Research On The Joint State Estimation Method For Lithium-ion Batteries Used In Electric Vehicle

The “14th Five-Year Energy Plan” pointed out that it is necessary to accelerate the establishment of a multi-energy complementary energy system with energy storage as the core,and also promote the development of new energy vehicle.Lithium-ion batteries will be the main target and provide more and more energy in the process of structural adjustment of the national energy strategy.The battery state of charge and state of health estimation is the core technology in the battery management system of electric vehicles.Accurately estimate the state of battery can effectively enhance the safety and energy efficiency of the battery.Aiming at the estimation of the state of charge and state of health of lithium-ion batteries,this article mainly does the following research:(1)Improve the modeling accuracy of lithium-ion batteries for vehicles.Accurate equivalent modeling is the basis for battery state estimation.Aiming at the time-varying problem of model parameters under complex working conditions,based on the Thevenin model,an equivalent modeling method for the full life cycle of lithium-ion batteries is proposed,in order to achieve a full description and equivalent of battery aging characteristics and temperature characteristics.(2)To improve the robustness of the traditional state estimation algorithm,an adaptive unscented H-infinity filtering algorithm is proposed.To comprehensively evaluate the performance of the algorithm,under several typical verification conditions,the robustness,maximum absolute error,mean estimation error and root mean square error of the state estimation are evaluated.The results show that the proposed algorithm has higher performance.(3)At present,many state estimation methods can obtain better estimation accuracy,but they basically assume that the actual battery capacity is known,the accuracy and reliability of state of charge estimation under uncertain aging conditions need to be improved.Based on the multi-model theory,this paper explores a multi-model fusion state-of-charge estimation method to achieve accurate state-of-charge estimation,and provides a feasible idea for solving the problem of state-of-charge estimation under uncertain aging conditions.(4)To test the performance of the above algorithms,experiments were carried out under different temperatures and health conditions to comprehensively evaluate the performance of the algorithm and model.The verification results show that the multi-model fusion joint state estimation method based on adaptive unscented H-infinity filtering has high estimation accuracy,the maximum estimation error is 2.35%,the capacity estimation result also has high accuracy,and the maximum estimation error is 2.81 Ah.