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Convolutional neural network–bagged decision tree: a hybrid approach to reduce electric vehicle’s driver’s range anxiety by estimating energy consumption in real-time

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Abstract

To overcome range anxiety problem of electric vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV’s driver with information about the remaining range in real time. A hybrid CNN–BDT approach has been developed, in which convolutional neural network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery’s SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, bagged decision tree (BDT) is used to fine-tune the estimate. Unlike existing techniques, the proposed approach does not require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. The comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.

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Abbreviations

EV:

Electric vehicle

CNN:

Convolutional neural network

SOC:

State of charge

BDT:

Bagged decision tree

PCE:

Power consumption estimation

RMSE:

Root mean square error

MAE:

Mean absolute error

Corr:

Correlation

\(\hbox {MAE}_{\mathrm{dev}}\) :

Mean absolute energy deviation

MPTDC:

Mean prediction time per drive cycle

UDDS:

Urban dynamometer driving schedule

SFTP:

Supplemental federal test procedures

FASTSim:

Future automotive systems technology simulator

\(\hbox {veh}_{\mathrm{sp}}\) :

Vehicle’s speed

\(\hbox {road}_{\mathrm{el}}\) :

Road elevation

\(\hbox {veh}_{\mathrm{acc}}\) :

Vehicle’s acceleration

\(\hbox {aux}_{\mathrm{ld}}\) :

Auxiliary loads

\(\hbox {wind}_{\mathrm{sp}}\) :

Wind speed

\(\hbox {batt}_{\mathrm{soc}}\) :

State of charge of battery

\(\hbox {env}_{\mathrm{temp}}\) :

Environmental temperature

\(\hbox {SOC}_i\) :

Battery’s state of charge at ith time instant

\(E_{\mathrm{cap}}\) :

Battery’s rated energy capacity

\(\hbox {Est}_{\mathrm{pow}}\) :

Power consumption estimated by the proposed approach

\(\hbox {Act}_{\mathrm{pow}}\) :

Actual power consumption as given in dataset

\(\overline{\hbox {Est}_{\mathrm{pow}}}\) :

Mean of estimated power consumption

\(\overline{\hbox {Act}_{\mathrm{pow}}}\) :

Mean of actual power consumption

\(P_{\mathrm{reg}}\) :

Regenerative power

\(\eta _{\mathrm{te}}\) :

Transmission efficiency

\(\delta \) :

Driving efficiency

m :

EV’s weight related coefficient

\(\rho \) :

Air density

\(C_\mathrm{D}\) :

Aerodynamic drag coefficient

\(P_{\mathrm{accessory}}\) :

Power consumed by accessories

\(\eta _\mathrm{m}\) :

Motor efficiency

k :

Percentage of energy restored by the motor during braking

References

  • Abu Arqub O (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610

    Article  Google Scholar 

  • Abu Arqub O, AL-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20(8):3283–3302

    Article  Google Scholar 

  • Alvarez AD, Garcia FS, Naranjo JE, Anaya JJ, Jimenez F (2014) Modeling the driving behavior of electric vehicles using smartphones and neural networks. IEEE Intell Transp Syst Mag 6(3):44–53

    Article  Google Scholar 

  • Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24(16):12501–12522

    Article  Google Scholar 

  • Arqub OA, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21(23):7191–7206

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  • Brooker A, Gonder J, Wang L, Wood E, Lopp S, Ramroth L (2015) Fastsim: a model to estimate vehicle efficiency, cost and performance. In: SAE 2015 World Congress & Exhibition. SAE International

  • Burress T (2012) Benchmarking of competitive technologies. Oak Ridge National Laboratory

  • Cano ZP, Banham D, Ye S, Hintennach A, Lu J, Fowler M, Chen Z (2018) Batteries and fuel cells for emerging electric vehicle markets. Nat Energy 3(4):279–289

