Synthetic Duty Cycles from Real-World Autonomous Electric Vehicle Driving: Accompanying Data

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Abstract/Contents

Abstract: Electric connected autonomous vehicles (ECAVs) provide increased safety and efficient, low-carbon-emissions travel at scale. Proper and efficient management of the ECAV lithium-ion battery (LIB) system is key to guaranteeing that all of the benefits associated with ECAVs are achieved. This requires that the LIB system design and control be informed by data representative of ECAV LIB system operation. This paper generates a synthetic duty cycle dataset from real-world ECAV driving for accelerated LIB characterization and development. We demonstrate a methodology for generating laboratory-friendly synthetic duty cycles directly from ECAV driving data, enabling LIB cell experiments which represent a wide range of different driving conditions and LIB system sizes. We share data collected from 31 LIB cells during these cycling experiments, providing the academic community with a rich and diverse set of ECAV-specific current inputs and voltage and temperature outputs induced by the synthetic duty cycles. This dataset will have an immediate impact towards robust on-board battery management systems which can handle a diverse range of battery excitation modes, as well as the evaluation of LIB cells for application-specific system design, expediting the wide-scale adoption and deployment of ECAVs.

Type of resource	Dataset
Publication date	March 30, 2023

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License: This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 4.0 International license (CC BY-NC-SA).

Preferred citation: Moy, K., Ganapathi, D., Geslin, A., Onori, S., and Chueh, W. (2023). Synthetic Duty Cycles from Real-World Autonomous Electric Vehicle Driving: Accompanying Data. Stanford Digital Repository. Available at https://purl.stanford.edu/ky011nj6376. https://doi.org/10.25740/ky011nj6376.

Stanford Research Data

Contact: dganapat@stanford.edu; kmoy14@stanford.edu; geslina@stanford.edu; sonori@stanford.edu; wchueh@stanford.edu

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