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

1
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino 10129, Italy
2
Podium Advanced Technologies, Pont Saint Martin 11026, Italy
*
Authors to whom correspondence should be addressed.
Batteries 2019, 5(2), 47; https://doi.org/10.3390/batteries5020047
Received: 1 April 2019 / Revised: 2 May 2019 / Accepted: 7 May 2019 / Published: 1 June 2019
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. View Full-Text
Keywords: state of charge; estimation; artificial neural networks; computational cost; Lithium battery; electric vehicles state of charge; estimation; artificial neural networks; computational cost; Lithium battery; electric vehicles
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MDPI and ACS Style

Bonfitto, A.; Feraco, S.; Tonoli, A.; Amati, N.; Monti, F. Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries. Batteries 2019, 5, 47.

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