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
Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first pr...Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.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.
文摘Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
