An Energy Efficient Control Strategy for Electric Vehicle Driven by In-Wheel-Motors Based on Discrete Adaptive Sliding Mode Control

This paper presents an energy-efficient control strategy for electric vehicles(EVs)driven by in-wheel-motors(IWMs)based on discrete adaptive sliding mode control(DASMC).The nonlinear vehicle model,tire model and IWM model are established at first to represent the operation mechanism of the whole system.Based on the modeling,two virtual control variables are used to represent the longitudinal and yaw control efforts to coordinate the vehicle motion control.Then DASMC method is applied to calculate the required total driving torque and yaw moment,which can improve the tracking performance as well as the system robustness.According to the vehicle nonlinear model,the additional yaw moment can be expressed as a function of longitudinal and lateral tire forces.For further control scheme development,a tire force estimator using an unscented Kalman filter is designed to estimate real-time tire forces.On these bases,energy efficient torque allocation method is developed to distribute the total driving torque and differential torque to each IWM,considering the motor energy consumption,the tire slip energy consumption,and the brake energy~?recovery.Simulation results of the proposed control strategy using the co-platform of Matlab/Simulink and CarSim way.

Deep reinforcement learning based energy management strategy for fuel cell/battery/supercapacitor powered electric vehicle

Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy.Therefore,the current automobile market is mainly dominated by fuel cell hybrid vehicles.In this study,the fuel cell hybrid commercial vehicle is taken as the research object,and a fuel cell/battery/supercapacitor energy topology is proposed,and an energy management strategy based on a double-delay deep deterministic policy gradient is designed for this topological structure.This strategy takes fuel cell hydrogen consumption,fuel cell life loss,and battery life loss as the optimization goals,in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery,providing more optimization ranges for the optimization of fuel cells and batteries.Compared with the deep deterministic policy gradient strategy(DDPG)and the nonlinear programming algorithm strategy,this strategy has reduced hydrogen consumption level,fuel cell loss level,and battery loss level,which greatly improves the economy and service life of the power system.The proposed EMS is based on the TD3 algorithm in deep reinforcement learning,and simultaneously optimizes a number of indicators,which is beneficial to prolong the service life of the power system.

Segmented trajectory planning strategy for active collision avoidance system

This paper presents a segmented trajectory planning strategy for active collision avoidance system.Considering the longitudinal and lateral movement of the obstacle vehicle,as well as the ego vehicle and obstacle outer contour limitations,the collision avoidance trajectory is divided into three segments:lane changing,overtaking and back to original lane.The starting point and end point of lane-change are decided based on longitudinal and lateral safety distance model according to the relative speed and distance as well as the outer contour of the two vehicles.Based on system objective function and lane-change trajectory cluster,vehicle states,dynamic constraints and vehicle body kinematics constraints,the optimal trajectory can be selected,which can monitor the relative location of the obstacle vehicle constantly and then ensure the vehicle can accomplish the collision avoidance safely and smoothly.Simulation and experiment results demonstrate the effectiveness and feasibility of proposed trajectory planning strategy for the active collision avoidance.

Key technologies for electric vehicles

1.Introduction Electric vehicles(EVs)are playing an increasingly important role in decarbonizing the transportation sector.They constitute a promising solution to a set of global challenges such as climate change and air pollution.EVs are an integration of a wide spectrum of techniques,such as battery monitoring,battery safety and vehicle energy management.In this regard,the EV development still faces significant challenges,which necessitate innovations in EV technologies.Given this,Green Energy and Intelligent Transportation(GEITS)organizes a special issue of“Key Technologies for Electric Vehicles”that attempts to advance knowledge in the area of EVs and provides a platform for researchers and engineers to share recent research results and discuss critical challenges in this field.A wide spectrum of topics are discussed,including but not limited to the following.

Mode Switching and Consistency Control for Electric-Hydraulic Hybrid Steering System

Electric-hydraulic hybrid power steering(E-HHPS)system,a novel device with multiple modes for commercial electric vehicles,is designed to realize both superior steering feel and high energy efficiency.However,inconsistent steering perfor-mance occurs in the mode-switching process due to different dynamic characteristics of electric and hydraulic components,which even threatens driving safety.In this paper,mode-switching strategy and dynamic compensation control method are proposed for the E-HHPS system to eliminate the inconsistency of steering feel,which comprehensively considers ideal assistance characteristics and energy consumption of the system.Then,the influence of disturbances on system stability is analyzed,and H_(∞)robust controller is employed to guarantee system robustness and stability.The experimental results dem-onstrate that the proposed strategy can provide a steering system with natural steering feel without apparent inconsistency and effectively minimize energy consumption.