Research on Degradation Characteristics and Real-Time Reliability Assessment of Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are increasingly used in electric vehicles and energy storage systems because of their high energy density and extended cycle life. However, the nonlinear and time-varying nature of their degradation processes severely hampers accurate real-time reliability assessment. This study systematically investigates the degradation behavior of LIBs via controlled cycling tests. According to the experimental results, a novel real-time reliability assessment framework utilizing the inverse Gaussian process as the core modeling approach is developed. The proposed model incorporates a Bayesian updating strategy to dynamically capture the progression of degradation. Moreover, it applies the Expectation–Maximization algorithm to achieve efficient and adaptive parameter estimation. Experimental validation using 18650-type LIBs confirms the model’s effectiveness in capturing capacity degradation trends and reflecting the evolution of decay rates over time. The findings demonstrate that the presented approach offers a reliable and practical solution for the real-time reliability management of LIBs in dynamic application scenarios.