WeChat Mini Program
Old Version Features

A Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

World electric vehicle journal(2021)

Hunan Univ

Cited 9|Views4
Abstract
Due to the complex changes in battery state, the accurate and fast estimation of battery state of charge (SOC) is still a great challenge. Here, a fast estimation method of battery impedance and SOC based on a multi-level PI observer is proposed. The observer model reflects the change of the battery state characteristics through the dynamic impedance, and then the system compensation factor is added to the observer to dynamically adjust the parameters of the battery model. The effectiveness of the algorithm is verified by the compound dynamic stress test (DST) experiment. The results show that the introduction of the compensation factor enables the system to tolerate a certain degree of impedance fluctuation and capacity attenuation and the maximum SOC estimation error can be kept within 2%.
More
Translated text
Key words
battery,state of charge,impedance,state observer,compensation factor
求助PDF
上传PDF
Bibtex
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
Summary is being generated by the instructions you defined