World : Machine Learning Predicts Battery Health

Rechargeable batteries, including the Li-ion batteries found in electric vehicles and in many grid-level and behind-the-meter energy storage devices, gradually lose their storage capacity with repeated charge-discharge cycles. One responsibility of a battery management system (BMS) is to monitor a battery’s state of health (SoH) in order to optimize charging and predict its remaining life span. Currently, a BMS will determine SoH by monitoring voltage and current levels while charging and discharging, but that doesn’t give the complete picture. Researchers at Cambridge and Newcastle Universities developed an artificial intelligence (AI) system to more accurately assess SoH. By sending brief electrical pulses to a battery and measuring its response, a machine learning algorithm detects subtleties in the battery’s output.

This information can help the BMS control the charging process to maximize the battery’s life. In addition, the data helps create better battery models that can be used in a digital twin situation.

Source : https://www.engineering.com/ElectronicsDesign/ElectronicsDesignArticles/ArticleID/20271/This-Week-in-Green-Tech-Offshore-Energy-Production-Stabilizing-Perovskite-Solar-Cells-AI-Battery-Management.aspx

Smart Grid Bulletin April 2020


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