Transport Energy Research Articles

Synergistic Ability and Effect of Leaf Color and Leaf Thickness to Improve the Photosynthetic Performance of Wheat

Leaf traits are important factors affecting the photosynthetic capacity of crops. In Bainong 4199 (BN4199) and Aikang 58 (AK58) wheat, the role of leaf color and leaf thickness in improving wheat photosynthetic performance and its influence on material accumulation and yield were studied in the field environment. Compared with AK58, BN4199 has a deeper leaf color and thicker leaves. Further study on photosynthetic physiological characteristics showed that the photosynthetic capacity of BN4199 with deep color and thick leaves was higher than that of AK58 at flowering stage, 7 days after flowering, 15 days after flowering, and 20 days after flowering regardless of low light in the morning and evening or light at noon. During the flowering stage, the light saturation point and compensation point were 1% higher and 15.23% lower, respectively, in BN4199 than AK58. According to the diurnal variation in chlorophyll content in different growth stages, BN4199 was generally higher than AK58, and the chlorophyll content was the highest at each time point 7 days after flowering. The chlorophyll content was highest at each time point 7 days after flowering. Chlorophyll fluorescence parameters and light reflectance analyses indicated that BN4199 has significantly higher photosynthetic electron transport and population light energy absorption and utilization capacity than AK58. The 2-year field yields indicated significantly higher material accumulation in BN4199 than AK58. In summary, thick leaves with deep color were resistant to both strong light and weak light, thus, markedly increasing photosynthetic efficiency. Improvement in leaf color and leaf thickness might serve as an important index to enhance the photosynthetic performance of wheat, and achieve improvement and breeding of wheat with high light efficiency.

The Truth About Co-Benefits: A Multidimensional Feasibility Assessment for Thailand and Beyond

Abstract Research has yielded increasingly robust estimates of the co-benefits from mitigating climate change while reducing air pollution, improving health, and meeting other development needs. Though quantifying these often hidden benefits could ease cost concerns and lower technological constraints for development-friendly climate solutions, achieving co-benefits frequently requires overcoming difficult-to-measure social and institutional barriers. This study extends insights from research focusing on quantitatively assessing the feasibility of a 1.5oC future to build a multidimensional framework for measuring different barriers to achieving co-benefits. The framework offers a novel yet generalizable approach for bringing context-appropriate assessments of different dimensions of feasibility into the integrated assessment modelling that underpins work on co-benefits. It then outlines five steps for applying that framework to evaluate the size of different barriers for transport, agricultural and residential energy co-benefit solutions in Thailand. The results demonstrate that the sum of the delays from social/institutional barriers exceed economic/technological barriers for four out of six studied solutions. These delays also lead to increases of 24% to 31% in PM2.5 emissions relative to a no-barriers effective implementation scenario between 2015 and 2030 and 2040. The feasibility framework can be integrated into not only national policy scenarios but also project assessments, following trends in carbon finance. An international barriers database as well as strengthening links to work on barriers and technological diffusion, transaction costs, and multi-level transitions can also help spread multi-dimensional feasibility assessments across countries and scales.

Photothermally Cross-Linkable Polymeric Hole Transport Material Functionalized with Azide for High-Performance Quantum Dot Light-Emitting Diodes.

Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-(4,4'-(N-(4-butylphenyl)))] (TFB) is a widely used hole transport material (HTM) in quantum dot light-emitting diodes (QLEDs). However, TFB-based solution-processed QLEDs face several challenges, including interlayer erosion, low hole mobility, shallow energy level of the highest occupied molecular orbital, and current leakage, which compromise the device efficiency and stability. To overcome these challenges, bromine and azide-based photothermally cross-linkable TFB derivatives, i.e., TFB-Br and TFB-N3, were designed and synthesized. TFB-N3 photothermally cross-linked under 254 nm ultraviolet light at 140 °C exhibited excellent solvent resistance within 30 s. Furthermore, the photothermally cross-linked TFB-N3 formed a compact three-dimensional (3D) network in QLEDs, enhancing hole transport and reducing the leakage current. Moreover, the HOMO energy level in photothermally cross-linked TFB-N3 decreased to -5.39 eV from that in TFB (-5.30 eV), reducing the hole transport energy barrier. Thus, the charge balance in the quantum dot (QD) layer was enhanced, and the current leakage was reduced, improving the overall QLED performance. The photothermally cross-linked TFB-N3-based QLEDs achieved a maximum external quantum efficiency of 19.53%, i.e., 61% higher than that of devices using TFB. Moreover, the T90 lifetime of the photothermally cross-linked TFB-N3-based QLEDs was 4.49 times longer than that of TFB-based devices. The proposed strategy demonstrates that incorporating azide groups into polymeric HTMs can considerably enhance their hole transport and solvent resistance and reduce leakage current, improving QLED efficiency and stability.

