Energy Consumption Analysis for the Prediction of Battery Residual Energy in Electric Vehicles
Received: 19 March 2023 | Revised: 20 April 2023 | Accepted: 22 April 2023 | Online: 2 June 2023
Corresponding author: Sushil Thale
Abstract
The emergence of Electric Vehicles (EVs) is a turning point in decarbonizing the road transport sector. In spite of the various apprehensions of the customers, such as range anxiety, long charging times, higher costs, and the lack of charging infrastructures, EVs have managed to considerably penetrate into the market. Appreciable subsidies in EV purchase and possibilities of renewable energy-based local charging equipment have encouraged more and more people to own EVs. Electrifying road transport also calls for scaling up of all stages of the supply chain as it involves a lot of raw materials and critical metals used for battery technology. One of the most important factors determining the range of an EV is the energy density of the battery, which has reached over 300 Wh/kg, from 100-150 Wh/kg a decade ago. This clearly means that the same vehicle can travel double the distance with the same mass. Understanding and modeling the energy consumption in an EV is quintessential in alleviating the fear of range anxiety. This paper presents a detailed mathematical equation-based energy consumption analysis of a particular EV model for Indian roads. Very few researchers have worked on drive cycles suitable for India. The novelty of the current work is that the energy consumption calculation can be worked out for any EV model or vehicle type through simple mathematical equations.
Keywords:
electric vehicle, Electric Vehicle Charging Stations (EVCSs), residual energy, aerodynamic drag, kinematics, tractive forces, rolling force, power electronicsDownloads
References
C. Hull, J. H. Giliomee, K. A. Collett, M. D. McCulloch, and M. J. Booysen, "High fidelity estimates of paratransit energy consumption from per-second GPS tracking data," Transportation Research Part D: Transport and Environment, vol. 118, May 2023, Art. no. 103695. DOI: https://doi.org/10.1016/j.trd.2023.103695
Y. Muratoglu and A. Alkaya, "Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles," Engineering, Technology & Applied Science Research, vol. 9, no. 6, pp. 4876–4882, Dec. 2019. DOI: https://doi.org/10.48084/etasr.3111
A. Khadhraoui, T. Selmi, and A. Cherif, "Energy Management of a Hybrid Electric Vehicle," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8916–8921, Aug. 2022. DOI: https://doi.org/10.48084/etasr.5058
K. Liu, J. Wang, T. Yamamoto, and T. Morikawa, "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, vol. 227, pp. 324–331, Oct. 2018. DOI: https://doi.org/10.1016/j.apenergy.2017.08.074
N. A. Zainurin, S. a. B. Anas, and R. S. S. Singh, "A Review of Battery Charging - Discharging Management Controller: A Proposed Conceptual Battery Storage Charging – Discharging Centralized Controller," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7515–7521, Aug. 2021. DOI: https://doi.org/10.48084/etasr.4217
I. Miri, A. Fotouhi, and N. Ewin, "Electric vehicle energy consumption modelling and estimation—A case study," International Journal of Energy Research, vol. 45, no. 1, pp. 501–520, 2021. DOI: https://doi.org/10.1002/er.5700
C. Fiori, K. Ahn, and H. A. Rakha, "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, vol. 168, pp. 257–268, Apr. 2016. DOI: https://doi.org/10.1016/j.apenergy.2016.01.097
P. Lebeau, C. De Cauwer, J. Van Mierlo, C. Macharis, W. Verbeke, and T. Coosemans, "Conventional, Hybrid, or Electric Vehicles: Which Technology for an Urban Distribution Centre?," The Scientific World Journal, vol. 2015, Jul. 2015, Art. no. e302867. DOI: https://doi.org/10.1155/2015/302867
K. Sarrafan, D. Sutanto, K. M. Muttaqi, and G. Town, "Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency," IET Electrical Systems in Transportation, vol. 7, no. 2, pp. 117–124, 2017. DOI: https://doi.org/10.1049/iet-est.2015.0052
K. Vatanparvar, S. Faezi, I. Burago, M. Levorato, and M. A. Al Faruque, "Extended Range Electric Vehicle With Driving Behavior Estimation in Energy Management," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2959–2968, Feb. 2019. DOI: https://doi.org/10.1109/TSG.2018.2815689
X. Yuan, C. Zhang, G. Hong, X. Huang, and L. Li, "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, vol. 141, pp. 1955–1968, Dec. 2017. DOI: https://doi.org/10.1016/j.energy.2017.11.134
R. Ristiana, A. S. Rohman, C. Machbub, A. Purwadi, and E. Rijanto, "A New Approach of EV Modeling and its Control Applications to Reduce Energy Consumption," IEEE Access, vol. 7, pp. 141209–141225, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2941001
C. De Cauwer, W. Verbeke, T. Coosemans, S. Faid, and J. Van Mierlo, "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, vol. 10, no. 5, May 2017, Art. no. 608. DOI: https://doi.org/10.3390/en10050608
E. A. Grunditz and T. Thiringer, "Performance Analysis of Current BEVs Based on a Comprehensive Review of Specifications," IEEE Transactions on Transportation Electrification, vol. 2, no. 3, pp. 270–289, Sep. 2016. DOI: https://doi.org/10.1109/TTE.2016.2571783
C. De Cauwer, M. Messagie, S. Heyvaert, T. Coosemans, and J. Van Mierlo, "Electric vehicle use and energy consumption based on real-worldelectric vehicle fleet trip and charge data and its impact on existing EV research models," in 28th International Electric Vehicle Symposium and Exhibition, Goyang, Korea, Dec. 2015, pp. 645–655.
