Battery Hybrid System Research Articles

CO2 emissions of fuel-cell battery hybrid system for large ships

ABSTRACT The needs of solid oxide fuel cells (SOFC) and batteries in large ships are analyzed by estimating the amount of fuel consumption and CO2 emissions. Three types of systems are proposed: 1) fuel cells-battery-generator engine system, 2) battery-generator engine system, and 3) generator engine system. This study is based on the time-varying electric power demand and adopted the sequential quadratic programming (SQP) method to find the optimal power distribution of mixed power sources. CO2 emissions in the SOFC hybrid system are 11.6% lower than in the conventional generator engine system, and those in the battery-generator engine system are 5.1% lower than in the conventional system. The reduction of CO2 emissions come from three factors: the operation of the generator engine at a relatively high load rate, the battery sharing the operation of the generator engine, and the high efficiency of the SOFC running. The SOFC hybrid system can be a sustainable technology in large vessels.

Resilience-centered optimal sizing and scheduling of a building-integrated PV-based energy system with hybrid adiabatic-compressed air energy storage and battery systems

This study investigates the economic and resilience co-optimization of a decentralized hybrid energy system (HES) within scenarios involving limited energy sources and a hybrid energy storage solution. The HES is comprised of a building-integrated Photovoltaic (PV) system incorporating an adiabatic compressed air energy storage (A-CAES) and batteries, with the main grid, serving as a backup. A two-stage sizing-scheduling model is proposed to optimize the configuration, minimize lifetime costs, and enhance both long and short-term resiliency while achieving an optimal schedule. The charging/discharging transition time of A-CAES is also adopted in the operational model. The results indicate a substantial annual resiliency improvement of approximately 41.1 %, with optimal sizing and energy storage integration. Additionally, they highlight the cost-effectiveness of the PV/A-CAES system while emphasizing the higher self-sufficiency achieved by the PV/A-CAES/battery system, attaining an electrical load management ratio of 47.3 % and a PV self-consumption rate of 96 %, a 6 % improvement over HESs with individual A-CAES. Furthermore, it is presented that under an optimal operational condition, even with the highest PV power availability during grid interruptions, HES relying solely on individual A-CAES could meet 94 % of the load demand. Integrating a fast-response battery enhances this resiliency to 100 %, effectively reducing PV-power curtailment, particularly during grid interruptions and A-CAES transitions.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Semi-Analytical Model for Hybrid and Redox Targeting Flow Battery Systems

Redox flow battery represents an economically viable energy storage technology that can integrate intermittent renewable energies from solar and wind power into existing electric grids. Besides the conventional all-liquid redox flow battery, there are two different architectures of flow batteries: hybrid and redox targeting flow batteries. Because the reactive solid phase is involved in the system, the energy storage density increases significantly. Therefore, they are considered as ones of the potential solutions for the future large-scale deployment of long-duration energy storage systems (LDES). A digital twin (DT) has been demonstrated as a promising numerical framework and platform to assist in the design, deployment, and operation of LDES. DTs usually require a fast-response model to provide an estimation of the flow battery's performance, which can be a fast-forward physics-based model or a machine learning-based regression model. Many pioneering works and our previous studies have introduced several fast-forward physics-based models for the conventional all-liquid redox flow battery in both inorganic and organic systems. In this study, a semi-analytical model, which can adapt to DTs, is proposed to provide an estimation of the performance of the hybrid and the redox targeting flow battery at different operating conditions.

An operating mode control method for photovoltaic (PV) battery hybrid systems

Depending on the PV power, load power, and battery status, the system may operate in different modes. The control loop may have to switch between operating modes. In practice, it is difficult to implement control loop switching because the transition and dynamic process are difficult to control. As a result, this paper presents a generalized mode control method that avoids loop switching across modes. First, system structure and topology are introduced. The operating conditions for both grid-connected and off-grid modes are then divided into six sub-cases. Furthermore, the control architecture, control loop, and reference transition for various scenarios are described. Finally, an experimental platform is built, and the results are presented to verify the proposed method.

Power Allocation based on ANN for Hybrid Battery and Supercapacitor Storage System in EV

The paper focuses on presented an ArtificialNeural Network (ANN) approach to allocate power for a hybridenergy storage system (HESS) in an Electric Vehicle (EV). TheHESS is comprised of a battery and supercapacitor, and theANN algorithm aims to optimize power allocation between thesetwo energy storage devices. The data for ANN training wasbased on cost optimization-based power allocation fromprevious research. While optimization can often take highcomputational resource and time, it is expected that a welltrained ANN can allocate power for the EV HESS more quickly.In this research, the inputs to the ANN are the required powerderived from the drive cycle, energy and power capacity of thebattery and supercapacitor, and state of charge (SoC) of thebattery and supercapacitor. The trained ANN was trained withvarious inputs not used in the training and it shows satisfactoryperformance.

