Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

• Published in: Batteries (Basel) 2019-06, Vol.5 (2), p.47
• Main Authors: Bonfitto, Angelo, Feraco, Stefano, Tonoli, Andrea, Amati, Nicola, Monti, Francesco
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; computational cost; Computational efficiency; Computing costs; Cost analysis; Datasets; Design; Electric vehicles; Electronic control; estimation; Hybrid electric vehicles; Layouts; Lithium; Lithium batteries; Lithium battery; Neural networks; Noise measurement; State of charge; Training
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries5020047

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.