Search Results (26)

To address the limitations of conventional single-stack fuel cell hybrid systems using equivalent hydrogen consumption strategies, this study proposes a multi-stack energy management strategy incorporating fuel cell health degradation. Leveraging a fuel cell efficiency decay model and lithium-ion battery cycle life assessment, power distribution is reformulated as an equivalent hydrogen consumption optimization problem with stack degradation constraints. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) approach achieves global optimization. The experimental results demonstrate that compared with the Frequency Decoupling (FD) method, the GA-PSO strategy reduces hydrogen consumption by 7.03 g and operational costs by 4.78%; compared with the traditional Particle Swarm Optimization (PSO) algorithm, it reduces hydrogen consumption by 3.61 g per operational cycle and decreases operational costs by 2.66%. This strategy ensures stable operation of the marine power system while providing an economically viable solution for hybrid-powered vessels. Full article

The decarbonization of offshore infrastructures is relevant to advancing global climate goals. This study presents a renewable-based energy system tailored for the Oceanic Platform of the Canary Islands (PLOCAN), designed to achieve full energy autonomy and eliminate greenhouse gas emissions. A hybrid configuration integrating photovoltaic panels, vertical-axis wind turbines, lithium-ion batteries, a proton exchange membrane (PEM) electrolyzer, and a PEM fuel cell was developed and evaluated through detailed resource assessment, system simulation, and techno-economic analysis under real offshore constraints. The results confirm that complete decarbonization is technically feasible, with a net present cost approximately 15% lower than the current diesel-based system and a total suppression of pollutant emissions. Although the transition entails a higher initial investment, the long-term economic and environmental gains are substantial. Offshore green hydrogen emerges as a key vector for achieving energy resilience and sustainability in isolated marine infrastructures, offering a replicable pathway towards fully decarbonized ocean platforms. Full article

Driven by increasingly stringent carbon emission regulations from the International Maritime Organization (IMO), the maritime industry increasingly requires eco-friendly power systems and enhanced energy efficiency. Lithium-ion batteries, a core component of these systems, necessitate precise temperature management to ensure safety, performance, and longevity, especially under high-temperature conditions owing to the inherent risk of thermal runaway. This study proposes a sensorless temperature estimation method using a long short-term memory network. Using key parameters, including state of charge, voltage, current, C-rate, and depth of discharge, a MATLAB-based analysis program was developed to model battery dynamics. The proposed method enables real-time internal temperature estimation without physical sensors, demonstrating improved accuracy via data-driven learning. Operational data from the training vessel Hannara were used to develop an integrated organic Rankine cycle–energy storage system model, analyze factors influencing battery temperature, and inform optimized battery operation strategies. The results highlight the potential of the proposed method to enhance the safety and efficiency of shipboard battery systems, thereby contributing to the achievement of the IMO’s carbon reduction goals. Full article

The transition to environmentally sustainable maritime operations has gained urgency with the International Maritime Organization’s (IMO) 2023 GHG reduction strategy, aiming for net-zero emissions by 2050. While alternative fuels like LNG and methanol serve as transitional solutions, lithium-ion battery energy storage systems (ESSs) offer a viable low-emission alternative. However, safety concerns such as thermal runaway, overcharging, and internal faults pose significant risks to marine battery systems. This study presents an AI-based fault prediction algorithm to enhance the safety and reliability of lithium-ion battery systems used in electric propulsion ships. The research employs a Long Short-Term Memory (LSTM)-based predictive model, integrating electrochemical impedance spectroscopy (EIS) data and voltage deviation analyses to identify failure patterns. Bayesian optimization is applied to fine-tune hyperparameters, ensuring high predictive accuracy. Additionally, a recursive multi-step prediction model is developed to anticipate long-term battery performance trends. The proposed algorithm effectively detects voltage deviations and pre-emptively predicts battery failures, mitigating fire hazards and ensuring operational stability. The findings support the development of safer and more reliable energy storage solutions, contributing to the broader adoption of electric propulsion in maritime applications. Full article

