Data plays a key role in the life cycle of a battery, ranging from the discovery and optimization of new materials to lifetime prediction models and battery management systems. Batteries can vary widely in their chemistry and use cases, resulting in a corresponding variance in generated data. Different research groups rely on internal best practices for data collection, and it is often difficult for external parties to leverage those datasets in their own research due to incompleteness of what is made publicly available. Standards for battery data acquisition and presentation are crucial for more accurate benchmarking and accelerating research and development.
Many recent efforts have sought to compile publicly available battery datasets in order to provide a foundation for future research. Within these repositories, there is little to no standardization between constituent datasets. The goal of this effort is to provide guidelines such that collected data can not only be directly compared across research groups, but also validated to ensure an accurate framework for training models and informing future experimental design.
The scope of this Research Topic is on standardizing best practices for the collection and validation of high quality battery data. To that end, this issue is primarily interested in compiling methods, perspectives, and opinions from contributors. Relevant technical areas include, but are not limited to:
• materials level characterization (e.g., what are minimum requirements for a given characterization technique, or what is necessary information that may be specific to a particular chemistry)
• electrochemical cell performance
• lifetime modelling
• operational controls (e.g., battery management systems)
• practical needs from an industry or policy perspective to execute decisions
• lifetime, degradation and safety modeling.
Beyond data collection, contributions related to best practices for validating external data are also encouraged.
Data plays a key role in the life cycle of a battery, ranging from the discovery and optimization of new materials to lifetime prediction models and battery management systems. Batteries can vary widely in their chemistry and use cases, resulting in a corresponding variance in generated data. Different research groups rely on internal best practices for data collection, and it is often difficult for external parties to leverage those datasets in their own research due to incompleteness of what is made publicly available. Standards for battery data acquisition and presentation are crucial for more accurate benchmarking and accelerating research and development.
Many recent efforts have sought to compile publicly available battery datasets in order to provide a foundation for future research. Within these repositories, there is little to no standardization between constituent datasets. The goal of this effort is to provide guidelines such that collected data can not only be directly compared across research groups, but also validated to ensure an accurate framework for training models and informing future experimental design.
The scope of this Research Topic is on standardizing best practices for the collection and validation of high quality battery data. To that end, this issue is primarily interested in compiling methods, perspectives, and opinions from contributors. Relevant technical areas include, but are not limited to:
• materials level characterization (e.g., what are minimum requirements for a given characterization technique, or what is necessary information that may be specific to a particular chemistry)
• electrochemical cell performance
• lifetime modelling
• operational controls (e.g., battery management systems)
• practical needs from an industry or policy perspective to execute decisions
• lifetime, degradation and safety modeling.
Beyond data collection, contributions related to best practices for validating external data are also encouraged.