当前位置: X-MOL 学术J. Electr. Eng. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Proficient Li-Ion Battery State of Charge Estimation Based on Event-Driven Processing
Journal of Electrical Engineering & Technology ( IF 1.9 ) Pub Date : 2020-05-25 , DOI: 10.1007/s42835-020-00458-x
Saeed Mian Qaisar

The lithium-ion batteries are recurrently used in a variety of applications. To assure an effective battery utilization and longer life, the battery management systems (BMSs) are employed. Recent BMSs are becoming sophisticated and causes a higher consumption overhead on the battery. To enhance the BMS power efficiency, this work exploits the input signal non-stationary nature. The idea is to employ event-driven sensing and processing. In contrast to the traditional counterparts, the battery cells parameters like voltages and currents are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, this non-uniformly partitioned information is employed by a novel event-driven Coulomb counting algorithm for a real-time determination of the State of Charge (SoC). The estimated SoCis calibrated by using an original event-driven Open Circuit Voltage (OCV) to SoC curve relation. The devised system comparison is made with the traditional counterparts. Results demonstrate a more than third-order of magnitude outperformance of the proposed system in terms of compression gain and computational efficiency while assuring an analogous SoC estimation precision.

中文翻译:

基于事件驱动处理的精通锂离子电池荷电状态估计

锂离子电池经常用于各种应用。为了确保有效的电池利用率和更长的使用寿命,采用了电池管理系统 (BMS)。最近的 BMS 变得越来越复杂,并导致更高的电池消耗开销。为了提高 BMS 功率效率,这项工作利用了输入信号的非平稳性。这个想法是采用事件驱动的传感和处理。与传统对应物相比,电压和电流等电池单元参数不再定期捕获,而是基于事件获取。它导致显着的实时数据压缩。之后,这种非均匀分区的信息被一种新颖的事件驱动库仑计数算法用于实时确定荷电状态 (SoC)。通过使用原始事件驱动的开路电压 (OCV) 与 SoC 曲线关系校准估计的 SoC。设计的系统与传统的同行进行了比较。结果表明,在确保类似 SoC 估计精度的同时,所提出的系统在压缩增益和计算效率方面的性能超过了三个数量级。
更新日期:2020-05-25
down
wechat
bug