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This paper proposes a new approach for the mode transition control in a parallel hybrid electric vehicle (HEV) with an engine clutch. During mode transition, a noticeable jerk or torque fluctuation in the hybrid powertrain, which may result in the vibration of the drivetrain and an unpleasant driving sensation, occurs frequently because of discontinuity of the dynamics and the characteristics of the powertrain system. Therefore, ensuring smooth vehicle operation during the mode transitions is an important objective of the HEV control. This paper presents a four-phase control strategy during transition from the electric vehicle mode to the hybrid mode by using disturbance observers. The disturbance observer estimates and compensates for disturbances to improve control accuracy, tracking performance, and drivability. The vehicle model is developed to represent the characteristics of the transient response of the target system and is used to simulate and verify the proposed control algorithms. The simulation results show the validity of the proposed control algorithms.

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This paper presents a new robust road bank angle estimation method that does not require a differential global positioning system or any additional expensive sensors. A modified bicycle model, which is less sensitive to model uncertainties than is the conventional bicycle model, is proposed. The road bank angle estimation algorithm designed using this model can improve robustness against modelling errors and uncertainties. A proportional-integral H N filter based on the game theory approach, which is designed for the worst cases with respect to the sensor noises and disturbances, is used as the estimator in order to improve further the stability and robustness of the bank estimation. The effectiveness and performance of the proposed estimation algorithm are verified by simulations and tests, and the results are compared with those of previous road bank angle estimation methods.

Reliability indexed sensor fusion (RISF) is a new estimation techique which uses process and measurement noise covariances as the reliability index in an adaptive Kalman filter framework. In RISF, noise covariances are assumed to be highly uncertain and determined by engineering knowledge. The uniform boundedness of the RISF with incorrect noise covariances is proved in the sense that the error covariance is bounded if specified conditions are satisfied. The RISF technique is then applied to the vehicle longitudinal and lateral velocity estimation. Multiple sensors, such as the whell speed sensors, the accelerometers, the yaw rate sensor, and the steering angle sensor, are used for the velocity estimation. Test results show the accuracy of the vehicle velocity estimation by the proposed RISF technique.

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.