Common-Mode Voltage Reduction Method-Based Flexible Power Control for Multiport DC–AC Converter in PV–Battery Hybrid System

Common-Mode Voltage Reduction Method-Based Flexible Power Control for Multiport DC–AC Converter in PV–Battery Hybrid System

Demonstration of participation in the German balancing power market using a large‐capacity hybrid battery storage system

AbstractAs one of NEDO's international demonstration projects, a large‐capacity hybrid battery storage system which consists of two types of battery cells with different charge‐discharge characteristics was constructed in the state of Lower Saxony in the Federal Republic of Germany. By using the hybrid battery system has a lithium‐ion which has an advantage in short time and high rate, and a sodium‐sulfur battery (NAS) which has an advantage in long time and low rate, the tender participation in the balancing power market in Germany had been executed. The hybrid battery storage system could successfully drive and provided services for primary and secondary control reserves, elimination of power imbalance, and reactive power injection, as well as a combination of these services.

Design of a Charging Station for Electric Vehicles Based on a Photovoltaic-Biodiesel Hybrid Renewable Energy System Combined with Battery Storage

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions.

Hydrogen as a deep sea shipping fuel: Modelling the volume requirements

Recent targets have increased pressure for the maritime sector to accelerate the uptake of clean fuels. A potential future fuel for shipping is hydrogen, however there is a common perception that the volume requirements for this fuel are too large for deep sea shipping. This study has developed a range of techniques to accurately simulate the fuel requirements of hydrogen for a case study vessel. Hydrogen can use fuel cells, which achieve higher efficiencies than combustion methods, but may require a battery hybrid system to meet changes in demand. A series of novel models for different fuel cell types and other technologies have been developed. The models have been used to run dynamic simulations for different energy system setups. Simulations tested against power profiles from real-world shipping data to establish the minimum viable setup capable of meeting all the power demand for the case study vessel, to a higher degree of accuracy than previously achieved. Results showed that the minimum viable setup for hydrogen was with liquid storage, a 105.6 MW PEM fuel cell stack and 6.9 MWh of batteries, resulting in a total system size of 8934 m3. Volume requirement results could then be compared to other concepts such as systems using ammonia and methanol, 8970 m3 and 6033 m3 respectively.

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Hybrid PV and Battery System Sizing for Commercial Buildings in Malaysia: A Case Study of FKE-2 Building in UTeM

Hybrid PV and Battery System Sizing for Commercial Buildings in Malaysia: A Case Study of FKE-2 Building in UTeM

Investigations of Regenerative Braking and Vibration Energy Conservation for Efficient Charging of Lithium-Ion Battery and Supercapacitor Hybrid System in Electric Vehicle Applications

This study aims to determine new methods of making electric vehicles more energy efficient by focusing on regenerative braking and vibration energy conservation in a hybrid energy storage system integrating lithium-ion batteries and supercapacitors. The first part of the research identifies the regenerative braking system, evaluates its effectiveness, and proposes enhancements for capturing and storing braking energy. Additionally, an investigation is made into whether it would be possible to conserve vibrating energies through creative integration with energetic harvesting technologies. The purpose is to capture and convert vibrational power produced when a car moves into an electrical charge using piezoelectric device for hybrid power storage. More energy efficiency is achieved through this method while also trying to overcome the shortcomings of regenerative braking in some driving situations. This experiments involve Modeling, Simulation and development and testing of prototype systems under different driving and vibrating conditions as well as real-world performance evaluation. It will help to determine how effective the hybrid system that has been suggested enhances energy recovery and storage efficiency in electric vehicles.

Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively.

Techno-economic and environmental analysis of an off-grid hybrid system using solar panels, wind turbine, diesel generator, and batteries for a rural health clinic considering

Abstract Middle East has significant potential for independent solar and wind power generation due to its vast land area and dispersed settlements. Enhancing the standard of living in remote areas and meeting the increasing demand for healthcare services worldwide are crucial objectives. Finding the most reliable and affordable method of supplying energy and clean water to rural healthcare institutions is the main goal of the research. The aim of this research is to evaluate the financial and environmental impacts of employing a hybrid energy system to supply power to a clinic in Rijal Almaa, Saudi Arabia. Utilizing the HOMER software, the investigation determined that the most efficient hybrid configuration includes 360 batteries, a 25 kW DG, a 2 kW wind turbine, 33.3 kW of solar panels, and an 18.4 kW converter. The NPC (Net Present Cost) associated with this optimized system amounts to $109 307, while its COE is 0.103 $/kWh. It was found that this efficient system necessitates an initial capital outlay of $72 281, coupled with an annual operational expense of $2361. The renewable fraction (RF) of 84.7%, excess electricity generation of 8.81%, and fuel consumption of 4135 L/yr are notable features of the system. The system also exhibits the lowest annual CO2 emissions at 10825 kg/yr, indicating a positive environmental impact. The findings can be applied globally, particularly in hot, arid regions. The analysis suggests that reducing the costs of hybrid solar panels, DG, wind turbine, and battery systems could significantly reduce overall costs, making them a feasible solution for developing nations.

