In order to develop an efficient and humanized adaptive cruise control (ACC) system for the extended range electric vehicle (EREV), a driving behavior based novel adaptive cruising strategy is developed using ACC system and EREV co-simulation model. The virtual driving platform is constructed and virtual driving experiments for various drivers are performed to obtain the core characteristics of different driver types. The constant time headway (CTH) safety distance model based on core characteristics and car-following model based on model predictive control (MPC) algorithm are developed to meet expectations of different driver types. An adaptive cruising strategy of ACC system in EREV is developed. Specially, the vehicle drive cost is investigated while the battery life and equivalent fuel consumption are considered. It is seen from the simulation results that the ACC system can meet the requirement of different driver types. Compared with the other three control strategies, the proposed adaptive cruising strategy can effectively reduce the vehicle drive cost.
KummethaVCKondyliASchrockSD. Analysis of the effects of adaptive cruise control on driver behavior and awareness using a driving simulator. J Transp Saf Secur2020; 12(5): 587–610.
2.
LiTChenDZhouH, et al. Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments. Transp Res B Methodol2021; 147: 67–91.
3.
LiXXieNWangJ. A variable weight adaptive cruise control strategy based on lane change recognition of leading vehicle. Automatika2022; 63(3): 555–571.
4.
WuXQinGYuH, et al. Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control. Optik2016; 127(6): 3445–3450.
5.
ChaturvediSKumarN. Design and implementation of an optimized PID controller for the adaptive cruise control system. IETE J Res. Epub ahead of print 23December2021. DOI: 10.1080/03772063.2021.2012282.
6.
HuBLiJYangJ, et al. Reinforcement learning approach to design practical adaptive control for a small-scale intelligent vehicle. Symmetry2019; 11(9): 1139.
7.
WangYGuoZWuJ, et al. Research on vehicle adaptive cruise control based on BP neural network working condition recognition. J Eng2022; 2022(2): 132–147.
8.
NieZFarzanehH. Adaptive cruise control for eco-driving based on model predictive control algorithm. Appl Sci2020; 10(15): 5271.
9.
ChenCGuoJGuoC, et al. Adaptive cruise control for cut-in scenarios based on model predictive control algorithm. Appl Sci2021; 11(11): 5293.
10.
PanCHuangAWangJ, et al. Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control. Energy2022; 241: 122793.
11.
WangMYuHDongG, et al. Dual-mode adaptive cruise control strategy based on model predictive control and neural network for pure electric vehicles. In: 2019 5th international conference on transportation information and safety (ICTIS), Liverpool, UK, 14–17 July 2019, pp.1220–1225. Liverpool: IEEE.
12.
RosenfeldABareketZGoldmanCV, et al. Learning drivers’ behavior to improve adaptive cruise control. J Intell Transp Syst2015; 19(1): 18–31.
13.
ManolisDSpiliopoulouAVandorouF, et al. Real time adaptive cruise control strategy for motorways. Transportation Res C2020; 115: 102617.
14.
YuXZ. Research on psychological evaluation and humanized control of adaptive cruise control system. MS Thesis, Jilin University, China, 2018.
15.
BagweRMByerlyAdos SantosECJr, et al. Adaptive rule-based energy management strategy for a parallel HEV. Energies2019; 12(23): 4472.
16.
PeiJSuYZhangD. Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Sci China Technol Sci2017; 60(3): 425–433.
17.
TaoSChenWGanR, et al. Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway. Railway Eng Sci2021; 29(3): 299–313.
18.
LüXWuYLianJ, et al. Energy management of hybrid electric vehicles: a review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Convers Manag2020; 205: 112474.
19.
NguyenBHGermanRTrovaoJPF, et al. Real-time energy management of battery/supercapacitor electric vehicles based on an adaptation of Pontryagin’s minimum principle. IEEE Trans Veh Technol2019; 68(1): 203–212.
20.
WangYWangXSunY, et al. Model predictive control strategy for energy optimization of series-parallel hybrid electric vehicle. J Clean Prod2018; 199: 348–358.
21.
LiGGörgesD. Ecological adaptive cruise control and energy management strategy for hybrid electric vehicles based on heuristic dynamic programming. IEEE Trans Intell Transp Syst2019; 20(9): 3526–3535.
22.
HeYZhouQMakridisM, et al. Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs. IEEE Trans Transp Electrification2020; 6(1): 346–355.
23.
YuYJiangJMinZ, et al. Research on energy management strategies of extended-range electric vehicles based on driving characteristics. World Electr Veh J2020; 11(3): 54.
24.
RahmanKMJurkovicSStancuC, et al. Design and performance of electrical propulsion system of extended range electric vehicle (EREV) Chevrolet volt. IEEE Trans Ind Appl2015; 51(3): 2479–2488.
25.
LiLLiuQ. Acceleration curve optimization for electric vehicle based on energy consumption and battery life. Energy2019; 169: 1039–1053.
26.
SockeelNShahverdiMMazzolaM. Impact of the state of charge estimation on model predictive control performance in a plug-in hybrid electric vehicle accounting for equivalent fuel consumption and battery capacity fade. In: IEEE transportation electrification conference and expo (ITEC), Detroit, MI, USA, 19–21 June 2019, pp.1–6. Detroit: IEEE.
27.
BaiYHeHLiJ, et al. Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy. J Clean Prod2019; 237: 117841.
28.
ChuJZ. Research on adaptive cruise control strategy considering driver style. MA Thesis, Chang’an University, China, 2020.
29.
LiuZYuanQNieG, et al. A multi-objective model predictive control for vehicle adaptive cruise control system based on a new safe distance model. Int J Autom Technol2021; 22(2): 475–487.
30.
YangYZhangYTianJ, et al. Adaptive real-time optimal energy management strategy for extender range electric vehicle. Energy2020; 197: 117237.
31.
VatanparvarKFaeziSBuragoI, et al. Extended range electric vehicle with driving behavior estimation in energy management. IEEE Trans Smart Grid2019; 10(3): 2959–2968.
32.
TianJXuRWangY, et al. Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries. Energy2021; 221: 119682.
