摘 要
随着全球对环保和节能减排的日益重视，电动汽车作为新能源汽车的代表，其市场普及率逐年上升。电动汽车的核心部件之一——电池，其健康状态直接影响到车辆的性能、续航里程及使用寿命。因此，对电动汽车电池健康状态的准确评估与预测显得尤为重要。本文首先概述了电动汽车电池健康状态评估的多个关键指标，包括电池容量保持率、内部电阻、物理形变、充放电性能、自放电速率以及系统警报等。电池容量保持率是评估电池老化程度的重要参数，通常认为保持在初次的80%或以上表示电池状况良好。内部电阻的增长则反映了电池工作效率和寿命的下降，特别是当内阻增加20-30%时，电池健康度已明显降低。此外，电池的物理形变、充放电性能及自放电速率也是评估电池健康状态不可忽视的方面。在预测方面，本文探讨了基于模型和数据的方法在电池健康状态预测中的应用。基于电路或电化学模型的方法将电池健康状态估计问题视为模型参数估算问题，而基于数据的方法则利用大量电池运行数据，通过机器学习算法进行预测。其中，长短期记忆神经网络（LSTM）因其对时间序列数据的优秀处理能力，在电池健康状态预测中展现出较高的准确性。通过综合评估与预测，本文旨在为电动汽车电池的维护、更换及优化管理提供科学依据。通过定期检测电池的各项性能指标，并结合先进的预测模型，可以及时发现并解决电池潜在问题，延长电池使用寿命，提高电动汽车的整体性能和经济性。
关键词：电动汽车电池 健康状态评估 预测模型
Abstract
With the increasing attention of the world to environmental protection and energy saving and emission reduction, the market penetration rate of electric vehicles, as a representative of new energy vehicles, has increased year by year. One of the core components of electric vehicles, the health of the battery, directly affects the performance of the vehicle, driving range and service life. Therefore, it is particularly important to accurately evaluate and predict the health status of electric vehicle batteries. In this paper, several key indicators of electric vehicle battery health status assessment are summarized, including battery capacity retention rate, internal resistance, physical deformation, charge-discharge performance, self-discharge rate, and system alarm. The battery capacity retention rate is an important parameter to evaluate the degree of battery aging, and it is generally believed that maintaining the initial 80% or more indicates that the battery is in good condition. The increase in internal resistance reflects the decline in the working efficiency and life of the battery, especially when the internal resistance is increased by 20-30%, the battery health has been significantly reduced. In addition, the physical deformation, charge and discharge performance and self-discharge rate of the battery are also aspects that can not be ignored to evaluate the health status of the battery. In terms of prediction, this paper discusses the application of model - and data-based methods in the prediction of battery health status. Circuit or electrochemical model-based approaches treat the battery health estimation problem as a model parameter estimation problem, while data-based approaches utilize a large amount of battery operation data to make predictions through machine learning algorithms. Among them, long short-term memory neural network (LSTM) shows high accuracy in the prediction of battery health status because of its excellent processing ability of time series data. Through comprehensive evaluation and prediction, this paper aims to provide scientific basis for the maintenance, replacement and optimal management of electric vehicle batteries. By regularly detecting various performance indicators of the battery, combined with advanced predictive models, potential battery problems can be found and solved in time, extending battery life, and improving the overall performance and economy of electric vehicles.
Keyword:Electric vehicle batteries Health assessment Predictive models
目 录
1引言 1
2电池健康状态评估方法 1
2.1评估指标体系构建 1
2.2数据采集与处理 1
2.3评估模型建立 2
3电池健康状态监测系统设计 2
3.1系统架构设计 3
3.2系统实现技术 3
3.3系统测试与维护 4
3.4系统的可靠性与实用性分析 4
4实际应用案例分析 5
4.1案例背景与数据来源 5
4.2评估与预测流程 5
4.3结果分析与讨论 6
5结论 6
参考文献 8
致谢 9