S. C. Yang, M. Li, Y. Lin, and T. Q. Tang, "Electric vehicle’s electricity consumption on a road with different slope," Physica A: Statistical Mechanics and its Applications, vol. 402, pp. 41–48, May 2014. DOI: https://doi.org/10.1016/j.physa.2014.01.062
A. Ficht and M. Lienkamp, "Rolling resistance modeling for electric vehicle consumption," in 6th International Munich Chassis Symposium, Munich, Germany, Jun. 2015, pp. 775–798. DOI: https://doi.org/10.1007/978-3-658-09711-0_49
T. Miwa, H. Sato, and T. Morikawa, "Range and Battery Depletion Concerns with Electric Vehicles," Journal of Advanced Transportation, vol. 2017, Oct. 2017, Art. no. e7491234. DOI: https://doi.org/10.1155/2017/7491234
"Automotive Testing | Automotive R&D Org. in India | ARAI." https://www.araiindia.com/.
"Indian Road Congress." https://www.irc.nic.in/.
G. James, An Introduction to Statistical Learning: with Applications in R. New York, NY, USA: Springer, 2013.
A. N. M. M. I. Mukut and M. Z. Abedin, "Review on Aerodynamic Drag Reduction of Vehicles," International Journal of Engineering Materials and Manufacture, vol. 4, no. 1, pp. 1–14, Mar. 2019. DOI: https://doi.org/10.26776/ijemm.04.01.2019.01
P. S. Pillai, "Inflation pressure effect on whole tyre hysteresis ratio and radial spring constant," Indian Journal of Engineering & Materials Sciences, vol. 13, pp. 110–116, Apr. 2006.
S. Modi, J. Bhattacharya, and P. Basak, "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 Computing, vol. 25, no. 3, pp. 2399–2416, Feb. 2021. DOI: https://doi.org/10.1007/s00500-020-05310-y
S. Modi, J. Bhattacharya, and P. Basak, "Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver’s range anxiety," ISA Transactions, vol. 98, pp. 454–470, Mar. 2020. DOI: https://doi.org/10.1016/j.isatra.2019.08.055
L. Zhao, W. Yao, Y. Wang, and J. Hu, "Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles," IEEE Access, vol. 8, pp. 212423–212441, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3039815
Y. Khawaja et al., "Battery management solutions for li-ion batteries based on artificial intelligence," Ain Shams Engineering Journal, Mar. 2023, Art. no. 102213. DOI: https://doi.org/10.1016/j.asej.2023.102213
H. A. Yavasoglu, Y. E. Tetik, and K. Gokce, "Implementation of machine learning based real time range estimation method without destination knowledge for BEVs," Energy, vol. 172, pp. 1179–1186, Apr. 2019. DOI: https://doi.org/10.1016/j.energy.2019.02.032
S. Sun, J. Zhang, J. Bi, and Y. Wang, "A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles," Journal of Advanced Transportation, vol. 2019, Jan. 2019, Art. no. e4109148. DOI: https://doi.org/10.1155/2019/4109148
R. Shankar and J. Marco, "Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions," IET Intelligent Transport Systems, vol. 7, no. 1, pp. 138–150, 2013. DOI: https://doi.org/10.1049/iet-its.2012.0114
A. Amirkhani, A. Haghanifar, and M. R. Mosavi, "Electric Vehicles Driving Range and Energy Consumption Investigation: A Comparative Study of Machine Learning Techniques," in 5th Iranian Conference on Signal Processing and Intelligent Systems, Shahrood, Iran, Dec. 2019, pp. 1–6. DOI: https://doi.org/10.1109/ICSPIS48872.2019.9066042
Downloads
How to Cite
License
Copyright (c) 2023 Keerthi Unni, Sushil Thale
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.