiency. By introducing deep learning models for precise modeling of battery aging characteristics and developing adaptive control algorithms to address performance fluctuations under complex operating conditions, the study achieves notable improvements. Experimental results demonstrate that the proposed optimization method can increase the energy utilization rate of battery packs by approximately 8% while extending the cycle life by over 15%. Additionally, the stability of the system under extreme temperature conditions has been effectively validated. The primary innovation of this research lies in the deep integration of artificial intelligence technologies with traditional BMS architectures, achieving higher-precision state estimation and superior resource allocation strategies, thereby providing significant reference for the intelligent development of future NEV battery management systems.
Keywords：Battery Management System; Multi-ob jective Optimization Algorithm; Deep Learning Model
目 录
摘要 I
Abstract II
一、绪论 1
(一) 新能源汽车电池管理系统研究背景 1
(二) 电池管理系统优化设计的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与内容安排 2
二、电池管理系统关键问题分析 2
(一) 电池状态监测技术研究 2
(二) 电池均衡策略分析 3
(三) 系统热管理需求评估 3
三、电池管理系统优化设计方法 4
(一) 数据采集与处理方案设计 4
(二) 控制算法优化策略 4
(三) 硬件架构改进方向 5
四、优化设计的验证与应用 6
(一) 实验平台搭建与测试方法 6
(二) 性能评估与结果分析 6
(三) 实际应用场景探讨 7
结 论 8
参考文献 9
