NDLI: A machine learning approach to predict future power demand in real-time for a battery operated car

Please wait, while we are loading the content...

A machine learning approach to predict future power demand in real-time for a battery operated car

Content Provider	IEEE Xplore Digital Library
Author	Pradhan, S. Roychaudhury, J.
Copyright Year	2014
Description	Author affiliation: Embedded Syst. Lab., Central Mech. Eng. Res. Inst., Durgapur, India (Pradhan, S.; Roychaudhury, J.)
Abstract	For any battery-employed system, it is essential for the battery management system to correctly predict the present operational condition of the battery. The fail safe operation of a safety critical system like battery operated car or any other lifesaving systems are heavily depend upon earlier prediction of battery life. SOC or State-of-Charge estimation is one of the well-known method to predict the runtime of a battery. Various approaches are adapted by automotive society to correctly predict the runtime or the SOC of a battery like Kalman filter, UKF and many others. This paper proposes a new approach, the method of regression to predict the future power demand of a car while running on the road. The aim is to identify that, the battery will support the run of the car in next 10 seconds or not. The runtime prediction of a battery, not only depends upon the starting SOC but also depends upon other factors like battery health and road profile imposed. To overcome this type of difficulties the self-corrective regression model is proposed and implemented. Experiments performed on different road profiles, validate demanded power by the car in up-coming 10 seconds of its run. The major problem of SoC estimation is to determine initial SoC of a battery. Extensive experiments needed to calculate the initial SoC and which may also vary with the life of the battery. The novelty of this work shows, the method to predict the future power demand by updating its model parameters and without any initial SoC calculation. Model parameters are updated by the introducing new current and voltage sample in the model.
Sponsorship	IEEE Bangalore Sect. CE Chapt.
Starting Page	49
Ending Page	56
File Size	492273
Page Count	8
File Format	PDF
ISBN	9789332902640
DOI	10.1109/IMPETUS.2014.6775877
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	PES Institute of Technology, Bangalore
Subject Keyword	Data Driven Prognostic Model End of Discharge (EoD) Roads Polynomials Data models Batteries System-on-chip State Of Charge (SOC) Mathematical model
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine

Li-ion battery model exploring by intermittent discharging

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

The model of electromotive force-impedance estimating the SOC of battery online

Control of solar system's battery voltage based on state of charge estimation (SOC)

Analysis of the operating life for battery-operated wireless sensor nodes

Simulation and analysis of battery performance of a solar car using a model implemented in Simulink

Lithium-ion battery state of charge estimation using a fractional battery model

Offline Battery Pack Model optimization

A machine learning approach to predict future power demand in real-time for a battery operated car

Similar Documents

Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine

Li-ion battery model exploring by intermittent discharging

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

The model of electromotive force-impedance estimating the SOC of battery online

Control of solar system's battery voltage based on state of charge estimation (SOC)

Analysis of the operating life for battery-operated wireless sensor nodes

Simulation and analysis of battery performance of a solar car using a model implemented in Simulink

Lithium-ion battery state of charge estimation using a fractional battery model

Offline Battery Pack Model optimization

A machine learning approach to predict future power demand in real-time for a battery operated car