Recently, in order to make sure that the operation of Lithium-ion battery-powered devices and systems are safe, reliable and economic, it is very important to predict life and other performances for Lithium-ion batteries. Efficient and accurate state of life prediction for Lithium-ion batteries can be used to optimize the charging/discharging and operation strategy. Thus it could prevent operation failure due to unexpected power loss, and decrease the cost of consequent accidents. Among current prognostic methods, particle filter (PF) method is often used to estimate the life of batteries. However, in traditional PF methods, the original degenerative characters are much affected by outside interference. This means that it might not be suitable to apply these characters directly in prediction, because the accuracy of the prediction result is excessively depended on the prediction model. In this paper, we propose a prediction method based on degenerative characters and improved particle filter to estimate the cycle life of Lithium-ion batteries. The experimental results proved the feasibility of the proposed prediction method, which can provide potential application for remain useful life prediction.
Solar arrays are the main source of energy to the on-orbit satellite, whose output power largely determines the life cycle of on-orbit satellites. Monitoring and further forecasting the output power of solar arrays by using the real-time observational data are very important for the study of satellite design and on-orbit satellite control. In this paper, we firstly describe the dynamical model of output power with summarizing the influencing factors of attenuation for solar arrays and elaborating the evolution trend of influencing factors which change with time. Based on the empirical model, a particle filtering algorithm is formulated to predict the output power of solar arrays and update the model parameters, simultaneously. Finally, using eight-year observational data of voltage and current from a synchronous on-orbit satellite, an experiment is carried out to illustrate the reliability and accuracy of the particle filtering method. Comparative results with classical curve fitting also are presented with statistical root mean square error and mean relative error analysis.
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