Research On The State Of Charge Estimation For Power Battery Of New Energy Electric Vehicles

The energy crisis and ecological pollution are increasingly challenging the living environment of mankind,the transformation of the energy industry structure is imminent.Thus,the new energy electric vehicles powered by batteries are immediately generated and concerned.The State of Charge(SoC)is an important criterion to measure the quality of the battery management system,whose accurate values can effectively control the charging time of electric vehicles as well as reduce the concern of drivers and passengers caused by inaccurate endurance mileage.Therefore,the accurate estimation of SoC for power battery has become a current research hotspot.Based on the power battery,the main work of the dissertation are as follows:In order to intuitively reflect the internal polarization and determine the relationship between the input current and output voltage of the battery,a second-order resistance-capacitance circuit equivalent circuit is built to characterize the external characteristics the battery.Intermittent discharging and standard discharging experiments are carried out to analyze characteristic parameters such as battery voltage and internal resistance.For describing the mathematical relationship between the open circuit voltage and state of charge,a theoretical model is set up,which can characterize the internal chemical reaction process of the battery.On this basis,the Hybrid Pulse Power Characteristics experiment is conducted,the off-line identification method is used to identify the equivalent circuit model parameters.The data acquired by intermittent discharging experiment is fitted,the results show that the accuracy of the established open circuit voltage-state of charge theoretical model is 70% higher than other models.Since the actual operation of electric vehicles will be affected by multiple factors such as temperature,road conditions,speed and so on,the battery has been working under the impact of alternating loads.Therefore,it is necessary to perform real-time online identification of the battery parameters to make the battery adapt to complex and changeable vehicle conditions.In order to solve the problems of data saturation and complicate environment,the forgetting factor recursive least square method is used to complete the online identification of battery parameters.What’s more,the correctness and rationality of the algorithm are verified by fitting the discharging experimental data.Aiming at the high nonlinearity of the battery and the limitations of the extended Kalman filter,a joint algorithm combining the extended Kalman filter with forgetting factor recursive least square method is adopted.Based on the iterative update of the joint algorithm,the SoC values are evaluated and the real-time estimation of SoC is done.Intermittent discharging experiment is implemented to verify the effectiveness of the algorithm.The results show that the joint algorithm estimation errors are always kept within ±0.5%,the algorithm has high accuracy and fast response.According to the standard of "Freedom CAR Battery Test Manual",an improved joint charging and discharging scheme is proposed,which combining high current with low current.The battery testing system and incubator are used to experiment and collect data.By using the data and the open circuit voltage-state of charge theoretical model,the mathematical relationship of open circuit voltage and state of charge are gotten.Based on this,the model is applied to the joint algorithm for estimating the SoC of the battery.Similarly,the above process is also adopted to evaluate the SoC under dynamic working conditions in real time.The results show that the SoC evaluation errors are kept within±2%,which proves that the built estimation method can meet the working requirements of the battery management system under the actual operating conditions of electric vehicles.