Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data

IF 13 Q1 ENERGY & FUELS
Kosaku Nakano , Sophia Vögler , Kenji Tanaka
{"title":"Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data","authors":"Kosaku Nakano ,&nbsp;Sophia Vögler ,&nbsp;Kenji Tanaka","doi":"10.1016/j.adapen.2024.100188","DOIUrl":null,"url":null,"abstract":"<div><p>The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100188"},"PeriodicalIF":13.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242400026X/pdfft?md5=d77f0e82e70762f99f4b385e9253c7d3&pid=1-s2.0-S266679242400026X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266679242400026X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.

推进电动汽车的健康状况评估:基于变压器的利用真实世界数据的方法
电动汽车(EV)的广泛应用凸显了对创新方法的迫切需求,以估算其锂离子电池的健康状况(SOH),这对确保安全和效率至关重要。本研究介绍了 SOH-TEC,这是一种基于变压器编码器的模型,可处理来自单次电动汽车行程的原始时间序列电池和车辆相关数据,以估算 SOH。与依赖实验室实验电池循环数据的传统方法不同,SOH-TEC 利用真实世界的电动汽车运行数据,提高了实际应用能力。该模型在近三年来从三辆电动汽车收集的真实世界数据集上进行了训练和评估。该数据集包括通过使用底盘测功机进行定期恒流全放电测试获得的可靠 SOH 标签。尽管嘈杂的电动汽车真实世界数据带来了挑战,但该模型显示出很高的准确性,平均绝对误差为 0.72%,均方根误差为 1.17%。此外,我们提出的使用未标注数据进行预训练的策略,尤其是 SOH 排序比较,显著提高了模型的性能;仅使用 50%的标注数据就能获得与使用完整数据集几乎相同的结果。自我注意力图分析表明,该模型主要侧重于静止或持续驾驶时段来估计 SOH。虽然这项研究受到以重复驾驶模式为特征的数据集的限制,但它强调了变压器在电动汽车 SOH 估算方面的巨大潜力,并为未来的数据收集和模型开发提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信