State of charge estimation method for lithium-ion batteries in all-electric ships based on LSTM neural network

• Autorzy: Geng Pan , Xu Xiaoyan , Tarasiuk Tomasz

Identyfikatory

DOI

10.2478/pomr-2020-0051

• Języki publikacji: EN

Abstrakty

EN

All-electric ships (AES) are considered an effective solution for reducing greenhouse gas emissions as they are a platform to use clean energy sources such as lithium-ion batteries, fuel cells and solar cells instead of fossil fuel. Even though these batteries are a promising alternative, the accuracy of the battery state of charge (SOC) estimation is a critical factor for their safe and reliable operation. The SOC is a key indicator of battery residual capacity. Its estimation can effectively prevent battery over-discharge and over-charge. Next, this enables reliable estimation of the operation time of fully electric ferries, where little time is spent at the harbour, with limited time available for charging. Thus, battery management systems are essential. This paper presents a neural network model of battery SOC estimation, using a long short-term memory (LSTM) recurrent neural network (RNN) as a method for accurate estimation of the SOC in lithium-ion batteries. The current, voltage and surface temperature of the batteries are used as the inputs of the neural network. The influence of different numbers of neurons in the neural network’s hidden layer on the estimation error is analysed, and the estimation error of the neural network under different training times is compared. In addition, the hidden layer is varied from 1 to 3 layers of the LSTM nucleus and the SOC estimation error is analysed. The results show that the maximum absolute SOC estimation error of the LSTM RNN is 1.96% and the root mean square error is 0.986%, which validates the feasibility of the method.

Słowa kluczowe

EN

all-electric ships; lithium-ion battery; state of charge; neural network

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2020
• Tom: nr 3
• Strony: 100--108
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Geng Pan

• Shanghai Maritime University, 1550 Hai Gang Da Dao, 201306 Shanghai, China

autor

Xu Xiaoyan

• Shanghai Maritime University, 1550 Hai Gang Da Dao, 201306 Shanghai, China

autor

Tarasiuk Tomasz

• Gdynia Maritime University, Morska, 81-225 Gdynia, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).