Stochastic Range Estimation Algorithms For Electric Vehicles Using Data Driven Learning Models
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Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
Author: Scheubner, Stefan
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-06-03
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
Author: Thorgeirsson, Adam Thor
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-09-03
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.
Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle
Author: Jauch, Jens
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-03-01
This work describes a method for weighted least squares approximation of an unbounded number of data points using a B-spline function. The method can shift the bounded B-spline function definition range during run-time. The approximation method is used for optimizing velocity trajectories for an electric vehicle with respect to travel time, comfort and energy consumption. The trajectory optimization method is extended to a driver assistance system for automated vehicle longitudinal control.