A comprehensive model for battery State of Charge prediction

Bart  Homan; Gerardus Johannes Maria Smit; Richard Pieter van Leeuwen; Marnix Ten Kortenaar

doi:10.1109/PTC.2017.7980943

A comprehensive model for battery State of Charge prediction

Bart Homan, Gerardus Johannes Maria Smit, Richard Pieter van Leeuwen, Marnix Ten Kortenaar (Contributor)

Research output: Contribution to conference › Paper › Academic › peer-review

8 Citations (Scopus)

1480 Downloads (Pure)

Abstract

In this paper the relatively simple model for State of Charge prediction, based on energy conservation, introduced in [1] is improved and verified. The model as introduced in [1] is verified for Pb-acid, Li-ion and Seasalt batteries. The model is further improved to accommodate the rate capacity effect and the capacity recovery effect, the improvements are verified with lead-acid batteries. For further verification the model is applied on a realistic situation and compared to measurements on the behavior of a real battery in that situation. Furthermore the results are compared to results of the well-established KiBaM model. Predictions on the SoC over time done using the proposed model closely follow the SoC over time calculated from measured data.
The resulting improved model is both simple and effective, making it specially useful as part of smart control, and energy usage simulations.

Original language	English
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/PTC.2017.7980943
Publication status	Published - 20 Jul 2017
Event	12th IEEE PES PowerTech Conference: Towards and Beyond Sustainable Energy Systems - University of Manchester, Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jul 2017 Conference number: 12 http://ieee-powertech.org/

Conference

Conference	12th IEEE PES PowerTech Conference
Abbreviated title	PowerTech 2017
Country/Territory	United Kingdom
City	Manchester
Period	18/06/17 → 22/07/17
Internet address	http://ieee-powertech.org/

Keywords

Storage
Predictive model
Smart grid
Energy management

Access to Document

10.1109/PTC.2017.7980943Licence: Unspecified

A comprehensive model for battery State of Charge predictionAccepted author version, 3.16 MBLicence: Unspecified

Cite this

@conference{a892077300d9493d8356557e2452a603,

title = "A comprehensive model for battery State of Charge prediction",

abstract = "In this paper the relatively simple model for State of Charge prediction, based on energy conservation, introduced in [1] is improved and verified. The model as introduced in [1] is verified for Pb-acid, Li-ion and Seasalt batteries. The model is further improved to accommodate the rate capacity effect and the capacity recovery effect, the improvements are verified with lead-acid batteries. For further verification the model is applied on a realistic situation and compared to measurements on the behavior of a real battery in that situation. Furthermore the results are compared to results of the well-established KiBaM model. Predictions on the SoC over time done using the proposed model closely follow the SoC over time calculated from measured data.The resulting improved model is both simple and effective, making it specially useful as part of smart control, and energy usage simulations.",

keywords = "Storage, Predictive model, Smart grid, Energy management",

author = "Bart Homan and Smit, {Gerardus Johannes Maria} and {van Leeuwen}, {Richard Pieter} and {Ten Kortenaar}, Marnix",

year = "2017",

month = jul,

day = "20",

doi = "10.1109/PTC.2017.7980943",

language = "English",

pages = "1--6",

note = "12th IEEE PES PowerTech Conference : Towards and Beyond Sustainable Energy Systems, PowerTech 2017 ; Conference date: 18-06-2017 Through 22-07-2017",

url = "http://ieee-powertech.org/",

}

TY - CONF

T1 - A comprehensive model for battery State of Charge prediction

AU - Homan, Bart

AU - Smit, Gerardus Johannes Maria

AU - van Leeuwen, Richard Pieter

A2 - Ten Kortenaar, Marnix

N1 - Conference code: 12

PY - 2017/7/20

Y1 - 2017/7/20

N2 - In this paper the relatively simple model for State of Charge prediction, based on energy conservation, introduced in [1] is improved and verified. The model as introduced in [1] is verified for Pb-acid, Li-ion and Seasalt batteries. The model is further improved to accommodate the rate capacity effect and the capacity recovery effect, the improvements are verified with lead-acid batteries. For further verification the model is applied on a realistic situation and compared to measurements on the behavior of a real battery in that situation. Furthermore the results are compared to results of the well-established KiBaM model. Predictions on the SoC over time done using the proposed model closely follow the SoC over time calculated from measured data.The resulting improved model is both simple and effective, making it specially useful as part of smart control, and energy usage simulations.

AB - In this paper the relatively simple model for State of Charge prediction, based on energy conservation, introduced in [1] is improved and verified. The model as introduced in [1] is verified for Pb-acid, Li-ion and Seasalt batteries. The model is further improved to accommodate the rate capacity effect and the capacity recovery effect, the improvements are verified with lead-acid batteries. For further verification the model is applied on a realistic situation and compared to measurements on the behavior of a real battery in that situation. Furthermore the results are compared to results of the well-established KiBaM model. Predictions on the SoC over time done using the proposed model closely follow the SoC over time calculated from measured data.The resulting improved model is both simple and effective, making it specially useful as part of smart control, and energy usage simulations.

KW - Storage

KW - Predictive model

KW - Smart grid

KW - Energy management

U2 - 10.1109/PTC.2017.7980943

DO - 10.1109/PTC.2017.7980943

M3 - Paper

SP - 1

EP - 6

T2 - 12th IEEE PES PowerTech Conference

Y2 - 18 June 2017 through 22 July 2017

ER -

A comprehensive model for battery State of Charge prediction

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this