Effect of training algorithms in accurate state of charge estimation of lithium-ion batteries using NARX model Online publication date: Tue, 18-Jul-2023
by Namrata Mohanty; Neeraj Kumar Goyal; V.N. Achutha Naikan
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 30, No. 2, 2023
Abstract: Accurate state of charge (SOC) estimation is required to ensure the safe and reliable operation of lithium-ion batteries (LIBs) in electric vehicles. Battery SOC does the similar function or operation as the fuel gauge in IC engine-driven vehicles indicating the energy left inside the battery to power a vehicle. This paper proposes the use of non-linear autoregressive exogenous input (NARX) model for accurate SOC estimation of LIBs and studies the effect of two training algorithms namely, Levenberg Marquardt and scaled conjugate gradient, on accuracy of proposed neural time series network model. Datasets including dynamic stress test (DST) profiles were extracted from the Center for Advanced Life Cycle Engineering (CALCE). Accuracy is evaluated in terms of mean squared error (MSE) and coefficient of determination (R-Square or R2). Levenberg-Marquardt algorithm is better than scaled conjugate gradient algorithm in providing better results for estimating SOC with a MSE of 4.61306 × 10-6.
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