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Sensors 2018, 18(1), 9; https://doi.org/10.3390/s18010009

Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

1
Computer Science Department, Universidad de Oviedo, 33203 Gijón, Spain
2
Electrical Engineering Department, Universidad de Oviedo, 33203 Gijón, Spain
3
Statistics Department, Universidad de Oviedo, 33203 Gijón, Spain
*
Author to whom correspondence should be addressed.
Received: 20 October 2017 / Revised: 11 December 2017 / Accepted: 18 December 2017 / Published: 21 December 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Abstract

A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the electrodes of the battery. The proposed soft sensor is able to exploit the information contained in operational records of the vehicle better than the alternatives, this being particularly true when the charge or discharge currents are between moderate and high. The accuracy of the neural model has been compared to different alternatives, including data-driven statistical models, first principle-based models, fuzzy observers and other recurrent neural networks with different topologies. It is concluded that monotonic echo state networks can outperform well established first-principle models. The algorithms have been validated with automotive Li-FePO4 cells. View Full-Text
Keywords: soft sensor; battery model; monotonic model; echo state networks soft sensor; battery model; monotonic model; echo state networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sánchez, L.; Anseán, D.; Otero, J.; Couso, I. Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks. Sensors 2018, 18, 9.

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