Enhancing Estimating the Charge Level in Electric Vehicles: Leveraging Force Fluctuation and Regenerative Braking Data


Authors : Subimal Nandi; Bikram Dass; Rupak Chakraborty

Volume/Issue : Volume 9 - 2024, Issue 6 - June


Google Scholar : https://tinyurl.com/yc6j9bh2

Scribd : https://tinyurl.com/5vu6t5dx

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1862

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Accurate determination of the state of charge is vital to optimize the performance and lifespan of electric vehicle batteries. Traditional methods which rely on battery models and direct measurements can be error-prone due to fluctuating operating conditions and battery degradation over time. Regenerative braking systems are crucial in electric and hybrid vehicles for improving energy efficiency by transforming kinetic energy into electrical energy during braking. However, force fluctuation is a challenge that can affect the performance and comfort of regenerative braking. It is known to us that electric motors and generators used in regenerative braking have non- linear torque characteristics, especially at low speeds, leading to inconsistent braking force. Variations in road conditions, such as wet or uneven surfaces, can affect the grip of the tires, leading to fluctuations in deceleration. Interactions of regenerative braking system with conventional friction brakes can cause force fluctuations, especially during the transition between the two systems. This study introduces an improved state of charge estimation technique based on force fluctuation and a regenerative braking system. This research shows that this approach significantly enhances state of charge accuracy compared to traditional methods, especially in urban driving conditions with frequent braking. The findings underscore the potential of using regenerative braking as well as force fluctuation condition data as a valuable input for state of charge estimation, ultimately leading to better battery management and an extended electric vehicle range.

Keywords : Electric Vehicle (EV), Regenerative Braking, State of Charge (SOC).

References :

  1. Mohd Herwan Sulaiman and Zuriani Mustaffa State of charge estimation for electric vehicles using random forest. Green Energy and Intelligent Transportation, page 100177, 2024.
  2. Zhicheng He, Ziming Yang, Xiangyu Cui, and Eric Li. A method of state-of-charge estimation for ev power lithium- ion battery using a novel adaptive extended kalman filter. IEEE Transactions on Vehicular Technology, 69(12):14618–14630, 2020
  3. Zhihang Chen, Shiqi Qiu, M Abul Masrur, and Yi Lu Murphey. Battery state of charge estimation on a combined model of extended kalman filter and neural networks. In The 2011 International Joint Conference on Neural Networks. pages 2156–2163. IEEE, 2011.
  4. Sonia Carina Lopes da Costa, Armando Sousa Araujo, and Adriano da Silva Carvalho. Battery state of charge estimation using extended kalman filter. In 2016 international symposium on power electronics, electrical drives, automation and motion (SPEEDAM), pages 1085–1092. IEEE, 2016.
  5. Henrik Beelen, Henk Jan Bergveld, and MCF Donkers. Joint estimation of battery parameters and state of charge using an extended kalman filter: A single- parameter tuning approach. IEEE Transactions on Control Systems Technology, 29(3):1087–1101, 2020.
  6. Pierfrancesco Spagnol, Stefano Rossi, and Sergio M Savaresi. Kalman filter soc estimation for li-ion batteries. In 2011 IEEE International conference on control applications (CCA), pages 587–592. IEEE, 2011.
  7. Di Domenico, D., Fiengo, G. and Stefanopoulou, A., 2008, September. Lithium-ion battery state of charge estimation with a kalman filter based on a electrochemical model. In 2008 IEEE International Conference on Control Applications (pp. 702-707). Ieee.
  8. Ramachandran, R., Ganeshaperumal, D. and Subathra, B., 2019, December. Parameter estimation of battery pack in EV using extended kalman filters. In 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES) (pp. 1-5). IEEE
  9. Wang, W. and Mu, J., 2019. State of charge estimation for lithium-ion battery in electric vehicle based on Kalman filter considering model error. Ieee Access, 7, pp.29223-29235.
  10. Sangwan, V., Kumar, R. and Rathore, A.K., 2017, October. . State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). In 2017 IEEE Industry Applications Society Annual Meeting (pp. 1-6). IEEE

Accurate determination of the state of charge is vital to optimize the performance and lifespan of electric vehicle batteries. Traditional methods which rely on battery models and direct measurements can be error-prone due to fluctuating operating conditions and battery degradation over time. Regenerative braking systems are crucial in electric and hybrid vehicles for improving energy efficiency by transforming kinetic energy into electrical energy during braking. However, force fluctuation is a challenge that can affect the performance and comfort of regenerative braking. It is known to us that electric motors and generators used in regenerative braking have non- linear torque characteristics, especially at low speeds, leading to inconsistent braking force. Variations in road conditions, such as wet or uneven surfaces, can affect the grip of the tires, leading to fluctuations in deceleration. Interactions of regenerative braking system with conventional friction brakes can cause force fluctuations, especially during the transition between the two systems. This study introduces an improved state of charge estimation technique based on force fluctuation and a regenerative braking system. This research shows that this approach significantly enhances state of charge accuracy compared to traditional methods, especially in urban driving conditions with frequent braking. The findings underscore the potential of using regenerative braking as well as force fluctuation condition data as a valuable input for state of charge estimation, ultimately leading to better battery management and an extended electric vehicle range.

Keywords : Electric Vehicle (EV), Regenerative Braking, State of Charge (SOC).

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe