Skip to main content
SpringerLink
Log in

Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

As electric vehicle (EV) emerges, it is important to understand how driver's driving behavior is influencing power consumption in an electric vehicle. Driver's personal driving behavior is usually quite distinctive and can be recognized by means of driving patterns after some driving cycles. This paper presents a method combining several machine learning approaches to characterize driving behaviors of electric vehicles. The driving patterns are modeled according to power consumption monitored by the battery management system (BMS), in aspects of individual driver's personal and EV-fleet operations. First, we apply an unsupervised clustering approach to characterize a driver's behaviors by formulating driving patterns. Subsequently, the resulting clustered datasets were used to train machine-learning based classifiers for classification of dataset of EV and EV-fleet driving patterns. The work aims to provide a robust solution to help identify the characteristics of specific types of EVs and their driver behaviors, in order to allow automakers and EV-subsystem providers to gather valuable driving information for product improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

EV:

electric vehicle

EVs:

electric vehicles

BMS:

battery management system

GHSOM:

growing hierarchical self-organizing maps

SOC:

state-of-charge

SOF:

state-of-function

SOH:

state-of-health

VCU:

vehicle control unit

DCIR:

direct current internal resistance

SVM:

support vector machine

RNNs:

recurrent neural networks

CART:

classification and regression trees

RBF:

radial basis function

v e :

vehicle speed

v a :

vehicle acceleration

m :

weight of car

f r :

coefficient of rolling resistance

c d :

drag coefficient

A :

frontal area

η t :

efficiency

f :

transmitting efficiency

amax:

maximal grade climbing radian

xk:

power consumption at different speed

p v0 :

static energy of auxiliaries

V :

maximum speed of a vehicle

C rated :

rated capacity of the battery

η soc :

charge-discharge efficiency

i(t) :

battery current

C act :

actual capacity

C nom :

initial capacity of the battery.

R EOL :

resistance at end-of-life

R act :

actual resistance

R nom :

initial resistance of a battery module

References

  • Awad, M. and Khanna, R. (2015). Deep Neural Networks. Efficient Learning Machines. Apress. Berkeley, USA, 127–147.

    Chapter  Google Scholar 

  • Barandiaran, I. (1998). The random subspace method for constructing decision forests. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 8, 832–844.

    Article  Google Scholar 

  • Barre, A, Deguilhem, B., Grolleau, S., Gerard, M., Suard, F. and Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J. Power Sources, 241, 680–689.

    Article  Google Scholar 

  • Belt, J. R. (2010). Battery Test Manual for Plug-in Hybrid Electric Vehicles, 1–70.

    Google Scholar 

  • Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and Regression Trees. Chapman & Hall/CRC. New York, USA.

    MATH  Google Scholar 

  • Chang, W. Y. (2013). The state of charge estimating methods for battery: A review. Applied Mathematic, 2013, Article ID 953792, 1–8.

    MATH  Google Scholar 

  • Chen, Z., Mi, C. C, Fu, Y., Xu, J. and Gong, X. (2013). Online battery state of health estimation based on genetic algorithm for electric and hybrid vehicle applications. J. Power Sources, 240, 184–192.

    Article  Google Scholar 

  • Cuma, M. U. and Koroglu, T. (2015). A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renewable and Sustainable Energy Reviews, 42, 517–531.

    Article  Google Scholar 

  • Dubarry, M., Bonnet, M., Dailliez, B., Teeters, A. and Liaw, B. Y. (2005). Analysis of electric vehicle usage of a Hyundai Santa Fe fleet in Hawaii. J. Asian Electric Vehicles 3, 1, 657–663.

    Article  Google Scholar 

  • Duran, A., Ragatz, A., Prohaska, R., Kelly, K. and Kevin, W. (2014). Characterization of in-use medium duty electric vehicle driving and charging behavior. Proc. IEEE Int. Electric Vehicle Conf., Florence, Italy.

    Google Scholar 

  • Ecker, M., Nieto, N, Kabitz, S., Schmalstieg, J., Blanke, H., Warnecke, A. and Sauer, D. U. (2014). Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. J. Power Sources, 248, 839–851.

    Article  Google Scholar 

  • Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. J. Royal Statistical Society 28, 1, 100–108.