Exploration of Charging Technology for New Energy Electric Vehicles

As the global consensus on reducing carbon emissions and environmental protection strengthens, electric vehicles, as a kind of clean energy transportation, have received great attention in terms of market demand and technology development. Based on the impact of the "double carbon" target, carbon emissions will officially peak by 2030, and carbon neutrality can be fully achieved by 2060, which provides strong policy support for the development of electric vehicles and their charging technologies. In addition, the large-scale development of electric vehicles promotes the transformation of the energy industry to low-carbon, electrification, and intelligence, and the charging technology, as the key infrastructure for the popularization of electric vehicles, its technological innovation and optimization and upgrading are of great significance for promoting the development of the entire new energy vehicle industry. Therefore, this paper explores the charging technology of new energy electric vehicles through the assessment of the actual needs of industry development, policy orientation, promotion of technological innovation and market potential.

Energy Saving Technologies and Practices in Facility Agriculture in Cold Regions

With the growth of the global population, energy demand continues to rise, making cross-regional energy transportation less viable as a sustainable solution. As a result, the construction of facilities for agriculture in cold regions has become increasingly important in the development of modern agriculture. However, cold region facility agriculture faces significant energy and environmental challenges, necessitating the advancement and application of energy saving technologies. This study addresses the high energy consumption in cold region facility agriculture by experimentally evaluating the integrated effects of geothermal heat pumps, solar collectors, intelligent light control systems, LED plant lamps, and smart ventilation systems in saving energy. The focus is on analyzing the technical adaptability and economic feasibility of these technologies under extremely cold conditions in Heilongjiang. The research findings indicate an overall energy saving rate of 17.8%, with energy savings in heating, lighting, and ventilation systems being 17.6%, 18.6%, and 17.4%, respectively. Economic analysis shows that geothermal heat pumps and high efficiency insulation materials have a short investment payback period and are suitable for widespread adoption, while solar collectors and intelligent light control systems are more appropriate for long-term application. This study demonstrates that intelligent and integrated energy saving technologies have significant potential in cold region facility agriculture, providing both data support and technical references for the efficient, low-carbon development of agriculture in cold climates. This study not only provides scientific evidence for the sustainable development of cold region facility agriculture but also highlights the practical implications of these technologies for reducing energy consumption and promoting low-carbon agriculture.

Spatial Correlation Network and Influencing Factors of Carbon Emissions from Inter-provincial Tourism Transportation in China

Clarifying the spatial correlation network structure from tourism transportation carbon emissions and its influencing factors is crucial for China's tourism and transportation industry to coordinate the planning of carbon reduction governance and realize the sustainable development of the tourism transportation industry. Based on inter-provincial panel data from 2001 to 2021, China's carbon emissions from tourism transportation were measured, and the modified spatial gravity model was used to construct characteristics of provincial spatial networks and their influencing factors, which were analyzed using the social network analysis method and the QAP model. The study showed that ① China's total carbon emissions from tourism and transportation have been growing slowly year by year, showing a distribution pattern of "high in the southeast and low in the northwest," with obvious differences between the eastern and western regions. ② China's carbon emissions from tourism and transportation formed a multi-threaded and complex network of "dense in the east and sparse in the west." The "Matthew effect" in the spatial network was obvious, with eastern provinces such as Beijing, Shanghai, and Guangdong dominating the core and the northwestern and northeastern provinces such as Xinjiang, Qinghai, Heilongjiang, and Liaoning on the periphery. ③ China's carbon emissions from the tourism transportation block model had a clear division structure, and each block had a large number of correlations and received a spatial overflow of carbon emissions from other blocks. ④ Transportation energy intensity and transportation structure had a significant positive effect on the spatial correlation network, while spatial geographic distance, residents' consumption level, and tourism economic efficiency had a significant negative effect on the spatial network.

