Correlation Analysis of Battery Capacity, Range, and Charging Time in Electric Vehicles Using Pearson Correlation and MATLAB Regression

Yasa Sanusi, Sri Pudjiwati, Kontan Tarigan, Dianta Ginting, Farrah Anis Fazliatun Adnan, Gerald Ensang Timuda, Nono Darsono, Nuwong Chollacoop, Deni Shidqi Khaerudini

Abstract


The increasing adoption of electric vehicles (EVs) reflects growing global awareness of climate change and air pollution challenges. As a sustainable alternative to conventional internal combustion vehicles, EVs produce zero tailpipe emissions and can significantly reduce carbon emissions—particularly when powered by renewable energy sources. However, one of the primary barriers to widespread EV adoption remains the high cost of battery components, which are essential to vehicle performance and energy storage. In Indonesia, two dominant battery types used in EVs are Lithium Ferro Phosphate (LFP) and Nickel Manganese Cobalt (NMC), each offering distinct advantages. LFP batteries are recognized for their thermal stability and longer life cycles, making them suitable for everyday use, while NMC batteries offer higher energy density and are preferred for performance-focused and long-distance applications. This study aims to evaluate the correlation between battery capacity, driving range, and charging time for LFP and NMC batteries using Pearson correlation and regression analysis through MATLAB simulation. The results indicate a strong and statistically significant correlation among the key parameters, with a Pearson coefficient of 0.576 for battery capacity and range, and an R-square value of 0.99 for the regression model, demonstrating high predictive accuracy. Furthermore, the analysis reveals that LFP batteries have a higher average energy efficiency of 7.53 km/kWh compared to 6.84 km/kWh for NMC batteries, indicating more consistent performance in energy usage. These findings offer valuable insights for optimizing battery selection in EV applications and contribute to strategic planning for the development of more efficient electric vehicle systems. The combination of statistical and simulation-based analysis provides a robust foundation for future research and policy-making in the field of electric mobility.

Keywords


Electric vehicle; Lithium Ferro Phosphate (LFP); Nickel Manganese Cobalt (NMC); Pearson correlation

References


International Energy Agency (IEA), “Global EV outlook 2021,” Global EV Outlook 2021, p. 101, 2021, [Online]. Available: https://iea.blob.core.windows.net/assets/ed5f4484-f556-4110-8c5c-4ede8bcba637/GlobalEVOutlook2021.pdf

T. Gül, A. F. Pales, and E. Connelly, “Global EV outlook 2024 moving towards increased affordability,” Electric Vehicles Intitiative , p. 79, 2024, [Online]. Available: www.iea.org

F. Alanazi, “Electric vehicles: Benefits challenges and potential solutions,” Journal of Applied Scienc, vol. 13, pp. 1–23, 2023, doi: 10.3390/app13106016

AC Ventures, “Indonesia’s electric vehicle outlook - Supercharging tomorrow’s mobility,” AC Ventures, no. July, 2023, [Online]. Available: https://acv.vc/wp-content/uploads/2023/07/Report-Indonesias-Electric-Vehicle-Outlook-Supercharging-Tomorrows-Mobility_NEW.pdf

E. Yuliandari and L. N. Violie, “Electric vehicle policy based on juridical foundation to realize environmental resilience in Indonesia,” Pro-ceedings of the International Conference for Democracy and National Resilience (ICDNR 2021), vol. 620, no. x, pp. 37–44, 2022, doi: 10.2991/assehr.k.211221.007.

McKinsey Center for Future Mobility, “Making electric vehicles profitable,” McKinsey & Company, no. March, 2019, [Online]. Available: https://www.mckinsey.com/~/media/McKinsey/Industries/Automotive and Assembly/Our Insights/Making electric vehicles profita-ble/Making-electric-vehicles-profitable.pdf

M. Aziz, Y. Marcellino, I. A. Rizki, S. A. Ikhwanuddin, and J. W. Simatupang, “Studi analisis perkembangan teknologi dan dukungan pemerintah indonesia terkait mobil listrik,” TESLA: Jurnal Teknik Elektro, vol. 22, no. 1, p. 45, 2020, doi: 10.24912/tesla.v22i1.7898.

M. A. Pradhana, “Pengisi daya baterai lifepo4 sebagai sumber energi pada sepeda listrik,” Transient: Jurnal Ilmiah Teknik Elektro, vol. 11, no. 2, pp. 70–74, 2022, doi: 10.14710/transient.v11i2.70-74.

R. O’Malley, L. Liu, and C. Depcik, “Comparative study of various cathodes for lithium ion batteries using an enhanced Peukert capacity model,” J Power Sources, vol. 396, no. February, pp. 621–631, 2018, doi: 10.1016/j.jpowsour.2018.06.066.

S. Ohneseit et al., “Thermal and mechanical safety assessment of type 21700 Lithium-Ion Batteries with NMC, NCA and LFP cathodes–investigation of cell abuse by means of Accelerating Rate Calorimetry (ARC),” Batteries, vol. 9, no. 5, 2023, doi: 10.3390/batteries9050237.

