Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

被引:101
作者
Li, Guoqiang [1 ]
Goerges, Daniel [1 ]
机构
[1] Univ Kaiserslautern, Dept Elect & Comp Engn, D-67663 Kaiserslautern, Germany
关键词
Adaptive cruise control; energy management strategy; heuristic dynamic programming; OPTIMIZATION; ABILITY;
D O I
10.1109/TITS.2018.2877389
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.
引用
收藏
页码:3526 / 3535
页数:10
相关论文
共 29 条
[1]   MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle [J].
Borhan, Hoseinali ;
Vahidi, Ardalan ;
Phillips, Anthony M. ;
Kuang, Ming L. ;
Kolmanovsky, Ilya V. ;
Di Cairano, Stefano .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (03) :593-603
[2]   Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach [J].
Desjardins, Charles ;
Chaib-draa, Brahim .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2011, 12 (04) :1248-1260
[3]   A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC) [J].
Dey, Kakan C. ;
Yan, Li ;
Wang, Xujie ;
Wang, Yue ;
Shen, Haiying ;
Chowdhury, Mashrur ;
Yu, Lei ;
Qiu, Chenxi ;
Soundararaj, Vivekgautham .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (02) :491-509
[4]  
Johri R., 2011, SAE TECH PAP, DOI [10.4271/2011-24-0081, DOI 10.4271/2011-24-0081]
[5]  
Johri R, 2012, PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE AND BATH/ASME SYMPOSIUM ON FLUID POWER AND MOTION CONTROL (DSCC 2011), VOL 2, P279
[6]   Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle [J].
Kim, Namwook ;
Cha, Sukwon ;
Peng, Huei .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011, 19 (05) :1279-1287
[7]  
Lewis F. L., 2013, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
[8]   Fuel consumption optimization for smart hybrid electric vehicle during a car-following process [J].
Li, Liang ;
Wang, Xiangyu ;
Song, Jian .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 87 :17-29
[9]   Model Predictive Multi-Objective Vehicular Adaptive Cruise Control [J].
Li, Shengbo ;
Li, Keqiang ;
Rajamani, Rajesh ;
Wang, Jianqiang .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011, 19 (03) :556-566
[10]   Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System of Road Vehicles With Step-Gear Transmission [J].
Li, Shengbo Eben ;
Guo, Qiangqiang ;
Xin, Long ;
Cheng, Bo ;
Li, Keqiang .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (03) :2033-2043