Multi-interest dual-adaptive unscented Kalman Filter with online parameter optimization for nonlinear battery modeling

Reliable driving range prediction is a key requirement for modern electric mobility. However, it remains challenging due to the complex electrochemical dynamics of lithium-ion batteries and their sensitivity to varying operating conditions. To address these challenges, this study proposes a novel framework integrating an enhanced second-order RC equivalent circuit model with a multi-criterion adaptive Unscented Kalman Filter algorithm (i-DAUKF) for improved state estimation accuracy. Firstly, the enhanced RC model is developed to optimize the battery parameter selection using a moving window average algorithm, while incorporating online parameter optimization to dynamically update measurement and predictive coefficients. The i-DAUKF algorithm is designed to better capture nonlinear battery behavior under operational thermal fluctuations and degradation processes, ensuring robust estimation fidelity across diverse operating conditions. Unlike conventional filtering approaches that rely on static tuning, the proposed method continuously refines model parameters, significantly improving adaptability and robustness. The novel contributions include: (i) integration of a refined second-order RC model with adaptive parameter calibration, (ii) dynamic optimization of predictive and measurement gains for sustained stability, and (iii) demonstration of application-specific improvements across transient and steady-state phases. Simulation results validate the effectiveness of the framework, achieving SOC estimation errors of 0.043 and 0.714% (RMSE), and voltage errors of 0.320 and 1.42% (RMSE), respectively. Comparative evaluations further reveal that i-DAUKF consistently achieves the lowest estimation errors (SOC RMSE < 2 . 5 % , voltage 1%–3%) with negligible computational cost, thereby establishing its superiority in accuracy, robustness, and real-time efficiency for nonlinear battery modeling under dynamic driving conditions. • Proposes a 3rd-order RC model for accurate EV battery SOC and range estimation. • Integrates trust-region algorithm to improve prediction accuracy under real conditions. • Validates model using experimental data from multiple EV driving cycles. • Demonstrates improved SOC and range estimation over existing linear models. • Offers a scalable method for real-time EV energy management applications.