There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems.

对于在现实世界中运行的电池的现代诊断系统的需求不断增长，特别是用于例如通过其剩余容量来估计其健康状态。由于电池老化的动态特性以及操作中的电池可用的输入种类有限，因此在线估算电池容量是一项挑战。这项工作的范围是针对具有长期短期记忆能力的递归神经网络，在实际工作条件下为细胞开发数据驱动的容量估算模型。来自部分恒定电流相位充电曲线的电压-时间传感器数据用作输入，反映了现实中的输入可用性。该网络在最佳情况下的平均绝对百分比误差为0.76％，并且在处理输入噪声时非常强大。它还具有处理输入时间序列长度变化的能力，即使由于传感器错误而导致输入不完整，也可以生成可行的估计。在本地嵌入式设备中完成了具有多种场景的模型验证，突出了此类模型在未来电池管理系统中的用例。