In order to evaluate the state of health (SOH) of energy storage battery packs more efficiently

an early health status detection method based on voltage range characteristics is proposed. Firstly

the cyclic aging experiment is carried out based on the large-capacity lithium iron phosphate battery pack

and the voltage range signal of each cycle is measured

and the voltage characteristics of key time points are extracted from it. Secondly

health factors highly related to battery aging are screened based on Pearson correlation coefficient and grey correlation degree analysis (GRA) . Finally

the Sparrow search algorithm (SSA) is used to optimize Bi-bidirectional long short-term memory ( BiLSTM ) hyperparameters

and SSA-BiLSTM health state estimation model is built

and realize SOH evaluation of energy storage battery pack. The effectiveness of the health factor and the superiority of the estimation model are verified by the conventional machine learning algorithm. The results show that the extracted voltage range characteristics of charging and discharging for 30 min can effectively reflect the decline trend of battery pack capacity

and the estimated error of SOH is less than士0.8% under various models. The root mean square error (RMSE) of the SSA-BiLSTM model proposed in this paper is as low as 0. 07% . Therefore

this method can effectively monitor the SOH of large-capacity energy storage battery packs online.