Capacity Fast Prediction and Residual Useful Life Estimation of Valve Regulated Lead Acid Battery

The usable capacity of acid lead batteries is often used as the degradation feature for online RUL (residual useful life) estimation. In engineering applications, the “standard” fully discharging method for capacity measure is quite time-consuming and harmful for the high-capacity batteries. In this paper, a data-driven framework providing capacity fast prediction and RUL estimation for high-capacity VRLA (valve regulated lead acid) batteries is presented. These batteries are used as backup power sources on the ships. The relationship between fully discharging time and partially discharging voltage curve is established for usable capacity extrapolation. Based on the predicted capacity, the particle filtering approach is utilized to obtain battery RUL distribution. A case study is conducted with the experimental data of GFM-200 battery. Results confirm that our method not only reduces the prediction time greatly but also performs quite well in prediction accuracy of battery capacity and RUL.

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