State Of Charge Estimation Of Lithium-ion Batteries For Electric Vehicles In Their Whole Lifespan And Entire Operating Temperature Range

In the context of environmental protection and reduction of fossil energy consumption,electric vehicles with electric energy as the main consumption energy have been rapidly promoted and applied.Battery systems provide the power source for electric vehicles,and its performance directly determines the development speed of electric vehicle.To ensure the safety and efficiency of the battery system,the battery management system(BMS)plays an important role with providing state monitoring,internal state estimation,charge and discharge management and safety protection.As a key parameter to evaluate electrical performance of battery system and optimize power control strategy of vehicle,it is vital to accurately estimate the internal state of battery in the whole life under complex temperature conditions for the protection and extension of battery life.This paper focus on the modeling and state estimation of lithium-ion batteries in the whole life cycle under complex temperature environment,and the contributions of this research are as follows.Firstly,in order to improve poor accuracy of the lithium-ion battery model in the full operation temperature range and estimation accuracy of the state of charge(SOC),the performance tests of the lithium-ion battery under the full operating temperature range are carried out.A battery model with temperature compensation in the temperature range from-20℃ to 60℃ was constructed.Based on square root cubature Kalman filter(SRCKF)algorithm,a battery SOC estimation method under complex temperature environment was proposed.The estimation accuracy of the algorithm is verified in a wide-range and time-varying temperature environment.The result shows that the maximum estimation error is less than 2% in the whole operating temperature range.The SOC estimation accuracy in complex environment is greatly improved.Secondly,in order to obtain the aging law and estimate the available capacity of lithium-ion battery in their whole lifespan,the peak value and corresponding voltage of incremental capacity curve in charging process were selected as the aging features to estimate the available capacity based on Gaussian process regression(GPR)algorithm.At the same time,for the purpose of solving the problem of kernel function and hyperparameters optimization for the GPR,the artificial bee colony(ABC)algorithm is exploited to solve the hyperparameters.A remaining capacity estimation model of lithium-ion battery is established based on GPR and ABC algorithm,of which the estimation error of capacity is less than 2% in the whole cycle life.The result show that the algorithm can improve the estimation accuracy of battery meanwhile remaining available capacity,which will play a role in improving the estimation precision in their whole life cycle.Thirdly,to modify the SOC estimation accuracy after battery aging and capacity attenuation,a joint estimation method of SOC and remaining capacity with the consideration of the effects of battery aging and ambient temperature was developed.The particle swarm optimization algorithm is combined with the data accumulation and updating to realize the rule recognition and periodic updating of the parameters for the second-order RC equivalent circuit model.The long short-term memory recurrent neural network algorithm is utilized to accurately estimate the maximum available capacity for the lithium-ion batteries.In addition,based on the SRCKF filter algorithm,the constructed battery model and estimated capacity are fused to estimate the SOC in the whole life cycle of lithium-ion battery with wide temperature.Under the conditions of wide and time-varying temperature,the SOC and available capacity error can be controlled within 2% and 0.071 Ah,respectively.Finally,aiming at the problem that the SOC estimation schemes need to be adjusted according to the different topological structures of the battery pack,a weighted method for SOC estimation was proposed.By comparing the difference between the maximum and minimum SOC of battery cell,the weight factor and deviation were designed to iteratively calculate the SOC of the battery pack.The proposed SOC estimation method was validated using the real-world operation data of electric scooters that are monitored by the built big data platform.The designed SOC estimation strategy for battery pack keeps tracking the minimum SOC value during the discharge process,which ensures a good adaptability.This demonstrates that the proposed method not only has a preferable accuracy for SOC and capacity estimation,but also greatly reduces the calculational burden of BMS.