Optimal energy management strategy of fuel-cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs
Corresponding Author
Yanbiao Feng
Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Correspondence
Yanbiao Feng, Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada.
Email: [email protected]
Search for more papers by this authorZuomin Dong
Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Search for more papers by this authorCorresponding Author
Yanbiao Feng
Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Correspondence
Yanbiao Feng, Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada.
Email: [email protected]
Search for more papers by this authorZuomin Dong
Department of Mechanical Engineering, and Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Search for more papers by this authorFunding information: Dennis & Phyllis Washington Foundation; Seaspan; The Natural Sciences and Engineering Research Council of Canada
Summary
Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.
REFERENCES
- 1Lajunen A. Energy Efficiency of Conventional, Hybrid Electric, and Fuel Cell Hybrid Powertrains in Heavy Machinery. No. 2015-01-2829. SAE Technical Paper. 2015. https://doi.org/10.4271/2015-01-2829.
10.4271/2015-01-2829 Google Scholar
- 2Lajunen A, Sainio P, Laurila L, Pippuri-Mäkeläinen J, Tammi K. Overview of powertrain electrification and future scenarios for non-road Mobile machinery. Energies. 2018; 11(5): 1184.
- 3Hannan MA, Azidin FA, Mohamed A. Multi-sources model and control algorithm of an energy management system for light electric vehicles. Energ Conver Manage. 2012; 62: 123-130.
- 4Martinez JS, John RI, Hissel D, Péra M-C. A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles. Inform Sci. 2012; 190: 192-207.
- 5Ahmadi S, Bathaee SMT, Hosseinpour AH. Improving fuel economy and performance of a fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy. Energ Conver Manage. 2018; 160: 74-84.
- 6Koubaa R. Double layer metaheuristic based energy management strategy for a fuel cell/ultra-capacitor hybrid electric vehicle. Energy. 2017; 133: 1079-1093.
- 7Zhang W, Li J, Xu L, Ouyang M. Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimization strategy. Energ Conver Manage. 2017; 134: 59-69.
- 8Zheng C, Cha SW, Park Y-i, Lim WS, Xu G. PMP-based power management strategy of fuel cell hybrid vehicles considering multi-objective optimization. Int J Precis Eng Manuf. 2013; 14(5): 845-853.
- 9Fares D, Chedid R, Panik F, Karaki S, Jabr R. Dynamic programming technique for optimizing fuel cell hybrid vehicles. Int J Hydrogen Energy. 2015; 40(24): 7777-7790.
- 10Zhou W, Yang L, Cai Y, Ying T. Dynamic programming for new energy vehicles based on their work modes part II: fuel cell electric vehicles. J Power Sources. 2018; 407: 92-104.
- 11Torreglosa JP, Jurado F, García P, Fernández LM. Hybrid fuel cell and battery tramway control based on an equivalent consumption minimization strategy. Control Eng Pract. 2011; 19(10): 1182-1194.
- 12Ettihir K, Boulon L, Agbossou K. Optimization-based energy management strategy for a fuel cell/battery hybrid power system. Appl Energy. 2016; 163: 142-153.
- 13Pelletier S, Jabali O, Laporte G, Veneroni M. Battery degradation and behaviour for electric vehicles: review and numerical analyses of several models. Transport Res Part B: Methodol. 2017; 103: 158-187.
- 14Wang J, Liu P, Hicks-Garner J, et al. Cycle-life model for graphite-LiFePO4 cells. J Power Sources. 2011; 196(8): 3942-3948.
- 15Sarasketa-Zabala E, Gandiaga I, Martinez-Laserna E, Rodriguez-Martinez LM, Villarreal I. Cycle ageing analysis of a LiFePO4/graphite cell with dynamic model validations: towards realistic lifetime predictions. J Power Sources. 2015; 275: 573-587.
- 16Song Z, Li J, Han X, et al. Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles. Appl Energy. 2014; 135: 212-224.
- 17Vora AP, Jin X, Hoshing V, Shaver G, Varigonda S, Tyner WE. Integrating battery degradation in a cost of ownership framework for hybrid electric vehicle design optimization. Proc Ins Mech Eng, Part D: J Automobile Eng. 2019; 233(6): 1507-1523.
- 18Xie S, Hu X, Zhang Q, Lin X, Mu B, Ji H. Aging-aware co-optimization of battery size, depth of discharge, and energy management for plug-in hybrid electric vehicles. J Power Sources. 2020; 450:227638.
- 19Chen L, Tong Y, Dong Z. Li-ion battery performance degradation modelling for the optimal design and energy management of electrified propulsion systems. Energies. 2020; 13(7): 1629.
- 20Tang L, Rizzoni G, Onori S. Energy management strategy for HEVs including battery life optimization. IEEE Trans Transport Electrif. 2015; 1(3): 211-222.
- 21Pham TH, Kessels JTBA, Van Den Bosch PPJ, Huisman RGM. Analytical solution to energy management guaranteeing battery life for hybrid trucks. IEEE Trans Vehicular Technol. 2015; 65(10): 7956-7971.
- 22Hu X, Martinez CM, Yang Y. Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: a unified cost-optimal approach. Mech Sys Signal Process. 2017; 87: 4-16.
- 23Zhang S, Hu X, Xie S, Song Z, Hu L, Hou C. Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses. Appl Energy. 2019; 256:113891.
- 24Jahnke T, Futter G, Latz A, et al. Performance and degradation of proton exchange membrane fuel cells: state of the art in modelling from atomistic to system scale. J Power Sources. 2016; 304: 207-233.
- 25Zhang X, Yang D, Luo M, Dong Z. Load profile based empirical model for the lifetime prediction of an automotive PEM fuel cell. Int J Hydrogen Energy. 2017; 42(16): 11868-11878.
- 26Chen H, Pei P, Song M. Lifetime prediction and the economic lifetime of proton exchange membrane fuel cells. Appl Energy. 2015; 142: 154-163.
- 27Ravey A, Mohammadi A, Bouquain D. Control strategy of fuel cell electric vehicle including degradation process. IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society. Yokohama, Japan: IEEE; 2015: 3508-3513.
10.1109/IECON.2015.7392644 Google Scholar
- 28Li H, Ravey A, N'Diaye A, Djerdir A. Online adaptive equivalent consumption minimization strategy for fuel cell hybrid electric vehicle considering power sources degradation. Energ Conver Manage. 2019; 192: 133-149.
- 29Sun C, Sun F, He H. Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Appl Energy. 2017; 185: 1644-1653.
- 30Fletcher T, Thring R, Watkinson M. An energy management strategy to concurrently optimise fuel consumption & PEM fuel cell lifetime in a hybrid vehicle. Int J Hydrogen Energy. 2016; 41(46): 21503-21515.
- 31Yue M, Jemei S, Zerhouni N. Health-conscious energy Management for Fuel Cell Hybrid Electric Vehicles based on prognostics-enabled decision-making. IEEE Trans Vehicular Technol. 2019; 68(12): 11483-11491.
- 32Hu X, Zou C, Tang X, Liu T, Hu L. Cost-optimal energy management of hybrid electric vehicles using fuel cell/battery health-aware predictive control. IEEE Trans Power Electron. 2019. 35(1): 382–392.
- 33Li Q, Chen W. Design of energy management system of a PEMFC–battery–supercapacitor hybrid tramway. Urban Transp Syst. London: IntechOpen; 2017; 21–39.
10.5772/64696 Google Scholar
- 34 MTU report, available at: https://www.mtu-report.com/pdf/0313/en/2013_03_mining_never_say_die.pdf
- 35Thompson ST, Papageorgopoulos D. Platinum group metal-free catalysts boost cost competitiveness of fuel cell vehicles. Nat Catal. 2019; 2(7): 558-561.
- 36Feng Y, Dong Z. Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck. J Power Sources. 2020; 454:227948.
- 37Ryan P. Electricity demand and implications of electric vehicle and battery storage adoption. Transition Towards 100% Renewable Energy. Cham: Springer; 2018: 391-398.
10.1007/978-3-319-69844-1_35 Google Scholar
- 38Wang X, Chow JC, Kohl SD, Percy KE, Legge AH, Watson JG. Real-world emission factors for Caterpillar 797B heavy haulers during mining operations. Particuology. 2016; 28: 22-30.
- 39Onori S, Serrao L, Rizzoni G. Hybrid Electric Vehicles: Energy Management Strategies. Vol 13. Berlin Heidelberg: Springer; 2016.
10.1007/978-1-4471-6781-5 Google Scholar
- 40Feng Y, Dong Z. Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost. Appl Energy. 2020; 270:115164.
- 41Feng Y, Dong Z. Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement. Energy. 2019; 189:116276.
- 42Dong H, Sun S, Song B, Wang P. Multi-surrogate-based global optimization using a score-based infill criterion. Struct Multidiscip Optim. 2019; 59(2): 485-506.
- 43Dong H, Song B, Dong Z, Wang P. Multi-start space reduction (MSSR) surrogate-based global optimization method. Struct Multidiscip Optim. 2016; 54(4): 907-926.
- 44Pei D, Leamy MJ. Dynamic programming-informed equivalent cost minimization control strategies for hybrid-electric vehicles. J Dyn Sys Meas Contr. 2013; 135(5):051013.
