Abstract:
The Senate Research Committee (SRC) promotes research by giving funding for research. In addition to
the monthly stipend payment, the allocated payments for publications, hardware purchasing, and
transportation is highly imperative to achieve the proposed objectives successfully within the time
duration. Moreover, guidance of the supervisor who provided the SRC grant for the research is also
essential factor to meet the requirements of the research.
Microgrids and Battery Energy Storage Systems play a major role in the sustainable and clean energy
sector. In this study, an accurate battery model is developed which can represent the electrical and the
thermal behavior of the battery and all the battery parameters are investigated experimentally by
implementing a test bench for the proposed model and moreover, each of the parameters is validated
theoretically by developing a MATLAB simulation. To improve the accuracy of the battery
parametrization, battery state of charge (SOC) level is estimated via machine learning algorithms.
According to the results, the accuracy of the comprehensive electro-thermal battery model based on
electrical and thermal parameters is at a satisfactory level and proves that designing such a model that
achieves excellent accuracy and realistic behavior in real-time platform simulators. There are six
different time series models are used to estimate the SOC level and according to the performance, auto
regressive (AR) model and seasonal auto regressive integrated moving average (SARIMA) models are the
best machine learning models for SOC level estimation when battery parametrization.
Description:
Following papers were published based on the results of this research project.
1. A.M.S.M.H.S.Attanayaka, J.P.Karunadasa, K.T.M.U.Hemapala. Estimation of state of charge for lithium-ion batteries - A Review[J]. AIMS Energy, 2019, 7(2): 186-210. doi:
10.3934/energy.2019.2.186
2. Attanayaka, A. M. S., Karunadasa, J. P., & Hemapala, K. T. (2021). Comprehensive
electro‐thermal battery‐model for Li‐ion batteries in microgrid applications. Energy Storage, 3(3), e230.
3.J. M. D. S. Jeewandara, J. P. Karunadasa and K. T. M. U. Hemapala, "SOC Level Estimation of Lithium-ion Battery Based on Time Series Forecasting Algorithms for Battery Management System," 2021 3rd International Conference on Electrical Engineering (EECon), 2021, pp. 43-49, doi: 10.1109/EECon52960.2021.9580869.
4.J. M. D. S. Jeewandara, J. P. Karunadasa and K. T. M. U. Hemapala, "Comprehensive Study of Kalman Filter Based State of Charge Estimation Method for Battery Energy Management System in Microgrid," 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021, pp. 01-06, doi: 10.1109/ICECCME52200.2021.9590949.