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Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

To take advantage of the high tidal current velocity near the free surface, the horizontal axis turbine is installed, which inevitably causes hydrodynamic characteristics to effect the turbine by the waves. In this article, we established a numerical calculation method for the hydrodynamic load of a horizontal axis turbine under wave-current conditions. Based on the numerical calculation results, the hydrodynamic loads were decomposed and the influence rules of wave parameters and blade tip immersion depth on the hydrodynamic load were obtained. The study found the following: (1) the multi-frequency fluctuations based on the rotation frequency and incident wave frequency occurred in instantaneous values of the axial load coefficients and energy utilization ratios, and the fluctuation amplitude decreased with the increase of the blade tip immersion depth; (2) the fluctuation amplitude, according to rotation frequency, changed less with the increase of wave period and wave height, and was smaller according to wave frequency; (3) the fluctuation amplitude based on wave frequency increased linearly with the increase of wave height and wave period. The research results can provide the basis and reference for the design and engineering application of tidal current power station.

The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance.

Abstract Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision‐making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple‐driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta‐supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre‐training, fine‐tune‐training and real‐time prediction, which is tested to predict the hybrid electric city buses’ future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s−1, which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energy‐saving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition.

The private motor vehicles are significantly important means of transportation in modern lifestyle, however, these also contribute to a large proportion of the total air pollution and primary energy consumption. In order to develop green transportation system, it becomes imperative to use integrated technologies to achieve reduced emissions and utilize renewable energy. Electric vehicles (EVs) have been considered as one of these technologies to transform the traditional vehicle mix. However, the uptake of EV has been debated on factors like cost, performance (autonomous mileage), charging point infrastructure construction, energy saving, policy and end users’ adaptation. Present study investigates the technology feasibility (which usually refer to EVs’ cost, EV charging, supplier’s customer services quality, EV travel performance) and users’ adaptation of EV in Beijing, which is a key driver for the EV uptake into the Beijing transportation system. The relevant data have been collected and analyzed in the form of questionnaire survey around all of these factors. While considering the user perception and satisfaction, safety of charging and energy bills have also been investigated. According to the data analysis, it has been found the policy of ‘No traffic restrictions for EVs’ (the traffic restrictions means for certain date, from Monday to Friday the motor vehicles with the last register number of 1 and 6, 2 and 7, 3 and 8, 4 and 9, 5 and 0, are restricted to travel, respectively), the availability of the charging infrastructure and technical support are the most significant factors affecting the users’ opinions on using EVs.

Abstract Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.