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

Sensors

José Otero¹  Luciano Sánchez¹  David Anseán²  Inés Couso³ 

[1] Computer Science Department, Universidad de Oviedo, 33203 Gijón, Spain;Electrical Engineering Department, Universidad de Oviedo, 33203 Gijón, Spain;Statistics Department, Universidad de Oviedo, 33203 Gijón, Spain;

关键词: soft sensor;    battery model;    monotonic model;    echo state networks;   

DOI  :  10.3390/s18010009

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown