The Fórmula SAE Elétrico is a student competition of monoblock vehicles with propulsion from electric engines, according to the regulations of the organization; the vehicle must be completely electric, not having any connection with a electric network to recharge it. The main thing being researched by the teams is the optimization and the management of the energy storage systems (battery banks) in the electric vehicles. However, there are some parameters which negatively influences the search for such optimization, like: the approximations associated to the battery mathematic models and the determination of the SoC (State of Charge), loss of useful life due to the quantity of cycles and the wear and tear, caused by multiple factors like ambient temperature, pressure, humidity and others. With the goal to build a cellular battery model, it was developed a Neural Network Artificial (ANN), with a learning capacity and adaptation to the charge cycles, discharge and rest to the Lithium-Ion battery. According to the results, it was modeled a system to estimate the SoC. To find the values of every parameter used in the training of the ANN, a set of experiments were made in a measuring bench composed of a four quadrant source, a thermal bath and a data acquisition system. The experiments were made manipulating the current and temperature with charge cycles, discharge and rest of the cells, in which were collected voltage, temperature and current data injected in the cell. With that said, this paper has the objective of obtaining a model and a estimation from the SoC based in Neural Network Artificial (ANN).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Modeling and estimation of the state of charge of lithium-ion battery based on artificial neural network


    Weitere Titelangaben:

    Sae Technical Papers



    Kongress:

    2018 SAE Brasil Congress & Exhibition ; 2018



    Erscheinungsdatum :

    2018-09-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Battery state of charge estimation using an Artificial Neural Network

    Ismail, Mahmoud / Dlyma, Rioch / Elrakaybi, Ahmed et al. | IEEE | 2017


    EV battery state of charge: neural network based estimation

    Affanni, A. / Bellini, A. / Concari, C. et al. | Tema Archiv | 2003


    Neural Network based State of Charge Prediction of Lithium-ion Battery

    Sharma, Sakshi / Achlerkar, Pankaj Dilip / Shrivastava, Prashant et al. | IEEE | 2022



    A Cycle-based Recurrent Neural Network for State-of-Charge Estimation of Li-ion Battery Cells

    Savargaonkar, Mayuresh / Chehade, Abdallah / Shi, Zunya et al. | IEEE | 2020