An Implementation of Quasi-Newton Algorithm for Fast-charging Lithium-Ion Battery (LIB) Optimization in Electric Vehicle Application

Mahmudda Mitra Anjarani Anjarani; Naufan Raharya

doi:10.62146/ijecbe.v2i2.54

Authors

Mahmudda Mitra Anjarani Universitas Indonesia
Naufan Raharya Universitas Indonesia

DOI:

https://doi.org/10.62146/ijecbe.v2i2.54

Keywords:

Quasi-Newton, Fast-charging, Lithium-Ion Battery

Abstract

Lithium-Ion Battery (LIB) is still an effective alternative technology in maximizing the efficiency of electric vehicles (EV). The application of EVs has had a significant impact in order to reduce the issue of global problems - reducing carbon gas emissions. The LIB charging mechanism with the fast-charging method is an alternative to the application of EVs on a more massive scale. However, the dynamics of the battery where the battery work function can decrease over time will affect battery performance. In addition, fast-charging efforts at LIB with maximum speed have the impact of increasing the risk of battery temperature and the existence of a larger gap in battery degradation. This paper proposes the application of Limited-Memory-Broyden-Fletcher-Goldfarb-ShannoBound Constrained (L-BFGS-B) algorithm for Lithium-Ion Battery (LIB) fast-charging optimization as an innovative solution approach in dealing with the complex LIB fastcharging dynamics. The results show that this approach is able to improve fast-charging speed and efficiency.

Author Biographies

Mahmudda Mitra Anjarani, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Naufan Raharya, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

References

Carlos Vidal et al. “Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art”. In: IEEE Access 8 (2020), pp. 52796–52814. DOI: 10.1109/ACCESS.2020.2980961.

Adarsh Kumar et al. “A Novel Simulated-Annealing Based Electric Bus System Design, Simulation, and Analysis for Dehradun Smart City”. In: IEEE Access 8 (2020), pp. 89395–89424. DOI: 10.1109/ACCESS.2020.2990190.

Heba M. Abdullah, Adel Gastli, and Lazhar Ben-Brahim. “Reinforcement Learning Based EV Charging Management Systems–A Review”. In: IEEE Access 9 (2021), pp. 41506–41531. DOI: 10.1109/ACCESS.2021.3064354.

Zilin Zhang et al. “A Deep RL-Based Algorithm for Coordinated Charging of Electric Vehicles”. In: IEEE Transactions on Intelligent Transportation Systems 23.10 (2022), pp. 18774–18784. DOI: 10.1109/TITS.2022.3170000.

Zhongbao Wei et al. “Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery”. In: IEEE Transactions on Industrial Electronics 69.3 (2022), pp. 2588–2598. DOI: 10.1109/TIE.2021.3070514.

Xinfan Lin et al. “An Electro-thermal Model for the A123 26650 LiFePO4 Battery”. In: (Apr. 2013).