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Fuel cell electric vehicles (FCEVs) require multiple components to operate properly, and the fuel cell stack—the source of power—is one of the most important components. While the number of enterprises manufacturing and selling fuel cell stacks is increasing globaly year after year, the residual challenges of core components and technologies still need to be resolved in order to keep pace with the development of lithium-ion batteries (i.e., its primary competitor). Additionally, many production and distribution standards are seen as unsettled. These barriers make large-scale commercialization an issue. Use of Proton-exchange Membrane Fuel Cells in Ground Vehicles explores the opportunities and challenges within the PEMFC industry. With the help of expert contributors, a critical overview of fuel cells and the FCEV industry is presented, and core technology, applications, costs, and trends are analyzed.

The temperature management of the proton exchange membrane fuel cell cooling system is very important to the stability and life of the engine. The size and cycle switching of the cooling system and the rapid temperature rise of the system are inseparable from the thermostat. In this paper, a simulation model of the fuel cell cooling system is built for a 30KW fuel cell system, and its accuracy is verified by experiments. The temperature fluctuation of the system in the confluence mode is mainly studied, and the error is about 0.9 °C. The influence of the converging mode of the thermostat on the temperature of the cooling system is researched through the simulation platform. Based on the simulation model, the influence of thermostats with different external environments and opening degrees on the size cycle switching of cooling system is predicted, which provides optimization and guidance for the system control strategy.

The characteristics of three-way-catalyst during engine start process were investigated based on a simulated start/stop test system for HEV application. Although the catalyst has already reached its light-off temperature, the conversion efficiency is poor during engine start process due to the deviation of lambda from stoichiometric. The high concentration hydrocarbon emission spike can be stored by catalyst substrate temporarily, then it is released. This dynamic process decreases the conversion efficiency for the following exhaust hydrocarbon emission. When the initial temperature of catalyst substrate before engine start increased from 150°C to 400°C, the conversion efficiency for both the hydrocarbon and NO are increased.

This paper is contributed to determining model parameters for DMFCs. Theoretical evaluations are carried out to set up the relationship between the unknown and measurable parameters or variables. A laboratory-scale liquid-feed cell was simulated under different operating conditions. The resulting measurable static performance curves are used as basic information. Some key kinetic and physical parameters can be determined or estimated for a DMFC model.

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.

Fuel cells’ soft output characteristics and mismatched voltage levels with subordinate electrical devices necessitate the use of DC/DC converters, which are an important part of the power electronic subsystem of the fuel cell system. The staggered parallel Boost topology is commonly employed in fuel cell DC/DC converters. This paper focuses on the control characteristics of the two-phase interleaved parallel Boost topology in the context of a fuel cell system. Specifically, we derive the small-signal model and output-control transfer function of the topology, and design a controller based on frequency characteristic analysis. Our proposed controller uses a cascaded double-ring structure and supports both constant current and constant voltage switching modes. To evaluate the effectiveness of our proposed control strategy, we conduct simulation and prototype testing.

To study the state of health (SOH) of the proton exchange membrane fuel cell (PEMFC), a novel hybrid method combining the advantages of both the model-based and data-driven methods is proposed. Firstly, the model-based method is proposed based on the voltage degradation model to estimate the variation trend, and three parameters reflecting the performance degradation are selected. Secondly, the data-driven (long short-term memory (LSTM)) method is presented to estimate the variation fluctuation. Moreover, the core step of the hybrid method is returning the results of the LSTM method to the power degradation model as the “observation” and modifying related parameters to improve the estimation accuracy. Finally, the sliding window method is applied to solve the problem of the data increase with the increase of the operating time. The results show that the power estimation is better than the current estimation for the SOH estimation.

To improve the prediction accuracy of the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC), an integrated health index (IHI) including electrical and non-electrical parameters of PEMFC is established, and the RUL prediction is conducted based on the above index. Firstly, several operating conditions including the PEMFC degradation information are selected according to the information theory method. Moreover, the IHI is established by the sequential quadratic programming method. Secondly, RUL predictions based on the power and IHI are conducted by the adaptive neuro fuzzy inference system (ANFIS), respectively. Finally, different results comparisons including power and IHI differences, differences between experimental and training/predicting results, amounts of different differences in training and predicting phases, and RUL prediction results are presented in detail.

To study the cold start of muti-stack fuel cell system (MFCS), a novel thermal management subsystem structure and corresponding cold start strategies are proposed. Firstly, leveraging the distinctive configuration of the MFCS that can be sequentially initiated, we augmented the existing thermal management subsystem with the incorporation of two additional collection valves and two bypass diverter valves, which affords an increased degree of flexibility in the formulation of cold-start strategies. Secondly, we innovatively propose a hierarchical auxiliary heating cold start strategy and an average auxiliary heating cold start tailored for MFCS consisting of power levels of 20 kW, 70 kW, and 120 kW. Furthermore, we have developed a controller to address temperature control challenges during the start-up process.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

When studying on how to identify the proton exchange membrane fuel cell model parameters accurately and quickly, the model frequently used is a lumped parameter model. Compared to this kind of model, one-dimensional dynamic proton exchange membrane fuel cell model can correlate the physical parameters with output characteristics of fuel cell to predict the effects of design parameters, materials and environmental conditions, thus reducing the need for experimentation. However, there is little literature about parameter identification for one-dimensional dynamic models currently. In this paper, a one-dimension dynamic proton exchange membrane fuel cell model with many assumptions for reducing the complexity of calculation is realized in Matlab-Simulink environment. The model consists of five interacting subsystems.

