在电动汽车电池中使用群优化与开放式学习进行增强的充电状态估计
Muhammad Zeshan Afzal1, Fushuan Wen2, Nimrah Saeed3
1College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China. 12110095@zju.edu.cn.
Scientific reports
|March 29, 2025
概括
本研究引入了一种新方法,通过将群优化 (CSO) 与开放式学习 (OEL) 结合起来,来估计电动汽车 (EV) 电池的充电状态 (SoC). 这种混合方法提高了准确性和电池寿命.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 电动汽车 (EV) 作为可持续的交通工具越来越受欢迎.
- 有效的充电状态 (SoC) 管理对于电动汽车电池的性能和寿命至关重要.
- 准确和快速的SoC估计对于优化电动汽车电池运行至关重要.
研究的目的:
- 开发一种创新的方法,用于对电动汽车电池的SoC估计.
- 通过改进的SoC管理来提高EV电池性能和延长电池寿命.
- 将群优化 (CSO) 与开放式学习 (OEL) 集成在一起,以进行高级 SoC 估计.
主要方法:
- 在电动汽车电池中用于SoC估计的CSO和OEL的混合化.
- 利用民间社会组织的集体智能和OEL的自适应性学习能力.
- 动态适应不同的环境和用户行为条件,以进行强大的SoC估计.
主要成果:
- 进行了广泛的模拟,以评估拟议的CSO-OEL方法.
- 与传统优化方法的性能比较显示出优异的结果.
- 观察到充电和放电效率的显著提高.
结论:
- CSO-OEL集成为EV电池SoC估计提供了强大而准确的方法.
- 这种方法促进了更顺的电力传输,并减少了电池的整体磨损.
- 这种方法有助于优化电动汽车电池性能并最大限度地延长使用寿命.
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