    Article  Google Scholar 

  • Carley S, Krause RM, Lane BW, Graham JD (2013) Intent to purchase a plug-in electric vehicle: a survey of early impressions in large us cites. Transp Res Part D Transp Environ 18:39–45

    Article  Google Scholar 

  • Dai H, Guo P, Wei X, Sun Z, Wang J (2015) Anfis (adaptive neuro-fuzzy inference system) based online soc (state of charge) correction considering cell divergence for the ev (electric vehicle) traction batteries. Energy 80:350–360

    Article  Google Scholar 

  • De Cauwer C, Verbeke W, Coosemans T, Faid S, Van Mierlo J (2017) A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies 10(5):608

    Article  Google Scholar 

  • Devineau G, Moutarde F, Xi W, Yang J (2018) Deep learning for hand gesture recognition on skeletal data. In: 2018 13th IEEE international conference on automatic Face Gesture Recognition (FG 2018), pp 106–113

  • Downloadable Dynamometer Database, Argonne National Library. Transportation Technology R&D Center (2015). http://www.transportation.anl.gov/D3/. Accessed 12 Oct 2019

  • Energy Efficiency & Renewable Energy Vehicle Technologies Program (2013) 2013 Nissan Leaf advanced vehicle testing—baseline testing results. Tech. rep., U.S. Department of Energy

  • Felipe J, Amarillo JC, Naranjo JE, Serradilla F, Diaz A (2015) Energy consumption estimation in electric vehicles considering driving style. In: 2015 IEEE 18th international conference on intelligent transportation systems, pp 101–106

  • Feng W, Figliozzi M (2013) An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: a case study from the usa market. Transp Res Part C Emerg Technol 26:135–145

    Article  Google Scholar 

  • Fetene GM, Kaplan S, Mabit SL, Jensen AF, Prato CG (2017) Harnessing big data for estimating the energy consumption and driving range of electric vehicles. Transp Res Part D Transp Environ 54(Supplement C):1–11

    Article  Google Scholar 

  • Fiori C, Ahn K, Rakha HA (2016) Power-based electric vehicle energy consumption model: model development and validation. Appl Energy 168(Supplement C):257–268

    Article  Google Scholar 

  • Galvin R (2017) Energy consumption effects of speed and acceleration in electric vehicles: laboratory case studies and implications for drivers and policymakers. Transp Res Part D Transp Environ 53(Supplement C):234–248

    Article  Google Scholar 

  • Gao DW, Mi C, Emadi A (2007) Modeling and simulation of electric and hybrid vehicles. Proc IEEE 95(4):729–745

    Article  Google Scholar 

  • Genikomsakis KN, Mitrentsis G (2017) A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications. Transp Res Part D Transp Environ 50(Supplement C):98–118

    Article  Google Scholar 

  • Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  • Grubwinkler S, Brunner T, Lienkamp M (2014) Range prediction for EVs via crowd-sourcing. In: IEEE Vehicle Power and Propulsion Conference (VPPC), pp 1–6

  • Hackbarth A, Madlener R (2013) Consumer preferences for alternative fuel vehicles: a discrete choice analysis. Transp Res Part D Transp Environ 25:5–17

    Article  Google Scholar 

  • Halmeaho T, Rahkola P, Tammi K, Pippuri J, Pellikka AP, Manninen A, Ruotsalainen S (2017) Experimental validation of electric bus powertrain model under city driving cycles. IET Electr Syst Transp 7(1):74–83

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778

  • Jebson S (2007) Fact sheet number 6: the Beaufort Scale. http://cedadocs.ceda.ac.uk/259/1/factsheet06.pdf. Accessed 12 Oct 2019

  • Kalapanidas E, Avouris N, Craciun M, Neagu D (2003) Machine learning algorithms: a study on noise sensitivity. In: Proceedings of 1st Balcan Conference in Informatics, pp 356–365

  • Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  • Kularatna N (2014) Rechargeable battery technologies: an electronic engineer’s view point. In: Energy storage devices for electronic systems: rechargeable batteries and supercapacitors. Elsevier, pp 29–61

