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Energy management strategy considering energy storage system degradation for hydrogen fuel cell ship

Cao Wei, Geng Pan, Xu Xiaoyan, Tarasiuk Tomasz

Polish Maritime Research

2023

nr 2

95--104

A hybrid energy system (HES) including hydrogen fuel cell systems (FCS) and a lithium-ion (Li-ion) battery energy storage system (ESS) is established for hydrogen fuel cell ships to follow fast load transients. An energy management strategy (EMS) with hierarchical control is presented to achieve proper distribution of load power and enhance system stability. In the high-control loop, a power distribution mechanism based on a particle swarm optimization algorithm (PSO) with an equivalent consumption minimization strategy (ECMS) is proposed. In the low-level control loop, an adaptive fuzzy PID controller is developed, which can quickly restore the system to a stable state by adjusting the PID parameters in real time. Compared with the rule-based EMS, hydrogen consumption is reduced by 5.319%, and the stability of the power system is significantly improved. In addition, the ESS degradation model is developed to assess its state of health (SOH). The ESS capacity loss is reduced by 2% and the daily operating cost of the ship is reduced by 1.7% compared with the PSO-ECMS without considering the ESS degradation.

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

Geng Pan, Xu Xiaoyan, Tarasiuk Tomasz

Polish Maritime Research

2020

nr 3

100-108

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.