Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network

Sakshi Sharma; Pankaj Dilip Achlerkar; Prashant Shrivastava; Akhil Garg; Bijaya Ketan Panigrahi

doi:10.1109/PEDES56012.2022.10080110

Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network

Sakshi Sharma, Pankaj Dilip Achlerkar, Prashant Shrivastava, Akhil Garg, Bijaya Ketan Panigrahi

Indian Institute of Technology Delhi

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

7 Citations (Scopus)

Abstract

The estimation of state of charge (SoC) and state of energy (SoE) serves the premise of an efficient Battery Management System(BMS). The estimation technique should be able to capture the dynamics the battery is subjected to, along with its inherent non-linear behaviour. This study proposes a combined SoC and SoE estimation framework using multi-layer feedforward neural network. The experimental results validate the higher accuracy and robustness of the proposed method under dynamic driving and temperature conditions. The Mean Square Error(MSE) obtained during the testing of the algorithm with various drive cycles is found to be quite promising.

Original language	English
Title of host publication	10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665455664
DOIs	https://doi.org/10.1109/PEDES56012.2022.10080110
Publication status	Published - 2022
Externally published	Yes
Event	10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022 - Jaipur, India Duration: 14 Dec 2022 → 17 Dec 2022

Publication series

Name	10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022

Conference

Conference	10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
Country/Territory	India
City	Jaipur
Period	14/12/22 → 17/12/22

Keywords

Li-ion battery(LIB)
Neural network(NN)
State of Charge(SoC)
State of Energy(SoE)

UN SDGs

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

Access to Document

10.1109/PEDES56012.2022.10080110

Cite this

Sharma, S., Achlerkar, P. D., Shrivastava, P., Garg, A., & Panigrahi, B. K. (2022). Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network. In 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022 (10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PEDES56012.2022.10080110

Sharma, Sakshi ; Achlerkar, Pankaj Dilip ; Shrivastava, Prashant et al. / Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network. 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022).

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title = "Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network",

abstract = "The estimation of state of charge (SoC) and state of energy (SoE) serves the premise of an efficient Battery Management System(BMS). The estimation technique should be able to capture the dynamics the battery is subjected to, along with its inherent non-linear behaviour. This study proposes a combined SoC and SoE estimation framework using multi-layer feedforward neural network. The experimental results validate the higher accuracy and robustness of the proposed method under dynamic driving and temperature conditions. The Mean Square Error(MSE) obtained during the testing of the algorithm with various drive cycles is found to be quite promising.",

keywords = "Li-ion battery(LIB), Neural network(NN), State of Charge(SoC), State of Energy(SoE)",

author = "Sakshi Sharma and Achlerkar, {Pankaj Dilip} and Prashant Shrivastava and Akhil Garg and Panigrahi, {Bijaya Ketan}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022 ; Conference date: 14-12-2022 Through 17-12-2022",

year = "2022",

doi = "10.1109/PEDES56012.2022.10080110",

language = "English",

series = "10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022",

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Sharma, S, Achlerkar, PD, Shrivastava, P, Garg, A & Panigrahi, BK 2022, Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network. in 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022. 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022, Institute of Electrical and Electronics Engineers Inc., 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022, Jaipur, India, 14/12/22. https://doi.org/10.1109/PEDES56012.2022.10080110

Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network. / Sharma, Sakshi; Achlerkar, Pankaj Dilip; Shrivastava, Prashant et al.
10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network

AU - Sharma, Sakshi

AU - Achlerkar, Pankaj Dilip

AU - Shrivastava, Prashant

AU - Garg, Akhil

AU - Panigrahi, Bijaya Ketan

PY - 2022

Y1 - 2022

N2 - The estimation of state of charge (SoC) and state of energy (SoE) serves the premise of an efficient Battery Management System(BMS). The estimation technique should be able to capture the dynamics the battery is subjected to, along with its inherent non-linear behaviour. This study proposes a combined SoC and SoE estimation framework using multi-layer feedforward neural network. The experimental results validate the higher accuracy and robustness of the proposed method under dynamic driving and temperature conditions. The Mean Square Error(MSE) obtained during the testing of the algorithm with various drive cycles is found to be quite promising.

AB - The estimation of state of charge (SoC) and state of energy (SoE) serves the premise of an efficient Battery Management System(BMS). The estimation technique should be able to capture the dynamics the battery is subjected to, along with its inherent non-linear behaviour. This study proposes a combined SoC and SoE estimation framework using multi-layer feedforward neural network. The experimental results validate the higher accuracy and robustness of the proposed method under dynamic driving and temperature conditions. The Mean Square Error(MSE) obtained during the testing of the algorithm with various drive cycles is found to be quite promising.

KW - Li-ion battery(LIB)

KW - Neural network(NN)

KW - State of Charge(SoC)

KW - State of Energy(SoE)

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BT - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022

Y2 - 14 December 2022 through 17 December 2022

ER -

Sharma S, Achlerkar PD, Shrivastava P, Garg A, Panigrahi BK. Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network. In 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022). doi: 10.1109/PEDES56012.2022.10080110

Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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