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This paper describes a methodology to estimate yaw rate of a 4-wheel-drive electric vehicle, in which wheel driven torque can be independently controlled by electric motor. Without non-driven wheels it would be difficult to estimate the vehicle yaw rate precisely, especially when some of the four wheels have large slip ratio. Therefore, a model based estimation methodology is put forward, which uses four wheel speeds, steering wheel angle and vehicle lateral acceleration as input signals. Firstly the yaw rate is estimated through three different ways considering both vehicle kinematics and vehicle dynamics. Vehicle kinematics based method has good estimation accuracy even when the vehicle has large lateral acceleration. However, it can not provide satisfying results when the wheel has large slip ratio. In contrast, vehicle dynamics based method is not so sensitive to wheel slip ratio.

This paper describes a methodology to estimate longitudinal velocity of a 4-wheel-drive electric vehicle, in which wheel driven torque can be independently controlled by electric motor. Without non-driven wheels it would be difficult to estimate the vehicle longitudinal velocity precisely, especially when all of four wheels have large slip ratio. Therefore, an estimation methodology based on fuzzy logic is put forward, which uses four wheel speed and longitudinal acceleration as input signals. However, this method works not very well when two or more wheels have large slip ratio. In order to improve estimation effect, a state variable filter is designed to calculate wheel acceleration signals, which are used as additional signals to the fuzzy logic observer. Furthermore, the possibility of using four wheel driving torque signals to improve the estimation precision is also discussed.