Synthetic duty cycles from real-world autonomous electric vehicle driving
Source abstract
Abstract
Connected/autonomous electric vehicles (C/AEVs) have the potential to provide safe, efficient, and low-carbon-emission transportation solutions. Proper and efficient management of the C/AEV lithium-ion battery (LIB) system is key to guaranteeing that all benefits associated with C/AEVs are achieved. This requires that the design and control of LIBs be informed by the C/AEV system operation. This article first demonstrates that C/AEV operation is distinct from human-driven electric vehicle operation, thus necessitating the development of application-specific testing protocols to properly characterize and model their LIBs. Laboratory-prone synthetic duty cycles are generated from C/AEV driving data, enabling a design of experiments representing a wide range of C/AEV driving modes and LIB system sizes. We share data collected from 31 LIB cells undergoing the synthetic cycling experiments. This paper provides the academic community with an application-specific C/AEV LIB dataset for the design and calibration of data-driven battery models for real-time control and operation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Vehicle emissions and performance · Electric and Hybrid Vehicle Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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