Electrochemical models offer great potential for onboard monitoring of lithium-ion batteries,yet their complexity and dependence on high-quality data have limited their engineering deployment.To address this challenge...Electrochemical models offer great potential for onboard monitoring of lithium-ion batteries,yet their complexity and dependence on high-quality data have limited their engineering deployment.To address this challenge,this paper proposed an engineering-adaptive modelling framework that enabled reduced-order electrochemical models to remain accurate and robust under temperature variations and battery aging in real-world vehicle operation.The framework was developed by applying a transfer-function-based reduction of the single particle model with electrolyte dynamics(SPMe)model and reformulating it into a state-space structure,supporting real-time iteration and internal state tracking.Furthermore,a practical parameter identification scheme was introduced,combining interpolation-enhanced preprocessing,long-term open-circuit voltage extraction,and polarization resistance estimation.This allowed one-shot particle swarm optimization(PSO)-based parameter identification using sparse onboard data,where PSO adaptively estimated internal parameters from voltage trends.The resulting lightweight process supported periodic updates and cross-platform deployment.While only three cells were used for initial parameter identification,the model was validated across 96 cells in the full battery pack,demonstrating scalability and long-term stability,with average residuals maintained around 12 mV throughout a full year of cross-seasonal operation.Based on residual trend analysis,a fault diagnosis method was further developed to detect and isolate subtle faults such as data loss and micro-overcharge.The results highlighted the framework's diagnostic capability and engineering adaptability,providing a practical path for the large-scale deployment of physics-based battery models in real vehicles.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2021YFF0601101)。
文摘Electrochemical models offer great potential for onboard monitoring of lithium-ion batteries,yet their complexity and dependence on high-quality data have limited their engineering deployment.To address this challenge,this paper proposed an engineering-adaptive modelling framework that enabled reduced-order electrochemical models to remain accurate and robust under temperature variations and battery aging in real-world vehicle operation.The framework was developed by applying a transfer-function-based reduction of the single particle model with electrolyte dynamics(SPMe)model and reformulating it into a state-space structure,supporting real-time iteration and internal state tracking.Furthermore,a practical parameter identification scheme was introduced,combining interpolation-enhanced preprocessing,long-term open-circuit voltage extraction,and polarization resistance estimation.This allowed one-shot particle swarm optimization(PSO)-based parameter identification using sparse onboard data,where PSO adaptively estimated internal parameters from voltage trends.The resulting lightweight process supported periodic updates and cross-platform deployment.While only three cells were used for initial parameter identification,the model was validated across 96 cells in the full battery pack,demonstrating scalability and long-term stability,with average residuals maintained around 12 mV throughout a full year of cross-seasonal operation.Based on residual trend analysis,a fault diagnosis method was further developed to detect and isolate subtle faults such as data loss and micro-overcharge.The results highlighted the framework's diagnostic capability and engineering adaptability,providing a practical path for the large-scale deployment of physics-based battery models in real vehicles.
