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 :
- Mohd Herwan Sulaiman and Zuriani Mustaffa State of charge estimation for electric vehicles using random forest. Green Energy and Intelligent Transportation, page 100177, 2024.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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).