L. V. Thomas, O. Schmidt, A. Gambhir, S. Few, and I. Staffell, “Comparative life cycle assessment of lithium-ion battery chemistries for resi-dential storage,” J Energy Storage, vol. 28, no. June, 2020, doi: 10.1016/j.est.2020.101230.

M. K. Tran, A. Dacosta, A. Mevawalla, S. Panchal, and M. Fowler, “Comparative study of equivalent circuit models performance in four com-mon lithium-ion batteries: LFP, NMC, LMO, NCA,” Batteries, vol. 7, no. 3, Sep. 2021, doi: 10.3390/batteries7030051.

B. Long, X. Gao, P. Li, and Z. Liu, “Multi-parameter optimization method for remaining useful life prediction of lithium-ion batteries,” IEEE Access, vol. 8, pp. 142557–142570, 2020, doi: 10.1109/ACCESS.2020.3011625.

C. White, B. Thompson, and L. G. Swan, “Comparative performance study of electric vehicle batteries repurposed for electricity grid energy arbitrage,” Appl Energy, vol. 288, no. February, p. 116637, 2021, doi: 10.1016/j.apenergy.2021.116637.

J. Guo, Y. Li, K. Pedersen, and D. I. Stroe, “Lithium-ion battery operation, degradation, and aging mechanism in electric vehicles: An over-view,” Energies (Basel), vol. 14, no. 17, 2021, doi: 10.3390/en14175220.

C. Geisbauer, K. Wöhrl, D. Koch, G. Wilhelm, G. Schneider, and H. G. Schweiger, “Comparative study on the calendar aging behavior of six different lithium‐ion cell chemistries in terms of parameter variation,” Energies (Basel), vol. 14, no. 11, 2021, doi: 10.3390/en14113358.

P. P. Mishra et al., “Analysis of degradation in residential battery energy storage systems for rate-based use-cases,” Appl Energy, vol. 264, no. November 2019, p. 114632, 2020, doi: 10.1016/j.apenergy.2020.114632.

Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Statistics Statistical formulae and tables. Pearson Education Limited, 2017.

H. Wang, J. Li, X. Liu, J. Rao, Y. Fan, and X. Tan, “Online state of health estimation for lithium-ion batteries based on a dual self-attention multivariate time series prediction network,” Energy Reports, vol. 8, pp. 8953–8964, Nov. 2022, doi: 10.1016/j.egyr.2022.07.017.

W. Wu, Z. Chen, W. Liu, and E. Pan, “Correlation based-graph neural network for health prognosis of non-fully charged and discharged lithi-um-ion batteries,” 2024. [Online]. Available: https://ssrn.com/abstract=4932318

Y. Han, H. Yuan, J. Li, J. Du, Y. Hu, and X. Huang, “Study on influencing factors of consistency in manufacturing process of vehicle lithium-ion battery based on correlation coefficient and multivariate linear regression model,” Adv Theory Simul, vol. 4, no. 8, Aug. 2021, doi: 10.1002/adts.202100070.

J. Chen, D. Chen, X. Han, Z. Li, W. Zhang, and C. S. Lai, “State-of-health estimation of lithium-ion battery based on constant voltage charging duration,” Batteries, vol. 9, no. 12, Dec. 2023, doi: 10.3390/batteries9120565.

X. Zhou, X. Han, Y. Wang, L. Lu, and M. Ouyang, “A data-driven LiFePO4 battery capacity estimation method based on cloud charging data from electric vehicles,” Batteries, vol. 9, no. 3, Mar. 2023, doi: 10.3390/batteries9030181.

BYD, “BYD Han,” BYD Website. Accessed: Nov. 14, 2024. [Online]. Available: https:/www.byd.com/en/vehicles/han/specs

Wuling, “Wuling Cloud Ev.” Accessed: Nov. 14, 2024. [Online]. Available: https://wuling.id/id/cloud-ev

Chery International, “Chery Omoda 5.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.cheryinternational.com/omoda5

MG Motors, “MG 4.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.mgmotors.id/mgmodels/mg4ev

Hyundai, “Hyundai Ioniq 5.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.hyundai.com/worldwide/en/eco/ioniq5/specs

KIA, “KIA Ev 7.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.kia.com/uk/new-cars/7-seat-family-electric-cars/

Tesla, “Tesla Model 3.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.tesla.com/model3

Toyota, “Toyota bz4x.” Accessed: Nov. 14, 2024. [Online]. Available: https://www.toyota.com/bz4x/




DOI: http://dx.doi.org/10.22441/ijimeam.v7i3.31800

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Copyright (c) 2025 Yasa Sanusi, Sri Pudjiwati, Kontan Tarigan, Dianta Ginting, Farrah A. F. Adnan, Gerald E. Timuda, Nono Darsono, Nuwong Chollacoop, Deni S. Khaerudini

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