E3S Web Conf.
Volume 87, 20191st International Conference on Sustainable Energy and Future Electric Transportation (SeFet 2019)
|Number of page(s)||8|
|Published online||22 February 2019|
Capacity Fade Modeling of Li-Ion Battery using Evolutionary Algorithm
1 Associate Professor, Department of Electrical and Electronics Engineering, SSN College of Engineering, Chennai, Tamilnadu, India, firstname.lastname@example.org
2 Professor, Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Shamshabad, Hyderabad, India, email@example.com
3 Associate Professor, Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Shamshabad, Hyderabad, India, firstname.lastname@example.org
* Corresponding author: email@example.com
Renewable sources are seasonal and cannot be considered as available energy source as their generation varies with time. The insufficient forecasting techniques lead to thought of storage of energy. Even though many techniques of energy storage are available, batteries play a vital role as the time taken to start delivering the stored energy is very less. The life period of the battery depends upon the charging and discharging characteristics which in turn depend on the internal parameters such as life period, charge rate, discharge rate of the battery. The energy stored in the battery can be calculated by finding these parameters. In this paper these parameters are estimated for a Sony lithium ion battery by evolutionary algorithm CMA-ES under different Charging and discharging rates. As the batteries are charged and discharged there is capacity loss in the battery. This loss is modelled by modified Arrhenius equation on practical conditions. Capacity loss of the sample battery is modelled for five different cycles starting from 50th cycle to 100th cycle in an interval of 10 cycles. The results are validated with those of manufacturer catalogue. The optimized battery capacity loss are found to coincide with the measured values.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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