An accurate prognosis of the marine lithium-ion battery capacity is significant in guiding electric ships’ optimal operation and maintenance. Under real-world operating conditions, lithium-ion batteries are exposed to various external factors, making accurate capacity prognostication a complex challenge. The paper develops a marine lithium-ion battery capacity prognostic method based on ICPO-Bi-LSTM under dynamic operating conditions to address this. First, the battery is simulated according to the actual operating conditions of an all-electric ferry, and in each charge/discharge cycle, the sum, mean, and standard deviation of each parameter (current, voltage, energy, and power) during battery charging, as well as the voltage difference before and after the simulated operating conditions, are calculated to extract a series of features that capture the complex nonlinear degradation tendency of the battery, and then a correlation analysis is performed on the extracted features to select the optimal feature set. Next, to address the challenge of determining the neural network’s hyperparameters, an improved crested porcupine optimization algorithm is proposed to identify the optimal hyperparameters for the model. Finally, to prevent the interference of test data during model training, which could lead to evaluation errors, the training dataset is used for parameter fitting, the validation dataset for hyperparameter adjustment, and the test dataset for the model performance evaluation. The experimental results demonstrate that the proposed method achieves high accuracy and robustness in capacity prognostics of lithium-ion batteries across various operating conditions and types. Full article

The inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This study examines the fault alarm system of marine battery management systems in conjunction with the unique operating conditions of ships, focusing on the system’s latency. To facilitate prompt fault detection, a fault diagnosis method based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed, utilizing the voltage data of battery clusters. Results indicate that the DBSCAN clustering algorithm demonstrates superior effectiveness and accuracy in identifying irregular battery clusters. Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. Experimental findings suggest that this model can effectively predict consistency faults and over-/under-voltage conditions based on battery cluster voltage values and corresponding fault thresholds. Full article

One of the main challenges of Lombok Island, Indonesia, is the significant disparity between peak load and base load, reaching 100 MW during peak hours, which is substantial considering the island’s specific energy dynamics. Battery energy storage systems provide power during peak times, alleviating grid stress and reducing the necessity for grid upgrades. By 2030, one of the proposed capacity development scenarios on the island involves deploying large-scale lithium-ion batteries to better manage the integration of solar generation. This paper focuses on the life cycle assessment and life cycle costing of a lithium iron phosphate large-scale battery energy storage system in Lombok to evaluate the environmental and economic impacts of this battery development scenario. This analysis considers a cradle-to-grave model and defines 10 environmental and 4 economic midpoint indicators to assess the impact of battery energy storage system integration with Lombok’s grid across manufacturing, operation, and recycling processes. From a life cycle assessment perspective, the operation subsystem contributes most significantly to global warming, while battery manufacturing is responsible for acidification, photochemical ozone formation, human toxicity, and impacts on marine and terrestrial ecosystems. Recycling processes notably affect freshwater due to their release of 4.69 × 10⁻⁴ kg of lithium. The life cycle costing results indicate that over 85% of total costs are associated with annualized capital costs at a 5% discount rate. The levelized cost of lithium iron phosphate batteries for Lombok is approximately 0.0066, demonstrating that lithium-ion batteries are an economically viable option for Lombok’s 2030 capacity development scenario. A sensitivity analysis of input data and electricity price fluctuations confirms the reliability of our results within a 20% margin of error. Moreover, increasing electricity prices for battery energy storage systems in Lombok can reduce the payback period to 3.5 years. Full article

A fire in a marine energy storage system (ESS) has a high risk because of the special situation of the sea compared with land systems. To mitigate serious damage in the event of a fire in marine ESSs, initial suppression of the fire is extremely important. In this study, a unit module-based fire extinguishing system was constructed for the initial suppression of an ESS fire, and a unit module fire suppression test was conducted. In addition, multiple modules were constructed to evaluate the impact of unit module fire suppression on adjacent modules. Novec 1230 and F-500, which are adaptable to ESS fire control, were used as extinguishing agents. The fire suppression test results showed that both extinguishing agents could effectively suppress the ESS fire in the initial stage using the proposed fire extinguishing system. The results of this study will contribute to the development of maritime safety protocols and practical measures for reinforcing preparation for ESS-related fire accidents. Full article

