Ice Nucleation Mechanisms on Platinum Surfaces in PEM Fuel Cells: Effects of Surface Morphology and Wettability.

Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL.

High emission of black carbon in heavy-duty diesel vehicles: Insights from microscopic operating conditions.

The in-depth investigation of the high black carbon (BC) emission scenarios of heavy-duty diesel vehicles (HDDVs) is a crucial step toward developing effective control strategies. Chassis dynamometer tests were conducted for three in-use HDDVs, namely, vehicle #1, #2, and #3, focusing on the instantaneous BC characterizations during multiple driving conditions, i.e., speed phases and acceleration intervals. BC emission was found to increase with positive acceleration, and high acceleration could result in instantaneous BC spikes. The total BC emissions during velocity-acceleration interval 15-60 km h-1 and 0.1-0.9 m s-2 contributed to 43.4 ± 10.2 % of the whole-cycle emissions, while the proportions of time spent in the velocity-acceleration interval to the whole cycle were 23.1 ± 7.6 %. The cold-start microscopic operating condition was assessed by the cold-start extra emissions (CSEEs). Under various pre-defined cold-start durations, the proportions of CSEEs in the total cycle emissions were 9.4-21.0 %, 0-9.1 %, and 6.8-39.4 % for vehicles #1, #2, and #3, respectively. The CSEEs exhibited an initial rise, followed by a plateau as the assumed cold-start durations extended. A uniform cold-start duration of 600 s was established based on the criterion that the relative standard deviation (RSD) of CSEEs during the plateau period was <10 %. We proposed that the updated cold-start duration can enhance the accuracy and consistency of cold-start corrections in emission inventory models.

HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Dataset of vehicle chase measurements in real-world subfreezing winter conditions.

This dataset comprises thorough measurements of light-duty vehicles emissions conducted in Siilinjärvi and Kuopio, Finland, during February 2021, using a mobile laboratory. The measurements focused on subfreezing conditions to capture emissions nuances during cold weather. Measurements were carried out on minimally trafficked roads to diminish external disturbances. The dataset includes a large number of variables from gas and particle emissions. Gaseous emissions of CO, CO2, and NOx were measured. Measured variables of particle emissions were number concentration (CPC), size distribution (ELPI+), black carbon concentration (AE33), and chemical composition (SP-AMS). A total of six light-duty vehicles were investigated, featuring three diesel and three gasoline engines. The measurements incorporated three distinct drive scenarios: subfreezing-cold start, preheated-cold start (utilizing either electrical or fuel-operated auxiliary heaters), and hot start (where a vehicle engine has reached the optimal temperature through prior driving). Each drive type was replicated twice, resulting in six driven rounds per vehicle and 36 rounds in total. Additionally, daily background measurements were conducted by following the same route without chasing a specific vehicle. Meteorological conditions during the measurements were representative of winter in Finland, with outside temperatures ranging from -9 °C to -28 °C. The effect of weather conditions on the measurements were minimal. Only a minor effect was due to the occasional snowfall, especially on the last day when the road surface was snowy, and the car being chased lifted the snow from the road surface. We didn't recognize other factors, such as high wind speeds or major road dust emissions, that could have affected the measurement results. This dataset serves as a valuable resource for comparing emissions under diverse environmental conditions, particularly in real-life winter settings where data are scarce. Furthermore, it provides an opportunity for meta-analysis of emission factors from various passenger vehicle types. The dataset's richness and specificity make it a valuable contribution to the understanding of winter-time vehicular emissions.

Effects of fuel and driving conditions on particle number emissions of China-VI gasoline vehicles: based on corrections to test results.

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300 s (the cold start period) was 7.2 times that of the 300 s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode.

Fairness-aware recommendation with meta learning.

Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.

Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to be applied to unseen (i.e., never-seen-before) proteins and compounds. In this study, we propose SgCPI to incorporate local known interacting networks to predict CPI interactions. SgCPI randomly samples the local CPI network of the query compound-protein pair as a subgraph and applies a heterogeneous graph neural network (HGNN) to embed the active/inactive message of the subgraph. For unseen compounds and proteins, SgCPI-KD takes SgCPI as the teacher model to distillate its knowledge by estimating the potential neighbors. Experimental results indicate: (1) the sampled subgraphs of the CPI network introduce efficient knowledge for unseen molecular prediction with the HGNNs, and (2) the knowledge distillation strategy is beneficial to the double-blind interaction prediction by estimating molecular neighbors and distilling knowledge.

