Comprehensive Review on State of Charge Estimation in Battery Management System
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : R Sivapriyan, Sakshi N, C V Mohan, Lavan Raj
|DOI : 10.14445/22315381/IJETT-V70I7P218|
How to Cite?
R Sivapriyan, Sakshi N, C V Mohan, Lavan Raj, "Comprehensive Review on State of Charge Estimation in Battery Management System" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 169-179, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P218
This paper describes the latest methods and enhanced techniques used to determine the precise State of Charge (SOC). The three primary factors influencing SOC accuracy are the environmental temperature, current, and open-circuit voltage for a typical battery. It is essential to know the SOC that judges the battery’s life. This paper analyzes five different methods to estimate the SOC using different Algorithms and Neural networks. These methods are state-of-the-art methods that can be used to check the correctness of the measurement of SOC in batteries. This paper discusses and analyses the Regression algorithm, time series algorithm, K nearest neighbor algorithm, AGA-based RBF neural network, and Back Propagation neural network to determine the précised SOC. Each method's advantages and disadvantages were discussed and compared with other models to show their superiority. A sample of data was fed to these models, and the result was noted for all five methods. Later, the data were analyzed for their accuracy.
AGA-based RBF neural network, Backpropagation neural network, K nearest neighbor algorithm, Regression algorithm, State of charge, Time series algorithm.
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