Research On Joint Estimation For Model Parameters And SOC Of LiFePO₄Battery

In today’s society with excessive energy consumption and serious environmentalpollution, electric vehicle of energy diversification and clean emission has becomethe hotspot in the research and development in the field of cars at present. The standor fall of battery performance in the electric vehicle directly produces an effect on thevehicle mileage, acceleration ability and braking energy recovery efficiency. Thecirculation service life and cost affect the vehicle reliability and cost. Batterymanagement system is one of the key technologies of electric vehicles and also thebiggest bottleneck of electric vehicle development. Battery state of charge estimationis the most important part of battery management system. Accurate estimation ofbattery SOC can prevent battery over-charging and over-discharging to extend batterylife; and can validly predict the travel distance of electric vehicle to prevent it stoprunning as a result of power estimation error; in addition, the accuracy of SOCestimation directly affect the energy distribution of electric vehicle. The battery modelparameters and state charge in real time in the working process of battery, so buildingan accurate battery model and study the method of estimating battery modelparameters associated with SOC are of great significance. This paper aims at a kindof LiFePO4battery whose Electric core material is FC-F45-R5D5J0-01SE and furtherstudy the joint estimation method of battery model parameters associated with SOC.The main research contents are focused on the following aspects:First of all, starting from the working principle of LiFePO4battery, the paperintroduced several main technical parameters; and studied the characteristics ofLiFePO4battery through experiment; and analyzed the influence factors of batterysafety in the user’s angel; and pointed out the defects of traditional SOC definitionmethod based on the influence factors of battery capacity and then presented thedynamic SOC definition.Then, based on the four classic equivalent circuit model and the characteristicsof LiFePO4battery, the paper established its equivalent circuit model; and thenadopted offline method and online method to identify the model parameters,respectively; and then verify the validity of the battery model and that online method can track the time-varying characteristic of the battery better and can further improvethe model accuracy through emulation and experiment.Finally, based on the established battery model, the paper used extended Kalmanfilter algorithm to estimate the battery SOC. The algorithm combined theampere-hour integral method and open circuit voltage method. The SOC stateequation can be built in the method of ampere-hour method and state variables can bemodified through detecting the battery voltage and current in real time. So thisalgorithm did not depend on initial values neither need longer stock-still time and hadhigh accuracy, convergence and feasibility. In order to track the time-varyingcharacteristics of battery, this paper studied the joint estimation method of batterymodel parameters associated with SOC and finally verified that the joint method hadhigher accuracy and convergence through simulation and experiment.