State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications

Terala, Pranaya K.; Ogundana, Ayodeji S.; Foo, Simon Y.; Amarasinghe, Migara Y.; Zang, Huanyu

doi:10.3390/mi13091397

Open AccessArticle

State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications

by

Pranaya K. Terala

,

Ayodeji S. Ogundana

,

Simon Y. Foo

^*,

Migara Y. Amarasinghe

and

Huanyu Zang

Department of Electrical and Computer Engineering, Florida A&M University-Florida State University, 2525 Pottsdamer St., Tallahassee, FL 32310, USA

^*

Author to whom correspondence should be addressed.

Micromachines 2022, 13(9), 1397; https://doi.org/10.3390/mi13091397

Submission received: 12 August 2022 / Revised: 22 August 2022 / Accepted: 23 August 2022 / Published: 26 August 2022

(This article belongs to the Topic Energy Equipment and Condition Monitoring)

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Abstract

Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture.

Keywords: machine learning; energy storage; state-of-charge estimation; bi-directional LSTM; encoder–decoder hybrid; robust estimator; deep neural network

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MDPI and ACS Style

Terala, P.K.; Ogundana, A.S.; Foo, S.Y.; Amarasinghe, M.Y.; Zang, H. State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications. Micromachines 2022, 13, 1397. https://doi.org/10.3390/mi13091397

AMA Style

Terala PK, Ogundana AS, Foo SY, Amarasinghe MY, Zang H. State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications. Micromachines. 2022; 13(9):1397. https://doi.org/10.3390/mi13091397

Chicago/Turabian Style

Terala, Pranaya K., Ayodeji S. Ogundana, Simon Y. Foo, Migara Y. Amarasinghe, and Huanyu Zang. 2022. "State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications" Micromachines 13, no. 9: 1397. https://doi.org/10.3390/mi13091397

APA Style

Terala, P. K., Ogundana, A. S., Foo, S. Y., Amarasinghe, M. Y., & Zang, H. (2022). State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications. Micromachines, 13(9), 1397. https://doi.org/10.3390/mi13091397

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications

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