基于anfiss的锂离子电池充电优化控制新方法

Salam Hussein, Ahmed Jabbar Abid, A. Obed
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引用次数: 0

摘要

锂离子可充电电池被认为是目前在多种便携式电子设备中使用最多的储能电池之一。不明智地给锂离子电池充电会损坏或缩短电池寿命。充电方法有几种,但可能与制造商的建议相冲突,导致电池寿命缩短,效率降低。本文提出了一种基于厂商建议,采用CC-CV(恒流恒压)方法控制锂离子电池快速充电的自适应神经模糊推理系统(ANFIS)系统。ANFIS模型已经根据制造商的建议进行了培训。仿真结果表明,在电流为1.28 mA(占总容量的0.044%)的情况下,该系统的精度为1.28 mA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANFIS-Based New Approach for an Optimal Lithium-Ion Battery Charging Control
Lithium-ion rechargeable batteries are considered one of the most energy storage batteries that are used in several portable electrical devices at present. Unwise charges of lithium-ion batteries can damage or reduce the battery life. Charging methods are several but may conflict with manufacturers- recommendations which leads to shortening battery life and reduced efficiency. This paper presents an Adaptive neuro-fuzzy inference system (ANFIS) system that controls fast charging based on manufacturer recommendations using the CC-CV (Constant Current and Constant Voltage) method on Lithium-Ion batteries. The ANFIS model has been trained based on the recommendations of the manufacturer. Based on the simulation results, the proposed system offers accuracy in the matter of current is 1.28 mA (0.044% of the total capacity).
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