摘要
随着全球能源危机和环境污染问题日益严重，电动汽车作为绿色交通的重要组成部分，其动力系统效率优化与能量管理策略成为研究热点。本文针对电动汽车动力系统效率优化展开深入研究，旨在通过改进能量管理策略提升车辆整体性能并降低能耗。基于对现有电动汽车动力系统结构及工作原理的分析，提出一种融合多源信息的能量管理算法，该算法综合考虑电池状态、行驶工况以及环境因素，实现了对电机驱动系统和储能系统的协同控制。采用仿真平台对所提算法进行验证，结果表明，在多种典型工况下，相较于传统控制方法，新算法可使整车平均能耗降低约15%，续航里程有效增加12%左右。此外，创新性地引入了机器学习技术用于预测驾驶行为模式，进一步提高了能量利用效率。研究还探讨了不同温度条件下电池性能变化对能量管理策略的影响，提出了适应宽温域工况的能量分配方案。本研究为提高电动汽车实际运行效率提供了理论依据和技术支持，对推动新能源汽车产业发展具有重要意义。
关键词：电动汽车能量管理；动力系统效率优化；多源信息融合算法
Abstract
As global energy crises and environmental pollution become increasingly severe, electric vehicles (EVs) have emerged as a critical component of green transportation, with the optimization of their power system efficiency and energy management strategies becoming a focal point of research. This study delves into the efficiency optimization of EV power systems, aiming to enhance overall vehicle performance and reduce energy consumption through improved energy management strategies. Based on an analysis of existing EV power system structures and operational principles, a multi-source information fusion energy management algorithm is proposed. This algorithm integrates battery status, driving conditions, and environmental factors to achieve coordinated control of the motor drive system and energy storage system. The proposed algorithm was validated using a simulation platform, demonstrating that under various typical driving conditions, compared to traditional control methods, the new algorithm can reduce average vehicle energy consumption by approximately 15% and increase driving range by about 12%. Additionally, machine learning techniques were innovatively introduced to predict driving behavior patterns, further improving energy utilization efficiency. The study also examined the impact of battery performance changes under different temperature conditions on energy management strategies and proposed an energy distribution scheme adaptable to wide temperature ranges. This research provides theoretical foundations and technical support for enhancing the actual operational efficiency of electric vehicles, which is of significant importance for promoting the development of the new energy vehicle industry.
Keywords：Electric Vehicle Energy Management; Power System Efficiency Optimization; Multi-source Information Fusion Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法与技术路线 2
二、动力系统效率影响因素分析 2
(一) 电机系统效率优化 2
(二) 电池管理系统改进 3
(三) 能量回收机制评估 4
三、能量管理策略设计与优化 4
(一) 基于工况的能量分配 4
(二) 智能充电策略研究 5
(三) 热管理系统优化 6
四、实验验证与结果分析 6
(一) 实验平台搭建 6
(二) 效率测试与对比 7
(三) 结果讨论与改进建议 8
结 论 9
参考文献 10
随着全球能源危机和环境污染问题日益严重，电动汽车作为绿色交通的重要组成部分，其动力系统效率优化与能量管理策略成为研究热点。本文针对电动汽车动力系统效率优化展开深入研究，旨在通过改进能量管理策略提升车辆整体性能并降低能耗。基于对现有电动汽车动力系统结构及工作原理的分析，提出一种融合多源信息的能量管理算法，该算法综合考虑电池状态、行驶工况以及环境因素，实现了对电机驱动系统和储能系统的协同控制。采用仿真平台对所提算法进行验证，结果表明，在多种典型工况下，相较于传统控制方法，新算法可使整车平均能耗降低约15%，续航里程有效增加12%左右。此外，创新性地引入了机器学习技术用于预测驾驶行为模式，进一步提高了能量利用效率。研究还探讨了不同温度条件下电池性能变化对能量管理策略的影响，提出了适应宽温域工况的能量分配方案。本研究为提高电动汽车实际运行效率提供了理论依据和技术支持，对推动新能源汽车产业发展具有重要意义。
关键词：电动汽车能量管理；动力系统效率优化；多源信息融合算法
Abstract
As global energy crises and environmental pollution become increasingly severe, electric vehicles (EVs) have emerged as a critical component of green transportation, with the optimization of their power system efficiency and energy management strategies becoming a focal point of research. This study delves into the efficiency optimization of EV power systems, aiming to enhance overall vehicle performance and reduce energy consumption through improved energy management strategies. Based on an analysis of existing EV power system structures and operational principles, a multi-source information fusion energy management algorithm is proposed. This algorithm integrates battery status, driving conditions, and environmental factors to achieve coordinated control of the motor drive system and energy storage system. The proposed algorithm was validated using a simulation platform, demonstrating that under various typical driving conditions, compared to traditional control methods, the new algorithm can reduce average vehicle energy consumption by approximately 15% and increase driving range by about 12%. Additionally, machine learning techniques were innovatively introduced to predict driving behavior patterns, further improving energy utilization efficiency. The study also examined the impact of battery performance changes under different temperature conditions on energy management strategies and proposed an energy distribution scheme adaptable to wide temperature ranges. This research provides theoretical foundations and technical support for enhancing the actual operational efficiency of electric vehicles, which is of significant importance for promoting the development of the new energy vehicle industry.
Keywords：Electric Vehicle Energy Management; Power System Efficiency Optimization; Multi-source Information Fusion Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法与技术路线 2
二、动力系统效率影响因素分析 2
(一) 电机系统效率优化 2
(二) 电池管理系统改进 3
(三) 能量回收机制评估 4
三、能量管理策略设计与优化 4
(一) 基于工况的能量分配 4
(二) 智能充电策略研究 5
(三) 热管理系统优化 6
四、实验验证与结果分析 6
(一) 实验平台搭建 6
(二) 效率测试与对比 7
(三) 结果讨论与改进建议 8
结 论 9
参考文献 10
