http://iet.metastore.ingenta.com
1887

Machine learning an alternate technique to estimate the state of charge of energy storage devices

Machine learning an alternate technique to estimate the state of charge of energy storage devices

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
— Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.2677
Loading

Related content

content/journals/10.1049/el.2017.2677
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address