摘要
提出一种面向微控制单元(MCU)的轻量化极限学习机(ELM),并将其应用于锂电池健康状态(SOH)预测与估计。通过对锂电池老化数据的分析和研究,对传统的ELM算法进行了一系列的改进,以提升其计算效率降低其对硬件资源的占用。基于NASA数据集在不同型号的电池、多种编程语言与各种MCU上进行了实验。结果表明:该方法可以实现高精度、高效率的锂电池SOH估计。
A lightweight Extreme Learning Machine(ELM)tailored for Microcontroller Units(MCU)has been proposed,with a specific application in lithium battery State of Health(SOH)prediction.By analyzing data on lithium battery aging,this study introduces several enhancements to the conventional ELM,aimed at enhancing its computational efficiency and minimizing hardware resource consumption.Utilizing the NASA dataset,extensive experiments were conducted across different battery types,multiple programming languages,and a variety of MCUs.The results demonstrate that our proposed methodology is capable of delivering highly accurate and efficient lithium battery SOH estimations.
作者
郭肖勇
严玮演
李勇进
常淑敏
GUO Xiaoyong;YAN Weiyan;LI Yongjin;CHANG Shumin(College of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300457,China)
出处
《华南师范大学学报(自然科学版)》
北大核心
2024年第6期44-50,共7页
Journal of South China Normal University(Natural Science Edition)
基金
国家自然科学基金项目(72104177,11604240)。
关键词
电池健康状态
碳中和
极限学习机
battery state of health
carbon neutral
extreme learning machine
