WeChat Mini Program
Old Version Features

Life Prediction Model and Performance Degradation of Lithium-Ion Battery under Different Cut-Off Voltages

SOLID STATE IONICS(2025)

Yancheng Inst Technol

Cited 0|Views8
Abstract
Battery lifetime prediction is critical to successfully introducing new products to the market, and a long testing time will affect the promotion of the product. In this paper, The prediction model of battery cycle life composed of cut-off voltages and state of health (SOH) is established based on an inverse power law equation to evaluate the NCM(811)battery. It is found that the capacity is more sensitive to the charge cut-off voltages (CCOV) than to the discharge cut-off voltages (DCOV). The capacity degrades to 67.3 % at 180th cycle in the range of 3-4.4 V, while it is 65.8 % at 380th cycle in the range of 2.5-4.2 V (the normal work voltage of battery is 3-4.2 V). The internal resistance and capacity degradation of the battery is analyzed by the incremental capacity curve and the hybrid pulse power characterization (HPPC) test. The error between prediction and measurement is less than 3 % within 400 cycles, and the model can predict the battery lifetime under different conditions (SOH, voltage). It helps to shorten the test time of new products and optimize the operating conditions of battery.
More
Translated text
Key words
Lithium-ion battery,Capacity degradation,Accelerated life test,Life prediction model
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种基于截止电压和健康状态(SOH)的锂离子电池循环寿命预测模型,通过逆幂律方程评估NCM(811)电池的性能退化,有助于缩短新产品的测试时间和优化电池的运行条件。

方法】:通过建立包含截止电压和SOH的电池循环寿命预测模型,并使用逆幂律方程进行评估。

实验】:研究分析了电池在不同截止电压下的容量衰减情况,使用增量容量曲线和混合脉冲功率特性(HPPC)测试分析了电池内部电阻和容量退化,实验数据集名称未提及,误差在400个周期内小于3%,模型能够预测不同条件下的电池寿命。