【作者】 王红；

【导师】 彭宇；

【作者基本信息】 哈尔滨工业大学 ， 仪器科学与技术， 2013， 硕士

【摘要】 锂离子电池由于具有工作电压高、体积小、重量轻、比能量高、寿命长和自放电率小等优点，成为替代传统氢镍、镍镉电池的第三代卫星用储能电源。电源子系统作为卫星全寿命周期可靠运行的基础，锂离子电池的在轨状态监测、性能预计和系统管理，已经成为航天领域的研究热点和挑战问题之一。本文针对数据驱动的卫星锂离子电池剩余寿命预测展开研究。首先，针对空间应用的锂离子电池在轨运行实际容量数据难以监测导致难以实现在轨性能预计的问题，提出一种基于可测外参数作为间接健康因子实现电池剩余寿命预测的预测方法框架。其次，将回声状态网络（ESN）用于锂离子电池间接剩余寿命预测框架，利用ESN的高度非线性逼近能力实现锂离子电池退化建模，等压降放电时间预测以及剩余寿命预测。然后，针对等压降放电时间单步迭代预测过程中，初始预测值不能准确跟踪真实值的下降趋势，导致误差逐步累计的问题，提出一种基于单调回声状态网络（MONotonic Echo State Network, MONESN）的锂离子电池间接RUL预测方法，将单调先验知识加入ESN的训练过程，限制MONESN的输入和输出之间的严格单调关系以符合剩余寿命轨迹特性，提高等压降放电时间序列的预测精度。最后，针对基于MONESN的电池RUL预测输出不稳定的问题，采用集成学习方法建立多个MONESN子模型，利用模型输出多样性降低泛化误差并提高输出稳定性。此外，面向航天实际应用需求，采用集成单调回声状态网络（EnsembleMONotonic Echo State Network, EnMONESN）的锂离子电池RUL预测算法为基础，充分发挥其良好的预测精度和输出稳定性，结合实验室已有的RUL预测算法研究基础，采用LabVIEW和MATLAB混合编程的方式，完成基于EnMONESN，GPR和NDAR的卫星锂离子电池剩余寿命预测应用软件的开发和验证。采用NASA公开数据集和实际卫星锂离子电池状态监测数据完成的大量实验表明，基于EnMONESN的锂离子电池间接RUL预测框架可以实现准确而且稳定的锂离子电池剩余寿命预测。卫星锂离子电池剩余寿命预测软件的可靠运行，表明本文提出的基于可测外参数的间接剩余寿命预测方法可以实现空间应用的锂离子电池的在轨性能预计。更多还原

【Abstract】 The lithium-ion battery becomes the third generation power system for satellite dueto its high voltage, small size, light weight, high energy density, long lifetime, and lowself-discharge rate. The state monitoring in-orbit, remaining useful life (RUL)prediction, and system management have become the research focuses and challenges inaerospace because the power system is the key component of the satellite. In this work,therefore, the degradation modeling and remaining useful life prediction for satellitelithium-ion battery based on data-driven method is implemented.Firstly, the capacity is difficult to monitor when the lithium-ion battery in in-orbitfor space application, as a result, it is not easy for researchers to predict the performanceof lithium-ion battery. Thus, we propose an indirect RUL prediction method based onthe external parameters that can be measured directly. The indirect health indicator isextracted with the time interval of equal discharging voltage difference. Secondly, theframework for indirect RUL prediction of lithium-ion battery based on Echo StateNetwork (ESN) is proposed. This framework can establish an accurate degradationmodel for lithium-ion battery based on the highly nonlinear approximation ability ofESN. Thirdly, due to the first prediction of time interval of equal discharging voltagedifference cannot track the decreasing tread of the true data in the predicting process,which leads to the error accumulation step by step. We propose Monotonic Echo Statenetwork (MONESN) which combines monotonic prior knowledge to the trainingprocess of ESN to solve this drawback. The output and input unit of MONESN areforced to meet the monotonic relationship, which improves the accuracy of RULprediction. Fourthly, we find that the RUL prediction of single MONESN model isunstable, which can lead to larger RUL prediction error some times. So, we adoptensemble method for RUL prediction of lithium-ion battery. The Ensemble MonotonicEcho State Network (EnMONESN) is proposed to improve the stability of predictionmodel. At the same time, the EnMONESN can reduce the generalization error by thediversity of predictions. Lastly, we apply the above methods in this thesis, Gaussianprocess regression, and NDAR in the software for indirect RUL prediction of lithium-ion battery. The software is based on LabVIEW and MATLAB.A large number of experiments, which use NASA and satellite lithium-ion batterymonitoring data are carried out, the results indicate that the indirect RUL predictionalgorithm based on EnMONESN can achieve more accurate and stable RUL prediction.The stable operation of the software for indirect RUL prediction of lithium-ion batteryproved that the indirect RUL prediction algorithm is an effective way to achieve theperformance prediction of lithium-ion battery in-orbit.更多还原