Details
| Original language | English |
|---|---|
| Title of host publication | 2024 European Control Conference, ECC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 711-717 |
| Number of pages | 7 |
| ISBN (electronic) | 9783907144107 |
| ISBN (print) | 979-8-3315-4092-0 |
| Publication status | Published - 25 Jun 2024 |
| Event | 2024 European Control Conference, ECC 2024 - Stockholm, Sweden Duration: 25 Jun 2024 → 28 Jun 2024 |
Abstract
The use of predictive energy management systems can improve the efficiency of multi-energy storage vehicles. However, current systems have limitations, such as short prediction horizons, the requirement for input data that is not publicly available, or the training of the Neural Networks on the routes on which the prediction is made. To overcome these challenges, this paper introduces a novel method for long-horizon energy prediction, utilizing readily available data such as route geometry and traffic information. Our study compares Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), and Transformer Networks optimized using the Asynchronous Successive Halving Algorithm (ASHA). The models were evaluated in a simulated environment using the Simulation of Urban MObility (SUMO) and further tested on real-world driving data, demonstrating that we are able to predict the consumed energy over a 45km stretch of highway with a median RMSE of 0.018 kWh/km for practical application. The energy prediction developed in this study has the potential to enhance predictive energy management systems, thereby optimizing energy usage and contributing to CO2 emission reduction.
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Mathematics(all)
- Modelling and Simulation
Sustainable Development Goals
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2024 European Control Conference, ECC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 711-717.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Neural Network-Based Prediction of Vehicle Energy Consumption on Highways
AU - Bank, Dennis
AU - Fink, Daniel
AU - Ehlers, Simon F.G.
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024/6/25
Y1 - 2024/6/25
N2 - The use of predictive energy management systems can improve the efficiency of multi-energy storage vehicles. However, current systems have limitations, such as short prediction horizons, the requirement for input data that is not publicly available, or the training of the Neural Networks on the routes on which the prediction is made. To overcome these challenges, this paper introduces a novel method for long-horizon energy prediction, utilizing readily available data such as route geometry and traffic information. Our study compares Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), and Transformer Networks optimized using the Asynchronous Successive Halving Algorithm (ASHA). The models were evaluated in a simulated environment using the Simulation of Urban MObility (SUMO) and further tested on real-world driving data, demonstrating that we are able to predict the consumed energy over a 45km stretch of highway with a median RMSE of 0.018 kWh/km for practical application. The energy prediction developed in this study has the potential to enhance predictive energy management systems, thereby optimizing energy usage and contributing to CO2 emission reduction.
AB - The use of predictive energy management systems can improve the efficiency of multi-energy storage vehicles. However, current systems have limitations, such as short prediction horizons, the requirement for input data that is not publicly available, or the training of the Neural Networks on the routes on which the prediction is made. To overcome these challenges, this paper introduces a novel method for long-horizon energy prediction, utilizing readily available data such as route geometry and traffic information. Our study compares Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), and Transformer Networks optimized using the Asynchronous Successive Halving Algorithm (ASHA). The models were evaluated in a simulated environment using the Simulation of Urban MObility (SUMO) and further tested on real-world driving data, demonstrating that we are able to predict the consumed energy over a 45km stretch of highway with a median RMSE of 0.018 kWh/km for practical application. The energy prediction developed in this study has the potential to enhance predictive energy management systems, thereby optimizing energy usage and contributing to CO2 emission reduction.
UR - http://www.scopus.com/inward/record.url?scp=85200548021&partnerID=8YFLogxK
U2 - 10.23919/ECC64448.2024.10590711
DO - 10.23919/ECC64448.2024.10590711
M3 - Conference contribution
AN - SCOPUS:85200548021
SN - 979-8-3315-4092-0
SP - 711
EP - 717
BT - 2024 European Control Conference, ECC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 European Control Conference, ECC 2024
Y2 - 25 June 2024 through 28 June 2024
ER -