Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic Data

Ziegmann, Johannes; Schmid, Michael; Endisch, Christian

doi:10.1109/ines.2018.8523987

2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) 2018

DOI: 10.1109/ines.2018.8523987

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Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic Data

Johannes Ziegmann

Michael Schmid

Christian Endisch

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2024

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Cited by 1 publication

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Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Schäfers,

Iravanimanesh,

Franke

et al. 2024

Automot. Innov.

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Accurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

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Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Schäfers,

Iravanimanesh,

Franke

et al. 2024

Automot. Innov.

View full text Add to dashboard Cite

show abstract

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Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic Data

Cited by 1 publication

References 9 publications

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

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