Abstract: The estimation method of state of health（SOH）of lead-acid battery based on chaotic particle swarm

optimization-Gaussian process regression（CPSO-GPR）was proposed. Firstly，the voltage and current curves of leadacid battery during charging process were investigated，and the characteristics of constant current charging were analyzed and compared. The Gaussian process regression（GPR）model of constant current charging time and battery capacity attenuation was established.Aiming at the problem that the traditional intelligent algorithm is easy to fall into the local optimal solutions，the chaotic process was introduced into the traditional particle swarm optimization algorithm to enhance the breadth and depth of its optimization，and forms the chaotic particle swarm optimization （CPSO）algorithm to optimize the super parameters in the regression model，so as to obtain higher quality of the super parameter solution and improve the prediction accuracy of the regression model. The CPSO-GPR algorithm was formed by the cooperation of the two algorithms. The experimental results show that CPSO-GPR algorithm can achieve accurate estimation and online monitoring of SOH of lead-acid batteries，and the estimation accuracy of new data points is less than 3%.

      Key words: chaotic particle swarm optimization（CPSO）algorithm；lead-acid battery；state of health（SOH）

estimation；energy storage