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Implementation of ANN Based SOC Estimation for Lithium-Ion Battery

Vishnu R., P. Maruthupandi, K. Yasoda

Abstract


Battery technology is the bottleneck of the Electric Vehicle. The SOC is one of the crucial parameters in Lithium-ion battery. This project presents an improved nonlinear characteristic in SOC estimation of Li-ion battery by using Artificial Neural Network (ANN). However, the accuracy of Artificial Neural Network depends on the amount of Input order, output order, and Hidden layer neurons. The contributions are brief as the computational ability of ANN model which does not need battery model and parameters somewhat than only desires current, voltage and temperature sensors. The technique contribution of the improved ANN based SOC estimation is developed by using new innovative soft computing method of RAO algorithm. The contributions are summarized to computational capability of ANN technique require the parameters rather than only needs voltage, current and temperature sensor. The performance of the proposed model is Back propagation neural network using dspic30F4011 controller. The consequences show that the projected ANN attains higher correctness with less computational time than other standing SOC algorithm under diverse Electrical vehicle drive cycle.

 

Keywords: Electrical vehicles, state of charge, Li-ion battery, back propagation neural network, RAO algorithm


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References


Environmental Protection Agency. Global Greenhouse Gas Emissions Data, Greenhouse Gas (GHG) Emissions. Accessed: Jan. 4, 2018. https.epa.gov/ghgemissions/global- greenhouse-gas-emissions-data

Tremblay, O., Dessaint, L. A., & Dekkiche, A. I. (2007, September). A generic battery model for the dynamic simulation of hybrid electric vehicles. In 2007 IEEE Vehicle Power and Propulsion Conference (pp. 284-289). IEEE.

Dragomir, F., Dragomir, O. E., Oprea, A., Olteanu, L., Olariu, N., & Ursu, V. (2017, October). Simulation of lithium-ion batteries from an electric vehicle perspective. In 2017 Electric Vehicles International Conference (EV) (pp. 1-5). IEEE.

Carter, B., Matsumoto, J., Prater, A., & Smith, D. (1996, August). Lithium ion battery performance and charge control. In IECEC 96. Proceedings of the 31st Intersociety Energy Conversion Engineering Conference (Vol. 1, pp. 363-368). IEEE.

Wahyuddin, M. I., Priambodo, P. S., & Sudibyo, H. (2018, August). State of Charge (SoC) Analysis and Modeling Battery Discharging Parameters. In 2018 4th International Conference on Science and Technology (ICST) (pp. 1-5). IEEE.

Liu, F., Liu, T., & Fu, Y. (2015, December). An improved SoC estimation algorithm based on artificial neural network. In 2015 8th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 2, pp. 152-155). IEEE.

Jakupović, A., Kovačević, Ž., Gurbeta, L., & Badnjević, A. (2018, June). Review of artificial neural network application in nanotechnology. In 2018 7th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE.

Rahim, N. A., Mekhilef, S., Chan, E. L., & Ping, H. W. (2006). Fuzzy-controlled battery charger state-of-charge controller. International Journal of Modelling and Simulation, 26(2), 106-111.

Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2017). Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access, 6, 1832-1843.

Santhanapoongodi, R., & Rajini, V. (2016, April). A new state of charge estimation algorithm for lead acid battery. In 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC) (pp. 326-330). IEEE.

Lipu, M. S. H., Hannan, M. A., Hussain, A., Saad, M. H., Ayob, A., & Blaabjerg, F. (2018). State of charge estimation for lithium-ion battery using recurrent NARX neural network model based lighting search algorithm. IEEE access, 6, 28150-28161.

Rao, R. (2020). Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations, 11(1), 107-130.

Wu, T. H., Wang, J. K., Moo, C. S., & Kawamura, A. (2016, June). State-of-charge and state-of-health estimating method for lithium-ion batteries. In 2016 IEEE 17th Workshop on Control and Modeling for Power Electronics (COMPEL) (pp. 1-6). IEEE.


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