Aiming at the problem of low estimation accuracy of Liion battery state of health (SOH), a method based on principal component analysis (PCA) and improved LevenbergMarquardt algorithmdouble Gaussian kernel RBF (ILMDGRBF) neural network was proposed, which realized the accurate estimation of SOH. Firstly, the health indicator (HI) highly related to the capacity decline was extracted, and PCA method was used for dimensional reduction processing to reduce the redundancy between HI. Secondly, a double Gaussian kernel RBF neural network was created, and improved LM algorithm was used to realize the online learning of neural network parameters to establish ILMDGRBF neural network. Thirdly, ILMDGRBF was trained with the enhanced battery test data to realize SOH estimation. The verification shows that the principal component 1 obtained by PCA dimensionality reduction can effectively reflect the aging trend of Liion battery, and can be used for SOH estimation; Compared with other models, the established ILMDGRBF model has higher estimation accuracy and better robustness, and the error of the estimation results is controlled within 15%. Finally, based on this method, a new SOH intelligent estimation system was constructed to provide a reference basis for battery safety management.