Planetary hybrid powertrain systems are widely utilized due to their high-efficiency planetary gear coupling mechanism, in which the energy management strategy (EMS) directly affects system efficiency and fuel economy. To address the difficulty in optimizing energy distribution across different operating modes of a double-planetary-gear hybrid system, this paper proposes a Mode-Adaptive Equivalent Consumption Minimization Strategy (MA-ECMS). Based on the dynamic programing (DP) algorithm for energy management, the proposed strategy improves a rule-based EMS by extracting the distribution characteristics of operating modes and fitting their boundary curves. Validation results show that the improved rule-based strategy reduces fuel consumption by approximately 2.68% compared with conventional rule-based strategies, while also improving the final state of charge (SOC) of the battery, confirming its effectiveness and practical value. Furthermore, the Equivalent Consumption Minimization Strategy (ECMS) is incorporated. By integrating ECMS with varying initial SOC levels and operating mode conditions, a piecewise linear adjustment function based on SOC deviation feedback is introduced to dynamically adjust the equivalent factor. An equivalent factor MAP is then constructed, enabling adaptive optimization of the energy management strategy under different operating states. Simulation results demonstrate that the proposed MA-ECMS not only achieves a 2.82% reduction in fuel consumption compared to conventional ECMS, but also significantly mitigates battery SOC fluctuations, thereby optimizing both fuel economy and battery lifespan. The strategy offers a solution with strong engineering applicability for real-time energy management in multi-mode hybrid powertrain systems.
ZhaoXWangLZhouY, et al. Energy management strategies for fuel cell hybrid electric vehicles: classification, comparison, and outlook. Energy Convers Manag2022; 270: 116179.
2.
ZhuDPritchardEDadamSR, et al. Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming. arXiv. arXiv:2207.06450, 2022.
3.
ChengYChenKChanC, et al. Global modeling and control strategy simulation for a hybrid electric vehicle using electrical variable transmission. In: 2008 IEEE vehicle power and propulsion conference, 2008, pp.1–5.
4.
JohnsonVHWipkeKBRausenDJ. HEV control strategy for real-time optimization of fuel economy and emissions. SAE technical paper 2000.
5.
RezaeiHAbdollahiSEAbdollahiS, et al. Energy management strategies of battery-ultracapacitor hybrid storage systems for electric vehicles: review, challenges, and future trends. J Energy Storage2022; 53: 105045.
6.
LüXHeSXuY, et al. Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles. Elect Power Syst Res2024; 232: 110372.
7.
BellmanRKalabaR. On the role of dynamic programming in statistical communication theory. IRE Trans Inf Theory1957; 3(3): 197–203.
8.
Azim MohseniNBayatiNEbelT. Energy management strategies of hybrid electric vehicles: a comparative review. IET Smart Grid2024; 7(3): 191–220.
9.
MazouziAHadrougNAlayedW, et al. Comprehensive optimization of fuzzy logic-based energy management system for fuel-cell hybrid electric vehicle using genetic algorithm. Int J Hydrogen Energy2024; 81: 889–905.
10.
MinDSongZChenH, et al. Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition. Appl Energy2022; 306: 118036.
11.
KimNChaSPengH. Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle. IEEE Trans Control Syst Technol2010; 19(5): 1279–1287.
12.
QuanRGuoHLiX, et al. A real-time energy management strategy for fuel cell vehicle based on Pontryagin’s minimum principle. IŞÇ2024; 27(4): 109473.
13.
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.
14.
ShiDLiuSCaiY, et al. Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information. Appl Energy2021; 286: 116467.
15.
DuCHuangSJiangY, et al. Optimization of energy management strategy for fuel cell hybrid electric vehicles based on dynamic programming. Energies2022; 15(12): 4325.
16.
DengTWuSChenQ, et al. Multi-objective energy management strategy for PHEVs based on working condition information prediction and time-varying equivalence factor ECMS. Automot Innov2024; 7(4): 698–715.
17.
QuanSWangYXXiaoX, et al. Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation. Appl Energy2021; 304: 117845.
18.
AbideenFZUKhalidHAKhanMS, et al. Direct model predictive control of fuel cell and ultra-capacitor based hybrid electric vehicle. IEEE Access2024; 12: 46774–46784.
