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State of Charge Estimation of Lithium-Ion Batteries Employing Deep Neural Network with Variable Learning Rate
https://orcid.org/http://orcid.org/0000-0003-3483-2377
Deep learning (DL) has gained a lot of attention in the domain of estimating the State of Charge (SoC) of lithium-ion batteries used in electric vehicles (EVs). However, it is still challenging to develop an estimation model that is accurate, trustworthy, and low cost to compute. This research proposes a deep neural network (DNN) employing different learning rate optimization strategies. The proposed approach is compared with the conventional learning rate-based strategy. Further, existing and well-established neural networks, namely long short-term memory, bi-directional long short-term memory, and gated recurrent unit are employed and tested under identical conditions. The proposed architecture is trained and tested using different dynamic discharge profiles. The computational cost and the results of various performance metrics show the accuracy of the proposed approach.
State of Charge Estimation of Lithium-Ion Batteries Employing Deep Neural Network with Variable Learning Rate
https://orcid.org/http://orcid.org/0000-0003-3483-2377
Deep learning (DL) has gained a lot of attention in the domain of estimating the State of Charge (SoC) of lithium-ion batteries used in electric vehicles (EVs). However, it is still challenging to develop an estimation model that is accurate, trustworthy, and low cost to compute. This research proposes a deep neural network (DNN) employing different learning rate optimization strategies. The proposed approach is compared with the conventional learning rate-based strategy. Further, existing and well-established neural networks, namely long short-term memory, bi-directional long short-term memory, and gated recurrent unit are employed and tested under identical conditions. The proposed architecture is trained and tested using different dynamic discharge profiles. The computational cost and the results of various performance metrics show the accuracy of the proposed approach.
State of Charge Estimation of Lithium-Ion Batteries Employing Deep Neural Network with Variable Learning Rate
https://orcid.org/http://orcid.org/0000-0003-3483-2377
Deep learning (DL) has gained a lot of attention in the domain of estimating the State of Charge (SoC) of lithium-ion batteries used in electric vehicles (EVs). However, it is still challenging to develop an estimation model that is accurate, trustworthy, and low cost to compute. This research proposes a deep neural network (DNN) employing different learning rate optimization strategies. The proposed approach is compared with the conventional learning rate-based strategy. Further, existing and well-established neural networks, namely long short-term memory, bi-directional long short-term memory, and gated recurrent unit are employed and tested under identical conditions. The proposed architecture is trained and tested using different dynamic discharge profiles. The computational cost and the results of various performance metrics show the accuracy of the proposed approach.
State of Charge Estimation of Lithium-Ion Batteries Employing Deep Neural Network with Variable Learning Rate
J. Inst. Eng. India Ser. B
Madhavan Namboothiri, Kannan (Autor:in) / K., Sundareswaran (Autor:in) / Nayak, P. Srinivasa Rao (Autor:in) / Simon, Sishaj P (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 104 ; 277-284
01.02.2023
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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