Terms Of Energy Cost Research Articles

Optimization of single and double pass solar air heater-phase change material (SAH-PCM) system based on thickness to length ratio

This paper presents a two-dimensional transient model for a solar air heater with phase change material (SAH-PCM), focusing on the thickness-to-length ratio (t/L) of the PCM container. Verified through experiments, the model considers single (SP) and double pass (DP) flow configurations, assessing liquid fraction, dead length, outlet temperature, energy, exergy, and system efficiency during PCM charging and discharging. Increasing the t/L ratio improves peak liquid fraction, reduces dead length, and delays PCM discharge in both configurations, enhancing system energy and exergy efficiencies. In a SP flow system, optimizing t/L at 0.042 yields a peak liquid fraction of 0.928, a 92.28% dead length reduction, and a 2 h and 22 min delay for PCM discharge (base-case t/L ratio: 0.0182), achieving a highest discharging efficiency of 39.1%. For a DP flow system, the optimized t/L ratio of 0.058 results in a peak liquid fraction of 0.976 and a 1 hour and 34 min delay for PCM discharge, attaining the highest discharging efficiency of 63.34%. Moreover, an improvement in the energy payback time (EPBT) is observed for both SP and DP flow as the t/L is increased. For the DP flow, a minimum EPBT of 0.68 years (energy basis) and 6.55 years (exergy basis) is observed at a t/L ratio of 0.058. In terms of energy cost, SP flow reaches a minimum of Rs. 20.824 Rs/kWh at a t/L ratio of 0.042. For DP flow, the minimum energy cost is 13.033 Rs/kWh at a t/L ratio of 0.058, representing a noTable 37.42% improvement compared to SP flow. These findings underscore the critical role of optimizing system parameters, specifically the t/L ratio, for enhanced energy and exergy performance, and economic viability of SAH-PCM.

Integrating Cargo Bikes and Drones into Last-Mile Deliveries: Insights from Pilot Deliveries in Five Greek Cities

Currently, there is a growing consensus that the use of more sustainable urban freight transportation has the potential to offer major energy and efficiency benefits which can be achieved through the appropriate combination of cargo bikes and the integration of drones into the urban logistics system. This study presents the results of a stepwise regression analysis that examines the role, benefits, and impact of electric vehicles such as e-bikes, cargo bikes, and drones for intermodal freight transportation in five Greek cities to support the green logistics paradigm. The results show that between routes of almost equal length, the most bicycle-friendly routes, such as routes along pedestrian areas or residential streets, should be avoided, as they reduce delivery speed and increase delivery costs in terms of energy and time expenditure. In addition, priority roads, which usually have higher traffic speeds and more trucks, are preferred by cyclists due to the quality of the road surface, even though the feeling of safety is lower on such roads. Finally, regarding drones, energy consumption is relatively low in the 0–5 mph and 6–10 mph wind speed ranges, indicating efficient energy use. In the 11–15 mph and 16–20 mph wind speed ranges, energy efficiency is significantly lower than the battery capacity, indicating that the cargo drone exhibits excessive energy consumption under these conditions.

Plasma nitrogen fixation system with dual-loop enhancement for improved energy efficiency and its efficacy for lettuce cultivation

Plasma nitrogen fixation (PNF) has been emerging as a promising technology for greenhouse gas-free and renewable energy-based agriculture. Yet, most PNF studies seldom address practical application-specific issues. In this work, we present the development of a compact and automatic PNF system for on-site agricultural applications. The system utilized a gliding-arc discharge as the plasma source and employed a dual-loop design to generate from air and water under atmospheric conditions. Experimental results showed that the system with a dual-loop design performs well in terms of energy costs and production rates. Optimal operational parameters for the system were determined through experimentation, resulting in an energy cost of 13.9 MJ mol−1 and an energy efficiency of 16 g kWh−1 for production, respectively. Moreover, the concentration of exhausted NO x was below the emission standards. Soilless lettuce cultivation experiments demonstrated that produced by the PNF system could serve as liquid nitrate nitrogen fertilizer. Overall, our work demonstrates the potential of the developed PNF system for on-site application in the production of green-leaf vegetables.