An assessment of the effects of manual energy and material haulage on the initial embodied environmental impact of residential buildings

The energy inputs associated with all the stages of production of a building are known as initial embodied energy. These stages range from the mining of natural materials to manufacturing, transportation, and construction. . Inadequate local data in the formulation of climate change mitigation strategies have made studies on the effects of construction and transportation energy/emissions very significant. The study aims to appraise the impact of material haulage and site construction processes on the initial embodied energy and emission in the Nigerian context with the view of identifying the effects of manual labour and material haulage. The objectives are: to estimate the transportation energy, and site construction, and identify the percentage of energy from manual work. The study adopted a case study methodology using multiple case studies and integrated the international energy/emission protocol. Construction energy and carbon emissions accounted for 3.9% and 1.52% respectively. Manual energy was found to be significant with an average manual energy intensity for the study area at 9.5MJ/m2. Also, transportation energy accounted for 11.65% of the initial embodied energy and 6.95% of emissions. Thus, recommended sustainable haulage approaches such as the reduction and enforcement of age restrictions on imported used trucks from <15 years to <5 years.

Performance metrics and mechanistic considerations for the development of 3D batteries.

There is an urgent need for improved energy storage devices to enable advances in markets ranging from small-scale applications (such as portable electronic devices) to large-scale energy storage for transportation and electric-grid energy. Next-generation batteries must be characterized by high energy density, high power density, fast charging capabilities, operation over a wide temperature range and safety. To achieve such ambitious performance metrics, creative solutions that synergistically combine state-of-the-art material systems with advanced architectures must be developed. The development of 3D batteries is a promising solution for achieving these targets. However, considerable challenges remain related to integrating the various components of a battery into an architecture that is truly 3D. In this Review, we describe the status of 3D batteries, highlight key advances in terms of mechanistic insights and relevant performance descriptors, and suggest future steps for translating current concepts into commercially relevant solutions.

State-of-the-art advances in homogeneous molecular catalysis for the Guerbet upgrading of bio-ethanol to fuel-grade bio-butanol.

The upgrading of ethanol to n-butanol marks a major breakthrough in the field of biofuel technology, offering the advantages of compatibility with existing infrastructure while simultaneously offering potential benefits in terms of transport efficiency and energy density. With its lower vapour pressure and reduced corrosiveness compared to ethanol, n-butanol is easier not only to manage but also to transport, eliminating the need for costly infrastructure changes. This leads to improved fuel efficiency and reduced fuel consumption. These features position n-butanol as a promising alternative to ethanol in the future of biodiesel. This review article delves into the cutting-edge advancements in upgrading ethanol to butanol, highlighting the critical importance of this transformation in enhancing the value and practical application of biofuels. While traditional methods for making butanol rely heavily on fossil fuels, those that employ ethanol as a starting material are dominated by heterogeneous catalysis, which is limited by the requirement of high temperatures and a lack of selectivity. Homogeneous catalysts have been pivotal in enhancing the efficiency and selectivity of this conversion, owing to their unique mode of operation at the molecular level. A comprehensive review of the various homogeneous catalytic processes employed in the transformation of feedstock-agnostic bio-ethanol to fuel-grade bio-n-butanol is provided here, with a major focus on the key advancements in catalyst design, reaction conditions and mechanisms that have significantly improved the efficiency and selectivity of these Guerbet reactions.

