桂通电子锂电池热管理智能调控数据

本数据集合由光纤光栅温度传感器阵列与红外热像仪采集生成，包含锂电池包三维温度场分布、热管理控制策略执行参数等关键信息。数据实时记录电池包在充放电过程中的温度梯度、产热速率及冷却效率，用于构建数字孪生模型并优化强化学习控制算法，实现温差≤3℃的精准热管理。通过分析温度异常特征数据，可提前200秒预警热失控风险，降低快充温升50%以上。

This dataset is collected and generated by fiber Bragg grating (FBG) temperature sensor arrays and infrared thermal imagers, covering critical information such as the 3D temperature field distribution of lithium-ion battery packs and the execution parameters of thermal management control strategies. The dataset captures real-time data on the temperature gradient, heat generation rate and cooling efficiency of the battery pack during charging and discharging processes. It is utilized to construct digital twin models and optimize reinforcement learning-based control algorithms, enabling precise thermal management with a temperature difference of ≤3℃. By analyzing abnormal temperature feature data, early warnings of thermal runaway risks can be issued 200 seconds in advance, and the temperature rise during fast charging can be reduced by over 50%.

搜集汇总

背景与挑战

背景概述

该数据集由东莞市桂通电子科技有限公司提供，利用光纤光栅温度传感器和红外热像仪采集锂电池包的三维温度场及热管理控制参数，结合强化学习算法实现智能调控。其核心特点包括提前200秒预警热失控风险、降低快充温升50%以上，并支持温差≤3℃的精准热管理，为锂电池安全优化和寿命预测提供关键数据支撑。