Reduce Hydrogen Consumption Research Articles

System Optimization of Multistack and Multimotor Powertrain for Fuel Cell Electric Vehicles

Multistack and multimotor powertrain systems have significant potential for improving the efficiency and performance of fuel cell electric vehicles (FCEVs) compared to conventional powertrain systems. To achieve a superior powertrain system, the major components such as the stack, motor, and transmission of the multistack and multimotor systems should be optimized. To analyze the energy efficiency and dynamic performance of the FCEV, an FCEV analysis model was developed. This model included a two‐stack and two‐motor powertrain system (2S2M) employing a stack power and motor torque distribution strategy. An optimization problem was formulated with stack transition power, motor torque distribution, and transmission gear ratios as the optimization variables and hydrogen consumption and acceleration time as the objectives for efficiency and performance measures, respectively. An artificial neural network (ANN) model‐based optimization method was used to address the computational burden of multiobjective optimization. The optimization results highlighted the Pareto front for the FCEV employing 2S2M, showing a trade‐off relationship between the efficiency and performance of the FCEV. Compared to the conventional powertrain system, the 2S2M can reduce hydrogen consumption and acceleration time by up to 7.9% and 6.2%, respectively. An analysis of the distribution of optimal solutions and a comparison of the Pareto fronts for each optimization variable highlighted the necessity for the proposed system optimization method. Furthermore, a comparison between the FCEV and ANN models in terms of computational time for the optimization demonstrated the effectiveness of the ANN model‐based multiobjective optimization.

Model-based Evaluation of Advanced Energy Management Control Strategies for Heavy-Duty Fuel Cell Powertrains

<div>In recent years, fuel cell electric vehicles (FCEV) have become a promising alternative to battery electric vehicles in medium- and heavy-duty on-road applications, which specifically require long vehicle range, high payload capacity, and fast refueling times. While FCEVs are more likely to meet these requirements, they come with their own challenges of high upfront system cost, reduced system efficiency at high load, on-board hydrogen storage system packaging, and fuel cell system (FCS) durability. To address these challenges, it is critical to ensure optimal propulsion system component sizing during the concept phase as well as ensure optimal propulsion system energy management during vehicle operation. In a previous publication, authors presented a model-based approach for system sizing and optimization of FCEV propulsion system components for a Class 8 long-haul application. In this study, the authors have evaluated and optimized multiple advanced propulsion system energy management control strategies to maximize the FCEV propulsion system efficiency during vehicle operation.</div> <div>Specifically, several energy management strategies were evaluated with the primary objective of reducing hydrogen consumption through efficient power split between FCS and high-voltage battery, while maintaining vehicle performance and sustaining battery state of charge (SOC). A 1D multi-physics-based plant model of the vehicle propulsion and thermal system was developed in GT-SUITE and validated against vehicle test data. The validated plant model was then used for model-in-loop (MiL) simulations to evaluate multiple control strategies such as rule-based, equivalent consumption minimization strategy (ECMS), and dynamic programming (DP), on real-world drive cycles.</div>

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

Research on state machine control optimization of double-stack fuel cell/super capacitor hybrid system.

To ensure the continuous high-efficiency operation of fuel cell systems, it is essential to perform real-time estimation of the maximum efficiency point and maximum power point for multi-stack fuel cell systems. The region between these two power points is commonly referred to as the "high-efficiency operating region." Initially, a transformation of the general expression for hydrogen consumption in multi-stack fuel cell systems is conducted to obtain an algebraic expression for the efficiency curve of multi-stack fuel cells. Utilizing a polynomial differentiation approach, the parameter equation for the maximum system efficiency is computed. Subsequently, a reverse deduction is carried out using the maximum efficiency and its corresponding power of underperforming subsystems to enhance the maximum efficiency of multi-stack fuel cell systems.Furthermore, an equivalent hydrogen consumption minimization method is introduced for real-time optimization of hybrid energy systems. The state machine control method serves as an auxiliary strategy, imposing the high-efficiency operating region as a boundary constraint for the equivalent hydrogen consumption minimization strategy's results. This ensures that the multi-stack fuel cell system operates as much as possible within the high-efficiency operating region.Through simulation validation using MATLAB/Simulink, the proposed approach comprehensively leverages the advantages of the state machine and equivalent hydrogen consumption. This approach enables effective identification of the high-efficiency operating region of fuel cells, while concurrently enhancing the operational range efficiency of the system, reducing hydrogen consumption, and elevating system stability.

