Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory
Laura Rieger, Eibar Flores, Kristian Frellesen Nielsen, Poul Norby, Elixabete Ayerbe, Ole Winther, Tejs Vegge, Arghya Bhowmik
Abstract
We present an interpretable uncertainty-aware machine learning model to predict battery degradation trajectories. Using LSTM Recurrent Neural Networks, we reach an RMSE of 106 and MAPE of 10.6%.
Topics & Concepts
TrajectoryDegradation (telecommunications)Battery (electricity)Computer scienceArtificial intelligenceMachine learningReliability engineeringEngineeringThermodynamicsTelecommunicationsPhysicsPower (physics)AstronomyAdvanced Battery Technologies ResearchAdvancements in Battery MaterialsReliability and Maintenance Optimization