A Pareto frontier-driven multi-objective energy management control strategy for fuel cell hybrid electric vehicles incorporating driving styles in adaptive cruise control scenarios
Restricted accessResearch articleFirst published online 2026
A Pareto frontier-driven multi-objective energy management control strategy for fuel cell hybrid electric vehicles incorporating driving styles in adaptive cruise control scenarios
Traditional adaptive cruise control (ACC) systems relying on fixed algorithms cannot fulfill the comprehensive demands of fuel cell hybrid electric vehicles in driving safety, energy consumption, and component lifespan as they overlook driver behavior and traffic variability. This study proposes an integrated ACC strategy using a convolutional neural network for driving style recognition, a trip distance adaptive equivalent consumption minimization strategy, and Pareto multi-objective optimization via Non-dominated Sorting Genetic Algorithm-II. Key innovations include dynamic equivalent factor adjustment based on state of charge and driving style, and a multi-objective function balancing tracking performance, hydrogen consumption, and fuel cell degradation. Simulations and hardware-in-the-loop experiments demonstrate a 3.96% improvement in fuel economy, 15.9% reduction in fuel cell degradation, and enhanced tracking accuracy compared to sequential quadratic programming.
AlthoffMMaierhoferSPekC. Provably-correct and comfortable adaptive cruise control. IEEE Trans Intell Veh2021; 6(1): 159–174.
2.
GuoNZhangXZouY, et al. Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation. Energy2021; 214: 119070.
3.
LeninpugalhanthiPLathaR. Adaptive PID controller based direct torque control of fuel cell fed BLDC drive for industrial drives applications. Therm Sci Eng Prog2024; 55: 102931.
4.
ZhaoDLiSChuL, et al. A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs. Energy2024; 310: 133188.
5.
ZhangRWuNWangZ, et al. Constrained hybrid optimal model predictive control for intelligent electric vehicle adaptive cruise using energy storage management strategy. J Energy Storage2023; 65: 107383.
6.
LinY-CNguyenHLT. Adaptive neuro-fuzzy predictor-based control for cooperative adaptive cruise control system. IEEE Trans Intell Transp Syst2020; 21(3): 1054–1063.
7.
ShenDLimC-CShiP. Fuzzy model based control for energy management and optimization in fuel cell vehicles. IEEE Trans Veh Technol2020; 69(12): 14674–14688.
8.
XuXGrizzleJWTabuadaP, et al. Correctness guarantees for the composition of lane keeping and adaptive cruise control. IEEE Trans Autom Sci Eng2018; 15(3): 1216–1229.
9.
GaoBCaiKQuT, et al. Personalized adaptive cruise control based on online driving style recognition technology and model predictive control. IEEE Trans Veh Technol2020; 69(11): 12482–12496.
10.
WangQZhaoYLinF, et al. Integrated control for distributed in-wheel motor drive electric vehicle based on states estimation and nonlinear MPC. Proc Inst Mech Eng D J Automob Eng2021; 236(5): 893–906.
11.
WangLHornBKP. On the stability analysis of mixed traffic with vehicles under car-following and bilateral control. IEEE Trans Autom Control2020; 65(7): 3076–3083.
12.
LiMHeHFengL, et al. Hierarchical predictive energy management of hybrid electric buses based on driver information. J Clean Prod2020; 269: 122374.
13.
BejaniMMGhateeM. Convolutional neural network with adaptive regularization to classify driving styles on smartphones. IEEE Trans Intell Transp Syst2020; 21(2): 543–552.
14.
ZhangFQiZXiaoL, et al. Co-optimization on ecological adaptive cruise control and energy management of automated hybrid electric vehicles. Energy2025; 314: 133542.
15.
ZhengWMaMXuE, et al. An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain. Energy2024; 312: 133544.
16.
ZhouWYangLCaiY, et al. Dynamic programming for new energy vehicles based on their work modes part I: electric vehicles and hybrid electric vehicles. J Power Sources2018; 406: 151–166.
17.
LiYWangFTangX, et al. Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles. Energy2022; 257: 124672.
18.
GaoSZongYJuF, et al. Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving. Energy2024; 304: 132028.
19.
MehrabanZZadehAYKhayyamH, et al. Fuzzy adaptive cruise control with model predictive control responding to dynamic traffic conditions for automated driving. Eng Appl Artif Intell2024; 136(Pt B): 109008.
20.
YangHHuYGongX, et al. Energy management strategy for fuel cell hybrid electric vehicles considering the inaccuracy of predicted vehicle speed. IEEE Trans Transp Electrif2024; 10(4): 8246–8262.
21.
ChaiHZhaoXYuQ, et al. Adaptive equivalent consumption minimization strategy based on road grade estimation for a plug-in hybrid electric truck. J Energy Storage2023; 70: 108035.
22.
IrshayyidAChenJXiongG. A review on reinforcement learning-based highway autonomous vehicle control. Green Energy Intell Transp2024; 3(4): 100156.
23.
LiGGorgesD. 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.
24.
LiuZELiYZhouQ, et al. Real-time energy management for HEV combining naturalistic driving data and deep reinforcement learning with high generalization. Appl Energy2025; 377: 124350.
25.
HuangYSurawskiNCOrganB, et al. Fuel consumption and emissions performance under real driving: comparison between hybrid and conventional vehicles. Sci Total Environ2019; 659: 275–282.
26.
LiuCChenbYXuaR, et al. Co-optimization of energy management and eco-driving considering fuel cell degradation via improved hierarchical model predictive control. Green Energy Intell Transp2024; 3(6): 100176.
27.
XieSHuXLiuT, et al. Predictive vehicle-following power management for plug-in hybrid electric vehicles. Energy2019; 166: 701–714.
28.
BorekJGroelkeBEarnhardtC, et al. Economic optimal control for minimizing fuel consumption of heavy-duty trucks in a highway environment. IEEE Trans Control Syst Technol2020; 28(5): 1652–1664.
29.
ChenBEvangelouSALotR. Series hybrid electric vehicle simultaneous energy management and driving speed optimization. IEEE/ASME Trans Mechatron2019; 24(6): 2756–2767.
30.
QiuSQiuLQianL, et al. Hierarchical energy management control strategies for connected hybrid electric vehicles considering efficiencies feedback. Simul Model Pract Theory2019; 90: 1–15.
31.
HeYZhouQMakridisM, et al. Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs. IEEE Trans Transp Electrif2020; 6(1): 346–355.
32.
ZhuLTaoFFuZ, et al. Multiobjective optimization of safety, comfort, fuel economy, and power sources durability for FCHEV in car-following scenarios. IEEE Trans Transp Electrif2023; 9(1): 1797–1808.
33.
HuJFuPLiJ, et al. Intelligent energy management for the fuel cell electric vehicle: a real-time, deep reinforcement learning application to multi-objective optimization. Green Energy Intell Transp2025; 2025; 100300.
