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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 This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel–metal hydride (Ni–MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni–MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

The advent of intelligent transportation system technology has expedited the advancement of eco-driving controls. Research on energy-saving cooperative control of intelligent connected vehicles in intelligent transportation environments is still in need of ongoing refinement. This article proposes a dual-level ecological driving control strategy for a pure electric vehicle platoon with dual-motor dual-axis drive in an urban multi-intersection environment. The upper-level design incorporates optimal platoon speed decision-making based on the nonlinear model predictive control algorithm. The multi-objective optimization function considers three scenarios: energy-optimal, time-optimal, and energy-time-optimal. It also takes into account platoon following control and passing efficiency, ensuring smooth passage through multi-intersections without interruptions. Built on the upper level’s optimal speed design, an energy management strategy is proposed for achieving optimal torque distribution of pure electric vehicles with front and rear independent drives. Finally, the upper and lower levels are jointly simulated in real-time. The results indicate that, compared to the energy-optimal mode, the average passage time decreased by 14.6% and 5.97% in the time-optimal and energy-time-optimal modes, respectively. Under average torque distribution, the time-optimal and energy-time-optimal modes increased the energy consumption of the vehicle platoon by 21.05% and 5.44%, respectively, compared to the energy-optimal mode. Under the optimal torque allocation strategy, the time-optimal and energy-time-optimal modes increased energy consumption by 15.31% and 6.11%, respectively, compared to the energy-optimal mode. In contrast to the average torque allocation strategy, the optimal torque allocation strategy for the dual-motor vehicles reduced energy consumption by 10.18%, 14.44%, and 9.61% in the energy-optimal mode, time-optimal mode and energy-time-optimal mode, respectively.

An efficient pack-level battery thermal management system is essential to ensure the safe driving experience of electric vehicles. In this work, we perform three-dimensional modeling of a liquid thermal management system for a real-world battery pack powering electrical vehicles. The effects of system structures, coolant flow direction layout, coolant flow rates, and inlet temperatures on the thermal performance are investigated. Under 2.0 C discharge, the system structure with interspersed cooling plates reduces the maximum temperature of the battery pack to 36.3 °C, which is 15% lower than that of the conventional design with bottom-mounted cooling plates. With the present system structure, around 62.2% of the heat produced by the batteries is dissipated. Moreover, the temperature difference is decreased by 10.5% with appropriately arranging coolant flow directions in different liquid cooling plates. In addition, increasing the coolant flow rate and lowering the inlet coolant temperature can greatly reduce the maximum battery temperature, while the latter parameter shows little influence on the pack temperature difference, which only changes 0.3 °C when the inlet temperature is decreased by 6 °C. This work provides valuable guidance for the development of pack-level liquid battery thermal management systems in electric vehicles.

Based on the available energy sources, the electric vehicle (EV) cannot compete with the conventional vehicle in terms of driving range and initial cost. In the near future, the hybrid EV (HEV) is not only an interim solution for implementation of zero emission vehicles but a practical solution for commercialization of super-ultra-low-emission vehicles. This paper firstly presents an overview of latest HEVs, with emphasis on power management. Based on the power management strategy of the drive train, a new classification approach for HEVs is proposed. Hence, the corresponding system configurations are identified. The power flow control for various HEVs is also elaborated. Finally, the development trends of HEVs and EVs are delineated.

Abstract International shipping carries around 80 per cent of global trade by volume and over 70 per cent by value. However, there is concern that the greenhouse gas (GHG) emissions from international shipping lead to adverse effects on climate, human health and marine ecosystems. Currently the international climate change regime under the United Nations Framework Convention on Climate Change ( UNFCCC ) process and the IMO through its Marine Environment Protection Committee are grappling with this issue, and GHG emissions from international shipping have been partially regulated by amendments to Annex VI to the International Convention for the Prevention of Pollution from Ships ( MARPOL 73/78 ) in 2011 and 2014. These amendments aim to reduce GHG emissions from international shipping by means of technical and operational measures. However, research indicates that the adopted technical and operational measures alone would not achieve absolute emissions reduction due to projected growth of international seaborne trade. Currently it is still controversial whether it is time to consider market-based measures (MBMs) in furthering the reduction of shipping GHG emissions. This article examines whether it is necessary to adopt MBMs, proposes a preferred MBM, and suggests that a MBM be considered in or after 2016.

AbstractAimExperimental simulation of near‐future ocean acidification (OA) has been demonstrated to affect growth and development of echinoderm larval stages through energy allocation towards ion and pH compensatory processes. To date, it remains largely unknown how major pH regulatory systems and their energetics are affected by trans‐generational exposure to near‐future acidification levels.MethodsHere, we used the common sea star Asterias rubens in a reciprocal transplant experiment comprising different combinations of OA scenarios, to study trans‐generational plasticity using morphological and physiological endpoints.ResultsAcclimation of adults to pHT 7.2 (pCO2 3500 μatm) led to reductions in feeding rates, gonad weight and fecundity. No effects were evident at moderate acidification levels (pHT 7.4; pCO2 2000 μatm). Parental pre‐acclimation to pHT 7.2 for 85 days reduced developmental rates even when larvae were raised under moderate and high pH conditions, whereas pre‐acclimation to pHT 7.4 did not alter offspring performance. Microelectrode measurements and pharmacological inhibitor studies carried out on larval stages demonstrated that maintenance of alkaline gastric pH represents a substantial energy sink under acidified conditions that may contribute up to 30% to the total energy budget.ConclusionParental pre‐acclimation to acidification levels that are beyond the pH that is encountered by this population in its natural habitat (eg, pHT 7.2) negatively affected larval size and development, potentially through reduced energy transfer. Maintenance of alkaline gastric pH and reductions in maternal energy reserves probably constitute the main factors for a reduced juvenile recruitment of this marine keystone species under simulated OA.

Abstract Blue carbon produced by marine ranching has tremendous economic and ecological value. A sound blue carbon trading mechanism, a fair market environment, and an effective supervision framework will help to transform and upgrade the marine economy through blue carbon trading. The present study develops a three-party evolutionary game model, that consists of the government supervisory agency, the blue carbon trading platform, and the news media, to analyse the evolutionary equilibrium conditions and each party’s evolutionarily stable strategy. It also performs numerical simulations on the evolution strategies. The study results demonstrate: (1) More strict supervision from the government will prevent the platform from engaging in opportunistic behaviours, and ensure fairness in blue carbon trading. (2) The news media’s participation as a watchdog can play a role in restraining the platform’s non-compliance behaviours, but a free-rider issue may also arise, which leads to an absence of supervision. (3) Consumers’ recognition of marine ranching products and the proportion of blue carbon produced by marine ranching that is available for trading will indirectly affect the platform’s course of action. Lastly, the paper makes policy recommendations for the development of a well-functioning blue carbon trading mechanism, a fair market environment, and an effective supervision framework.

Abstract The ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead–acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries.