This study presents a real-time coordinated control strategy for optimizing fuel efficiency and NOx emissions in commercial vehicles operating in urban environments. First, data-fitting vehicle-level models are developed to characterize fuel consumption, NOx emissions, and after-treatment system conversion efficiency, accounting for the coupled dynamics of the engine and exhaust treatment system. Next, a real-time optimization framework is established by integrating traffic signal information, preceding vehicle states, and system dynamics. To enable rapid computation, a hybrid reasoning-iteration approach based on Pontryagins maximum principle is proposed, significantly reducing computational complexity while yielding an explicit solution. Simulation results demonstrate the effectiveness of the proposed method, and hardware-in-the-loop experiments further validate its practical feasibility.
ZhangFHuXLangariR, et al. Energy management strategies of connected HEVs and PHEVs: recent progress and outlook. Prog Energy Combust Sci2019; 73: 235–256.
3.
YangDJiangKZhaoD, et al. Intelligent and connected vehicles: current status and future perspectives. Sci China (Technol Sci)2018; 61(10): 1446–1471.
4.
MatinADiaH.Impacts of connected and automated vehicles on road safety and efficiency: a systematic literature review. IEEE Trans Intell Transp Syst2023; 5(4): 2705–2736.
5.
ShenDKarbowskiDRousseauA.A minimum principle-based algorithm for energy-efficient eco-driving of electric vehicles in various traffic and road conditions. IEEE Trans Intell Veh2020; 5(4): 725–737.
6.
JoaEChoiEYBorrelliF. Eco-driving under localization uncertainty for connected vehicles on urban roads: data-driven approach and experiment verification. In: 2024 IEEE intelligent vehicles symposium (IV), Jeju Island, Republic of Korea, 2–5 June 2024, pp.2904–2909. New York: IEEE.
7.
BakibillahASMKamalMASTanCP, et al. Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads. Heliyon2024; 10(1): 23586.
8.
ZhangYAiZChenJ, et al. Energy-saving optimization and control of autonomous electric vehicles with considering multiconstraints. IEEE Trans Cybern2022; 52(10): 10869–10881.
9.
QiXWuGHaoP, et al. Integrated-connected eco-driving system for PHEVs with co-optimization of vehicle dynamics and powertrain operations. IEEE Trans Cybern2022; 57(6): 3393–3401.
10.
LiangXGulerSIGayahVV.Decentralized arterial traffic signal optimization with connected vehicle information. J Intell Transp Syst2023; 27(2): 145–160.
11.
YounesMBBoukercheA.Traffic efficiency applications over downtown roads: a new challenge for intelligent connected vehicles. ACM Comput Surv2020; 53(5): 102–131.
12.
MarkertDMasrurA.A two-speed synchronous traffic protocol for intelligent intersections: from single-vehicle to platoon crossing. ACM Trans Cyber Phys Syst2023; 7(2): 1–21.
13.
KaurGSharmaS.Correlation between traffic lights and emergency vehicles in intelligent transportation system. Intell Cyber Phys Syst Auton Transp2021; 9: 153–165.
14.
TanCCaoYBanX, et al. Connected vehicle data-driven fixed-time traffic signal control considering cyclic time-dependent vehicle arrivals based on cumulative flow diagram. IEEE Trans Intell Transp Syst2024; 25(8): 8881–8897.
15.
PerronnetFBuissonJLombardA, et al. Deadlock prevention of self-driving vehicles in a network of intersections. IEEE Trans Intell Transp Syst2019; 20(11): 4219–4233.
16.
MaCHaoWWangA, et al. Developing a coordinated signal control system for urban ring road under the vehicle-infrastructure connected environment. IEEE Access2018; 6: 52471–52478.
17.
LeVAMalikopoulosAA.Distributed optimization for traffic light control and connected automated vehicle coordination in mixed-traffic intersections. IEEE Control Syst Lett2024; 8: 2721–2726.
18.
ZhongWLiKShiJ, et al. Reservation-prioritization-based mixed-traffic cooperative control at unsignalized intersections. IEEE Trans Intell Veh2024; 9(5): 4917–4930.
19.
LanghorstJChanKWMeerpohlC, et al. Computing safe stop trajectories for autonomous driving utilizing clustering and parametric optimization. Vehicles2024; 6: 590–610.
20.
PatilSVHashimotoKKishidaM.A robust traffic flow control using connected vehicle technology: signal spatio-temporal logic-based approach. IEEE Trans Intell Transp Syst2024; 25(12): 19658–19674.
21.
ShiYWangZLaClairTJ, et al. A novel deep reinforcement learning approach to traffic signal control with connected vehicles. Appl Sci2023; 13: 2750–2773.
22.
GuoLGaoBLiuQ, et al. On-line optimal control of the gearshift command for multi speed electric vehicles. IEEE ASME Trans Mechatron2017; 22(4): 1519–1530.
23.
GuoNZhangWLiJ, et al. Predictive energy management of fuel cell plug-in hybrid electric vehicles: a co-state boundaries-oriented PMP optimization approach. Appl Energy2024; 362: 122882.
24.
GuoNZhangWChenW, et al. Universal bounds estimation and efficient tuning for equivalent factor in real-time cost-optimal predictive ECMS of PHEVs. IEEE Trans Transp Electrif2025; 11(1): 2796–2813.
25.
BakibillahASMKamalMASTanCP, et al. Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads. Appl Soft Comput2021; 99: 106875.
26.
JaysonTBakibillahASMTanCP, et al. Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption. J Environ Manage2024; 368: 122245.
27.
KamalMASMukaiMMurataJ, et al. Model predictive control of vehicles on urban roads for improved fuel economy. IFAC Proc Vol2011; 44(1): 3843–3848.
28.
KamalMASMukaiMMurataJ, et al. Ecological driving based on preceding vehicle prediction using MPC. IEEE Trans Control Syst Technol2012; 21(3): 831–841.
29.
FengSChuCLingJ.Simulation research on catalytic reduction reaction in SCR system. Agric Equip Veh Eng2019; 57(2): 76–81.
30.
ChenHGuoLDingH, et al. Real-time predictive cruise control for eco-driving taking into account traffic constraints. IEEE Trans Intell Transp Syst2018; 20(8): 2858–2868.