    MATH  Google Scholar 

  • Helmbrecht, M., Olaverri-Monreal, C, Bengler, K., Vilimek, R. and Keinath, A. (2014). How electric vehicles affect driving behavioral patterns. IEEE Intelligent Transportation Systems Magazine 6, 3, 22–32.

    Article  Google Scholar 

  • Hinton, G. E., Osindero, S. and The, Y W. (2006). A fast learning algorithm for deep belief nets. Neural Computation 18, 7, 1527–1554.

    Article  MathSciNet  MATH  Google Scholar 

  • Ho, T. K. (1995). Random decision forests. Proc. 3rd Int. Conf. Document Analysis and Recognition, Montreal, Quebec, Canada.

    Google Scholar 

  • Jung, M. F., Sirkin, D., Gur, T. M. and Steinert, M. (2015). Displayed uncertainty improves driving experience and behavior: The case of range anxiety in an electric car. Proc. 33rd Annual ACM Conf. Human Factors in Computing Systems, Seoul, Korea.

    Google Scholar 

  • Lee, C. H. and Wu, C. H. (2015). A novel big data modeling method for improving driving range estimation of EVs. IEEE Access, 3, 1980–1993.

    Article  Google Scholar 

  • Lee, C. H. and Wu, C. H. (2016). A data driven approach to create an extensible EV driving data model. Proc. 3rd Multidisciplinary Int. Social Networks Conf. SocialInformatics, Union, New Jersey, USA.

    Google Scholar 

  • Liaw, B. Y. (2004). Fuzzy logic based driving pattern recognition for driving cycle analysis. J. Asian Electric Vehicles 2, 1, 551–556.

    Article  Google Scholar 

  • Liaw, B. Y. and Dubarry, M. (2007). From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation. J. Power Sources 174, 1, 76–88.

    Article  Google Scholar 

  • Liu, Z., Wu, Q., Christensen, L., Rautiainen, A. and Xue, Y (2015). Driving pattern analysis of nordic region based on national travel surveys for electric vehicle integration. J. Modern Power Systems and Clean Energy 3, 2, 180–189.

    Article  Google Scholar 

  • Loisel, R., Pasaoglu, G. and Thiel, C. (2014). Large-scale deployment of electric vehicles in Germany by 2030: An analysis of grid to vehicle and vehicle to grid concepts. Energy Policy, 65, 432–443.

    Article  Google Scholar 

  • Lu, L., Han, X., Li, J., Hua, J. and Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources, 226, 272–288.

    Article  Google Scholar 

  • Meissner, E. and Richter, G. (2003). Battery monitoring and electrical energy management: Precondition for future vehicle electric power systems. J. Power Sources 116, 1-2, 79–98.

    Article  Google Scholar 

  • Mwasilu, F., Justo, J. J., Kim, E. K., Do, T. D. and Jung, J. W (2014). Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and Sustainable Energy Reviews, 34, 501–516.

    Article  Google Scholar 

  • Rauber, A., Merkl, D. and Dittenbach, M. (2000). The growing hierarchical self-organizing map. Proc. IEEE-INNS-ENNS Int. Joint Conf. Neural Networks, Como, Italy.

    Google Scholar 

  • Rauber, A., Merkl, D. and Dittenbach, M. (2002). The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Trans. Neural Networks 13, 6, 1331–1341.

    Article  MATH  Google Scholar 

  • Ripley, B. D. (2007). Pattern Recognition and Neural Networks. Cambridge University Press. Cambridge, UK.

    MATH  Google Scholar 

  • Saxena, S., Floch, C. L., MacDonald, J. and Moura, S. (2015). Quantifying EV battery end-of-life through analysis of travel needs with vehicle powertrain models. J. Power Sources, 282, 265–276.

    Article  Google Scholar 

  • Smart, J. and Schey, S. (2012). Battery electric vehicle driving and charging behavior observed early in the EV project. Int. J. Alternative Powertrains 1, 1, 27–33.

    Article  Google Scholar 

  • Staackmann, M., Liaw, B. Y. and Yun, D. Y. Y (1997). Dynamic driving cycle analyses using electric vehicle time-series data. Proc. Thirty-Second Intersociety Energy Conversion Engineering Conf., Honolulu, Hawaii, USA.

