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Soft Computing
Erschienen in: Soft Computing 3/2021

02.10.2020 | Methodologies and Application

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

verfasst von: Shatrughan Modi, Jhilik Bhattacharya, Prasenjit Basak

Erschienen in: Soft Computing | Ausgabe 3/2021

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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.
Vorheriger Artikel A novel hybrid dynamic fireworks algorithm with particle swarm optimization
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Literatur
Abu Arqub O (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610CrossRef
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):3283–3302CrossRef
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):44–53CrossRef
Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24(16):12501–12522CrossRef
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):7191–7206CrossRef
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MATH
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):279–289CrossRef
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 18:39–45CrossRef
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 80:350–360CrossRef
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):608CrossRef
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
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 26:135–145CrossRef
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):1–11CrossRef
Fiori C, Ahn K, Rakha HA (2016) Power-based electric vehicle energy consumption model: model development and validation. Appl Energy 168(Supplement C):257–268CrossRef
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):234–248CrossRef
Gao DW, Mi C, Emadi A (2007) Modeling and simulation of electric and hybrid vehicles. Proc IEEE 95(4):729–745CrossRef
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):98–118CrossRef
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 25:5–17CrossRef
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):74–83CrossRef
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
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
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):1351–1360CrossRef
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
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 IIICrossRef
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):818CrossRef
Manzetti S, Mariasiu F (2015) Electric vehicle battery technologies: from present state to future systems. Renew Sustain Energy Rev 51:1004–1012CrossRef
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 98:454–470CrossRef
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 summerCrossRef
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):677–684CrossRef
Sweeting WJ, Hutchinson AR, Savage SD (2011) Factors affecting electric vehicle energy consumption. Int J Sustain Eng 4(3):192–201CrossRef
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 105:262–282CrossRef
Wu X, Freese D, Cabrera A, Kitch WA (2015) Electric vehicle’s energy consumption measurement and estimation. Transp Res Part D Transp Environ 34:52–67CrossRef
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
Yi Z, Bauer PH (2017) Effects of environmental factors on electric vehicle energy consumption: a sensitivity analysis. IET Electr Syst Transp 7(10):3–13CrossRef
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):177–187CrossRef
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 220:527–535CrossRef
Metadaten
Titel
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
verfasst von
Shatrughan Modi
Jhilik Bhattacharya
Prasenjit Basak
Publikationsdatum
02.10.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05310-y

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