Hydrocarbon adsorption mechanism of modern automobile engines and methods of reducing hydrocarbon emissions during cold start process: A review.

With the global emphasis on environmental protection and increasingly stringent emission regulations for internal combustion engines, there is an urgent need to overcome the problem of large hydrocarbon (HC) emissions caused by unstable engine cold starts. Synergistic engine pre-treatment (reducing hydrocarbon production) as well as after-treatment devices (adsorbing and oxidizing hydrocarbons) is the fundamental solution to emissions. In this paper, the improvement of hydrocarbon emissions is summarized from two aspects: pre-treatment and after-treatment. The pre-treatment for engine cold start mainly focuses on summarizing the intake control, fuel, and engine timing parameters. The after-treatment mainly focuses on summarizing different types of adsorbents and modifications (mainly including different molecular sieve structures and sizes, preparation conditions, silicon aluminum ratio, ion exchange modification, and heterogeneity, etc.), adsorptive catalysts (mainly including optimization of catalytic performance and structure), and catalytic devices (mainly including coupling with thermal management equipment and HC trap devices). In this paper, a SWOT (strength, weakness, opportunity, and threat) analysis of pre-treatment and after-treatment measures is conducted. Researchers can obtain relevant research results and seek new research directions and approaches for controlling cold start HC emissions.

The application prospect and challenge of the alternative methanol fuel in the internal combustion engine.

In the context of global carbon neutrality, the internal combustion engines aim to further reduce the carbon emission and improve the fuel economy for the transportation sector. Methanol is treated as a renewable, reliability, and applicability energy, which also shows some superior physicochemical properties compared to the traditional fossil fuels. However, some challenges such as cold start issue, low fuel economy, high unregulated emissions need to address before the methanol widely applies in the engines. This article comprehensively reviews the physicochemical properties and production processes of the methanol, the cold start issue of the methanol engine, and emission and combustion characteristics of the methanol engine for evaluating its potential effect of emission reduction and energy saving in the transportation sector. In addition, different optimization strategies and advanced technologies are proposed and comprehensively discussed in this paper for addressing the issues of the cold start, combustion and emissions of the methanol engines in the real application. Finally, the conclusions and prospects of the methanol engine are presented for promoting its application in the transportation sector and further reducing the carbon emission in the near future, thereby achieving the carbon peak and carbon neutrality in the China.

Novel insights into the NOx emissions characteristics in PEMS tests of a heavy-duty vehicle under different payloads.

Real Drive Emission (RDE) test with Portable Emission Measurement System (PEMS) is a widely adopted way to assess vehicle emission compliance. However, the current NOx emissions calculation method stipulated in the China VI emission standard easily ignores the NOx emissions during cold start and low-power operation. To study the effect of cold start and low-power operation on the calculation of NOx emissions in the PEMS test, in this study, a China VI Heavy-Duty Vehicle (HDV) for urban use was used to conduct PEMS tests under various vehicle payload conditions. The data analysis results show that the increase in vehicle payload is beneficial to reducing the specific NOx emissions and passing the NOx emission compliance test because the increased payload improves the NOx conversion efficiency of the SCR system. Cold start duration has no obvious relationship with vehicle payload, accounting for only about 4∼6% in each test, but contributing more than 30% of NOx emissions. Due to the effect of the power threshold and the 90th cumulative percentile, the cold start data has little influence on the result of the NOx emissions assessment and the maximum variation of the NOx emissions result in this study is an 8% rise. For the HDV for urban use, the variation of the power threshold resulting from vehicle payload is small, no more than 2% in this study. The presence of the power threshold makes almost only the low-power operation in the second half of urban driving have an impact on the NOx emissions calculation, which may make more than 50% of NOx emissions in the PEMS test be neglected. The impact of the low-power operation on NOx emissions calculation result will be significantly enhanced if all windows are considered in the Moving Average Window (MAW) method. In the meantime, the degree of variation is closely related to the NOx emissions level during the first half of urban driving. The maximum deterioration of NOx emission assessment result can be more than 90% in this study.

Hazardous particles during diesel engine cold-start and warm-up: Characterisation of particulate mass and number under the impact of biofuel and lubricating oil.

