Summary
This 2022 review examines the application of deep learning techniques to predict the operational state of lithium-ion batteries, specifically focusing on state of health and state of charge estimation. The paper synthesises literature on neural network-based approaches and their performance relative to conventional battery management methods. The review does not directly address agricultural, soil health, or food systems research.
UK applicability
This paper is not applicable to UK agricultural policy, farming systems, soil health, or nutritional research. It concerns battery technology and energy storage, which falls outside the scope of Vitagri's Pulse Brain catalogue.
Key measures
As suggested by the title, metrics likely include state of health (SOH) and state of charge (SOC) prediction accuracy using various deep learning architectures.
Outcomes reported
This paper appears to review deep learning approaches and their application to predicting the state of health (SOH) and state of charge (SOC) of lithium-ion batteries. The review synthesises methodologies and performance metrics from existing research in this domain.
Topic tags
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