Search Results

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

Developing pure electric powertrains have become an important way to reduce reliance on crude oil in recent years. This paper concerns energy management of dual motor-driven electric vehicles. In order to obtain a predictive energy management strategy with good performance in computation and energy efficiency, we propose a hybrid algorithm that combines model predictive control (MPC) and convex programming to minimize electrical energy use in real time control. First, few changes are occurred in original component models in order to convert the original optimal control problem into convex programming problem. Then convex optimization algorithm is used in the prediction horizon to optimize torque allocation between two electric motors with different size. To verify the effectiveness of the hybrid algorithm, a real city driving cycle is simulated. Furthermore, different predictive horizons are performed to illustrate the robustness and time efficiency of the proposed method.