Shuwei Zhang

Intelligent Hybrid Electric Vehicle ACC With Coordinated Control of Tracking Ability, Fuel Economy, and Ride Comfort

Adaptive cruise control (ACC) of hybrid electric vehicles (HEVs) has been traditionally developed without an efficient integration with active safety and energy management systems of hybrid power-trains, mainly for facilitating its implementation. This, however, leads to a compromise in the fuel economy of HEVs, since the predictive driving information provided by ACC is not exploited by the energy management system. In order to enhance the energy efficiency and control system integration, a novel ACC system for intelligent HEVs (i-HEV ACC) is developed in this study. The controller is proposed within the framework of nonlinear model predictive control, and a position-based nonlinear longitudinal intervehicle dynamics model is developed. A coordinated optimal control problem for both the tracking safety and the fuel consumption is formulated subject to the constraints on stable tracking. A multistep offline dynamic programming optimization and an online lookup table are used to implement the real-time control algorithm. Experiments are further conducted, which demonstrate that the proposed i-HEV ACC achieves enhanced performance and cooperation in traffic safety, fuel efficiency, and ride comfort.

Predictive Energy Management Strategy for Fully Electric Vehicles Based on Preceding Vehicle Movement

This paper presents an energy-efficient and terrain-information-and-preceding-vehicle-information-incorporated energy management strategy for fully electric vehicles (FEVs) equipped with in-wheel motors. Saving driving energy with terrain preview and preceding vehicle movement prediction are crucial to prolong the driving distance for an FEV. Unlike conducting energy optimization under the assumption that the preceding vehicle movements are already known in most studies, the front vehicle movements are predicted during each control cycle based on the vehicle-to-vehicle communication, and the FEV vehicle velocity and motor torque distribution are optimized by a nonlinear model predictive controller to reduce energy consumption. The energy-saving objective is achieved by including, in the cost function, the motor energy consumption in each control cycle, while the safety objective is accomplished by keeping a suitable relative distance from the preceding vehicle. Since the nonlinear vehicle longitudinal model is applied, the gridding initial torque plane is utilized in each time step to search for the global minimum. Simulation results show that this method has a better energy-saving performance than the control method without using the preceding vehicle movement information, and the algorithm proposed here has a wide applicability under various driving conditions.

Predictive energy management strategy for electric vehicles based on estimation of preceding vehicle future movements

In this paper, a model predictive controller is designed to control an FEV based on the road terrain grade information and the front vehicles movement prediction. With the vehicle-to-vehicle and vehicle-to-infrastructure wireless communication technologies, two front vehicles movement information are obtained, and a Bayes Network model is applied to predict the movement of the closer preceding vehicle. With a recursive optimization of the control algorithm, the velocity trajectory and front and rear motor torques of the FEV are optimized to realize the energy-efficient driving performance. Simulation results show that this method has a better energy saving performance than the traditional one, and this algorithm has a wider applicability under various driving conditions.

Predictive energy management strategy for fully electric vehicles based on hybrid model predictive control

Limited travelling range is a major concern for fully electric vehicles (FEVs), and enlarging the total driving range is a main research topic of FEVs. Recently, with the development of intelligent transportation systems, traffic information can be available for the vehicle control systems, like terrain profile and velocity and acceleration of preceding vehicles. With such information, energy-efficient control for FEVs could further prolong the total driving range. In this paper, a hybrid model predictive control method is introduced to control an FEV considering the road terrain profile ahead and preceding vehicle movement information. With the vehicle-to-vehicle wireless communication technology, the movement of preceding vehicle can be predicted in each control cycle. While keeping a suitable distance from the preceding vehicle, the driving energy consumption is optimized. Simulation results show that this method has a better energy saving performance than the traditional methods under various driving conditions even with communication packet losses.

Multi-objective route search for electric vehicles using ant colony optimization

In this paper, a temporal multi-objective ant colony optimization (ACO) algorithm is proposed to generate the routing plan for electric vehicles to fulfill the various requirements of drivers under a time-dependent stochastic traffic environment. The algorithm optimizes the route length, traveling time, energy, battery recycling lifetime and cabin temperature integrally, and meets constraints on arrival time and state of charge limitation. Simulation experiments are conducted and three different evaluation indexes are adopted. The performance of the algorithm is compared with two other multi-objective ACO methods. The proposed algorithm is proven to process higher convergence performance, and the solutions are also widely distributed, which suggests the effectiveness of this newly presented algorithm.