Abstract
Electric vehicles (EVs) have the advantages of energy saving and environmental protection, which are favoured by major vehicle companies nowadays. However, the problem of how to effectively improve the economy has been a hot spot and difficult research point of the vehicle control strategy. Therefore, this chapter introduced the mainstream algorithms currently used as energy management strategies, and analysed the advantages of each method. This chapter begins with an introduction to energy integrated control for electric vehicles. Since the control scheme is related to architecture, this chapter then introduces the common architectures of EVs. Finally, the rule-based energy management strategy and the optimization-based energy management strategy are highlighted, and the vehicle architectures to which the different strategies are adapted are analyzed. Finally, the development and characteristics of the strategies are summarized.
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References
S.H. Mohr, Ward J, Ellem G, et al., Projection of world fossil fuels by country. Fuel (2015)
S. Sorrell, J. Speirs, R. Bentley et al., Global oil depletion: a review of the evidence. Energy Policy 38(9), 5290–5295 (2010)
M. Kebriaei, A.H. Niasar, B. Asaei, Hybrid electric vehicles: an overview, in 2015 International Conference on Connected Vehicles and Expo (ICCVE) (IEEE, 2016)
S.G. Wirasingha, A. Emadi, Classification and review of control strategies for plug-in hybrid electric vehicles. IEEE Trans. Veh. Technol. 60(1), 111–122 (2011)
K.T. Chau, Y.S. Wong, Overview of power management in hybrid electric vehicles. Energy Convers. Manage. 43(15), 1953–1968 (2002)
T. Lundberg, J. Persson. Hybrid powertrain control—a predictive real-time energy management system for a parallel hybrid electric vehicle (2008)
J. Park, J. Oh, Y. Park, et al. Optimal power distribution strategy for series-parallel hybrid electric vehicles, in International Forum on Strategic Technology (IEEE, 2007)
K. Ahn, P.Y. Papalambros, Engine optimal operation lines for power-split hybrid electric vehicles. Proc. Inst. Mech. Eng., Part D: J. Automob. Eng. 223(9), 1149–1162 (2009). https://doi.org/10.1243/09544070J-AUTO1124
Y. Cheng, K. Chen, C.C. Chan, A. Bouscayrol, S. Cui, Global modeling and control strategy simulation for a hybrid electric vehicle using electrical variable transmission, in 2008 IEEE Vehicle Power and Propulsion Conference, Harbin (2008), pp. 1–5. https://doi.org/10.1109/VPPC.2008.4677794
M. Schulze, R. Mustafa, B. Tilch et al., Energy management in a parallel hybrid electric vehicle for different driving conditions. Sae Int. J. Alternat. Powertrains 3(2), 193–212 (2014)
H.I. Dokuyucu, M. Cakmakci, Concurrent design of energy management and vehicle traction supervisory control algorithms for parallel hybrid electric vehicles. IEEE Trans. Veh. Technol. 65(2), 555–565 (2016)
W. Shabbir, S.A. Evangelou, Exclusive operation strategy for the supervisory control of series hybrid electric vehicles. IEEE Trans. Control Syst. Technol. 24(6), 1–9 (2016)
M.L. Zhou, D.K. Lu, W.M. Li et al., Optimized fuzzy logic control strategy for parallel hybrid electric vehicle based on genetic algorithm. Appl. Mech. Mater. 274, 345–349 (2013)
Lu, Dengke, Li, et al., Fuzzy logic control approach to the energy management of parallel hybrid electric vehicles, 592–596 (2012)
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Zhang, Y., Hou, Z. (2023). Energy Efficient Control of Vehicles. In: Cao, Y., Zhang, Y., Gu, C. (eds) Automated and Electric Vehicle: Design, Informatics and Sustainability. Recent Advancements in Connected Autonomous Vehicle Technologies, vol 3. Springer, Singapore. https://doi.org/10.1007/978-981-19-5751-2_1
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DOI: https://doi.org/10.1007/978-981-19-5751-2_1
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