Lead–acid batteries are widely used, and their health status estimation is very important. To address the issues of low fitting accuracy and inaccurate prediction of traditional lead–acid battery health estimation, a battery health estimation model is proposed that relies on charging curve analysis using historical degradation data. This model does not require the assistance of battery mechanism models or empirical degradation models, instead, it is combined with improved deep learning algorithms. A long short-term memory (LSTM) regression model was established, and parameter optimization was performed using the bat algorithm (BA). The experimental results show that the proposed model can achieve an accurate capacity estimation of lead–acid batteries.