Machine learning an alternate technique to estimate the state of charge of energy storage devices
文献类型：期刊论文
| 作者 | Taimoor Zahid; Kun Xu; Weimin Li |
| 刊名 | Electronics Letters
 |
| 出版日期 | 2017 |
| 文献子类 | 期刊论文 |
| 英文摘要 | State of charge (SOC) estimation plays a critical role in the operation of an electric vehicle (EV) power battery. In this Letter, the authors propose machine learning (ML) algorithms as alternate to the existing filtering algorithms used for SOC estimation of an EV battery. The SOC estimation approach is evaluated by the simulation experiments in advanced vehicle simulator (ADVISOR). For the modelling of ML algorithms, the input parameters that affect the SOC estimation are battery current, battery module temperature, power out of the battery (available and requested), battery power loss and heat removed from the battery. Training and testing stages of the models are carried out using the data collected from ADVISOR. As the drive cycle conditions provided by ADVISOR are universal therefore present method is applicable to all kinds of batteries used in EVs including lithium ion, nickel metal hydride and lead acid batteries. Thus, the proposed models for SOC estimation provide an alternative approach in SOC estimation. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| WOS记录号 | WOS:000419110300025 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11583]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | Electronics Letters |
| 推荐引用方式 GB/T 7714 | Taimoor Zahid,Kun Xu, Weimin Li . Machine learning an alternate technique to estimate the state of charge of energy storage devices[J]. Electronics Letters,2017. |
| APA | Taimoor Zahid,Kun Xu,& Weimin Li .(2017).Machine learning an alternate technique to estimate the state of charge of energy storage devices.Electronics Letters. |
| MLA | Taimoor Zahid,et al."Machine learning an alternate technique to estimate the state of charge of energy storage devices".Electronics Letters (2017). |
入库方式： OAI收割
来源：深圳先进技术研究院
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