Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

Abstract

In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

Keywords

Plug-in hybrid electric vehicle trajectory prediction dynamic programming energy management strategy SOC trajectory planning

Get full access to this article

View all access options for this article.

References

Hamut

Dincer

Naterer

GF.

Analysis and optimization of hybrid electric vehicle thermal management systems. J Power Sourc 2014; 247: 643–654.

Kaur

Gaur

Saxena

, et al. Speed control of hybrid electric vehicle using artificial intelligence techniques. Int J Comput Netw Technol 2014; 2: 33–39.

Montazeri-Gh

Pourbafarani

Near-optimal SOC trajectory for traffic-based adaptive PHEV control strategy. IEEE Trans Vehi Technol 2017; 66: 9753–9760.

Banvait

Anwar

Chen

A rule-based energy management strategy for plug-in hybrid electric vehicle (PHEV). In: American control conference, St. Louis, MI, 10–12 June 2009. New York: IEEE.

Yang

Zhang

, et al. A rule-based energy management strategy for plug-in hybrid electric vehicle energy management strategy of plug-in hybrid electric bus for city-bus route. IEEE Trans Veh Technol 2015; 64: 2792–2803.

Kim

Rousseau

Lee

A jump condition of PMP-based control for PHEVs. J Power Source 2011; 196: 10380–10386.

Zeng

Wang

A two-level stochastic approach to optimize the energy management strategy for fixed-route hybrid electric vehicles. Mechatronics 2016; 38: 93–102.

Malikopoulos

AA.

Supervisory power management control algorithms for hybrid electric vehicles: a survey. IEEE Trans Intell Transp Syst 2014; 15: 1869–1885.

Yuan

Zhihong

Guangyu

, et al. An energy management strategy for fuel cell hybrid electric vehicle based on Markov decision process. Automot Eng 2006; 28(9): 798–797.

10.

Tribioli

Barbieri

Capata

, et al. A real time energy management strategy for plug-in hybrid electric vehicles based on optimal control theory. Energy Procedia 2014; 45: 949–958.

11.

Sivertsson

Adaptive control using map-based ECMS for a PHEV. IFAC Proc Vol 2012; 45: 357–362.

12.

Tianheng

Lin

Qing

, et al. A supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. IEEE Trans Veh Technol 2015; 64: 1691–1700.

13.

Musardo

Rizzoni

Staccia

A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management. Euro J Contr 2005; 11: 509–524.

14.

Tulpule

Marano

Rizzoni

. Effects of different PHEV control strategies on vehicle performance. In: American control conference, St. Louis, MI, 10–12 June 2009. New York: IEEE.

15.

Wang

Lukic

. Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. In: Vehicle power & propulsion conference, Chicago, IL, 6–9 September 2011. New York: IEEE.

16.

Larsson

Johannesson

Egardt

, et al. Benefit of route recognition in energy management of plug-in hybrid electric vehicles. In: American control conference, Montréal, QC, Canada, 27–29 June 2012. New York: IEEE.

17.

Tiesyte

Jensen

CS.

Similarity-based prediction of travel times for vehicles traveling on known routes. In ACM SIGSPATIAL international conference on advances in geographic information systems, Irvine, CA, 5–7 November 2008. New York: ACM.

18.

Chen

Gong

Lawson

, et al. Evaluating the feasibility of a passive travel survey collection in a complex urban environment: lessons learned from the New York City case study. Transp Res Part A: Policy Prac 2010; 44: 830–840.

19.

Xue

Zhang

Zheng

, et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: Proceedings of the 2013 IEEE 29th international conference on data engineering, Brisbane, QLD, Australia, 8–12 April 2013. New York: IEEE.

20.

Xue

Xie

, et al. Solving the data sparsity problem in destination prediction. VLDB J 2015; 24: 219–243.

21.

Alvarezgarcia

Ortega

Gonzalezabril

, et al. Trip destination prediction based on past GPS log using a Hidden Markov Model. Expert Syst Appl 2010; 37: 8166–8171.

22.

Zhan

Qin

Zeng

Energy management strategy of HEV based on driving cycle recognition using Genetic Optimized K-means Clustering Algorithm. China J Highway Transp 2016; 29: 130–137.

23.

Niu

Qin

, et al. Driving cycle identification and parameters optimization for parallel hybrid electric vehicle. J Anhui Univ Technol 2018; 35(3): 232–239, 255.

24.

Peng

Xiong

Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Appl Energy 2016; 185: 1633–1643.

25.

Padmarajan

Mcgordon

Jennings

PA.

Blended Rule-Based Energy Management for PHEV: system structure and strategy. IEEE Trans Veh Technol 2016; 65: 8757–8762.

26.

Lingling

Zhumu

Dongwei

Energy management strategy of PHEV based on traffic prediction. Comput Measure Contr 2017; 25(5): 207–211.

27.

Mozaffari

Vajedi

Azad

NL.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy. Front Mech Eng 2015; 10: 154–167.

28.

Chasse

Sciarretta

Supervisory control of hybrid powertrains: an experimental benchmark of offline optimization and online energy management. Contr Eng Pract 2011; 19: 1253–1265.

29.

Park

Development of equivalent fuel consumption minimization strategy for hybrid electric vehicles. Int J Auto Technol 2012; 13: 835–843.

30.

Vajedi

Taghavipour

Azad

, et al. A comparative analysis of route-based power management strategies for real-time application in plug-in hybrid electric vehicles. In: American control conference, Portland, OR, 4–6 June 2014. New York: IEEE.

31.

Paganelli

Delprat

Guerra

, et al. Equivalent consumption minimization strategy for parallel hybrid powertrains. In: IEEE vehicular technology conference, Birmingham, AL, 24–28 September 2002. New York: IEEE.

32.

Stockar

Marano

Canova

, et al. Energy-optimal control of plug-in hybrid electric vehicles for real-world driving cycles. IEEE Trans Veh Technol 2011; 60: 2949–2962.

33.

Chowdhury

Pisu

, et al. An energy optimization strategy for power-split drivetrain plug-in hybrid electric vehicles. Transport Res, Part C: Emerg Technol 2012; 22: 29–41.

34.

Sun

Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Appl Energy 2016; 185: 1644–1653.