Global Hybrid Simulation of Dayside Magnetopause Energy Transport Under Purely Southward Interplanetary Magnetic Field

AbstractIn this paper, dayside magnetopause energy transport (energy transport across the separatrix surface to the magnetopause boundary layer and energy transport inside the magnetopause boundary layer) and its dependence on the magnetopause dynamic evolution under purely southward interplanetary magnetic field (IMF) conditions are studied via a 3‐D global hybrid simulation. By investigating the energy transport across the separatrix surface, current layer surface, and magnetopause surface, we find that the energy transport from the magnetosheath to the magnetopause boundary layer is mainly in the form of electromagnetic energy, while the energy transport directly across the magnetopause surface to the magnetosphere is mainly in the form of plasma energy. The energy transport across the magnetopause surface exhibits temporal variability, driven by the dynamic evolution of reconnection and flux rope. During the development of multiple X‐lines reconnection and flux rope, a substantial portion of solar wind energy does not directly penetrate the dayside magnetopause to the magnetosphere. Instead, it is transported with the reconnection outflow and flux rope from low latitude to high latitude, and with the drifting flow from the subsolar region to the tail magnetopause within the magnetopause current layer. These results significantly improve our understanding of solar wind‐magnetosphere coupling at the dayside magnetopause.

Safeguarding India’s Energy Security: Challenges and Strategic Approaches

This paper focuses on India’s energy security. Data shows that India’s domestic energy reserves and production alone are insufficient to meet its rapidly growing energy demands, necessitating substantial energy imports from abroad. Major import sources include the Middle East, Russia, the United States, and Africa. However, this heavy reliance on energy imports poses risks to India’s energy security. To address these challenges, India can enhance its energy security by maintaining and expanding energy import channels, ensuring the safety of energy transportation, and vigorously developing clean energy sources.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New Energy Conservation

The key areas of interest in respect of which analyses are provided within the context of the paper are sustainable business practices that can help drive the change towards a low carbon economy. The study focuses on advanced techniques for managing businesses to advance the link between economic development and environmental conservation to optimize economic and environmental performance. Furthermore, based on the features, discussed in the paper, it is possible to identify the meaning of the concepts making the organizations introduce sustainability factors, as well as the possible effect these decisions may have on their future performance. This paper explores the role of machine learning (ML) and data-driven business strategies in promoting sustainable business development within smart cities. By integrating ML models across various sectors such as energy management, transportation, waste management, water usage, public safety, and urban planning, cities can optimize resources, reduce environmental impact, and enhance overall efficiency. Results from implementing these strategies in a smart city context show a 10% reduction in energy consumption (from 200,000 kWh to 180,000 kWh), a 50% increase in renewable energy share (from 20% to 30%), and a 40% decrease in CO2 emissions (from 500 tons to 300 tons) due to the use of autonomous electric vehicles. In transportation, average traffic speed improved by 16.67% (from 30 km/h to 35 km/h), and public transport efficiency increased by 21.43% (from 70% to 85%). In waste management, waste collection costs were reduced by 20% (from $50,000 to $40,000), and the recycling rate increased by 40% (from 25% to 35%). Water usage efficiency rose by 12.5% (from 80% to 90%), while water leak incidents decreased by 60% (from 50 to 20). Additionally, predictive policing reduced crime rates by 40% (from 5 to 3 per 1,000 residents), and emergency response times improved by 33.33% (from 15 minutes to 10 minutes). Finally, urban planning efficiency increased by 25% (from 60% to 75%) in land use, and green space area expanded by 40% (from 50 to 70 square kilometers).

Harnessing AI for sustainable university practices: toward a greener campus

Purpose This study aims to examine the potential of artificial intelligence (AI) to advance sustainability practices in universities, focusing on stakeholder perceptions and factors influencing the successful implementation of AI-driven initiatives. Design/methodology/approach Using a quantitative approach, a survey was administered to 539 university stakeholders (faculty, staff and students) from universities in Saudi Arabia. The survey measured awareness, perceived effectiveness and the importance of AI applications in energy management, waste reduction, transportation optimization and conservation. Findings Stakeholders exhibited moderate familiarity with AI for sustainability, with perceptions varying significantly by gender, university role and awareness level. Positive correlations were found between awareness and perceptions of AI’s effectiveness and importance. Transportation, energy efficiency and waste management emerged as key areas for AI application. Challenges identified include ethical concerns, financial constraints and infrastructure needs. Originality/value This research provides novel insights into integrating AI into university sustainability strategies. It highlights stakeholder perspectives, identifies priority areas for AI application and offers practical recommendations for enhancing AI-driven sustainability. The study emphasizes interdisciplinary collaboration and policy development to ensure ethical and effective AI use in higher education.

