Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy

This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load...

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Autores principales:	Seongwan Kim, Jongsu Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hybrid system neural network energy management system optimal energy control equivalent consumption minimization strategy Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/f52375ede2a24c34ad575bf8299dcd54
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spelling	oai:doaj.org-article:f52375ede2a24c34ad575bf8299dcd542021-11-25T18:04:31ZOptimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy10.3390/jmse91112282077-1312https://doaj.org/article/f52375ede2a24c34ad575bf8299dcd542021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1228https://doaj.org/toc/2077-1312This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load of the ship and the state of charge of the battery satisfy the target of energy optimization. Based on the rules, the load of the ship and the state of charge of the battery were the input in the neural network, and the outputs of two generators were recorded as the output values of the neural network. To optimize the weights of the neural network and reduce the error between the predicted values and results, the Bayesian regularization method was employed, and a single hidden layer with 20 nodes, 2 input layers, and 2 output layers were considered. For the hidden layer, the tansigmoid function was applied, and for the activation functions of the output layers, linear functions were adopted considering the correlation between the input and output data used for training the neural network. The propulsion motor was fitted with a speed controller to ensure a stable speed, and a torque load was applied on the propulsion motor. To verify the accuracy of the neural network learning, a generator–battery hybrid system simulation was conducted using MATLAB Simulink, and the neural network learned values were compared with the generator output command values obtained based on the load of the ship and the battery state of charge. Additionally, it was confirmed that the generator command values were consistent with the neural network learned values, and the stability of the system was maintained by controlling the speed, voltage, and current control of the propulsion motor under various loads of the ship and different battery charge statuses.Seongwan KimJongsu KimMDPI AGarticlehybrid systemneural networkenergy management systemoptimal energy controlequivalent consumption minimization strategyNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1228, p 1228 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid system neural network energy management system optimal energy control equivalent consumption minimization strategy Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	hybrid system neural network energy management system optimal energy control equivalent consumption minimization strategy Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Seongwan Kim Jongsu Kim Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
description	This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load of the ship and the state of charge of the battery satisfy the target of energy optimization. Based on the rules, the load of the ship and the state of charge of the battery were the input in the neural network, and the outputs of two generators were recorded as the output values of the neural network. To optimize the weights of the neural network and reduce the error between the predicted values and results, the Bayesian regularization method was employed, and a single hidden layer with 20 nodes, 2 input layers, and 2 output layers were considered. For the hidden layer, the tansigmoid function was applied, and for the activation functions of the output layers, linear functions were adopted considering the correlation between the input and output data used for training the neural network. The propulsion motor was fitted with a speed controller to ensure a stable speed, and a torque load was applied on the propulsion motor. To verify the accuracy of the neural network learning, a generator–battery hybrid system simulation was conducted using MATLAB Simulink, and the neural network learned values were compared with the generator output command values obtained based on the load of the ship and the battery state of charge. Additionally, it was confirmed that the generator command values were consistent with the neural network learned values, and the stability of the system was maintained by controlling the speed, voltage, and current control of the propulsion motor under various loads of the ship and different battery charge statuses.
format	article
author	Seongwan Kim Jongsu Kim
author_facet	Seongwan Kim Jongsu Kim
author_sort	Seongwan Kim
title	Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
title_short	Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
title_full	Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
title_fullStr	Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
title_full_unstemmed	Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy
title_sort	optimal energy control of battery hybrid system for marine vessels by applying neural network based on equivalent consumption minimization strategy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f52375ede2a24c34ad575bf8299dcd54
work_keys_str_mv	AT seongwankim optimalenergycontrolofbatteryhybridsystemformarinevesselsbyapplyingneuralnetworkbasedonequivalentconsumptionminimizationstrategy AT jongsukim optimalenergycontrolofbatteryhybridsystemformarinevesselsbyapplyingneuralnetworkbasedonequivalentconsumptionminimizationstrategy
_version_	1718411718441304064

Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy

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