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In this paper, a method of capacity trajectory prediction for lead-acid battery, based on the steep drop curve of discharge voltage and improved Gaussian process regression model, is proposed by analyzing the relationship between the current available capacity and the voltage curve of short-time discharging. The battery under average charging is discharged for a short time, and the voltage drop of short-time discharging during equal time intervals, which has the highest relevance with presently available capacity, is extracted as the health feature (HF), and the ergodic method is used to search the optimal time interval. Then, a Gaussian process regression (GPR) model, which reflects the capacity degradation of lead–acid battery, is established (with the HF series as input and current available capacity series as output). Considering the complex trend of capacity trajectory, the rational quadratic covariance function is used as the kernel function of GPR model, and the conjugate gradient algorithm is used for optimization, in order to improve the nonlinear mapping ability of GPR. Finally, the experimental results of lead-acid batteries under different charging cut-off voltages and operating temperatures show that the proposed method can effectively predict the capacity change trajectory of lead–acid batteries with a small training sample, showing high prediction accuracy and wide applicability.

The Prediction of Capacity Trajectory for Lead–Acid Battery Based on Steep Drop Curve of Discharge Voltage and Gaussian Process Regression