空间锂离子电池退化状态识别和剩余寿命预测方法研究

项目名称： 空间锂离子电池退化状态识别和剩余寿命预测方法研究

项目编号： No.61301205

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 刘大同

作者单位： 哈尔滨工业大学

项目金额： 24万元

中文摘要： 针对空间锂离子电池寿命预测和在轨管理问题，开展退化状态识别、物理退化模型优化及与数据驱动方法融合的寿命预测方法、基于FPGA可重构计算的电池管理系统等研究。首先，研究锂离子电池退化特征参数构建、特征参数选择和特征提取，实现基于电池状态监测参数的退化特征识别；然后，研究加速退化效应、容量再生效应和浅度放电的影响因素及建模，实现物理退化模型优化，并研究基于稀疏贝叶斯学习的在线数据驱动预测算法，再基于统计滤波方法实现物理模型与数据驱动方法的预测融合；最后，面向空间在轨应用，研究基于FPGA可重构计算的在线数据驱动方法的计算模式问题，解决硬件计算结构、计算体系和计算单元等问题。构建面向空间在轨的电池管理系统平台和地面试验寿命评估系统平台，实现方法验证和应用评估。课题将在计算方法、计算模式两个方面开展空间锂离子电池寿命预测的创新性研究，为锂离子电池未来的空间应用提供理论方法支撑和系统平台参考。

中文关键词： 故障预测和健康管理；锂离子电池；剩余寿命；退化特征识别；可重构计算

英文摘要： This research mainly focuses on the cycle life prediction and in-orbit battery management system (BMS) for the lithium-ion batteries in aerospace applications. The degradation identification methods, optimization of the physical degradation model, fusion of the physical degradation model and data-driven algorithm, and BMS development based on the Reconfigurable Computing (RC) with FPGA are involved. Firstly, the approaches to realize degradation feature parameters identification, feature selection and feature extraction will be studied to obtain the degradation feature parameters with the monitoring parameters. Then, the influences and mathematical modeling will be considered for the "accelerated" degradation effect, capacity regeneration phenomenon and depth-of-discharge in the degradation process.The optimization of the physical degradation model will be realized with the modeling factors for the lithium-ion batteries. Moreover, the on-line sparse Bayesian algorithm will be proposed to achieve on-line prediction with in-orbit monitoring data. Thus, the fusion of the optimized physical degradation model and data-driven sparse Bayesian learning algorithm will be achieved by combining the sparse Bayesian learning algorithm as the measure equation of the statistical filter method. Finally, for the limited computi

英文关键词： Prognostics and Healthmanagement；Lithium-ion Battery；Remaining Useful Life；Degradation Feature Extract；Reconfigurable Computing