Model‐Predictive Optimization for Pure Electric Vehicle during a Vehicle‐Following Process

Abstract

In this paper, a pure electric vehicle (PEV) equipped with adaptive cruise control (ACC) system is studied for a vehicle-following process. And a multiobjective optimization algorithm for ACC system is proposed in a model-predictive control (MPC) framework for optimizing safety, tracking capability, driving comfortability and energy consumption. The longitudinal dynamics of the ACC system are modeled, which not only considers the vehicle spacing and speed, but also introduces the acceleration and the change rate of acceleration (jerk) for the host vehicle and fully considers the influence of the acceleration of the leading vehicle. The improvement of driving comfortability and the reduction of energy consumption are achieved mainly by optimizing the acceleration and jerk of host vehicle. Some optimized reference trajectories are introduced to MPC for improving driving comfortability of host vehicle. The performances of the multiobjective upper level algorithm combined with the PEV model are evaluated for three representative scenarios. The results demonstrate the effectiveness of the proposed algorithm.

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

“…ADAS for the safe automatic driving mostly tackles the stabilization of the chassis longitudinal motion and the actuation of the emerging braking via a wide variety of control techniques. Among others, Model Predictive Control (MPC) has been effectively used in [24][25][26][27][28] in order to synthesize an ACC system. Regarding the AEB, typically event-based controllers have been realized through a continuous evaluation of the braking distance [29,30] or the collision time [31].…”

“…where µ is the estimated maximum available grip and τ H is the constant headway for an ideal dry road [28]. Note that Equation (25) ensures that the safety distance increases as the peak road friction decreases.…”

“…with a max ( µ) = u max ( µ) = min(2, µg), a min ( µ) = u min ( µ) = max(−4, − µg), where µ is the estimated maximum available grip and g the acceleration of gravity. The saturation values ( i.e., 2 and −4) are chosen as upper and lower limit, related to the ideal value for the grip set as 1 [28].…”

Robust Multiobjective Model Predictive Control with Computation Delay Compensation for Electric Vehicle Applications Using PMSM with Multilevel Inverter

“…e 3L-NPC inverter consists of three identical branches, each branch is composed with four switches and can be described by three switching states [1], [0], and [−1] (Table 1). us, there are 27 possible combinations of switching states corresponding to 19 inverter output voltages obtained in αβ-coordinate as follows [20]:…”

“…e Electric Vehicle (EV) has received more attention as an alternative solution of energy crisis and environmental challenges [1]. In the next years, with the research efforts and the constant improvement of the electrical drive systems and control strategies, the penetration of EVs will increase exponentially [2].…”