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Battery discharge capacity forecasting is critically essential for theapplications of lithium-ion batteries. The capacity degeneration can be treatedas the memory of the initial battery state of charge from the data point ofview. The streaming sensor data collected by battery management systems (BMS)reflect the usable battery capacity degradation rates under various operationalworking conditions. The battery capacity in different cycles can be measuredwith the temporal patterns extracted from the streaming sensor data based onthe attention mechanism. The attention-based similarity regarding the firstcycle can describe the battery capacity degradation in the following cycles.The deep degradation network (DDN) is developed with the attention mechanism tomeasure similarity and predict battery capacity. The DDN model can extract thedegeneration-related temporal patterns from the streaming sensor data andperform the battery capacity prediction efficiently online in real-time. Basedon the MIT-Stanford open-access battery aging dataset, the root-mean-squareerror of the capacity estimation is 1.3 mAh. The mean absolute percentage errorof the proposed DDN model is 0.06{\%}. The DDN model also performance well inthe Oxford Battery Degradation Dataset with dynamic load profiles. Therefore,the high accuracy and strong robustness of the proposed algorithm are verified.

Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting