Abstract
Lithium-ion batteries are widely used in many fields. Conducting Remaining Useful Life (RUL) assessment can effectively identify the aging state and degradation trends, addressing safety issues arising from untimely battery replacement. This paper proposes a battery RUL estimation method based on a Particle Filter (PF) algorithm-corrected empirical model. Firstly, aging experiments are conducted to obtain degradation data under different rates and depths of discharge (DOD) aging conditions, and the most suitable model is selected from four commonly used empirical models. Secondly, combining known aging data, the PF algorithm is employed to correct the parameters of the empirical model, making it applicable to other aging conditions. Experimental results demonstrate that under different aging conditions and with varying known data, the absolute average relative errors in battery capacity estimation are within , and the absolute relative errors in RUL estimation are within 10%.