In order to make an exact prediction for the density of the lead-acid battery electrolyte, this paper proposes a method by using a genetic algorithm to optimise the support vector regression. In this AGA-SVR model, a kind of adaptive genetic algorithm is exploited to choose the model parameters of support vector regression for obtaining better prediction performance. The proposed predicting model ... [Show full abstract] is applied to the density predicting for lead-acid battery. The experimental results indicate that both GA and AGA have good efficiency on parameter optimisation. Furthermore, the AGA-SVR model provides a superior prediction performance than the other three models including SPSO-SVR model, IPSO-SVR model and GA-SVR model. Therefore, the AGA method could be considered as an effective alternative method for predicting electrolyte density.