Study On Methods Of States And Parameters Estimation For In-wheel Motor Drive Electric Vehicle

With the increasing severe challenges of the environment, energy, developing greenautomotive,such as electric vehicles, became the central issue of the worlds. In-wheelmotor drive electric vehicle because of its dynamic layout flexibility, eliminated thetraditional mechanical transmission, motor torque responded rapidly and precisely made iteasier to realize chassis integrated control than the internal combustion engine vehicles.However, the introduction of the wheel motor lead to unsprung mass increases of in-wheeldrive electric vehicle, making the stability, safety, ride comfort etc. deterioration. Generally,vehicle’s stability control, active safety control, needed some important state information,such as sideslip angle, yaw rate, longitudinal speed, roll angle etc., as the feedback ofdecision-making control, but it was difficult to fully rely on car sensors to get the statesignals of the vehicle. In order to solve the problem that measurement difficulties and costlimits, in-wheel motor drive electric vehicles as the study object, estimated the states andparameters of the vehicle. Main works for this paper as followed:①Built an in-wheel motor drive electric vehicle simulation model. A pure electricvehicle as the platform to develop in-wheel motor drive system, used the way ofMATLAB/Simulink and the commercial software CarSim combined simulation to build anin-wheel motor drive electric vehicle model.②Estimated the key states of the chassis integrated control of in-wheel electricvehicle. Accurately, real-time acquiring the state information and parameters of vehicle wasthe prerequisite to the stability and active safety control. Using extended Kalman filtering,unscented Kalman filtering and strong tracking filtering algorithm to estmate the states ofin-wheel motor drive electrical vehicle based on3-dof vehicle model. Using virtualsimulation software, chose a single lane change conditions to verify the accuracy of thefiltering algorithm.③Improved the estimation algorithm. Based on the results of states estimation, builtthe dual extended Kalman filter to estimate the states and parameters by establishing a morecomplete state estimation model and introducing noise statistics estimators, simulationresults showed that more complete model allows the estimator to estimate more states,improved algorithm made the estimation results have higher accuracy.④Estimated road friction coefficient. Utilizing the states and parameters estimationresults, used extended Kalman filtering algorithm to estimate road adhesion coefficient of the vehicle.