Training and Validation Methodology for Range Estimation Algorithms
Patrick Petersen,Adam Thor Thorgeirsson,Adam Thor Thorgeirsson,Stefan Scheubner,Stefan Otten,Frank Gauterin,Eric Sax +6 more
- 12 Oct 2019
- pp 434-443
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TL;DR: This paper proposes a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches that considers the evaluation of driver-specific and driver-unspecific performance.
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Abstract: Estimating the range of battery electric vehicles is one of the most challenging topics for the current trend in the automotive industry, the electrification of vehicles. Range anxiety still limits the adoption of battery electric vehicles. Since the range estimation is dependent on different influencing factors, complex algorithms to accurately estimate the vehicles consumption are required. To evaluate the accuracy of data-driven machine learning algorithms, an exhaustive training and validation procedure is mandatory. In this paper, we propose a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches. The proposed methodology considers the evaluation of driver-specific and driver-unspecific performance. In addition, an error measure is introduced to assess the performance of range estimation algorithms. This approach is demonstrated and evaluated on a set of recorded real-world driving data. It is shown that our approach helps to analyze the performance of the range estimation algorithm and the influences of different parameter sets.
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A Data-driven Energy Estimation based on the Mixture of Experts Method for Battery Electric Vehicles
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- 01 Jan 2022
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3
A Fully Automated Methodology for the Selection and Extraction of Energy-Relevant Features for the Energy Consumption of Battery Electric Vehicles
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- 18 Jun 2022
TL;DR: In this article , the authors proposed a methodology to select and extract a subset of energy-relevant features from real-world data to investigate all types of influencing factors, including weather and traffic conditions, driving style, and travel route.
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A Fully Automated Methodology for the Selection and Extraction of Energy-Relevant Features for the Energy Consumption of Battery Electric Vehicles
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TL;DR: In this article , the authors proposed a methodology to select and extract a subset of energy-relevant features from real-world data to investigate all types of influencing factors, including weather and traffic conditions, driving style, and travel route.
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