Proton exchange membrane fuel cell (PEMFC) system is considered to be one of the ideal replacement for conventional Internal Combustion Engines (ICE) due to its zero emission, low operating temperature and high power density. The high-pressure PEMFC system has become the future development direction because the higher gas pressure can effectively improve the performance and power density of stack. However, the high gas pressure can lead to the damage of membrane electrode assembly (MEA) and even the failure of fuel cell stack. As the air supply system is a nonlinear system, the flow and pressure have a strong coupling relation. For this problem, firstly, the mathematical model of air supply system is established in MATLAB/Simulink. Then a dual-loop control strategy is used in controller.

Water management is a key research direction for the performance and lifetime of proton exchange membrane fuel cell (PEMFC) stacks. The paper is aimed to develop an online fault diagnosis method that distinguishes different degrees of flooding and drying within a fuel cell stack by unobservable variables. In our research, the equivalent circuit model is established and electrochemical impedance spectroscopy (EIS) is utilized. The mathematical methods are used to extract the fault features. Fuzzy C-means is used to classify the selected features and the diagnostic rules are automatically extracted from the data. Through verification, the interpretability and computational efficiency of the proposed method are achieved.

Polymer electrolyte membrane fuel cells (PEMFCs) are a good candidate for fuel cell electric vehicles (FCEVs) due to their high efficiency, high power density and zero-emission. However, the lifetime is one of the main barriers to overcome before their commercialization. The durability testing methods for PEMFCs are main include electrocatalyst cycle, catalyst support cycle, membrane electrode assembly (MEA) chemical stability, membrane mechanical cycle and so on. In addition, there is little research about MEA chemical stability test, which applies a continuous open-circuit voltage (OCV) to produce more free radicals and these will have a bad influence on PEMFCs. Based on this, the durability of PEMFC was studied under OCV operation at 30% relative humidity (RH), 90 °C and 150 kPa inlet pressure. The electrochemical performances such as in-situ cyclic voltammetry (CV), linear sweep voltage (LSV) and cell polarization were used to evaluate the durability of PEMFC.

Fuel cell vehicles have always garnered a lot of attention in terms of energy utilization and environmental protection. In the analysis of fuel cell performance, there are usually some outliers present in the raw experimental data that can significantly affect the data analysis results. Therefore, data cleaning work is necessary to remove these outliers. The polarization curve is a crucial tool for describing the basic characteristics of fuel cells, typically described by semi-empirical formulas. The parameters in these semi-empirical formulas are fitted using the raw experimental data, so how to quickly and effectively automatically identify and remove data outliers is a crucial step in the process of fitting polarization curve parameters. This article explores data-cleaning methods based on the Local Outlier Factor (LOF) algorithm and the Isolation Forest algorithm to remove data outliers.

Water content estimation is a key problem for studying the PEM fuel cell. When several hundred fuel cells are connected in serial and their active surface area is enlarged for sufficient power, the difference between cells becomes significant with respect to voltage and water content. The voltage of each cell is measurable by the cell voltage monitor (CVM) while it is difficult to estimate water content of the individual. Resistance of the polymer electrolyte membrane is monotonically related to its water content, so that the new online high frequency resistance (HFR) measurement technique is investigated to identify the uniformity of water content between cells and analyze its sensitivity to operating conditions in this paper. Firstly, the accuracy of the proposed technique is experimentally validated to be comparable to that of a commercialized electrochemical impedance spectroscopy (EIS) measurement equipment.

The problem of keeping the stable starting performance of diesel engine under high altitude and low temperature conditions has been done a lot of research in the field of diesel engine, but there is a lack of research on extreme conditions such as above 2000 meters above sea level and below 0°C. Aiming at solving the cold start problem of diesel engine in extreme environment, a set of chamber system of cold start environment diesel engine was constructed to simulate environment of 3000m altitude and -20°C. A series of experimental research was conducted on cold start efficiency optimization strategy of a certain type of diesel engine at 3000m altitude and -20°C. In parallel, a diesel engine model was constructed through Chemkin to explore the influence of the three parameters of compression ratio, stroke length, and fuel injection advance angle on the first cold start cycle of diesel engine at 4000m altitude and -20°C.

In recent years, the burgeoning applications of hydrogen fuel cells have ignited a growing trend in their integration within the transportation sector, with a particular focus on their potential use in multi-rotor drones. The heightened mass-based energy density of fuel cells positions them as promising alternatives to current lithium battery-powered drones, especially as the demand for extended flight durations increases. This article undertakes a comprehensive exploration, comparing the performance of lithium batteries against air-cooled fuel cells, specifically within the context of multi-rotor drones with a 3.5kW power requirement. The study reveals that, for the specified power demand, air-cooled fuel cells outperform lithium batteries, establishing them as a more efficient solution.