面向海洋能电站的储能电池组建模与健康特征提取方法研究

Detailed information is necessary for health status evaluation of energy store battery pack, but its acquisition method is far more difficult than the cell because of some issues such as battery pack configuration, this contradiction is the main factor restricting the application of health status evaluation for battery packs. This project intends to focus on the rapid extraction method of cell health features and health modeling of battery pack faced to energy store application of marine power station, the contradiction between information content and acquiring complexity will be mollified. For feature acquiring complexity, relationship between internal and external heathy features will be discussed, and feature extraction process will be accelerated by using efficient numerical modeling and rapid ICA and EIS testing, then an simplified extraction solution is expected to be proposed according to the degradation of key features based on aging tests. For information content, the performance and health of the battery pack are combined by using graph theory, and the relationship of health between cells in the pack will be established via modeling, then cell health features would be promoted to the battery pack. At the same time, the degradation of health features of the battery pack will be also investigated, more detailed and rich information of battery pack health status is expected to be acquired. This project can provide a systematic method for extracting health features of energy store battery pack, the findings will enrich the theoretical understanding of battery pack health and its degradation mechanisms, and provide strong support for health status evaluation in further.

储能电池组健康评估需要尽可能详细的健康信息，而电池组构型等问题使其健康特征获取复杂度远高于单体电池，这一问题制约了电池组健康评估的发展。本项目面向海洋能电站储能应用，拟从电池健康特征快速提取和电池组建模两方面开展研究，系统解决健康特征信息量与获取方法复杂度的矛盾。针对健康特征获取复杂度，利用高效数值建模、快速ICA和EIS测试等手段探索电池内外部健康特征的联系、加速健康特征提取进程，利用老化试验讨论关键特征的退化规律，有望获得简化的健康特征提取方案。针对健康特征信息量，尝试利用图论将电池组性能与健康有机结合，通过电池组模型描述各单体健康特征的关系，可将单体电池健康特征拓展到电池组，同时探索电池组健康特征的退化规律，有望提供丰富的电池组健康特征信息。本项目可系统形成针对储能电池组的健康特征快速提取方案，成果将丰富电池组健康及其退化机理的理论认识，为进一步深入电池组健康状态评估提供有力支撑。

海洋能电站包括多种形式，由于其获能装置原理、结构的不同，发电功率特性有很大差异，储能电池组的工况较为特殊。为保证储能系统安全、可靠工作，必须对电池的健康状态进行监控和研究，其关键问题在于：如何无损、快速的获得电池健康特征。本课题主要研究内容为：（1）海洋能电站储能工况简化。针对实际波浪能电站实测储能系统工况，提出一种简化方法，将复杂工况简化为恒功率充放电片段的组合。再利用多目标优化方法，得到片段划分最优解。从而使得工况简化的同时，保持与原工况一致的功率密度分布。针对小型电池做缩比加速试验设计，得到了三组加速老化试验工况。（2）快速EIS测试方法研究与样机研制。针对传统EIS测试方法耗时较长的问题，提出基于混频信号注入与改进FFT的快速EIS测试方法。相比于传统FFT提取的混频信号相位、幅值信息更准确，所构成的EIS曲线更加平滑，依此获得的电池模型参数更准确。基于上述理论，利用NI数据采集卡和LabVIEW，研制了一款8通道快速EIS测试仪样机，可以与现有电池测试系统直接连接，自动切换测量，测量时间缩短至传统方法的三分之一。（3）锂离子电池分数阶模型及其参数辨识。提出了一种基于遗传算法和粒子群算法的两步优化方法，同时提出将电池实测EIS数据和动态充放电数据作为两个目标函数，将二者融合进行模型参数辨识。对于分数阶模型中不敏感的参数，融合辨识得到的结果比单纯使用EIS或者动态充放电数据辨识出的结果更加准确。（4）电池老化试验及健康特征提取方法研究。应用加速老化试验工况，对小型锂离子电池进行加速老化试验。使用所提出的参数辨识方法，得到了不同老化阶段的模型参数值，分析发现仅有少数参数是随老化而明显变化的，这些参数可以作为关键健康特征看待。利用多元回归方法得到了这些参数对老化程度的详细规律。结果表明：未来仅对这些参数的值进行准确获取，即可得到关于电池老化程度的描述，且其变化规律可以用于电池寿命预测、维护决策等PHM研究中。