Lithium-ion batteries (LIBs) deployed in battery energy storage systems (BESS) can reduce the carbon intensity of the electricity-generating sector and improve environmental sustainability. The aim of this study is to use life cycle assessment (LCA) modeling, using data from peer-reviewed literature and public and private sources, to quantify environmental impacts along the supply chain for cobalt, a crucial component in many types of LIBs. The study seeks to understand where in the life cycle stage the environmental impacts are highest, thus highlighting actions that can be taken to improve sustainability of the LIB supply chain. The system boundary for this LCA is cradle-to-gate. Impact assessment follows ReCiPe Midpoint (H) 2016. We assume a 30-year modeling period, with augmentation occurring at the end of the 3rd, 7th, and 14th years of operations, before a complete replacement in the 21st year. Three refinery locations (China, Canada, and Finland), a range of ore grades, and five battery chemistries (NMC111, NMC532, NMC622, NMC811, and NCA) are used in scenarios to better estimate their effect on the life cycle impacts. Insights from the study are that impacts along nearly all pathways increase according to an inverse power-law relationship with ore grade; refining outside of China can reduce global warming potential (GWP) by over 12%; and GWP impacts for cobalt used in NCA and other NMC battery chemistries are 63% and 45–74% lower than in NMC111, respectively. When analyzed on a single-score basis, marine and freshwater ecotoxicity are prominent. For an ore grade of 0.3%, the GWP values for the Canada route decrease at a rate of 58% to 65%, and those for Finland route decrease by 71% to 76% from the base case. Statistical analysis shows that cobalt content in the battery is the highest predictor (R² = 0.988), followed by the ore grade (R² = 0.966) and refining location (R² = 0.766), when assessed for correlation individually. The results presented here point to areas where environmental burdens of LIBs can be reduced, and thus they are helpful to policy and investment decision makers. Full article

The emission reductions mandated by International Maritime Regulations present an opportunity to implement full electric and hybrid vessels using large-scale battery energy storage systems (BESSs). lithium-ionion batteries (LIB), due to their high power and specific energy, which allows for scalability and adaptability to large transportation systems, are currently the most widely used electrochemical storage system. Hence, BESSs are the focus of this review proposing a comprehensive discussion on the commercial LIB chemistries that are currently available for marine applications and their potential role in ship services. This work outlines key elements that are necessary for designing a BESS for ships, including an overview of the regulatory framework for large-scale onboard LIB installations. The basic technical information about system integration has been summarized from various research projects, white papers, and test cases mentioned in available studies. The aim is to provide state-of-the-art information about the installation of BESSs on ships, in accordance with the latest applicable rules for ships. The goal of this study is to facilitate and promote the widespread use of batteries in the marine industry. Full article

The increasing global warming problem has pushed the community to implement emission reduction measures in almost every segment of human life. Since the major source of anthropogenic Greenhouse Gases (GHGs) is fossil fuel combustion, in the shipping sector, these measures are oriented toward a reduction in tailpipe emissions, where the replacement of traditional internal combustion marine engines with zero-carbon technologies offers the ultimate emission reduction results. According to the International Maritime Organization (IMO) GHG strategy, vessels involved in international shipping must achieve a minimum 50% reduction in their GHG emissions by 2050. However, this requirement does not extend to fishing vessels, which are significant consumers of fossil fuels. This paper deals with the full electrification of two types of fishing vessels (purse seiners and trawlers), wherein different Lithium-ion Batteries (LiBs) are considered. To investigate their environmental footprint and profitability, Life-Cycle Assessments (LCAs) and Life-Cycle Cost Assessments (LCCAs) are performed. The comparison of all-electric fishing vessels with existing diesel-powered ships highlighted the Lithium Iron Phosphate (LFP) battery as the most suitable alternative powering option regarding environmental and economic criteria. Full article

