In this paper, a comparison is made on different torque vectoring strategies to find the best strategy in terms of improving handling, fuel consumption, stability and ride comfort performances. The torque vectoring differential strategies include superposition clutch, stationary clutch, four-wheel drive and electronic stability control. The torque vectoring differentials are implemented on an eight-DOF vehicle model and controlled using optimized fuzzy-based controllers. The vehicle model assisted with the Pacejka tyre model, an eight-cylinder dynamic model for engine, and a five-speed transmission system. Bee’s Algorithm is employed to optimize the fuzzy controller to ensure each torque vectoring differential works in its best state. The controller actuates the electronic clutches of the torque vectoring differential to minimize the yaw rate error and limiting the side-slip angle in stability region. To estimate side-slip angle and cornering stiffness, a combined observer is designed based on full order observer and recursive least square method. To validate the results, a realistic car model is built in Carsim package. The final model is tested using a co-simulation between Matlab and Carsim. According to the results, the torque vectoring differential shows better handling compared to electronic stability control, while electronic stability control is more effective in improving the stability in critical situation. Among the torque vectoring differential strategies, stationary clutch in handling and four-wheel drive in fuel consumption as well as ride comfort have better operation and more enhancements.
Rolling Stability Control of In-wheel Motor Electric Vehicle Based on Disturbance Observer
