Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles

To achieve a resource-efficient automotive traffic, modern driver assistance systems minimize the vehicle’s energy demand through speed optimization algorithms. Based on predictive route data, the required energy for upcoming operation points has to be determined. This paper presents a method to pre...

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Bibliographic Details
Published in:SN computer science 2024-01, Vol.5 (1), p.192, Article 192
Main Authors: Fink, Daniel, Maas, Oliver, Herda, Daniel, Ziaukas, Zygimantas, Schweers, Christoph, Trabelsi, Ahmed, Jacob, Hans-Georg
Format: Article
Language:English
Subjects:
Algorithms
Automation
Automobiles
Braking systems
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Control algorithms
Data Structures and Information Theory
Demand
Electric motors
Electric vehicles
Emission standards
Energy consumption
Energy efficiency
Gaussian process
Hybrid electric vehicles
Information Systems and Communication Service
Lookup tables
Neural networks
Optimization
Original Research
Pattern Recognition and Graphics
Powertrain
Software Engineering/Programming and Operating Systems
Test vehicles
Torque
Vehicle Technology and Intelligent Transport Systems
Vision
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Summary:To achieve a resource-efficient automotive traffic, modern driver assistance systems minimize the vehicle’s energy demand through speed optimization algorithms. Based on predictive route data, the required energy for upcoming operation points has to be determined. This paper presents a method to predict the energy demand of a hybrid electrical vehicle. Within this method, data-based approaches, such as neural networks, Gaussian processes, and look-up tables, are applied and assessed regarding their ability to predict the behavior of separate powertrain parts. The applied approaches are trained using measured data of a test vehicle. As a result, for every separate powertrain part, the best-suited data-based approach is selected to obtain an optimal energy demand prediction method. On a validation data set, this method is able to predict the transmission ratio of the gearbox causing a rmse of 0.426. The combustion engine’s torque prediction results in an rmse of 19.01 Nm and the electric motor torque prediction to 19.11 Nm. The root mean square error of the motor voltage results to 1.211 V.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-02475-9