This paper proposes a efficient model predictive controller (MPC) for the energy management problem of dual mode intelligent (DM-i) hybrid powertrain equipped vehicles in navigation driving scenarios. The controller is divided into two layers where the upper-layer plans the optimal state of charge (SOC) trajectory according to the dynamic traffic information provided by the navigation system, while the lower-layer determines the optimal operation mode and engine working points. The alternating direction multiplier method (ADMM) is employed in the upper-layer to obtain the optimal solution considering mode switching. Simulation results show that compared with the rule-based strategy, MPC operates the powertrain with 1.48% and 2.49% lower fuel consumption in urban and highway routes, respectively. In the complex routes composed of urban and highway, the fuel-saving rate of MPC is 6.2% and 8.92%, respectively. In the varying scenarios where the driver temporarily changes the driving route, the economy of MPC is still better than the rule-based strategy, which ensures that MPC has strong robustness in practical applications.
WuYZhangSHaoJ, et al. On-road vehicle emissions and their control in China: a review and outlook. Sci Total Environ2017; 574: 332–349.
2.
HannanMAAzidinFAMohamedA.Hybrid electric vehicles and their challenges: a review. Renew Sustain Energy Rev2014; 29: 135–150.
3.
TranD-DVafaeipourMEl BaghdadiM, et al. Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: topologies and integrated energy management strategies. Renew Sustain Energy Rev2020; 119: 109596.
4.
AgamlohEvon JouanneAYokochiA.An overview of electric machine trends in modern electric vehicles. Machines2020; 8(2): 20.
5.
YangLYuBYangB, et al. Life cycle environmental assessment of electric and internal combustion engine vehicles in China. J Clean Prod2021; 285: 124899.
6.
ZhuangWLiSZhangX, et al. A survey of powertrain configuration studies on hybrid electric vehicles. Appl Energy2020; 262: 114553.
7.
HuXJiangJEgardtB, et al. Advanced power-source integration in hybrid electric vehicles: multicriteria optimization approach. IEEE Trans Ind Electron2015; 62(12): 7847–7858.
8.
HuangYWangHKhajepourA, et al. Model predictive control power management strategies for HEVs: a review. J Power Sources2017; 341: 91–106.
9.
JalilNKheirNASalmanM. A rule-based energy management strategy for a series hybrid vehicle. In: Proceedings of the 1997 American control conference (cat. no. 97CH36041), Albuquerque, New Mexico, USA, June 4–6, 1997, vol. 1, pp.689–693. New York, NY: IEEE.
10.
SorniottiAHoldstockTEverittM, et al. A novel clutchless multiple-speed transmission for electric axles. Int J Powertrains2013; 2(2–3): 103–131.
11.
HuJZhengLJiaM, et al. Optimization and model validation of operation control strategies for a novel dual-motor coupling-propulsion pure electric vehicle. Energies2018; 11(4): 754.
12.
ZhangSZhangCHanG, et al. Optimal control strategy design based on dynamic programming for a dual-motor coupling-propulsion system. Sci World J2014; 2014(1): 958239.
13.
AmiriMEsfahanianMHairi-YazdiMR, et al. Minimization of power losses in hybrid electric vehicles in view of the prolonging of battery life. J Power Sources2009; 190(2): 372–379.
MainoCMisulDMusaA, et al. Optimal mesh discretization of the dynamic programming for hybrid electric vehicles. Appl Energy2021; 292: 116920.
16.
SongZHofmannHLiJ, et al. Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energy2015; 139: 151–162.
17.
MainoCMisulDMusaA, et al. Optimal mesh discretization of the dynamic programming for hybrid electric vehicles. Appl Energy2021; 292: 116920.
18.
WangWZhangZShiJ, et al. Optimization of a dual-motor coupled powertrain energy management strategy for a battery electric bus based on dynamic programming method. IEEE Access2018; 6: 32899–32909.
19.
HuJLiJHuZ, et al. Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming. Energy2021; 215: 118851.
