Recovering large-scale battery aging dataset with machine learning

Batteries are crucial for building a clean and sustainable society, and their performance is highly affected by aging status. Reliable battery health assessment, however, is currently restrained by limited access to sufficient aging data, resulting from not only complicated battery operations but also long aging experimental time (several months or even years). Refining industrial datasets (e.g., the field data from electric vehicle batteries) unlocks opportunities to acquire large-volume aging data with low experimental efforts. We introduce the potential of combining industrial data with accelerated aging tests to recover high-quality battery aging datasets, through a migration-based machine learning. A comprehensive dataset containing 8,947 aging cycles with 15 operational modes is collected for evaluation. While saving up to 90% experimental time, aging data can be recovered with ultra-low error within 1%. It provides an alternative solution to significantly improve data shortage issues for assessment of battery and energy storage aging

Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future

A new approach to the internal thermal management of cylindrical battery cells for automotive applications

Conventional cooling approaches that target either a singular tab or outer surface of common format cylindrical lithium-ion battery cells suffer from a high cell thermal resistance. Under an aggressive duty cycle, this resistance can result in the formation of large in-cell temperature gradients and high hot spot temperatures, which are known to accelerate ageing and further reduce performance. In this paper, a novel approach to internal thermal management of cylindrical battery cells to lower the thermal resistance for heat transport through the inside of the cell is investigated. The effectiveness of the proposed method is analysed for two common cylindrical formats when subject to highly aggressive electrical loading conditions representative of a high performance electric vehicle (EV) and hybrid electric vehicle (HEV). A mathematical model that captures the dominant thermal properties of the cylindrical cell is created and validated using experimental data. Results from the extensive simulation study indicate that the internal cooling strategy can reduce the cell thermal resistance by up to 67.8 ± 1.4% relative to single tab cooling, and can emulate the performance of a more complex pack-level double tab cooling approach whilst targeting cooling at a single tab