论文标题
基于注意的深度神经网络，用于电池放电能力预测
Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting
论文作者
论文摘要
电池排放能力的预测对于锂离子电池的应用至关重要。从数据的角度来看，容量变性可以视为最初电池电量状态的记忆。电池管理系统（BMS）收集的流传感器数据反映了各种操作工作条件下的可用电池容量降解率。可以根据注意机制从流传感器数据中提取的时间模式来测量不同循环中的电池容量。基于注意的第一个周期的相似性可以描述以下周期中的电池容量降解。深层降解网络（DDN）是通过注意机制来衡量相似性和预测电池容量的。 DDN模型可以从流传感器数据中提取与变性相关的时间模式，并实时在线进行电池容量预测。根据MIT-Stanford开放式电池老化数据集，容量估计的根平方错误为1.3 mAh。所提出的DDN模型的平均绝对百分比误差为0.06 {\％}。 DDN模型在具有动态载荷轮廓的牛津电池降解数据集中也表现良好。因此，验证了所提出的算法的高精度和强鲁棒性。
Battery discharge capacity forecasting is critically essential for the applications of lithium-ion batteries. The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point of view. The streaming sensor data collected by battery management systems (BMS) reflect the usable battery capacity degradation rates under various operational working conditions. The battery capacity in different cycles can be measured with the temporal patterns extracted from the streaming sensor data based on the attention mechanism. The attention-based similarity regarding the first cycle can describe the battery capacity degradation in the following cycles. The deep degradation network (DDN) is developed with the attention mechanism to measure similarity and predict battery capacity. The DDN model can extract the degeneration-related temporal patterns from the streaming sensor data and perform the battery capacity prediction efficiently online in real-time. Based on the MIT-Stanford open-access battery aging dataset, the root-mean-square error of the capacity estimation is 1.3 mAh. The mean absolute percentage error of the proposed DDN model is 0.06{\%}. The DDN model also performance well in the Oxford Battery Degradation Dataset with dynamic load profiles. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.