In Situ Investigation of Electrochemically Mediated Carbon Capture and Release Via Quinone Chemistry in Aqueous Media

The development of devices capable of capturing and sequestering carbon dioxide emissions directly from air or at point sources is motivated by mitigating climate change. Electrochemically driven carbon capture and release at ambient temperature and pressure through the formation of complexes with organic redox active molecules [1,2] or via pH swing caused by proton-coupled electron transfer during molecular redox events [3,4] has shown promising results for carbon capture and release in terms of energy cost, scalability and safety compared to the state of the art amine scrubbing technologies [5].Quinones are organic molecules containing an unsaturated six-member ring with two carbonyl groups. They exhibit two potential mechanisms of CO2 capture. (a) Upon electrochemical reduction, the carbonyl groups within quinones provide sites for nucleophilic addition of carbon dioxide which results in the formation of a quinone-CO2 adduct (CO2reversibly bound by quinones). (b) Depending on the pKa of the quinone and the local pH, the electrochemical reduction of the compound can be proton-coupled, creating hydroxide, which captures CO2 as carbonate or bicarbonate.In this work, we introduce in situ electrochemical characterization and in situ fluorescence microscopy to separately quantify the two aforementioned contributions toward capture of CO2 at partial pressures ranging from 0.1 bar to 0.4 mbar. The non-invasive in situ analytical methods introduced in this work are used to distinguish, with sub-second time resolution, between the oxidized, reduced and adduct species by their electrochemical and fluorescence signatures. The results of this study permits us to understand the underlying simultaneous contributions of quinone-CO2 adduct path and the pH-swing path toward carbon capture and release and, additionally, introduces powerful non-invasive methods for gathering insights on similar systems. 1X. Li, X. Zhao, Y. Liu, T.A. Hatton, and Y. Liu, "Redox-tunable lewis bases for electrochemical carbon dioxide capture", Nature Energy 7, 1065 (2022). 2Y. Liu, H.Z. Ye, K.M. Diederichsen, T. Van Voorhis, and T.A. Hatton, "Electrochemically mediated carbon dioxide separation with quinone chemistry in salt-concentrated aqueous media", Nat Commun 11, 2278 (2020). 3S. Jin, M. Wu, Y. Jing, R.G. Gordon, and M.J. Aziz, "Low energy carbon capture via electrochemically induced pH swing with electrochemical rebalancing", Nat Commun 13, 2140 (2022). 4S. Jin, M. Wu, R.G. Gordon, M.J. Aziz, and D.G. Kwabi, "pH swing cycle for CO2 capture electrochemically driven through proton-coupled electron transfer", Energy & Environmental Science 13, 3706 (2020). 5G.T. Rochelle, "Amine scrubbing for CO2 capture", Science 325, 1652.

Deep learning and optimization-based task scheduling algorithms for fog-cloud computing environment

Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models.

Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

Photovoltaic (PV) power generation exhibits stochastic and uncertain characteristics. In order to improve the economy and reliability of a photovoltaic-energy storage system (PV-ESS), it is crucial to optimize both the energy storage capacity size and the charging and discharging strategies of the ESS. An optimal scheduling model for PV-ESS is proposed in this paper, comprehensively considering factors in terms of energy cost and charging/discharging constraints of the PV-ESS. Moreover, the model employs a particle swarm optimization-backpropagation (PSO-BP) neural network to predict the PV power using historical generation data from a factory in Xiamen. The proposed two PV-ESS scheduling strategies are compared under three weather conditions. In the demand management strategy, the ESS can flexibly respond to different weather conditions and load demand changes, and effectively reduce the electricity cost for users.

A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times

We consider a problem from the context of energy-efficient underground railway timetabling, in which an existing timetable draft is improved by slightly changing departure and running times. In practice, synchronization between accelerating and braking trains to utilize regenerative braking plays a major role for the energy efficiency of a timetable. Because deviations from a planned timetable may lead to unnecessarily high energy consumption during actual operation, we include operational uncertainties in our model to create a timetable that remains energy efficient, even if deviations from the nominal timetable occur. To solve the problem, we use a scenario expansion model in conjunction with a Benders decomposition approach. As an alternative to solving the Benders subproblems, we present a heuristic sparse cut that can be computed efficiently. The resulting sparse-cut heuristic produces high-quality solutions on a set of real-world instances stemming from the Nürnberg underground system, outperforming the integrated mixed-integer programming approach as well as the basic Benders approach. Additionally, we evaluate two static recovery strategies—shortening dwell times as well as shortening dwell and running times—to determine the cost and benefit of handling delays using a simple static rule. In our experiments, we are able to reduce the energy consumption by up to 9.4% and confirm that delay recovery via shortening dwell times is an energy-efficient and effective way to increase punctuality at low cost in terms of energy. Funding: This research was supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy through the Center for Analytics – Data – Applications (ADA-Center) within the framework of “BAYERN DIGITAL II” (20-3410-2-9-8).

Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network

Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.

Modular Construction of Industrial Buildings and Lean Thinking—Identifying the Role of Daylight through a Case Study

This research looks at the optimisation of industrial buildings through the application of the principles of lean thinking and philosophy, with an emphasis on daylighting in the design of industrial buildings. With the use of multiparametric analysis tools, we provide a solution for the optimized design of a roof system for the provision of daylight, whilst maximising the benefits and minimising the cost during its lifetime, in different geographic and climatic regions in Greece. An optimisation algorithm has been proposed that improves the selection of the optimal roof opening type and geometry for industrial buildings in different geographical locations. The investigation of a roof system model was based on the maximum performance of daylighting, while reducing unnecessary energy use and cost. To reach our solution, we investigated the sawtooth roofing system in terms of energy cost (cooling, heating, and lighting), geography (orientation, location), and building variables (the opening dimensions and number). This has been achieved through the use of multi-parametric design, computational simulations, genetic algorithms, and the post-processing of results through statistical analysis. The use of natural lighting proved to be an effective sustainability strategy, providing energy savings of up to 20–30%, and offering economic advantages, hence presenting a comprehensive approach that benefits stakeholders and end-users by reducing the thermal loads, cooling requirements, initial HVAC costs, and overall waste. The developed algorithm has identified the optimal opening size and distance as ranging between 10 and 11 m for the conditions examined.

Performance Evaluation of High-Rise Buildings Integrated with Colored Radiative Cooling Walls in a Hot and Humid Region

Radiative sky cooling is an appealing form of heat exchange between terrestrial objects and outer space through thermal radiation, which is attracting worldwide interest due to its nature as passive cooling, that is, cooling without consuming energy. Due to a recent breakthrough in material science, sub-ambient daytime radiative sky cooling has been effectively achieved, which has significantly stimulated research interest in this field. In view of the numerous radiative coolers being reported as having excellent spectral properties and cooling ability under sunlight, integrating these superb cooling materials into building skins is a promising route to implementing radiative sky cooling technology. To this end, this study deploys state-of-the-art colored radiative cooling coatings as a new retrofitting strategy for building walls, and then conducts a comprehensive performance evaluation by considering a high-rise building situated in the hot-humid city of Hong Kong. Potential benefits of implementing differently colored cooling wall strategies, including their performance regarding thermal insulation, energy savings, economic viability, and environmental sustainability, were thoroughly investigated. The obtained results elucidate that for the utilization of the porous P(VdF-HFP)-based bilayer wall, relative to the monolayer, the frequency of the wall temperature exceeding the surrounding environment on an annual basis can be further reduced by up to 4.8%, and the yearly savings in cooling electricity vary from 855.6 to 3105.6 kWh (0.4–1.5%) with an average of 1692.4 kWh. Besides this, the yearly savings in net electricity cost vary from 1412.5 to 5127.3 HKD and the reduction in carbon emissions ranges from 1544.4 to 5606.1 kg with an average of 3055.0 kg. In addition, discussions of the combination of the super-cool roof strategy with blue porous polymer-based cooling walls reveal that the achievable savings in terms of energy costs and reductions in carbon emissions are 1.6 and 2.2 times more than either the application of the super-cool roof or porous polymer bilayer walls alone, respectively. This research offers new understandings of the deployment of colored cooling coatings on vertical building façades in hot and humid regions, which can considerably facilitate the realization of low-energy buildings in a passive approach for stakeholders.

Environmentally Sustainable Green Roof Design for Energy Demand Reduction

Green roofs are artificial ecosystems that provide a nature-based solution to environmental problems such as climate change and the urban heat island effect by absorbing solar radiation and helping to alleviate urban environmental, economic, and social problems. Green roofs offer many benefits in terms of heat and water conservation as well as in terms of energy costs. This work proposes the design of an extensive and environmentally sustainable green roof for the Faculty of Engineering building in Bilbao. The green roof will be made from the composting of food waste generated in the building’s own canteen. Therefore, the main objective of this study is to calculate the solar efficiency of a sustainable green roof, evaluate its thermal performance, and quantify the impact that its implementation would have on energy consumption and the thermal comfort of its users. The results obtained confirm that an environmentally sustainable green roof has a positive effect on summer energy consumption and that this effect is much greater when there is water on the roof, as shown by the difference in energy savings between the dry (−53.7%) and wet (−84.2%) scenarios. The data show that in winter the differences between a green roof and a non-vegetated roof are not significant. In this case, the estimated energy consumption penalty (0.015 kWh/m2) would be 10% of the summer gain.