The electrochemical ion pumping device is a promising alternative for the development of the industry of recovering metals from natural sources—such as seawater, geothermal water, well brine, or reverse osmosis brine—using electrochemical systems, which is considered a non-evaporative process. This technology is potentially used for metals like Li, Cu, Ca, Mg, Na, K, Sr, and others that are mostly obtained from natural brine sources through a combination of pumping, solar evaporation, and solvent extraction steps. As the future demand for metals for the electronic industry increases, new forms of marine mining processing alternatives are being implemented. Unfortunately, both land and marine mining, such as off-shore and deep sea types, have great potential for severe environmental disruption. In this context, a green alternative is the mixing entropy battery, which is a promising technique whereby the ions are captured from a saline natural source and released into a recovery solution with low ionic force using intercalation materials such as Prussian Blue Analogue (PBA) to store cations inside its crystal structure. This new technique, called “electrochemical ion pumping”, has been proposed for water desalination, lithium concentration, and blue energy recovery using the difference in salt concentration. The raw material for this technology is a saline solution containing ions of interest, such as seawater, natural brines, or industrial waste. In particular, six main ions of interest—Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl⁻, and SO₄²⁻—are found in seawater, and they constitute 99.5% of the world’s total dissolved salts. This manuscript provides relevant information about this new non-evaporative process for recovering metals from aqueous salty solutions using hexacianometals such as CuHCF, NiHCF, and CoHCF as electrodes, among others, for selective ion removal. Full article

Automotive drive cycles have existed since the 1960s. They started as requirements as being solely used for emissions testing. During the past decade, they became popular with scientists and researchers in the testing of electrochemical vehicles and power devices. They help simulate realistic driving scenarios anywhere from system to component-level design. This paper aims to discuss the complete history of these drive cycles and their validity when used in an electrochemical propulsion scenario, namely with the use of proton exchange membrane fuel cells (PEMFC) and lithium-ion batteries. The differences between two categories of drive cycles, modal and transient, were compared; and further discussion was provided on why electrochemical vehicles need to be designed and engineered with transient drive cycles instead of modal. Road-going passenger vehicles are the main focus of this piece. Similarities and differences between aviation and marine drive cycles are briefly mentioned and compared and contrasted with road cycles. The construction of drive cycles and how they can be transformed into a ‘power cycle’ for electrochemical device sizing purposes for electrochemical vehicles are outlined; in addition, how one can use power cycles to size electrochemical vehicles of various vehicle architectures are suggested, with detailed explanations and comparisons of these architectures. A concern with using conventional drive cycles for electrochemical vehicles is that these types of vehicles behave differently compared to combustion-powered vehicles, due to the use of electrical motors rather than internal combustion engines, causing different vehicle behaviours and dynamics. The challenges, concerns, and validity of utilising ‘general use’ drive cycles for electrochemical purposes are discussed and critiqued. Full article

Battery sensor data collection and transmission are essential for battery management systems (BMS). Since inaccurate battery data brought on by sensor faults, communication issues, or even cyber-attacks can impose serious harm on BMS and adversely impact the overall dependability of BMS-based applications, such as electric vehicles, it is critical to assess the durability of battery sensor and communication data in BMS. Sensor data are necessary for a BMS to perform every operation. Effective sensor fault detection is crucial for the sustainability and security of electric vehicle battery systems. This research suggests a system for battery data, especially lithium ion batteries, that allows deep learning-based detection and the classification of faulty battery sensor and transmission information. Initially, we collected the sensor data, and preprocessing was carried out using z-score normalization. The features were extracted using sparse principal component analysis (SPCA), and enhanced marine predators algorithm (EMPA) was used for feature selection. The BMS’s safety and dependability may be enhanced by the suggested incipient bat-optimized deep residual network (IB-DRN)-based false battery data identification and classification system. Simulations using MATLAB (2021a), along with statistics, machine learning, and a deep learning toolbox, along with experimental research, were used to show and assess how well the suggested strategy performs. It is shown to be superior to traditional approaches. Full article

Lithium-ion batteries are crucial building stones in many applications. Therefore, modeling their behavior has become necessary in numerous fields, including heavyweight ones such as electric vehicles and plug-in hybrid electric vehicles, as well as lightweight ones like sensors and actuators. Generic models are in great demand for modeling the current change over time in real-time applications. This paper proposes seven dynamic models to simulate the behavior of lithium-ion batteries discharging. This was achieved using NASA room temperature random walk discharging datasets. The efficacy of these models in fitting different time-domain responses was tested through parameter identification with the Marine Predator Algorithm (MPA). In addition, each model’s term’s impact was analyzed to understand its effect on the fitted curve. The proposed models show an average absolute normalized error as low as $0.0057$. Full article