论文标题
锂离子电池中最先进估计的时间卷积网络方法
A Temporal Convolution Network Approach to State-of-Charge Estimation in Li-ion Batteries
论文作者
论文摘要
在过去的几年中，电动汽车（EV）舰队已经大大扩展。电气化所有运输方式的兴趣已大大增加。电动汽车主要由储能系统（例如锂离子电池组）提供动力。总电池组容量转化为EV中的可用范围。充电状态（SOC）是可用电池容量与总容量的比率，并以百分比表示。准确估计SOC在使用时确定EV中的可用范围至关重要。在本文中，采用时间卷积网络（TCN）方法来估计SOC。这是用于SOC估计任务的TCN的首次实施。在1 C和25°Celsius的HWFET，LA92，UDDS和US06驱动周期等各种驱动周期上进行估计。发现TCN体系结构的准确性为99.1％。
Electric Vehicle (EV) fleets have dramatically expanded over the past several years. There has been significant increase in interest to electrify all modes of transportation. EVs are primarily powered by Energy Storage Systems such as Lithium-ion Battery packs. Total battery pack capacity translates to the available range in an EV. State of Charge (SOC) is the ratio of available battery capacity to total capacity and is expressed in percentages. It is crucial to accurately estimate SOC to determine the available range in an EV while it is in use. In this paper, a Temporal Convolution Network (TCN) approach is taken to estimate SOC. This is the first implementation of TCNs for the SOC estimation task. Estimation is carried out on various drive cycles such as HWFET, LA92, UDDS and US06 drive cycles at 1 C and 25 °Celsius. It was found that TCN architecture achieved an accuracy of 99.1%.
