Accurate prediction of battery temperature rise is very essential for designing an efficient thermal management scheme. In this paper, machine learning (ML) based prediction of Vanadium Redox Flow Battery (VRFB) thermal behavior during charge-discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; Linear Regression (LR), Support Vector Regression (SVR) and Extreme Gradient Boost (XGBoost) have been used for the prediction work. The training and validation of ML algorithms have been done by the practical dataset of a 1kW 6kWh VRFB storage under 40A, 45A, 50A and 60A charge-discharge currents and 10 L min-1 of flow rate. A comparative analysis among the ML algorithms is done in terms of performance metrics such as correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE). It is observed that XGBoost shows the highest accuracy in prediction of around 99%. The ML based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as indicator for further development of an optimized thermal management system.

翻译：准确预测电池温升对于设计高效热管理方案至关重要。本文首次展示了基于机器学习（ML）的钒氧化还原液流电池（VRFB）在充放电运行期间热行为预测。通过实验研究特定电解液流速下不同电流的千瓦级VRFB系统温度变化，采用三种不同机器学习算法：线性回归（LR）、支持向量回归（SVR）和极限梯度提升（XGBoost）进行预测。基于1kW 6kWh VRFB储能系统在40A、45A、50A和60A充放电电流及10 L min⁻¹流速条件下的实际数据集，完成了机器学习算法的训练与验证。通过相关系数（R²）、平均绝对误差（MAE）和均方根误差（RMSE）等性能指标对算法进行对比分析。结果表明，XGBoost在预测中达到约99%的最高精度。本工作获得的机器学习预测结果可有效用于控制VRFB运行期间的温升，并为优化热管理系统的进一步开发提供指示。