The battery pack inconsistency directly affects output energy, which is an important factor reflecting the driving range of electric vehicles. Therefore, this manuscript focuses on influence degree analysis of inconsistency on output energy. Firstly, a novel battery pack inconsistency model, consisting of Gaussian mixture model (GMM) which well describes the marginal distribution characteristics of single parameter and three-dimensional mixture Copula model (MCM) which well describes the correlation between parameters, is proposed. Secondly, the battery packs with different inconsistency characteristics are designed based on the built inconsistency quantification experimental platform, and the virtual ones with the same inconsistency are generated by the inconsistency parameters generation method. The output energy estimation errors between simulation and experiment are within $\pm$1%. Thirdly, multiple linear regression analysis is used to study the influence degree of GMM and MCM model parameters on output energy respectively. The analysis results show that in GMM, the variance of SOC and the mean of capacity and SOC have greater impacts than the mean of resistance and the variance of capacity and resistance, and in MCM, the parameters of Gumbel and Frank have greater influence than that of Gaussian and Clayton. The results could provide support for battery pack performance evaluation.