Lithium ion battery is widely used in weapon systems. The evaluation for its health status to lithium ion is of great significance to ensure the operational effectiveness of weapon systems. Aimed at the problems that building a physical model is comparatively difficult in the degradation process of lithium ion battery, simultaneously the process is very hard to be described accurately due to the uncertainty and incomplete data by the pure datadriven method. Being the extended belief rule base model combined with the characteristics of the knowledge structure and evidence reasoning, the uncertainty and incompleteness of data may be quantitatively described, but the parameters of the original model had a greater influence on the choice of its performance. In view of the mentioned above problems, an extended belief rule base (EBRB) model of center decenter particle swarm optimization is proposed, and the model is applied in the lithium ion battery health status evaluation. The EBRB based on the optimization of the CDPSO converts the rule set into rules in a datadriven way, the initial parameters with CDPSO are trained, and finally, the validity of the model with the test data set is tested. By comparing with the traditional method, the validity of the proposed method is verified.