The degradation of lithium-ion batteries (LIBs) during actual operation results from multiple degradation mechanisms, typically manifested through degraded performance parameters such as capacity fade, increased internal resistance, and energy loss. This inherent complexity makes it challenging to provide a reliable evaluation of LIBs performance based on a single performance parameter. Therefore, this study proposes a reliability evaluation method combing Hybrid Basis Functional Principal Component Analysis (HB-FPCA) and Copula functions. The HB-FPCA method introduces an adaptive basis function selection, dynamically integrating B-spline and Daubechies wavelet bases through least-squares reconstruction error, ensuring effective extraction of key parameters from multiple degradation features by functional principal component analysis. Then, a nonlinear Wiener process and Gaussian kernel density estimation is employed to estimate battery lifetime and its distribution. Finally, Copula functions are utilized to couple lifetime distribution derived from different performance parameters, and the reliability indices are calculated. Experimental results based on 124 LIBs demonstrated that the proposed method improves evaluation accuracy and credibility compared to conventional single-parameter approaches.