State of Charge Estimation Method for Lithium-Ion Batteries in All-Electric Ships Based on LSTM Neural Network

Pan Geng,Xiaoyan Xu,Tomasz Tarasiuk

doi:10.2478/pomr-2020-0051

Abstract

Abstract 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.

Highlights

Shipping is generally the most energy-effective mode of global mass cargo transportation
The use of batteries onboard vessels is growing rapidly, leading to the development of ships with hybrid power systems or fully all-electric ships (AES), which are charged when in harbour, or back zero-emission sources like fuel-cells, solar panels, thermo-electric generators, wind energy conversion systems etc. [3,4,5]. In the former, due to the limited time spent in dock, high-power wireless charging technologies are being developed [2,6]
The number of AES is increasing rapidly and they are becoming a promising tool for reducing greenhouse gas emissions and the dependency on fossil fuels [3,4]

Summary

INTRODUCTION

Shipping is generally the most energy-effective mode of global mass cargo transportation. In view of the shortcomings of the above three types of methods, this paper uses the long short-term memory (LSTM) cell based recurrent neural network (RNN) to reduce the absolute error of SOC estimation to less than 2% [27]. This paper will use the recurrent neural network with LSTM cells as a new machine learning technology, which can estimate the battery’s SOC by learning the parameters such as network weight and offset. The following formulae are used to evaluate the performance of the SOC prediction model based on the LSTM recurrent neural network: the root mean square error RMSE, the mean absolute error MAE, and the maximum absolute error MAX. LSTM neural network training selects three important factors, including the voltage, current, and battery surface temperature. Where xmax and xmin are the maximum and minimum values of the LSTM neural network input vector

RESULT

Findings

CONCLUSION