Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements

Roberta Di Fonso; Carlo Cecati; Remus Teodorescu; Daniel-Ioan Stroe; Pallavi Bharadwaj

doi:10.1109/PEDES56012.2022.10080375

Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements

Roberta Di Fonso, Carlo Cecati, Remus Teodorescu, Daniel-Ioan Stroe, Pallavi Bharadwaj

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

Having a good model for a Li-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target simulation environment. In this paper we focus on how to build a battery model using a data-driven approach. We present two different ways of creating the model: using datasheets provided by the manufacturer and using more extensive laboratory measurements. This hybrid method of using lab data on datasheet battery model is named here as advance datasheet battery model. We present a thorough report on the successful preparation of the data to be used in both models, and highlight the benefits and the disadvantages of both approaches. Furthermore, we show how the measurements-based advanced datasheet battery model is more robust to model battery behavior with 100% improvement in modeling accuracy compared to a pure datasheet based approach.

Original language	English
Title of host publication	2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)
Number of pages	6
Publisher	IEEE Communications Society
Publication date	17 Dec 2022
Pages	1-6
Article number	10080375
ISBN (Print)	978-1-6654-5567-1
DOIs	https://doi.org/10.1109/PEDES56012.2022.10080375
Publication status	Published - 17 Dec 2022
Event	2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) - Jaipur, India Duration: 14 Dec 2022 → 17 Dec 2022

Conference

Conference	2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)
Location	Jaipur, India
Period	14/12/2022 → 17/12/2022

Keywords

Lithium-ion batteries
Power measurement
Energy measurement
Predictive models
Prediction algorithms
Battery charge measurement
Power electronics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PEDES56012.2022.10080375

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Cite this

Fonso, R. D., Cecati, C., Teodorescu, R., Stroe, D-I., & Bharadwaj, P. (2022). Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements. In 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-6). Article 10080375 IEEE Communications Society. https://doi.org/10.1109/PEDES56012.2022.10080375

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title = "Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements",

abstract = "Having a good model for a Li-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target simulation environment. In this paper we focus on how to build a battery model using a data-driven approach. We present two different ways of creating the model: using datasheets provided by the manufacturer and using more extensive laboratory measurements. This hybrid method of using lab data on datasheet battery model is named here as advance datasheet battery model. We present a thorough report on the successful preparation of the data to be used in both models, and highlight the benefits and the disadvantages of both approaches. Furthermore, we show how the measurements-based advanced datasheet battery model is more robust to model battery behavior with 100% improvement in modeling accuracy compared to a pure datasheet based approach.",

keywords = "Lithium-ion batteries, Power measurement, Energy measurement, Predictive models, Prediction algorithms, Battery charge measurement, Power electronics",

author = "Fonso, {Roberta Di} and Carlo Cecati and Remus Teodorescu and Daniel-Ioan Stroe and Pallavi Bharadwaj",

year = "2022",

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doi = "10.1109/PEDES56012.2022.10080375",

language = "English",

isbn = "978-1-6654-5567-1",

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booktitle = "2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)",

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note = "2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) ; Conference date: 14-12-2022 Through 17-12-2022",

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Fonso, RD, Cecati, C, Teodorescu, R , Stroe, D-I & Bharadwaj, P 2022, Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements. in 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)., 10080375, IEEE Communications Society, pp. 1-6, 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 14/12/2022. https://doi.org/10.1109/PEDES56012.2022.10080375

Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements. / Fonso, Roberta Di; Cecati, Carlo; Teodorescu, Remus et al.
2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). IEEE Communications Society, 2022. p. 1-6 10080375.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

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AU - Fonso, Roberta Di

AU - Cecati, Carlo

AU - Teodorescu, Remus

AU - Stroe, Daniel-Ioan

AU - Bharadwaj, Pallavi

PY - 2022/12/17

Y1 - 2022/12/17

N2 - Having a good model for a Li-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target simulation environment. In this paper we focus on how to build a battery model using a data-driven approach. We present two different ways of creating the model: using datasheets provided by the manufacturer and using more extensive laboratory measurements. This hybrid method of using lab data on datasheet battery model is named here as advance datasheet battery model. We present a thorough report on the successful preparation of the data to be used in both models, and highlight the benefits and the disadvantages of both approaches. Furthermore, we show how the measurements-based advanced datasheet battery model is more robust to model battery behavior with 100% improvement in modeling accuracy compared to a pure datasheet based approach.

AB - Having a good model for a Li-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target simulation environment. In this paper we focus on how to build a battery model using a data-driven approach. We present two different ways of creating the model: using datasheets provided by the manufacturer and using more extensive laboratory measurements. This hybrid method of using lab data on datasheet battery model is named here as advance datasheet battery model. We present a thorough report on the successful preparation of the data to be used in both models, and highlight the benefits and the disadvantages of both approaches. Furthermore, we show how the measurements-based advanced datasheet battery model is more robust to model battery behavior with 100% improvement in modeling accuracy compared to a pure datasheet based approach.

KW - Lithium-ion batteries

KW - Power measurement

KW - Energy measurement

KW - Predictive models

KW - Prediction algorithms

KW - Battery charge measurement

KW - Power electronics

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DO - 10.1109/PEDES56012.2022.10080375

M3 - Article in proceeding

SN - 978-1-6654-5567-1

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EP - 6

BT - 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

PB - IEEE Communications Society

T2 - 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

Y2 - 14 December 2022 through 17 December 2022

ER -

Data-driven Modeling of Li-ion Battery based on the Manufacturer Specifications and Laboratory Measurements

Abstract

Conference

Keywords

UN SDGs

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