{"title":"多维机器学习框架:准确高效地估算电池充电状态","authors":"","doi":"10.1016/j.jpowsour.2024.235417","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate state of charge (SOC) estimation is essential for battery safe and efficient utilization. As artificial intelligence technologies evolve, data-driven methods have become mainstream for estimating SOC. However, the technique can significantly deteriorate model performance when encountering poor or insufficient data quality. In this paper, we apply median filtering to eliminate extreme noise and utilize continuous wavelet transform to extract time-frequency features from voltage signals. Additionally, we generate novel features via feature crossing. We then apply dimensionality reduction via the random forest method to decrease computational expense. Finally, we select a convolutional neural network (CNN) as the base model to learn optimized features for more precise SOC estimation. To confirm the efficacy of our proposed method, this study compares it with CNN, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and a CNN-BILSTM model combined with an attention mechanism. These comparisons are conducted under different temperatures and operating conditions. The results indicate that this method achieves a mean absolute error and a root mean square error of less than 2.89 % and 3.71 %, respectively, in SOC estimation, demonstrating superior accuracy compared to other models. This study underscores the significance of feature engineering techniques in SOC estimation.</p></div>","PeriodicalId":377,"journal":{"name":"Journal of Power Sources","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-dimensional machine learning framework for accurate and efficient battery state of charge estimation\",\"authors\":\"\",\"doi\":\"10.1016/j.jpowsour.2024.235417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate state of charge (SOC) estimation is essential for battery safe and efficient utilization. As artificial intelligence technologies evolve, data-driven methods have become mainstream for estimating SOC. However, the technique can significantly deteriorate model performance when encountering poor or insufficient data quality. In this paper, we apply median filtering to eliminate extreme noise and utilize continuous wavelet transform to extract time-frequency features from voltage signals. Additionally, we generate novel features via feature crossing. We then apply dimensionality reduction via the random forest method to decrease computational expense. Finally, we select a convolutional neural network (CNN) as the base model to learn optimized features for more precise SOC estimation. To confirm the efficacy of our proposed method, this study compares it with CNN, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and a CNN-BILSTM model combined with an attention mechanism. These comparisons are conducted under different temperatures and operating conditions. The results indicate that this method achieves a mean absolute error and a root mean square error of less than 2.89 % and 3.71 %, respectively, in SOC estimation, demonstrating superior accuracy compared to other models. This study underscores the significance of feature engineering techniques in SOC estimation.</p></div>\",\"PeriodicalId\":377,\"journal\":{\"name\":\"Journal of Power Sources\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Sources\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378775324013697\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Sources","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378775324013697","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
A multi-dimensional machine learning framework for accurate and efficient battery state of charge estimation
Accurate state of charge (SOC) estimation is essential for battery safe and efficient utilization. As artificial intelligence technologies evolve, data-driven methods have become mainstream for estimating SOC. However, the technique can significantly deteriorate model performance when encountering poor or insufficient data quality. In this paper, we apply median filtering to eliminate extreme noise and utilize continuous wavelet transform to extract time-frequency features from voltage signals. Additionally, we generate novel features via feature crossing. We then apply dimensionality reduction via the random forest method to decrease computational expense. Finally, we select a convolutional neural network (CNN) as the base model to learn optimized features for more precise SOC estimation. To confirm the efficacy of our proposed method, this study compares it with CNN, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and a CNN-BILSTM model combined with an attention mechanism. These comparisons are conducted under different temperatures and operating conditions. The results indicate that this method achieves a mean absolute error and a root mean square error of less than 2.89 % and 3.71 %, respectively, in SOC estimation, demonstrating superior accuracy compared to other models. This study underscores the significance of feature engineering techniques in SOC estimation.
期刊介绍:
The Journal of Power Sources is a publication catering to researchers and technologists interested in various aspects of the science, technology, and applications of electrochemical power sources. It covers original research and reviews on primary and secondary batteries, fuel cells, supercapacitors, and photo-electrochemical cells.
Topics considered include the research, development and applications of nanomaterials and novel componentry for these devices. Examples of applications of these electrochemical power sources include:
• Portable electronics
• Electric and Hybrid Electric Vehicles
• Uninterruptible Power Supply (UPS) systems
• Storage of renewable energy
• Satellites and deep space probes
• Boats and ships, drones and aircrafts
• Wearable energy storage systems