20.
SerraoLOnoriSRizzoniG.ECMS as a realization of Pontryagin’s minimum principle for HEV control. In: 2009 American control conference, St. Louis, Missouri, USA, June 10–12, 2009, pp.3964–3969. New York, NY: IEEE.
21.
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.
22.
BoydSParikhNChuE, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn2011; 3(1): 1–122.
23.
EastSCannonM. ADMM for MPC with state and input constraints, and input nonlinearity. In: 2018 annual American control conference (ACC), Milwaukee, Wisconsin, USA, June 27–29, 2018, pp.4514–4519. New York, NY: IEEE.
24.
EastSCannonM.Fast optimal energy management with engine on/off decisions for plug-in hybrid electric vehicles. IEEE Control Syst Lett2019; 3(4): 1074–1079.
25.
TateEDGrizzleJWPengH.SP-SDP for fuel consumption and tailpipe emissions minimization in an EVT hybrid. IEEE Trans Control Syst Technol2010; 18(3): 673–687.
26.
ZengXWangJ.A parallel hybrid electric vehicle energy management strategy using stochastic model predictive control with road grade preview. IEEE Trans Control Syst Technol2015; 23(6): 2416–2423.
27.
TateEDGrizzleJWPengH.SP-SDP for fuel consumption and tailpipe emissions minimization in an EVT hybrid. IEEE Trans Control Syst Technol2009; 18(3): 673–687.
28.
ZengXWangJ.A parallel hybrid electric vehicle energy management strategy using stochastic model predictive control with road grade preview. IEEE Trans Control Syst Technol2015; 23(6): 2416–2423.
29.
GreenwellWVahidiA.Predictive control of voltage and current in a fuel cell–ultracapacitor hybrid. IEEE Trans Ind Electron2009; 57(6): 1954–1963.
30.
LiYLinXHuX.Traffic information-based eco-driving for plug-in electric vehicles: a hierarchical control strategy. In: 2022 6th CAA international conference on vehicular control and intelligence (CVCI), 2022, pp.1–3. New York, NY: IEEE.
31.
HanJKhalatbarisoltaniAYangY, et al. Energy management in plug-in hybrid electric vehicles: preheating the battery packs in low-temperature driving scenarios. IEEE Trans Intell Transp Syst2023; 25(2): 1978–1991.
32.
PengJZhangFCoskunS, et al. Hierarchical optimization of speed planning and energy management for connected hybrid electric vehicles under multi-lane and signal lights aware scenarios. IEEE Trans Intell Transp Syst2023; 24(12): 14174–14188.
33.
WangLZhangFCuiY, et al. Stochastic velocity prediction for connected vehicles considering V2V communication interruption. IEEE Trans Intell Transp Syst2023; 24(11): 11654–1166.
34.
ZhaoNZhangFYangY, et al. Dynamic traffic prediction-based energy management of connected plug-in hybrid electric vehicles with long short-term state of charge planning. IEEE Trans Veh Technol2023; 72(5): 5833–5846.
35.
JuFWangQZhuangW, et al. Multi-lane eco-cruise for battery electric vehicles with terrain and traffic previews. IEEE Trans Transp Electrif2024; 10(4): 8362–8375.
36.
MehdiMahmoodi-kMontazeriMMadhanipour. Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition. Energy2021; 233: 121111.
37.
Montazeri-GhMortezaMahmoodi-kMehdi. Optimized predictive energy management of plug-in hybrid electric vehicle based on traffic condition. J Clean Prod2016; 139: 935–948.
38.
HuangSHHuangYHLeeHC, et al. A new hybrid algorithm for solving the vehicle routing problem with route balancing. Int J Ind Eng Manag2023; 14: 51–62.
39.
SalmanSAlaswadS.Mitigating the impact of congestion minimization on vehicles’ emissions in a transportation road network. Int J Ind Eng Manag2020; 11: 40–49.
