Joint estimation of SOC/SOP for lithium-ion batteries across a wide temperature range using an electro-thermal coupling model
Accurate state estimation is crucial for ensuring the safe and reliable operation of lithium-ion batteries.However,achieving simultaneous online estimation of multiple parameters across a broad temperature range is challenging due to strong nonlinearity and multi-parameter coupling.To address this,an electro-thermal coupling model was developed,and battery parameters were identified online using the extended Kalman filter algorithm,the model's accuracy was verified by voltage and temperature simulations.To enhance the utilization of historical data and address the limitations of unscented Kalman filter algorithm(UKF),the multi-innovation theory(MI)was introduced to improve the UKF.The root mean square error of state of charge(SOC)estimation with the improved algorithm in the non-voltage platform areas is reduced to under 1.2%,representing more than 30%improvement.A switching algorithm was also designed,integrating the ampere-hour method to overcome the MIUKF algorithm's limitation of not correcting SOC estimation errors through voltage feedback in the voltage platform areas of lithium iron phosphate batteries.This approach enabled accurate full-range SOC estimation under complex working conditions and at various temperatures.The combined algorithm's accuracy was validated across different initial SOC values,with a root mean square error of less than 3%,providing a reliable SOC value for state of power(SOP)estimation.Finally,under multiple constraint method,a temperature constraint was introduced in SOP estimation.The results show that at high temperature,temperature limitation plays a critical role in preventing excessive temperature rise,thereby reducing potential safety hazards.
lithium iron phosphate batterywide temperature rangeSOC/SOP joint estimationelectro-thermal coupling modelimproving UKFmultiple constraints
万方数据
维普