A high-performance magnesium/lithium hybrid-ion battery using a hollow multi-layered NiS/Co3S4/carbon cathode and an all-phenyl-complex-based electrolyte.

Magnesium-lithium hybrid batteries (MLHBs) using a dual-ion electrolyte and safe Mg anode have promising potential for high-performance energy storage. Here, we develop an MLHB constructed of a hollow multi-layered NiS/Co3S4/carbon cathode and an all-phenyl-complex/lithium chloride (APC-LiCl) electrolyte. The hollow multi-layered structure and carbon matrix accommodate volumetric expansion and facilitate electrolyte penetration. The APC-LiCl electrolyte displays a stable electrochemical window. The MLHB shows a high specific capacity of 398 mA h g-1 after 100 cycles at 0.2 A g-1, and a stable capacity at 1.0 A g-1 after cycling 500 times. Moreover, stable rate performance and temperature tolerance are achievable. These findings would enable this design to be promising for developing other hybrid battery systems.

Optimizing Microgrid Efficiency with Battery and Super Capacitor Hybrid Systems

Microgrids are small networks consisting of several renewable energy sources such as wind light, sunlight, geothermal, bioenergy, water and so on. But the drawback of this system is in fluctuating power and when the source does not produce power at a certain time. So that power storage media such as batteries and supercapacitors are needed to maintain contingency when the source does not supply enough load. The purpose of this study was toobtain the effect of adding supercapacitors with batteries and compare battery storage media without supercapacitors. From the results of the study, it was found that the addition of supercapacitors can maintain battery stability charging from 50% State of Charge (SOC) down to 47% and start It rises back by 50% at 1 second, as well as additional charging can be set to our liking. If the battery storage media is without supercapacitors, battery charging tends to drop from 50% and continue to fall without any increaseat all. So it can be concluded that with the addition of supercapacitors are able to maintain the performance of the battery in the microgrid system.

Hybrid solar-electric cart efficiency enhancement: A bibliometric analysis

The present study involves the development of an electric cart, with future research aiming to enhance its efficiency by creating a hybrid solar-electric cart. To achieve this goal, a bibliometric analysis of electric vehicle (EV) batteries is required. This study aims to identify research gaps in EV batteries through Bibliometric Analysis, utilizing Scopus Analyze and VOSViewer to analyze 1,276 documents obtained from the Scopus database, including articles (49.7%), conference papers (43.3%) and various other publications such as reviews, book chapters, reports, short surveys, notes, books, erratum, and editorials. The analysis reveals a substantial surge in EV battery research and publications within the Scopus database since 2013, and this trend is projected to continue until the end of 2023. Based on researchers’ affiliations, Chinese institutions have ranked first in contributions, followed by institutions from the United States, India, the United Kingdom, and Canada. Surprisingly, the University of Warwick secured the top among research institutions, with the Beijing Institute of Technology claiming the second position. The VOSViewer analysis generated six keyword clusters relevant to EV battery research. Of particular interest is Cluster 5, which emphasizes the significance of battery management techniques, establishing efficient battery swapping stations, optimizing energy management strategies, and exploring the role of EV batteries in building intelligent grids. These gaps identified in Cluster 5 will become the focal point for future research, especially concerning efficiency enhancement through developing a hybrid battery system capable of a hybrid solar-electric cart.

Achieving High Energy Efficiency: Recent Advances in Zn‐Air‐Based Hybrid Battery Systems

Rechargeable Zn‐air batteries (ZABs) are regarded as an attractive green energy storage technology, featured with large theoretical energy densities and intrinsic high safety factors. However, hindered by the sluggish kinetics of both oxygen reduction reaction and oxygen evolution reaction, rechargeable ZABs are confronted with some critical challenges such as low operating voltage, poor energy efficiency, and limited cycle life. Zn‐air‐based hybrid batteries (ZAHBs), integrating the advantages of a conventional ZAB with supplementary redox reactions, have emerged as a promising solution to address those challenges. Based on working principles, the hybrid batteries can be categorized into two groups: Zn‐M/air hybrid batteries (M = Ni, Co, Ag, Cu, and Mn) and Zn‐X/air hybrid batteries (X = KI, ethanol, and urea), which can achieve improved energy efficiency and density by optimizing charge–discharge voltage. Herein, a comprehensive overview of ZAHBs is provided, including the classification, latest progress, and electrochemical properties, as well as detailed discussion of relevant mechanisms. Moreover, the perspectives and opportunities for future research in the field of hybrid battery systems are also outlined. This review shall give helpful guidance on the design and application of ZAHBs, and provide important insights into the development of new electrochemical energy storage systems.