2010 · 13 citations · 6 references
ComputingComputer ScienceElectrical EngineeringArtificial IntelligenceMachine LearningPredictive AnalyticsBattery SystemsBattery TechnologyElectrochemical CellsNeural ModelingNeural NetworksState Of Charge EstimationBattery ModelingBattery State Of Charge ModelingMachine Learning For State Of Charge EstimationModel-based State Of Charge EstimationReal-time ProcessingReal-time State Of Charge EstimationState Of Charge ForecastingState Of Charge Prediction
Based on artificial neural networks and Thenvenin model, this paper uses an improved model predicting state of charge. We combine artificial neural networks model with Thevenin model, and predict state of charge in real time at the same time. When the difference between the predictive value of artificial neural networks model and the predictive value of Thevenin model is more than 10%, we revised the predictive value of artificial neural networks model by weighted average value. The results show that it can reduce the error of artificial neural networks model obvious and the average error is 4.72%. It is lower independence on initial state of charge than artificial neural networks model.
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Methods for state-of-charge determination and their applications
S. Piller, Marion Perrin, Andreas Jossen · Journal of Power Sources · 2001 · 1.1K citations
Natural Sciences, Chemistry, Physics +11
Battery performance models in ADVISOR
Valerie H. Johnson · Journal of Power Sources · 2002 · 621 citations