Nonlinear Model Predictive Control for Ecological Driver Assistance Systems in Electric Vehicles

Abstract

Battery Electric Vehicle (BEV) has one of the most promising drivetrain technology. However, the BEVs are facing the limited cruising range which generally reduces their share in the automotive market. Velocity profile, acceleration characteristics, road gradients, and drive techniques around curves have significant impacts on the energy consumption of the BEVs. A semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques is proposed to address the limitation. The main contribution of this paper is the design of a real-time nonlinear model predictive controller with improved inequality constraints handling and economic penalty function to plan the online cost-effective cruising velocity. This system is based on the extended cruise control driver assistance system which controls the longitudinal velocity of the BEV in a safe and energy efficient manner by taking advantage of road slopes, effective drive around curves, and respecting the traffic regulation. A real-time optimisation algorithm is adapted and extended with economic objective function. Instead of the conventional Euclidean norms, deadzone penalty functions are proposed to achieve the economic objectives. In addition, the states inequality constraints are handled based on the proposed soft nonlinear complementarity function aimed to preserve the relaxed complementary slackness to enhance the Pontryagin’s Minimum Principle (PMP) method. Obtained numerical simulation and field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of real-time energy-efficient velocity regulation and constraints satisfaction intended to improve the cruising range capability of the BEVs.