  • Liu K, Wang J, Yamamoto T, Morikawa T (2016) Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles. Appl Energy 183(Supplement C):1351–1360

    Article  Google Scholar 

  • Liu K, Yamamoto T, Morikawa T (2017) Impact of road gradient on energy consumption of electric vehicles. Transp Res Part D Transp Environ 54(Supplement C):74–81

    Google Scholar 

  • Liu K, Wang J, Yamamoto T, Morikawa T (2018) Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption. Appl Energy 227:324–331 transformative Innovations for a Sustainable Future—Part III

    Article  Google Scholar 

  • Ma X, Dai Z, He Z, Ma J, Wang YYY, Wang YYY (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors (Switzerland) 17(4):818

    Article  Google Scholar 

  • Manzetti S, Mariasiu F (2015) Electric vehicle battery technologies: from present state to future systems. Renew Sustain Energy Rev 51:1004–1012

    Article  Google Scholar 

  • Modi S, Bhattacharya J, Basak P (2020) Estimation of energy consumption of electric vehicles using deep convolutional neural network to reduce driver’s range anxiety. ISA Trans 98:454–470

    Article  Google Scholar 

  • Qi X, Wu G, Boriboonsomsin K, Barth MJ (2018) Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions. Transp Res Part D Transp Environ 64:36–52 the contribution of electric vehicles to environmental challenges in transport. WCTRS conference in summer

    Article  Google Scholar 

  • Shen WX, Chan CC, Lo EWC, Chau KT (2002) Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles. IEEE Trans Ind Electron 49(3):677–684

    Article  Google Scholar 

  • Sweeting WJ, Hutchinson AR, Savage SD (2011) Factors affecting electric vehicle energy consumption. Int J Sustain Eng 4(3):192–201

    Article  Google Scholar 

  • Weather API from open weather. https://openweathermap.org/api. Aaccessed 31 Oct 2019

  • Wolff S, Madlener R (2019) Driven by change: commercial driver’s acceptance and efficiency perceptions of light-duty electric vehicle usage in Germany. Transp Res Part C Emerg Technol 105:262–282

    Article  Google Scholar 

  • Wu X, Freese D, Cabrera A, Kitch WA (2015) Electric vehicle’s energy consumption measurement and estimation. Transp Res Part D Transp Environ 34:52–67

    Article  Google Scholar 

  • Yang S, Li M, Lin Y, Tang T (2014) Electric vehicle’s electricity consumption on a road with different slope. Physica A 402:41–48

  • Yao E, Yang Z, Song Y, Zuo T (2013) Comparison of electric vehicle’s energy consumption factors for different road types. https://doi.org/10.1155/2013/328757

  • Yi Z, Bauer PH (2017) Effects of environmental factors on electric vehicle energy consumption: a sensitivity analysis. IET Electr Syst Transp 7(10):3–13

    Article  Google Scholar 

  • Zhang R, Yao E (2015) Electric vehicle’s energy consumption estimation with real driving condition data. Transp Res Part D Transp Environ 41(Supplement C):177–187

    Article  Google Scholar 

  • Zhang H, Song X, Xia T, Yuan M, Fan Z, Shibasaki R, Liang Y (2018) Battery electric vehicles in Japan: human mobile behavior based adoption potential analysis and policy target response. Appl Energy 220:527–535

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147001, Punjab, India

    Shatrughan Modi & Jhilik Bhattacharya

  2. Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147001, Punjab, India

    Prasenjit Basak

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  1. Shatrughan Modi
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  2. Jhilik Bhattacharya
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  3. Prasenjit Basak
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Correspondence to Shatrughan Modi.

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Modi, S., Bhattacharya, J. & Basak, P. Convolutional neural network–bagged decision tree: a hybrid approach to reduce electric vehicle’s driver’s range anxiety by estimating energy consumption in real-time. Soft Comput 25, 2399–2416 (2021). https://doi.org/10.1007/s00500-020-05310-y

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