Journal
SENSORS
Volume 18, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s18010009
Keywords
soft sensor; battery model; monotonic model; echo state networks
Funding
- Spanish Ministry of Science and Innovation (MICINN)
- Regional Ministry of the Principality of Asturias
- Fondo Europeo de Desarrollo Regional (FEDER) [TIN2014-56967-R, TIN2017-84804-R, FC-15-GRUPIN14-073, TEC2016-80700-R]
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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.
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