The increasing share of using biofuels in vehicles (mandated by current regulations) leads to a reduction in particle size, resulting in increased particle toxicity. However, existing regulations disregarded small particles (sub-23 nm) that are more toxic. This impact is more significant during vehicle cold-start operation, which is an inevitable frequent daily driving norm where after-treatment systems prove ineffective. This study investigates the impact of biofuel and lubricating oil (as a source of nanoparticles) on the concentration, size distribution, median diameter of PN and PM, and their proportion at size ranges within accumulation and nucleation modes during four phases of cold-start and warm-up engine operation (diesel-trucks/busses application). The fuels used were 10% and 15% biofuel and with the addition of 5% lubricating oil to the fuel. Results show that as the engine warms up, PN for all the fuels increases and the size of particles decreases. PN concentration with a fully warmed-up engine was up to 132% higher than the cold-start. Sub-23 nm particles accounted for a significant proportion of PN (9%) but a smaller proportion of PM (0.1%). The fuel blend with 5% lubricating oil showed a significant increase in PN concentration and a decrease in particle size during cold-start.

Enhancing e-commerce recommendation systems through approach of buyer's self-construal: necessity, theoretical ground, synthesis of a six-step model, and research agenda.

The current recommendation system predominantly relies on evidential factors such as behavioral outcomes and purchasing history. However, limited research has been conducted to explore the use of psychological data in these algorithms, such as consumers' self-perceived identities. Based on the gap identified and the soaring significance of levering the non-purchasing data, this study presents a methodology to quantify consumers' self-identities to help examine the relationship between these psychological cues and decision-making in an e-commerce context, focusing on the projective self, which has been overlooked in previous research. This research is expected to contribute to a better understanding of the cause of inconsistency in similar studies and provide a basis for further exploration of the impact of self-concepts on consumer behavior. The coding method in grounded theory, in conjunction with the synthesis of literature analysis, was employed to generate the final approach and solution in this study as they provide a robust and rigorous basis for the findings and recommendations presented in this study.

3D graph neural network with few-shot learning for predicting drug-drug interactions in scaffold-based cold start scenario.

Understanding drug-drug interactions (DDI) of new drugs is critical for minimizing unexpected adverse drug reactions. The modeling of new drugs is called a cold start scenario. In this scenario, Only a few structural information or physicochemical information about new drug is available. The 3D conformation of drug molecules usually plays a crucial role in chemical properties compared to the 2D structure. 3D graph network with few-shot learning is a promising solution. However, the 3D heterogeneity of drug molecules and the discretization of atomic distributions lead to spatial confusion in few-shot learning. Here, we propose a 3D graph neural network with few-shot learning, Meta3D-DDI, to predict DDI events in cold start scenario. The 3DGNN ensures rotation and translation invariance by calculating atomic pairwise distances, and incorporates 3D structure and distance information in the information aggregation stage. The continuous filter interaction module can continuously simulate the filter to obtain the interaction between the target atom and other atoms. Meta3D-DDI further develops a FSL strategy based on bilevel optimization to transfer meta-knowledge for DDI prediction tasks from existing drugs to new drugs. In addition, the existing cold start setting may cause the scaffold structure information in the training set to leak into the test set. We design scaffold-based cold start scenario to ensure that the drug scaffolds in the training set and test set do not overlap. The extensive experiments demonstrate that our architecture achieves the SOTA performance for DDI prediction under scaffold-based cold start scenario on two real-world datasets. The visual experiment shows that Meta3D-DDI significantly improves the learning for DDI prediction of new drugs. We also demonstrate how Meta3D-DDI can reduce the amount of data required to make meaningful DDI predictions.

Ice Formation during PEM Fuel Cell Cold Start: Acceptable or Not?

Proton exchange membrane (PEM) fuel cell faces the inevitable challenge of the cold start at a sub-freezing temperature. Understanding the underlying degradation mechanisms in the cold start and developing a better starting strategy to achieve a quick startup with no degradation are essential for the wide application of PEM fuel cells. In this study, the comprehensive in situ non-accelerated segmented techniques are developed to analyze the icing processes and obtain the degradation mechanisms under the conditions of freeze-thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time. A detailed degradation mechanism map in the cold start of PEM fuel cells is proposed to demonstrate how much degradation occurs under different conditions, whether the ice formation is acceptable under the actual operating conditions, and how to suppress the ice formation. Moreover, an ideal starting strategy is developed to achieve the cold start of PEM fuel cells without degradation. This map is highly valuable and useful for researchers to understand the underlying degradation mechanisms and develop the cold start strategy, thereby promoting the commercialization of PEM fuel cells.

An integrated RF energy-harvesting system with broad input voltage range and high power conversion efficiency.

By 2050, countries around the world are expected to be gradually phasing out fossil fuels and implementing greener energy technologies. In this work, we present a system employing Energy harvesting, a self-powered technology that can recycle energy from the surrounding environment. A high-efficiency radio frequency (RF) energy-harvesting chip was designed and fabricated. With an off-chip antenna and rectifier, the system scavenges ambient RF energy and converts it into usable energy, which is then stored in energy storage elements (such as a supercapacitor or a rechargeable battery). The system can further be implemented as an energy source for charging smart devices. The system-on-chip design consists of a cold start block, a boost converter with maximum power point tracking functionalities, and a charging block. The chip was fabricated using AMS 350 nm technology. Although the system was optimized for harvesting RF energy, it can be easily adapted to harvest other energy sources (i.e., mechanical and thermal energy sources). Using an optimized cold start architecture, the circuit has a cold start voltage of 380 mV. With an improved control strategy of power conversion, the system is capable of continuously charging up to 4.5 V with a broad input voltage range of 100 mV to 10 V and has a peak charging efficiency of 82%.

Updating emission inventories for vehicular organic gases: Indications from cold-start and temperature effects on advanced technology cars.

How would the organic gas emission inventories of future urban vehicles change with new features of advanced technology cars? Here, volatile organic compounds (VOCs) and intermediate volatile organic compounds (IVOCs) from a fleet of Chinese light-duty gasoline vehicles (LDGVs) were characterized by chassis dynamometer experiments to grasp the key factors affecting future inventory accuracy. Subsequently, the VOC and IVOC emissions of LDGVs in Beijing, China, from 2020 to 2035, were calculated and the spatial-temporal variations were recognized under a scenario of fleet renewal. With the tightening of emission standards (ESs), cold start contributed a larger fraction of the total unified cycle VOC emissions due to the imbalanced emission reductions between operating conditions. It took 757.47 ± 337.75 km of hot running to equal one cold-start VOC emission for the latest certified vehicles. Therefore, the future tailpipe VOC emissions would be highly dependent on discrete cold start events rather than traffic flows. By contrast, the equivalent distance was shorter and more stable for IVOCs, with an average of 8.69 ± 4.59 km across the ESs, suggesting insufficient controls. Furthermore, there were log-linear relationships between temperatures and cold-start emissions, and the gasoline direct-injection vehicles performed better adaptability under low temperatures. In the updated emission inventories, the VOC emissions were more effectively reduced than the IVOC emissions. The start emissions of VOCs were estimated to be increasingly dominant, especially in wintertime. By winter 2035, the contribution of VOC start emissions could reach 98.98 % in Beijing, while the fraction of IVOC start emissions would decrease to 59.23 %. Spatially allocation showed that the high emission regions of tailpipe organic gases from LDGVs have transferred from road networks to regions of intense human activities. Our results provide new insights into tailpipe organic gas emissions of gasoline vehicles, and can support future emission inventories and refined assessment of air quality and human health risk.

Impacts on real-world extra cold start emissions: Fuel injection, powertrain, aftertreatment and ambient temperature.

Vehicles emit substantial amounts of pollutants during start periods. Engine starts mainly occur in urban areas, causing serious harm to humans. To investigate the impacts on extra cold start emissions (ECSEs), eleven China 6 vehicles with various control technologies (fuel injection, powertrain, and aftertreatment) were monitored with a portable emission measurement system (PEMS) at different temperatures. For conventional internal combustion engine vehicles (ICEVs), the average ECSEs of CO2 increased by 24%, while the average ECSEs of NOx and particle number (PN) decreased by 38% and 39%, respectively, with air conditioning (AC) on. Gasoline direct injection (GDI) vehicles had 5% lower CO2 ECSEs, but 261% higher NOx ECSEs and 318% higher PN ECSEs than port fuel injection (PFI) vehicles at 23 °C. The average PN ECSEs were significantly reduced by gasoline particle filters (GPFs). The GPF filtration efficiency was higher in GDI than PFI vehicles due to particle size distribution. Hybrid electric vehicles (HEVs) generated excessive PN extra start emissions (ESEs), resulting in a 518% increase compared to ICEVs. The start times of the GDI-engine HEV accounted for 11% of the whole test time, but the proportion of PN ESEs relative to total emissions were 23%. Linear simulation based on the decrease in ECSEs with increasing temperature underestimated the PN ECSEs from PFI and GDI vehicles by 39% and 21%, respectively. For ICEVs, CO ECSEs varied with temperature in a U shape with a minimum at 27 °C; NOx ECSEs decreased as ambient temperature increased; PFI vehicles generated more PN ECSEs at 32 °C than GDI vehicles, stressing the significance of ECSEs at high temperature. These results are useful for improving emission models and assessing air pollution exposure in urban aeras.

Effects of Hydrophobicity Treatment of Gas Diffusion Layers on Ice Crystallization in Polymer Electrolyte Fuel Cells.

The unassisted cold-start capability of polymer electrolyte fuel cells (PEFCs) remains challenging for large-scale automotive applications. Various studies have shown that the freezing of produced water at the cathode catalyst layer (CL) and gas diffusion layer (GDL) interface blocks the oxidant gas and leads to a cold-start failure. However, the impact of GDL properties, including substrate, size, and hydrophobicity, on the freezing behavior of supercooled water is yet to be thoroughly investigated. We use differential scanning calorimetry to perform non-isothermal calorimetric measurements on untreated and waterproofed GDLs (Toray TGP-H-060, Freudenberg H23). By conducting a large number of experiments (>100) for each type of GDL, we obtained the corresponding distribution of onset freezing temperature (Tonset) and found noticeable sample-to-sample variations in both untreated and waterproofed GDLs. Furthermore, ice crystallization is affected by GDL wettability, coating load, coating distribution, and GDL size, whereas the impact of the GDL substrate and saturation level is not apparent. The Tonset distribution allows for predicting the capability of PEFC freeze-start and the freezing probability of residual water at a given subzero temperature. Our work paves the way for GDL modifications toward the improved cold-start capability of PEFC by identifying and avoiding the features that systematically trigger the freezing of supercooled water with high probability.

Cold-Start NOx Mitigation by Passive Adsorption Using Pd-Exchanged Zeolites: From Material Design to Mechanism Understanding and System Integration.

It remains a major challenge to abate efficiently the harmful nitrogen oxides (NOx) in low-temperature diesel exhausts emitted during the cold-start period of engine operation. Passive NOx adsorbers (PNA), which could temporarily capture NOx at low temperatures (below 200 °C) and release the stored NOx at higher temperatures (normally 250-450 °C) to downstream selective catalytic reduction unit for complete abatement, hold promise to mitigate cold-start NOx emissions. In this review, recent advances in material design, mechanism understanding, and system integration are summarized for PNA based on palladium-exchanged zeolites. First, we discuss the choices of parent zeolite, Pd precursor, and synthetic method for the synthesis of Pd-zeolites with atomic Pd dispersions, and review the effect of hydrothermal aging on the properties and PNA performance of Pd-zeolites. Then, we show how different experimental and theoretical methodologies can be integrated to gain mechanistic insights into the nature of Pd active sites, the NOx storage/release chemistry, as well as the interactions between Pd and typical components/poisons in engine exhausts. This review also gathers several novel designs of PNA integration into modern exhaust after-treatment systems for practical application. At the end, we discuss the major challenges, as well as important implications, for the further development and real application of Pd-zeolite-based PNA in cold-start NOx mitigation.

Research on Cold Start of Proton-Exchange Membrane Fuel Cells Based on Model Predictive Control.

The cold start of fuel cells limits their wide application. Since the water produced by fuel cells takes up more space when it freezes, it may affect the internal structure of the stack, causing collapse and densification of the pores inside the catalytic layer. This paper mainly analyzes the influence of different startup strategies on the stack cold start, focusing on the change in the stack temperature and the ice volume fraction of the catalytic layer. When designing a startup strategy, it is important to focus not only on the optimization of the startup time, but also on the principle of minimizing the damage to the stack. A lumped parameter cold-start model was constructed, which was experimentally verified to have a maximum error of 8.9%. On this basis, a model predictive control (MPC) algorithm was used to control the starting current. The MPC cold-start strategy reached the freezing point at 17 s when the startup temperature was -10 °C, which is faster than other startup strategies. Additionally, the time to ice production was controlled to about 20 s. Compared with the potentiostatic strategy and maximum power strategy, MPC is optimal and still has great potential for further optimization.