The Nonlinear Dynamics of CO2 Emissions in Pakistan: A Comprehensive Analysis of Transportation, Electricity Consumption, and Foreign Direct Investment

CO2 emissions are major drivers of climate change, causing global warming, extreme weather, and biodiversity loss. They disrupt ecosystems, deplete resources, and threaten public health and economic stability. Reducing CO2 emissions is essential for climate stability and sustainability. This study explores the complex relationships between CO2 emissions and factors such as the transportation sector, electricity consumption, foreign direct investment (FDI), international trade, and gross domestic product (GDP). The focus is on small- and medium-sized enterprises (SME) in Pakistan. Using time series data from 2000 to 2022. This study applies advanced econometric techniques, including nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least squares (DOLS), and fully modified ordinary least squares (FMOLS). The findings highlight that increased electricity consumption, international trade, transportation activities, and FDI contribute to higher CO2 emissions. However, FDI can also help reduce emissions, particularly through investments in green technologies. This study emphasizes the importance of transitioning to renewable energy and adopting sustainable practices across sectors such as electricity, trade, and transportation. Specifically, transportation and electricity consumption were found to significantly impact CO2 emissions, with a 10% increase in transportation activities resulting in a 5% rise in emissions. Conversely, FDI can reduce emissions by approximately 3% per unit of investment, largely due to green technology adoption. Additionally, integrating renewable energy and energy-efficient technologies in transportation can lead to a 20% reduction in emissions. Policymakers and experts must prioritize strategies that promote renewable energy adoption and integrate sustainable practices to reduce CO2 emissions and ensure long-term environmental sustainability. This research is innovative in its analysis of the interconnected effects of electricity consumption, trade, transportation, and FDI on CO2 emissions. By applying sophisticated econometric methods, it highlights the potential of FDI, particularly green investments, to mitigate environmental damage. This study, focusing on Pakistan, offers insights into how economic growth can be balanced with environmental sustainability.

Smart Integration of Renewable Energy into Transportation: Challenges, Innovations, and Future Research Directions

The integration of renewable energy into transportation systems is essential for reducing greenhouse gas emissions and achieving global sustainability goals. This review explores the challenges, innovations, and future directions of incorporating renewable energy sources such as solar, wind, and bioenergy into transportation infrastructures. Key challenges include the intermittency of renewable energy, the need for advancements in energy storage systems, and the regulatory and economic barriers hindering widespread adoption. Innovations such as electric vehicles (EVs), vehicle-to-grid (V2G) technologies, and smart grids are pivotal in enabling this transition. Furthermore, artificial intelligence (AI) and machine learning (ML) offer significant potential to optimize energy management, enhance efficiency, and facilitate the smooth integration of renewable energy with transportation systems. The review also discusses successful case studies from different regions and examines policy frameworks supporting renewable energy in transportation. Future directions point toward increased collaboration between industries, technological advancements, and supportive policies to create a more sustainable, resilient transportation sector. Ultimately, this review aims to provide a comprehensive understanding of how smart integration of renewable energy into transportation can drive a cleaner, more sustainable future.

Precipitation Forecasting and Drought Monitoring in South America Using a Machine Learning Approach

Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management.

Impact of Arctic Sea Ice Changes on LNG Shipping in the Context of Rising Global Energy Demand

The need for and demand for energy rises quickly as the world's population grows and economic growth quickens as well. Given the rising need for energy worldwide, liquefied natural gas, or LNG, is playing a significant role in energy transportation. Russia has a lot of gas. Russia plays a significant role in the LNG trade. The Northern Sea Route has opened due to the melting of the Arctic Sea's ice due to global warming. There is great potential for LNG transportation in the area. EViews was employed in this study to ascertain the connection between Arctic Sea ice and LNG ship movement in the NSR. The data was prepared using the Vector Autoregressive Model and the Granger Causality Test. The outcome of the analysis was the correlation between Arctic Sea ice and LNG ship traffic. LNG ship traffic in the NSR in 2023, 2024, and 2025 was estimated using the Census X-12 Method, and the 5-year (2018–2022) dataset of LNG ships departing the Russian port of Sabetta was analyzed. According to this data, by 2025, an estimated 27.03% more LNG tankers will be visiting the Russian Arctic port of Sabetta.

