基于云神经网络的锂电池放电容量预测

Jing Wan, Qingdong Li
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引用次数: 1

摘要

锂电池放电容量预测是电池管理系统的主要任务之一。锂电池的放电容量与许多参数有关,包括放电电流、电压、温度以及过去的充放电历史。现有的锂电池放电容量预测方法大多缺乏学习能力和非线性预测能力,为了更准确地预测锂电池的放电容量,提出了一种基于云神经网络(CNN)的预测算法。在分析NASA实际数据的基础上,确定放电容量的相关影响因素,利用云模型建立相应的CNN预测模型,并利用云模型对学习速度进行自适应调整。仿真结果表明,与传统的神经网络方法相比,CNN预测模型具有较小的预测误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Discharge Capacity of Lithium Battery Based on Cloud Neural Network
The prediction of discharge capacity of lithium batteries was one of the main tasks of battery management system. The discharge capacity of lithium batteries was related with many parameters, including discharge current, voltage, temperature, and the past charge and discharge history. The prediction methods of existing lithium battery discharge capacity mostly have no learning capabilities and nonlinear prediction ability, in order to predict the discharge capacity of lithium battery more accurately, an algorithm Based on cloud neural network (CNN) was presented. On the basis of the analysis of the actual data of NASA, determine the related influence factors of discharge capacity, set up a corresponding CNN prediction model using cloud model, and use the cloud model for adaptive adjustment of the learning speed. Comparing with the traditional NN method, the simulation result demonstrates that the CNN prediction model has smaller prediction error.
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