A Neural Network Application for a Lithium-Ion Battery Pack State-of-Charge Estimator with Enhanced Accuracy
Gabriel C. S. Almeida, Antônio Carlos Zambroni de Souza, Paulo F. Ribeiro
Abstract
A State-of-Charge (SOC) real-time estimation plays an essential role in effective energy management. This paper proposes the use of an Artificial Neural Network (ANN) to design a state-of-charge estimator for a Graphite/LiCoO2 lithium-ion battery pack. The software MATLAB was used to develop and test several network configurations to find the ideal weights for the ANN. The results demonstrate that the Mean Squared Error (MSE) achieved renders the ANN as an effective technique. Thus, it predicted the battery bank’s SOC values with accuracy using only voltage, current, and charge/discharge time as inputs.
Topics & Concepts
State of chargeEstimatorBattery packMean squared errorArtificial neural networkBattery (electricity)MATLABVoltageComputer scienceSoftwareLithium (medication)Electronic engineeringControl theory (sociology)AlgorithmElectrical engineeringEngineeringArtificial intelligenceMathematicsControl (management)StatisticsPhysicsPower (physics)Operating systemEndocrinologyMedicineProgramming languageQuantum mechanicsAdvanced Battery Technologies ResearchAdvancements in Battery MaterialsElectric Vehicles and Infrastructure