Cruise Control Of Electric Vehicles Research Articles

Energy-Saving Driving Assistance System Integrated With Predictive Cruise Control for Electric Vehicles

Energy-Saving Driving Assistance System Integrated With Predictive Cruise Control for Electric Vehicles

Pacejka-Like Curve-Based Speed Reference Generators for Electric Vehicles Powered by in-Wheel Motors

Recently, motor speed reference generators have been designed for the cruise control of electric vehicles with either centralized electric motors (straight manoeuvres) or in-wheel motors (bend manoeuvres with sufficiently small constant steering angles). Steady-state operation at a safe (though conservative) tire longitudinal slip can be achieved, with no a priori knowledge regarding the occurrence of a specific external condition. Here, an innovative solution is presented. It is based on an ingenious use of Pacejka-like curves – representing the torque current-slip characteristics of the vehicle – to design a new contraction-mapping-based automatic tuning procedure for the longitudinal velocity of the vehicle. Such an innovative procedure overcomes the highly conservative nature of the previous approach in terms of unduly small longitudinal velocities (and yaw rates) under relatively favourable external conditions while guaranteeing a safe operating condition close to the maximum of the torque-slip characteristics with no knowledge of the tire-road adhesion coefficient. Comparative CarSim simulations illustrate the effectiveness of the proposed approach, in the presence of uncertain parameters and complex vehicle dynamics that are neglected at the control design stage.

Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric vehicles (EVs), and improve the energy economy, an ADHDP-based economic adaptive cruise control (Eco-ACC) strategy for EVs in car-following scenarios is proposed in this paper. First, the longitudinal dynamics of EVs is modeled, and the control objectives are presented; then, the actor-critic structure of ADHDP is introduced, and the policy iteration formulas of the critic and actor networks in the ADHDP framework are given; finally, after the state variables, control variables, unity function and value function are determined, the ADHDP-based Eco-ACC strategy for EVs is designed. Extensive simulation results under different driving cycles show that the proposed Eco-ACC strategy can not only ensure vehicle safety, improve riding comfort and reduce energy consumption, but also significantly reduce the battery capacity loss and extend the battery life compared with the benchmark algorithm. In addition, the proposed Eco-ACC strategy is model-free and real-time, and can be robust in different car-following scenarios.

Energy-Optimal Adaptive Cruise Control for Electric Vehicles Based on Linear and Nonlinear Model Predictive Control

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which plans the host car's speed trajectory in real time for higher energy efficiency by taking look-ahead traffic information and road conditions into consideration. After linear MPC (LMPC) is formulated in time domain, a nonlinear MPC (NMPC) formulation in space domain is proposed in this paper to overcome the drawbacks of LMPC in time domain. To cope with computational complexity of NMPC in space domain, the nonlinear equality constraints are relaxed to inequality constraints to yield a convex optimization problem. Moreover, it is proven that the relaxed optimization problem can be recast as a second-order cone programming (SOCP) problem, for which the efficient numerical optimizers exist. The performance of NMPC is evaluated in simulation, which is compared with the time domain LMPC and a theoretically optimal receding horizon dynamic programming (RH-DP) solution. Results indicate that the proposed space domain NMPC outperforms LMPC in time domain and the optimal solution of NMPC is very close to the result of RH-DP.

Energy-Optimal Adaptive Cruise Control for Electric Vehicles in Both Time and Space Domain based on Model Predictive Control

Abstract A novel energy-optimal adaptive cruise control (EACC) function based on model predictive control (MPC) is developed for electric vehicles (EV). Through exploiting the surrounding traffic information, MPC based EACC plans an optimal speed trajectory for the controlled host car, in order to reduce its energy consumption and track its preceding car at the same time. As applying MPC to control an EV faces the challenges of optimizing a non-convex cost function and dealing with non-linear system dynamics, this work proposes more appropriate MPC problem formulations in both time and space domain. The performance of four different MPC designs is compared in a test cycle, which meets the trip requirements of a real driving emissions (RDE) test, according to the European regulation (European Commission, 2016).

Automatic motor speed reference generators for cruise and lateral control of electric vehicles with in-wheel motors

Motor speed reference generators have been recently presented in the literature for the cruise control of electric vehicles powered by centralized electric motors. Steady-state operation at a safe tire longitudinal slip is guaranteed to be achieved in the presence of uncertain external conditions. In this paper, a generalization of the aforementioned result to electric vehicles that make use of two in-wheel motors and are required to perform a bend manoeuvre is provided. A new cruise and lateral control is presented. The two in-wheel rear motors provide propulsion, while being capable of acting as a rear differential. The key point relies on the definition of suitable references for the two motor speeds, so that motor speed control algorithms can be directly inherited from the electric machine control literature, while intrinsically robust motor speed control loops can be additionally incorporated. Both CarSim simulations and experiments are included to illustrate the practical effectiveness of the proposed approach.

Robust cascaded automatic cruise control of electric vehicles

SummaryThis article focuses on automatic cruise control for electrically driven vehicles. The objective is to track a given vehicle‐velocity profile. For this type of application, the so‐called wheel slip plays a key role, as it is a measure for the force transmitted from the wheel to the road. Conventional wheel‐ slip controllers are usually activated if the absolute value of the slip exceeds pre‐assumed thresholds. Furthermore, it is distinguished between a braking and acceleration maneuver using separately designed and implemented controllers. In contrast, the proposed concept requires neither an activation strategy for the slip controller nor a distinction between braking and acceleration. The cascaded control structure is based upon adaptive‐gains super twisting sliding‐mode algorithm, and the friction force estimator is realized as a second‐order sliding‐mode observer with constant gains. The effectiveness and robustness of the proposed concept are demonstrated in numerical simulations using a complex multibody vehicle model. Copyright © 2015 John Wiley & Sons, Ltd.

Fault-tolerant cruise control of electric vehicles with induction motors

A fault-tolerant control scheme is proposed for the cruise control of electric vehicles (trains, cars) that make use of induction motors. It relies on a rotor speed reference generator and on a flux observer which is adaptive with respect to the uncertain rotor and stator resistances and to the load torque as well. The closed loop on-line identification of those three critical uncertain parameters allows for: (i) on-line estimating and imposing the motor flux modulus reference value which minimizes power losses at steady-state and improves power efficiency; (ii) the on-line detection of speed sensor faults as well as the fast switching on redundant motor speed sensors. CarSim simulations illustrate the effectiveness of the proposed approach.