Personalized Cycle-to-Failure Correction Method for New Electric Vehicle Lithium Batteries Based on Safety State Constraints and Bayesian Hybrid Reasoning

Lithium-ion batteries hold significant promise for electric vehicle applications due to their high energy density and environmental benefits. Accurate prediction of battery cycle-to-failure (CTF) is crucial for ensuring system reliability, given the aging mechanisms and usage patterns involved. However, predicting battery cycle life is challenging as it involves multiple factors such as state of health (SOH), charge rate (Crate), and state of charge (SOC) usage habits, making it difficult to quantitatively evaluate CTF under practical conditions. This article first proposes a Bayesian hybrid model combined with a state of safety (SOS) constraint for personalized battery cycle life prediction. The model establishes a battery safety potential field by defining feasible regions for SOH and Crate variables, develops a Bayesian-Markov hybrid inference algorithm to extract SOC usage habits and calculates personalized damage factors (DF), and constructs a state of safety-damage factor (SOS-DF) coupling correction framework through Taylor expansion theory. The performance of the proposed model was validated using real vehicle operation data. The predicted distribution was highly consistent with the historical distribution, and the mean absolute error (MAE) and root mean square error (RMSE) of Bayesian inference were as low as 0.0058 and 0.0069, respectively. The model can effectively reflect the cycle life evolution of lithium batteries under complex usage conditions.