Mixed Conducting Oxide Coating for Lithium Batteries.

Thin, uniform, and conformal coatings on the active electrode materials are gaining more importance to mitigate degradation mechanisms in lithium-ion batteries. To avoid polarization of the electrode, mixed conductors are of crucial importance. Atomic layer deposition (ALD) is employed in this work to provide superior uniformity, conformality, and the ability to precisely control the stoichiometry and thickness of the desired coating materials. We provide experimental and computational guidelines for the need of mixed electronic and ionic conducting coating materials, especially in the case where highly uniform and conformal coatings are achieved. We report promising results for ALD-deposited protective films achieved by doping fluorine (F) into a lithium vanadate coating. The F-doped lithium vanadate coating at the optimal doping level exhibits an electrical conductivity of 1.2 × 10-5 S·cm-1. Density functional theory calculations corroborate enhanced mixed electronic and ionic conduction in F-doped lithium vanadate through band structure analysis and climbing-image nudge elastic band (CI-NEB) calculations. It has been demonstrated that the experimentally determined optimal doping concentration aligns well with that predicted by density functional theory calculations. CI-NEB calculations have shown that the activation energy for lithium-ion transport was the lowest for optimally doped lithium vanadate.

Multiscale Mass Transport Across Membranes: From Molecular Scale to Nanoscale to Micron Scale.

Multiscale mass transport across membranes occurs ubiquitously in biological systems but is difficult to achieve and long-sought-after in abiotic systems. The multiscale transmembrane transport in abiotic systems requires the integration of multiscale transport channels and energy ergodicity, making multiscale mass transport a significant challenge. Herein, emulsion droplets with cell-like confinement are used as the experimental model, and multiscale mass transport is achieved from molecular scale to nanoscale to micron scale, reproducing rudimentary forms of cell-like transport behaviors. By adjustment of the magnetic dipole interactions between adjacent superparamagnetic nanoparticles (MNPs), the assembled structure at the interface of emulsion droplets is successfully modified, which constructs transport channels of various scales at the interface. Simultaneously, the assembly process of MNPs induces self-emulsification, which increases entropy and further reduces Gibbs free energy, ultimately realizing multiscale mass transport that evolves in time visiting all possible microscopic states (energy ergodicity). This work represents the comprehensive identification and realization of a multiscale transmembrane transport in abiotic droplet systems, which offers opportunities for the development of high-order cell-like characteristics in emulsion droplet-based communities, synthetic cells, microrobots, and drug carriers.

Alternative Energy Sources Usable in Automotive Transport

The research focuses on the methodology of preparing a concept and application scenarios for alternative sources of energy in transportation. The ideas and interpretations are not strictly limited to automobile transport but also reach into other areas of using and processing energy. The conceptual approach to the choice of sources, with the aim of securing the efficient use of energy conversion, is illustrated on the model embodiment embedded in a passenger car; the relevant presentation is centered on an experiment focusing on the linear arrangement of a driven electromagnetic generator. This involves generating and collecting energy for not only the accumulation of electrical energy using relatively independent systems, but also for direct use within driving needs. In the modeled example, the supplied energy is assumed to be in a range of constant power from p = 10 W to 50 kW (200 kW). The given example of the design of the choice of energy conversion sources and the use of generators in a passenger car shows the possibilities, limitations, and variants for demonstrating the requirements relating to a simple driving mode. The application of a linear or cylindrical internal combustion engine is considered for a specific set mode of the car. Variants of suitable uses of the accumulated energy in compressed air are proposed. The use of light and thermal forms of energy is considered for additional forms. As an experimental example, the use of generators derived from vibration harvesters is shown. The proposed energy generation arrangement can be controlled and optimized for specific transport tasks. The generation and accumulation of energy can be employed in the form of electrical energy, as kinetic energy for direct use in driving, or to accumulate in compressed air for later use. Solar energy can be used directly or can be accumulated. The combustion unit can serve as a source of kinetic energy or also to store energy for further use. The concept of alternative sources is based on known methods of use in other industries. The model combination of resources and its simple analysis in the concept of resource selection is demonstrated on an example of an application in passenger cars.