Abstract
The state of charge (SOC) estimation of Li-ion batteries has attracted substantial interests in recent years. Kalman Filter has been widely used in real-time battery SOC estimation, however, to build a suitable dynamic battery state-space model is a key challenge, and most existing methods still use the off-line modelling approach. This paper tackles the challenge by proposing a novel sparse learning machine for real-time SOC estimation. This is achieved first by developing a new learning machine based on the traditional least squares support vector machine (LS-SVM) to capture the process dynamics of Li-ion batteries in real-time. The least squares support vector machine is the least squares version of the conventional support vector machines (SVMs) which suffers from low model sparseness. The proposed learning machine reduces the dimension of the projected high dimensional feature space with no loss of input information, leading to improved model sparsity and accuracy. To accelerate computation, mapping functions in the high feature space are selected using a fast recursive method. To further improve the model accuracy, a weighted regularization scheme and the differential evolution (DE) method are used to optimize the parameters. Then, an unscented Kalman filter (UKF) is used for real-time SOC estimation based on the proposed sparse learning machine model. Experimental results on the Federal Urban Drive Schedule (FUDS) test data reveal that the performance of the proposed algorithm is significantly enhanced, where the maximum absolute error is only one sixth of that obtained by the conventional LS-SVMs and the mean square error of the SOC estimations reaches to 10−7, while the proposed method is executed nearly 10 times faster than the conventional LS-SVMs.
Highlights
In recent years, the transportation electrification through mass roll-out of electric vehicles has been considered as an important measure to tackle the global challenges of climate change and environmental pollutions due to substantiveThe associate editor coordinating the review of this manuscript and approving it for publication was Bin Zhou .consumption of fossil fuels in the transportation sector [1]
In order to reduce the computation time incurred by existing approaches, a sparse learning machine (LM) trained only using a very small number of samples is developed for state of charge (SOC) estimation, in replacement of the conventional least squares support vector machine (LS-support vector machines (SVMs)) model
A novel sparse learning machine based on the LS-SVM formulation has been proposed for real-time SOC estimation in this paper
Summary
LI ZHANG 1, KANG LI 2, (Senior Member, IEEE), DAJUN DU 1, YUANJUN GUO3, MINRUI FEI1, AND ZHILE YANG 3,4, (Member, IEEE).
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