scholarly journals Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sheng Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Simulation Experiment ◽  
Hierarchical Control ◽  
Battery Electric Vehicle ◽  
Cruise Control ◽  
Car Following ◽  
Energy Economy

This paper studies the control strategy for adaptive cruise control (ACC) system on a battery electric vehicle (BEV) in the car-following process, and the highlight of this paper is that the regeneration braking of BEV is considered in the car-following process. The hierarchical control structure is adopted for the ACC system. And the structure contains an upper controller and a lower controller. In the upper controller, multiple objectives including the safety, tracking, comfort, and energy consumption are optimized by using the model predictive control (MPC) method. In the lower controller, the energy is recovered during braking. So the energy economy is improved by reducing energy consumption and increasing energy recovery. The proposed ACC strategy is evaluated in simulation experiment. In the simulation experiment, safe tracking for the front vehicle is guaranteed, and the comfort and the energy economy are improved greatly. So the proposed adaptive cruise control strategy can make ACC more widely used in BEVs.

scholarly journals Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

2021 ◽  
Vol 11 (24) ◽  
pp. 12137
Author(s):  
Fei-Xue Wang ◽  
Qian Peng ◽  
Xin-Liang Zang ◽  
Qi-Fan Xue
Keyword(s):  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Hierarchical Control ◽  
Driving Safety ◽  
Cruise Control ◽  
City Bus ◽  
Tracking Ability ◽  
Vehicle Acceleration ◽  
Energy Economy ◽  
Road Slope

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

scholarly journals Model-Predictive Optimization for Pure Electric Vehicle during a Vehicle-Following Process

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Sheng Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Energy Consumption ◽  
Multiobjective Optimization ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Change Rate ◽  
Upper Level ◽  
Reduction Of Energy Consumption ◽  
Reference Trajectories

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.

scholarly journals Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1516
Author(s):  
Sheng Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Objective Function ◽  
Adaptive Cruise Control ◽  
Multiple Objectives ◽  
Weight Matrix ◽  
Tracking Performance ◽  
Cruise Control ◽  
Constant Weight ◽  
Car Following ◽  
Energy Economy ◽  
Driving Conditions

This paper studies control strategies for adaptive cruise control (ACC) systems in battery electric vehicles (BEVs). A hierarchical control structure is adopted for the ACC system, and the structure contains an upper controller and a lower controller. This paper focuses on the upper controller. In the upper controller, model predictive control (MPC) is applied for optimizing multiple objectives in the car-following process. In addition, multiple objectives, including safety, tracking, comfort, and energy economy, can be transformed into a symmetric objective function with constraints in MPC. In the objective function, the corresponding weight matrix for the optimization of multiple objectives is implemented in symmetric form to reduce the computational complexity. The weights in the weight matrix are usually set to be constant. However, the motion states of the own vehicle and the front vehicle change with respect to time during a car-following process, resulting in variation of the driving conditions. MPCs with constant weights do not adapt well to changes in driving conditions, which limits the performance of the ACC system. Therefore, a strategy for weight adjustment is proposed in order to improve the tracking performance, in which some weights in MPC can be adjusted according to the relative velocity of two vehicles in real time. The simulation experiments are carried out to demonstrate the effectiveness of the strategy for weight adjustment. Based on achieving the other control objectives, the ACC system with the weight adjustment has better tracking performance than the ACC system with the constant weight. While the tracking is improved, the energy economy is also improved.

scholarly journals Research on adaptive cruise control strategy of pure electric vehicle with braking energy recovery

2017 ◽  
Vol 9 (11) ◽  
pp. 168781401773499 ◽  
Author(s):  
Chengwei Sun ◽  
Liang Chu ◽  
Jianhua Guo ◽  
Dapai Shi ◽  
Tianjiao Li ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Energy Recovery ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Braking Energy Recovery

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Researches on Adaptive Cruise Control system: A state of the art review

2021 ◽  
pp. 095440702110192
Author(s):  
Liangyao Yu ◽  
Ruyue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Energy Economy ◽  
Machine Interaction

Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems

2017 ◽  
Vol 2622 (1) ◽  
pp. 84-95 ◽  
Author(s):  
Mizanur Rahman ◽  
Mashrur Chowdhury ◽  
Kakan Dey ◽  
M. Rafiul Islam ◽  
Taufiquar Khan
Keyword(s):  
Vehicle Dynamics ◽  
Adaptive Cruise Control ◽  
User Acceptance ◽  
Evaluation Framework ◽  
Cruise Control ◽  
String Stability ◽  
Maximum Acceleration ◽  
Car Following ◽  
Cooperative Adaptive Cruise Control ◽  
Behavior Models

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, ( a) the optimum velocity model (OVM) and ( b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.

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