Life Prediction of Energy Storage LFP Batteries Based on Voltage Segment Health Indicators: A Comparative Study of Data-Driven and Arrhenius-Data Fusion Models
Abstract
Large-capacity lithium iron phosphate (LFP) batteries dominate energy storage systems, but their degradation characteristics differ from small-capacity cells. Most existing life prediction methods require complete voltage–current time-series data, which is hard to obtain in practical operation. This paper proposes two life prediction methods: a data-driven method and an empirical-data hybrid method. The data-driven method adopts Summed Voltage Falloff (SVF) extracted from partial voltage segments as the health indicator, which removes the dependence on full charge–discharge waveform data and enhances engineering practicability. It uses a unified numerical fitting framework with Gaussian process regression (GPR) residual correction, with tailored fitting strategies for 320 Ah and 298 Ah battery datasets. The hybrid method integrates the Arrhenius model with Kalman filtering for closed-loop online prediction correction. Both methods are validated using 281 cycles of 320 Ah battery data, and the data-driven method is further verified with 150 cycles of 298 Ah battery data. Results show that the data-driven method performs better with limited data, suitable for offline one-time inspection scenarios; the hybrid model achieves higher accuracy with sufficient data, applicable to long-term online remaining useful life monitoring. The data-driven method yields a worst-case cycle life of 3304 cycles at 2σ confidence level, and the hybrid model maintains error below 5% when forecasting 1000 cycles with 200 cycles of training data.