Context-aware state estimation in battery management systems: Leveraging nonlinear dynamics with physics-guided parameter identification

Abstract

Black accurate remaining range estimation remains a critical issue to promote plug-in and electric vehicle adoption, primarily due to underlying uncertainties in voltage and current-dependent state estimation. To overcome these challenges, the proposed work introduces a novel framework for range estimation while integrating an enhanced equivalent circuit model with a physics-guided temperature-compensated Extended Kalman Filter algorithm. Firstly, comprehensive mathematical models are developed and validated that integrate: (i) proposed enhanced 3rd-order equivalent circuit modeling (p-eTECM) with control parameter optimization, (ii) data-and-model-driven parameter identification using Coulomb counting and voltage scaling analysis, (iii) comprehensive sensitivity analysis to rank important parameters to improve accuracy, and (iv) application-specific model selection criteria based on performance trade-offs. However, unlike existing frameworks that incorporate higher-order RC models that are universally superior, the proposed work identifies that model selection should be application-dependent for different battery management functions. The novel contributions include: parameter & voltage optimization from the pack-level, while systematically eliminating voltage bias through online parameter optimization, and developing a comprehensive sensitivity analysis algorithm to validate the improvements. The proposed framework demonstrates that parameter calibration is more crucial with capacity correction and voltage scaling, to eliminate systematic biases that render models impractical. This study further reveals that 3rd-order model outperforms in voltage prediction (8.3 % improvement) while the 2nd-order model provides better SOC tracking (13 % improved accuracy), establishing clear application-specific selection criteria. Key results demonstrate that both models have achieved excellent performance in terms of SOC errors ($<$0.2 %), and range accuracy (155–170 km) with real-time computational efficiency, validating the practical applicability for diverse battery management applications while providing a systematic methodology for future battery modeling research.