19.
WangWGuoXYangC, et al. A multi-objective optimization energy management strategy for power split HEV based on velocity prediction. Energy2022; 238: 121714.
20.
VuTMMoezziRCyrusJ, et al. Parallel hybrid electric vehicle modelling and model predictive control. Appl Sci2021; 11(22): 10668.
21.
MontazeriGHMMahmoodiKM. Development a new power management strategy for power split hybrid electric vehicles. Transp Res D Transp Environ2015; 37: 79–96.
22.
TianHWangXLuZ, et al. Adaptive fuzzy logic energy management strategy based on reasonable SOC reference curve for online control of plug-in hybrid electric city bus. IEEE Trans Intell Transp Syst2017; 19(5): 1607–1617.
23.
HuangKDNguyenMKChenPT. A rule-based control strategy of driver demand to enhance energy efficiency of hybrid electric vehicles. Appl Sci2022; 12(17): 8507.
24.
LinXHuangHXuX, et al. Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle. Energy2024; 295: 130728.
25.
FanJChengZZhangZ. Optimization of hybrid electric vehicle energy management strategy based on dynamic programming. In: 4th International conference on mechanical, electronics, and electrical and automation control (METMS 2024), 2024, pp.726–733.
26.
TangWWangYJiaoX, et al. Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios. Energy2023; 265: 126264.
27.
WangPDouJSuW, et al. An energy management strategy based on dynamic programming for fuel cell hybrid trucks in ports. Int J Hydrogen Energy2024; 82: 123–133.
28.
GuoNZhangWChenW, et al. Universal bounds estimation and efficient tuning for equivalent factor in real-time cost-optimal predictive ECMS of PHEVs. IEEE Trans Transp Electrification2024; 11(1): 2796–2813.
29.
YangCDuXWangW, et al. A rolling convergent equivalent consumption minimization strategy for plug-in hybrid electric vehicles. IEEE Trans Vehicular Technol2023; 73(3): 3340–3353.
30.
LiJLiuYQinD, et al. Research on equivalent factor boundary of equivalent consumption minimization strategy for PHEVs. IEEE Trans Vehicular Technol2020; 69(6): 6011–6024.
31.
ZengTZhangCZhangY, et al. Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle. Energy2021; 227: 120305.
32.
PirasMDe BellisVMalfiE, et al. Adaptive ECMS based on speed forecasting for the control of a heavy-duty fuel cell vehicle for real-world driving. Energy Convers Manag2023; 289: 117178.
33.
GaoSZongYJuF, et al. Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving. Energy2024; 304: 132028.
34.
HeHShouYSongJ. An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine. Energy Rep2023; 10: 2006–2016.
35.
ChoiKByunJLeeS, et al. Adaptive equivalent consumption minimization strategy (A-ECMS) for the HEVs with a near-optimal equivalent factor considering driving conditions. IEEE Trans Vehicular Technol2021; 71(3): 2538–2549.
36.
DengTTangPSuZ, et al. Systematic design and optimization method of multimode hybrid electric vehicles based on equivalent tree graph. IEEE Trans Power Electron2020; 35(12): 13465–13474.
37.
TrinhHATruongHVADoTC, et al. Optimization-based energy management strategies for hybrid construction machinery: a review. Energy Rep2022; 8: 6035–6057.
38.
DaiHWangYLuoS, et al. Dynamic modeling and vibration analysis of planetary gear sets concerning mesh phasing modulation. Mech Syst Signal Process2023; 200: 110557.
39.
XueQZhangXTengT, et al. Dynamic coordinated control strategy of power on gear shifting for novel dual motor electric vehicle. Sustain Energy Technol Assess2023; 55: 102941.
40.
HongXWuJZhangN, et al. Energy efficiency optimization of Simpson planetary gearset based dual-motor powertrains for electric vehicles. Energy2022; 259: 124908.
41.
TaoDJunlinLHaishuoH, et al. Adaptive energy management strategy based on driving cycle identification for hybrid electric vehicles. J Xi’an Jiaotong Univ2018; 52(01): 77–83.
