Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model
| Autor: | Dominique Nelson-Gruel, Yann Chamaillard, Sylvain Franger, Jean Kuchly, Alain Goussian, Issam Baghdadi, Mathieu Merveillaut, Cédric Nouillant |
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| Přispěvatelé: | Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Stellantis - PSA Centre Technique de Vélizy |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Battery (electricity)
Artificial neural network Computer science 020209 energy Open problem 02 engineering and technology 021001 nanoscience & nanotechnology [SPI.AUTO]Engineering Sciences [physics]/Automatic Extended Kalman filter State of charge Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Coulomb Diffusion (business) 0210 nano-technology Algorithm ComputingMilieux_MISCELLANEOUS Voltage |
| Zdroj: | 6th IFAC Conference on Engine Powertrain Control, Simulation and Modeling E-COSM 2021 6th IFAC Conference on Engine Powertrain Control, Simulation and Modeling E-COSM 2021, Aug 2021, Tokyo, Japan. pp.336-343, ⟨10.1016/j.ifacol.2021.10.185⟩ |
| DOI: | 10.1016/j.ifacol.2021.10.185⟩ |
| Popis: | The State Of Charge (SOC) estimation of a Li-ion battery is still an open problem. The most classical method, Coulomb Counting (CC) is vulnerable to current measurement bias. Measuring the Open-Circuit Voltage (OCV) allows to correct the error accumulated by the CC method, but only after the battery has been unsolicited long enough. Regarding these deficiencies, advanced SOC estimation methods try to combine current and voltage information, and are either based on an Extended Kalman Filter (EKF), which represents a certain algorithmic complexity and is hard to calibrate, or on black-box methods. In particular, methods using a Neural Network (NN) have been investigated in the literature, but take usually into account only instantaneous information, failing to represent the dynamic of ion diffusion in the electrodes. By considering also a close-past current integral as an input, this paper proposes a NN model able to correct initial SOC estimation errors and handle current measurement bias, and achieving a better estimation performance than a classical NN model taking only instantaneous information as an input. |
| Databáze: | OpenAIRE |
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