LONGTERM OPTIMIZATION MODEL FOR THE GAMBIA’S ENERGY TRANSITION

The energy sector in the Gambia is entirely dependent on imported petroleum products for electricity generation. The country is not only vulnerable to global oil market disruptions but also has an unstable electricity supply system. As of 2020, a huge portion of the country’s electricity demand remains unsatisfied. Following recent government intervention to improve the energy system, this paper examined the optimal capacity expansion planning using the open-source energy modelling system (OSeMOSYS) on a time horizon of thirty years, (2020-2050). The three scenarios studied include the business as usual (BAU), which reflects the continuation with the existing power generation pattern, the second scenario (roadmap) aligns with the Gambia’s strategic electricity roadmap (2021-2040) and the third scenario considered high renewable penetration in the energy mix. Contrary to policy makers expectations, renewables accounted for only 19.2% of electricity generation in the roadmap by 2030 and 11% by 2050. CO2 emission in 2050 reduced by 31% in the roadmap scenario and 71% in the new scenario. The global cost of the new scenario is 29% more expensive than the roadmap scenario but becomes more competitive than the roadmap in terms of energy cost when the capital investment is fully subsidised in all scenarios.

Cooperative quantum information erasure

We demonstrate an information erasure protocol that resets N qubits at once. The method displays exceptional performances in terms of energy cost (it operates nearly at Landauer energy cost kTln⁡2), time duration (∼μs) and erasure success rate (∼99,9%). The method departs from the standard algorithmic cooling paradigm by exploiting cooperative effects associated to the mechanism of spontaneous symmetry breaking which are amplified by quantum tunnelling phenomena. Such cooperative quantum erasure protocol is experimentally demonstrated on a commercial quantum annealer and could be readily applied in next generation hybrid gate-based/quantum-annealing quantum computers, for fast, effective, and energy efficient initialisation of quantum processing units.

Prospects for the development of hydrogen technologies in the domestic metallurgy

The inevitability of the development of carbon-free steel production technologies in the domestic metallurgy is due not only under the pressure of environmental requirements for decarbonization, but also by the need to bring steelmaking technologies in line with the level of modern science. The electronic theory of oxidation/reduction can serve as a scientific basis for new technologies for the reduction of metals. From the standpoint of this theory, the final stage of the electrochemical process of reducing metals with any reducing agent, including hydrogen, is the electrolysis of oxides with the release of metal and gaseous oxygen under the action of an electron flow generated in the fuel cell during the combustion of the reducing agent due to the oxygen of the oxides. Comparison of two technology options ‒ iron reduction by “green” hydrogen or direct electrolysis of ore oxides using electricity from the electrical network shows a multiple advantage of electrolysis in terms of energy costs. It is concluded that the priority in the development strategy of the industry should not be given to the production and use of “green” hydrogen, but to the electrolysis of ore. The use of hydrogen as a reducing agent can be justified in the selective extraction of iron from complex ores in units such as plasma shaft furnaces, plasma reactors or fluidized bed furnace, in which, along with reduction, nitriding would also take place to convert soft iron into steel. In Russia, there is already an opportunity to master the production of steel using “green” or “turquoise” hydrogen. It is most rational to start the introduction of hydrogen reduction of iron on the basis of the deposits of titanomagnetites of the Kola Peninsula using the excess capacity of the Kola NPP to produce hydrogen

Design and Optimization of a Hybrid Solar–Wind Power Generation System for Greenhouses

The climate crisis and energy price increases make energy supply a crucial parameter in the design of greenhouses. One way to tackle both these issues is the local production of energy from renewable sources. Since the permitted photovoltaic power installation on a greenhouse roof is limited by the need for an adequate amount of photosynthetically active radiation at the crop level, the necessity of designing a hybrid production system combining different renewable sources, storage systems, and conventional sources arises. The present work addresses the multifactorial problem of the optimal design (in terms of energy production quality, produced electricity price and CO2 emissions) of a hybrid power generation system (photovoltaics/wind turbine/accumulators/oil generating unit) to meet greenhouse needs. The design accounts for the needs of production (for tomato cultivation) for different combinations of production and energy equipment (for microclimate management). Extended parametric studies for available solar and wind potential and energy demand are used to generalize the conclusions. Special attention is given to the contribution of various wind turbine sizes. The effect of greenhouse orientation and of photovoltaic modules arrangement on arched roofs is also examined and the different greenhouse energy systems are assessed in terms of energy cost and environmental footprint.

Minimizing Energy Cost in PV Battery Storage Systems Using Reinforcement Learning

This article addresses the development and tuning of an energy management for a photovoltaic (PV) battery storage system for the cost-optimized use of PV energy using of reinforcement learning (RL). An energy management concept based on the Proximal Policy Optimization algorithm in combination with recurrent Long Short-Term Memory neural networks is developed for data-based policy learning. As a reference system for the simulation-based investigations, a PV battery storage system is modelled, parametrized and implemented with an interface for the RL algorithm. To demonstrate the generalization capability of the learned energy management, 98 training and 12 evaluation episodes, each with a length of one year, are generated from an empirical dataset of global radiation and load power time series. To improve the convergence speed and stability of the RL algorithm as well as the learned policy with regards to techno-economic metrics, an extensive hyperparameter study is conducted by training 216 control policies with different hyperparameter configurations. Simulation-based benchmark tests of the learned energy management against conventional rule-based and model-predictive energy managements show that the RL-based concept can achieve slightly better results in terms of energy costs and the amount of energy fed into the grid than the commonly used model-predictive method.

A Graph-Based Approach to Generate Energy-Optimal Robot Trajectories in Polygonal Environments

As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude.

Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm

The purpose of this study is to develop an effective control method for a hybrid energy storage system composed by a flow battery for daily energy balancing and a lithium-ion battery to provide peak power. It is assumed that the system operates behind the meter, the goal is to minimize the energy cost in the presence of a PV installation (as an example of a local renewable source) and energy prices are determined by 3-zone tariffs. The article presents the application of an optimization method to schedule the operation of each battery in the system. The authors have defined an optimization method aimed at minimizing the total cost of the system, taking into account energy costs and batteries depreciation. The techno-economical model of the system, including battery degradation, is constructed and the cost optimization methods are implemented in Python. The results are validated with real energy and price profiles and compared with conventional control strategies. The advantages of optimization in terms of energy cost are discussed. The experiment shows that not only is a hybrid energy system successful in lowering the total operation cost and in increasing self-consumption but also that the implemented methods have slightly different properties, benefits and issues.

Minimum Scan Cover and Variants: Theory and Experiments

We consider a spectrum of geometric optimization problems motivated by contexts such as satellite communication and astrophysics. In the problem Minimum Scan Cover with Angular Costs , we are given a graph G that is embedded in Euclidean space. The edges of G need to be scanned, i.e., probed from both of their vertices. To scan their edge, two vertices need to face each other; changing the heading of a vertex incurs some cost in terms of energy or rotation time that is proportional to the corresponding rotation angle. Our goal is to compute schedules that minimize the following objective functions: (i) in Minimum Makespan Scan Cover (MSC-MS), this is the time until all edges are scanned; (ii) in Minimum Total Energy Scan Cover (MSC-TE), the sum of all rotation angles; and (iii) in Minimum Bottleneck Energy Scan Cover (MSC-BE), the maximum total rotation angle at one vertex. Previous theoretical work on MSC-MS revealed a close connection to graph coloring and the cut cover problem, leading to hardness and approximability results. In this article, we present polynomial-time algorithms for one-dimensional (1D) instances of MSC-TE and MSC-BE but NP-hardness proofs for bipartite 2D instances. For bipartite graphs in 2D, we also give 2-approximation algorithms for both MSC-TE and MSC-BE. Most importantly, we provide a comprehensive study of practical methods for all three problems. We compare three different mixed-integer programming and two constraint programming approaches and show how to compute provably optimal solutions for geometric instances with up to 300 edges. Additionally, we compare the performance of different meta-heuristics for even larger instances.

A Study on the Performance Modeling Method for a Deep-Sea Cobalt-Rich Crust Mining Vehicle

To mine the cobalt-rich crust deposits at the substrate of the slope of the seamount, the mining vehicle needs to walk on the slope and strip the cobalt-rich crust from the substrate. To achieve safety and control, the vehicle needs to stably walk on the slope. In this paper, we propose a modeling method for mining vehicle performance based on slope stability and antislip and antiskid conditions. The specific energy of three types of mining vehicles is compared based on dimensionless parameters. The dual-drum type is determined to be the best choice in terms of energy costs, followed by the down-milling type. The performance of the compact mining vehicle based on different slope angles and two sea trial results in the South China Sea indicate that the slope angle, substrate, and crust have significant effects on the stability. The modeling method proposed in this paper can help designers estimate the structure and power of mining vehicles based on mining stability.