Yufang Lu , Jiazhen Lin , Dongxu Guo , Jingzhao Zhang , Chen Wang , Guannan He , Minggao Ouyang
{"title":"实现真实世界的健康状况评估:第 1 部分:使用锂离子电池实验室数据的电池级方法","authors":"Yufang Lu , Jiazhen Lin , Dongxu Guo , Jingzhao Zhang , Chen Wang , Guannan He , Minggao Ouyang","doi":"10.1016/j.etran.2024.100338","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data\",\"authors\":\"Yufang Lu , Jiazhen Lin , Dongxu Guo , Jingzhao Zhang , Chen Wang , Guannan He , Minggao Ouyang\",\"doi\":\"10.1016/j.etran.2024.100338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.</p></div>\",\"PeriodicalId\":36355,\"journal\":{\"name\":\"Etransportation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.0000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Etransportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590116824000286\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116824000286","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data
Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.
期刊介绍:
eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation.
The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment.
Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.