Adaptive Flow Control Strategy for Power Lithium-Ion Batteries Based on LSTM-Encoder
ID：112
Submission ID：36 View Protection：ATTENDEE
Updated Time：2025-09-30 10:37:53
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Oral Presentation
Start Time：2025-10-12 16:05 （Asia/Shanghai）
Duration：15min
Session：[S8] AI, surrogate modeling and optimization » [S8-2] Session 8-2
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Abstract
This study presents an adaptive battery cooling strategy for electric vehicles using an LSTM-Encoder network to predict short-term temperature rise. By analyzing time-series data of current, voltage, and temperature, the model adjusts coolant flow in real time based on future thermal trends. Compared to fixed-flow systems, the approach improves response to dynamic heat loads, reduces energy consumption, and maintains battery temperature within a safe range, enhancing overall thermal management efficiency.
Keywords
Lithium-Ion Battery, Liquid Cooling, Adaptive Flow Control
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