2 research outputs found

    Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

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    Embedded distributed temperature sensing enabled multi-state joint observation of smart lithium-ion battery

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    Accurate monitoring of the internal statuses are highly valuable for the management of lithium-ion battery (LIB). This paper proposes a thermal model-based method for multi-state joint observation, enabled by a novel smart battery design with embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor (DFOS) internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to co-estimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical model-based method regarding the accuracy and the robustness to battery aging
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