    Google Scholar 

  • Tanim, T. R., Rahn, C. D. and Wang, C. Y. (2015). State of charge estimation of a lthium ion cell based on a temperature dependent and electrolyte enhanced single particle model. Energy, 80, 731–739.

    Article  Google Scholar 

  • Vapnik, V. (2013). The Nature of Statistical Learning Theory. Springer Science and Business Media. New York, USA

    MATH  Google Scholar 

  • Vapnik, V. and Chapelle, O. (2000). Bounds on error expectation for support vector machines. Neural Computation 12, 9, 2013–2036.

    Article  Google Scholar 

  • Vatanparvar, K., Wan, J. and Faruque, M. A. A. (2015). Battery-aware energy-optimal electric vehicle driving management. Proc. IEEE/ACM Int. Symp. Low Power Electronics and Design (ISLPED), Rome, Italy.

    Google Scholar 

  • Verwer, S., De Weerdt, M. and Witteveen, C. (2011). Learning driving behavior by timed syntactic pattern recognition. Proc. 22nd Int. Joint Conf. Artificial Intelligence, Barcelona, Spain.

    Google Scholar 

  • Walsh, C., Carroll, S., Eastlake, A. and Blythe, P. (2010). Electric Vehicle Driving Style and Duty Variation Performance Study. Cenex Corp. Loughborough, UK.

    Google Scholar 

  • Wang, L. and Wen, X. (1999). Dynamic match and optimizing design of electric vehicle powertrain. Proc. IEEE Int. Vehicle Electronics Conf. (IVEC'99), Changchun, China.

    Google Scholar 

  • Wangm, R. and Lukic, S. M. (2011). Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. Proc. IEEE Vehicle Power and Propulsion Conf, Chicago, Illinois, USA.

    Google Scholar 

  • Wu, Q., Nielsen, A. H., Ostergaard, J., Cha, S. T., Marra, F., Chen, Y and Traholt, C. (2010). Driving pattern analysis for electric vehicle (EV) grid integration study. Proc. IEEE PES Innovative Smart Grid Technologies Conf. Europe (ISGT Europe), Gothenberg, Sweden.

    Google Scholar 

  • Xiaohua, Z., Haitao, M., Xu, X. and Wang, Q. (2008). Parameter design for power train and performance simulation of electrical city bus. Proc. IEEE Vehicle Power and Propulsion Conf, Harbin, China.

    Google Scholar 

  • Yan, Q., Zhang, B. and Kezunovic, M. (2014). Optimization of electric vehicle movement for efficient energy consumption. Proc. North American Power Symp. (NAPS), Pullman, Washington, USA.

    Google Scholar 

  • Yang, H. C, Lee, C. H. and Chen, D. W (2009). A method for multilingual text mining and retrieval using growing hierarchical self-organizing maps. J. Information Science 35, 1, 3–23.

    Article  Google Scholar 

  • Younes, Z., Boudet, L., Suard, F., Gerard, M. and Rioux, R. (2013). Analysis of the main factors influencing the energy consumption of electric vehicles. Proc. Int. Electric Machines & Drives Conf, Chicago, Illinois, USA.

    Google Scholar 

  • Zhang, Q. and White, R. E. (2008). Calendar life study of Li-ion pouch cells: Part 2: Simulation. J. Power Sources 179, 2, 785–792.

    Article  Google Scholar 

  • Zhang, Y., Zhang, C. and Zhang, X (2013). State-of-charge estimation of the lithium-ion battery system with time-varying parameter for hybrid electric vehicles. IET Control Theory & Applications 8, 3, 160–167.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

The authors would like to give special thanks to the Luxgen Motor Co., Ltd. for their valuable supports for this research work.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan

    Chung-Hong Lee & Chih-Hung Wu

Authors
  1. Chung-Hong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chih-Hung Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chung-Hong Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, CH., Wu, CH. Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors. Int.J Automot. Technol. 20, 1263–1276 (2019). https://doi.org/10.1007/s12239-019-0118-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-019-0118-4

Key words

Search

Navigation