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Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning

In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probab...

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Main Author: Thorgeirsson, Adam Thor
Format: Online
Language:English
Published: KIT Scientific Publishing 2024
Subjects:
Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials
Online Access:https://library.oapen.org/handle/20.500.12657/93282
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Summary:In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.

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