摘要
文章建立了一种电池容量的动态预测方法,提出了滚动优化GM(1,1)模型、残差修正滚动GM(1,1)模型和Markov残差修正滚动GM(1,1)预测模型。研究结果证明了3种模型具有极好的预测性能,只是残差GM(1,1)模型的精度比其他2个低一些。且发现在仅有4个数据点建立的残差修正滚动GM(1,1)模型与Markov残差修正滚动预测模型也有相当高的预测精度。通过预测不同电池在不同充放电条件及温度条件下的电容容量,验证了滚动优化模型的普遍适用性。
A dynamic prediction method of battery capacity is proposed,and the rolling optimized GM(1,1) model,the residual error correction rolling GM(1,1) model and Markov residual error correction rolling GM(1,1) prediction model are presented. The results show that the three models have excellent prediction performance,but the residual error correction rolling GM (1,1) model has lower accuracy than the other two. It is found that the residual error correction rolling GM(1,1) model and Markov residual error correction rolling prediction model with only four data points have high prediction accuracy. The general applicability of the rolling optimization model is verified by predicting the capacitance of different batteries in different charging and discharging conditions and temperature.
作者
李守军
李光宇
马小平
LI Shoujun;LI Guangyu;MA Xiaoping(School of Mechanical and Electrical Engineering,Suqian College,Suqian 223800,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221008,China)
出处
《合肥工业大学学报（自然科学版）》
CAS
北大核心
2019年第6期763-769,共7页
Journal of Hefei University of Technology：Natural Science
基金
江苏高校品牌专业建设工程资助项目(PPZY2015C252)
宿迁市产业发展引导资金资助项目(M201612)
关键词
电池寿命
马尔科夫方法
滚动优化
残差修正
灰色预测
battery lifetime
Markov method
rolling optimization
residual error correction
grey prediction