Fuzzy Logic-Based Energy Management Strategy for Hybrid Fuel Cell Electric Ship Power and Propulsion System

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Hydrodeoxygenation of guaiacol over modified coconut carbon supported Ni nanoparticles catalysts under alkaline condition

Hydrodeoxygenation of guaiacol over modified coconut carbon supported Ni nanoparticles catalysts under alkaline condition

Performance analysis and optimization of a vehicular LT-PEMFC for maximum power density and efficiency based on NSGA-II algorithm

Based on finite temporal thermodynamics (FTT), a model for a low-temperature proton exchange membrane fuel cell (LT-PEMFC) is created. The link between the LT-PEMFC’s output power density and efficiency was determined using the aforementioned model. The experimental results confirm the algorithm model’s accuracy. Investigations are also conducted into how the design and operating parameters affect the LT-PEMFC’s output outcomes. Moreover, the NSGA-II technique is used to maximize the power density and efficiency of the LT-PEMFC for multi-objective optimization. The experiment outcomes demonstrate that the optimized LT-PEMFC model performs better at output. The related powertrain design scheme for the various output performances of Fuel cell vehicles (FCVs) is provided by LT-PEMFC optimization. It is demonstrated by the study of simulation data that the optimized LT-PEMFC achieves reduced hydrogen consumption and higher FCV efficiency.

Optimal power management for hybrid electrical vehicle

This paper introduces an innovative optimal energy management strategy tailored for a hybrid electric-powered hydraulic excavator system. The aim is to bolster power performance, extend the lifespan of power sources, and optimize hydrogen usage. In this system, the fuel cell serves as the primary power source, while integrating supercapacitors and batteries offers benefits such as enhancing power performance and storing regenerative energy for future use. To efficiently distribute power among these three sources and maximize the utilization of regenerative energy, we propose an energy management strategy. This strategy combines fuzzy logic control with a rule-based algorithm. Moreover, we optimize the parameters of the fuzzy logic system using a combination of the backtracking search algorithm and sequential dynamic programming. This optimization aims to reduce hydrogen consumption and prolong the lifespan of the power sources. Simulation results demonstrate that our proposed energy management strategy significantly enhances vehicle performance, improves fuel economy of the hybrid electric-powered hydraulic excavator system, and enhances the durability and efficiency of the battery and supercapacitor systems within the fuel cell system.

Energy management strategy of fuel cell electric vehicle based on work condition recognition

To reduce hydrogen consumption in fuel cell electric vehicles, mitigate power fluctuations, and maintain the battery system's state of charge, a novel transition process recognition method based on work condition recognition framework of energy management strategy is proposed. This method offers the advantages such as higher recognition rates and faster recognition speed comparing with the traditional condition recognition methods. A comparison is made with the commonly used LVQ recognition method, and the simulations are conducted to demonstrate the superiority of this condition recognition algorithm and the improved performance of the energy management strategy based on the present recognition method under mixed work conditions.

A temperature fluctuation suppression control method of fuel cell vehicles to reduce hydrogen consumption

A temperature fluctuation suppression control method of fuel cell vehicles to reduce hydrogen consumption

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Hierarchical Energy Management Strategy Based On M-DQN for Fuel Cell Hybrid Electric Vehicle

For fuel cell hybrid electric vehicles equipped with a fuel cell (FC), battery (BAT), and supercapacitor (UC), this paper proposes a hierarchical energy management strategy (EMS) based on the improved M-DQN algorithm to reduce hydrogen consumption, enhance the efficiency of the FC, and maintain the state of charge (SoC) of the BAT. Firstly, an adaptive fuzzy low-pass filter is employed to achieve the frequency decoupling of power demand, enabling the UC to provide/absorb the peak power demand. Secondly, an energy management framework based on M-DQN is designed, and the concept of an equivalent consumption minimization strategy is applied to construct the reward function, which includes penalty factors related to the efficiency of the FC and SoC deviation of the BAT, aiming at optimizing the power allocation between the FC and BAT. Simulations and platform tests were conducted under various typical driving cycles. The results indicate that, compared with the traditional M-DQN-based EMS, the proposed EMS can significantly maintain the SoC of the BAT, improve the efficiency of the FC, reduce hydrogen consumption, and even achieve an average improvement of 5.2% in fuel economy.

Sensitivity study of integrated carbon capture and methanation process using dual function materials

Sensitivity study of integrated carbon capture and methanation process using dual function materials

Effects of Fuel Cell Size and Dynamic Limitations on the Durability and Efficiency of Fuel Cell Hybrid Electric Vehicles under Driving Conditions

In order to enhance the durability of fuel cell systems in fuel cell hybrid electric vehicles (FCHEVs), researchers have been dedicated to studying the degradation monitoring models of fuel cells under driving conditions. To predict the actual degradation factors and lifespan of fuel cell systems, a semi-empirical and semi-physical degradation model suitable for automotive was proposed and developed. This degradation model is based on reference degradation rates obtained from experiments under known conditions, which are then adjusted using coefficients based on the electrochemical model. By integrating the degradation model into the vehicle simulation model of FCHEVs, the impact of different fuel cell sizes and dynamic limitations on the efficiency and durability of FCHEVs was analyzed. The results indicate that increasing the fuel cell stack power improves durability while reducing hydrogen consumption, but this effect plateaus after a certain point. Increasing the dynamic limitations of the fuel cell leads to higher hydrogen consumption but also improves durability. When considering only the rated power of the fuel cell, a comparison between 160 kW and 100 kW resulted in a 6% reduction in hydrogen consumption and a 10% increase in durability. However, when considering dynamic limitation factors, comparing the maximum and minimum limitations of a 160 kW fuel cell, hydrogen consumption increased by 10%, while durability increased by 83%.

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.

Energy management of an eVTOL aircraft with optimization based on the Equivalent Consumption Minimization Strategy (ECMS)

This article introduces an optimization-based energy management strategy developed and validated for electric vertical take-off and landing (eVTOL) aircraft. The primary objective of this strategy is to minimize hydrogen consumption while accounting for resource constraints, such as the battery and fuel cell, to enhance energy efficiency and sustainability while adhering to performance and safety requirements. The eVTOL employed in this research is an essential component of our “Viable” research project, featuring a hybrid propulsion system that combines batteries and fuel cells.To accomplish this, mathematical and computational techniques were employed using the equivalent consumption minimization strategy method to determine the optimal operational parameters for the eVTOL aircraft, considering the available energy resources. These techniques were implemented for the first time in MATLAB, enabling simulations of the aircraft’s performance under various conditions. The results demonstrate the efficacy of the energy management strategy in significantly reducing hydrogen consumption while maintaining optimal performance and safety. Graphs and comparative analyses are presented to highlight the evolution of hydrogen consumption compared to different parameters and to compare the approach with alternative methods.Furthermore, the article explores an alternative solution that offers related results and performance as MATLAB, utilizing the open-source software OpenModelica (OM). Energy management experiments using ECMS were conducted on OM, yielding highly satisfactory outcomes in terms of simulation accuracy and cost-effectiveness. The method was also evaluated in simulations of propulsive system resources, developed on OM, validating the results concerning energy distribution, compliance with constraints, and real-time feedback.In summary, this article presents a comprehensive strategy for effectively managing energy consumption in eVTOL aircraft. By leveraging simulations conducted in MATLAB and OM, the strategy’s effectiveness was assessed, with potential implications for advancing the sustainability of electric eVTOL aircraft.

Efficiency improvement strategy of fuel cell system based on oxygen excess ratio and cathode pressure two-dimensional optimization

Efficiency improvement strategy of fuel cell system based on oxygen excess ratio and cathode pressure two-dimensional optimization

Bimetallic nickel molybdenum nitride catalyst with low pressure and reduced hydrogen consumption for hydrogenation of dimethyl oxalate to ethanol: the impact of reduction temperature on catalytic performance

The bimetallic nickel molybdenum nitride (Ni3Mo3N) catalyst exhibits exceptional performance with a high EtOH yield (>97.5%) under low pressure (0.5 MPa) and minimal hydrogen consumption (H2/DMO (mol)) conditions.

Techno-economic analysis of a hydrogen fuel cell hybrid system and corresponding optimum matching design for hydrogen fuel cell forklifts

Conventional forklifts face serious issues, with internal combustion engine models causing indoor pollution, and lead-acid battery variants having slow charging and reduced power output as the charge diminishes. To address these drawbacks, this paper introduces a 2.5-tonne fuel cell forklift designed for Hong Kong's bustling logistics, warehousing, and transportation needs. It presents the development of dynamic simulation and cycle condition models, incorporating life cycle cost and average efficiency functions. Simulations reveal that selecting a 50-cell stack (rated at 11.8 kW) is the most cost-effective option, reducing hydrogen consumption by 2.3% using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimisation. Cycle conditions do not alter the stack's optimal working voltage. However, the stack's voltage is influenced by stack and hydrogen prices, requiring an optimal design based on Hong Kong's actual costs. This study provides a theoretical foundation for future fuel cell forklift design through techno-economic analysis.