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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.

Electricity supply in India is from a centralized grid. Many parts of the country experience grid interruptions. Life cycle energy and environmental analysis has been done for a 27 kWp photovoltaic system which acts as grid backup for 3 h outage in an Indian urban residential scenario. This paper discusses energy requirements and carbon emission for a PV storage system for five different battery technologies in Indian context. This can be used as a metric for comparative analysis for new batteries, with an undeveloped market. The energy requirements for the components are quantified and are compared in terms of energy payback time (EPBT) and Net Energy Ratio (NER). All the calculations are done for Indian context. EPBT is found to be in the range of 2–4.5 years for all the systems, while NER is in the range of 6.6–2.52. NaS has the highest emission factor of 0.67 kgCO2/kWh and the least for NiCd (0.091 kgCO2/kWh). These factors can be used to select a PV battery option and to target selection of materials and systems based on the reported values.

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.

Abstract This study developed a geographical information system-based methodology to maximize the environmental and economic efficiency of large-scale energy plants using different types of biomass. As a precursor to the optimization modelling, multi-criteria spatial analyses and then transport cost optimization are applied taking into account spatial biomass availability and existing road networks, in order to identify optimal biomass energy plant sites for different combinations of biomass types. A geographical information system integrated with a capacitated maximal covering location problem model was employed to assign biomass supply points to biomass energy plants. Capacity limits of the plants and a defined maximum transportation distance were taken into consideration. The resulting information was used to examine major challenges in establishing a large-scale biomass energy plant, biomass supply logistics, environment cost and anticipated traffic congestion. A sensitivity analysis was also carried out to review the influence of plant capacity and maximum transportation distance on economic and environmental sustainability. The proposed methodology was employed utilizing agricultural and forest residues for bioelectricity generation in Queensland, Australia. It was found that 100% bagasse had the highest eco-efficiency for bioelectricity generation from multiple biomass combinations. In contrast, the combination of 70% sawmill residues and 30% forest harvest residues showed the lowest level of traffic congestion (2 trucks per hour). In addition, the impact of traffic congestion of bulky sugarcane residues can be reduced by adding forest residues to the biomass supply. The advantages of the resulting solution are further enhanced when social impacts such as energy security, new income opportunities and job creation are taken into account in addition to the sustainability aspects.

Bus services are a fundamental component of transportation networks in Latin America, but buses often account for a disproportionately large number of environmental externalities. Electric buses (e-buses) are emerging as an effective and pragmatic option for reducing greenhouse gas emissions and local pollutants. However, e-buses are difficult to procure in Latin America because of existing procurement challenges in the region, especially as those challenges relate to forming contracts to deal with high upfront costs and unknown risks. To overcome these procurement issues, this paper presents a new contractual model, based on literature and case study research. This new model suggests the separation of bus service responsibilities into three separate actors: multiple bus procurement companies, one or multiple bus depots and charging infrastructure companies, and multiple bus operating companies. By separating bus service responsibilities, the proposed model would bring about three concrete improvements: lower costs to the transit system, better quality of service, and lower-emission fleet deployment.

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.

Transit bus passenger loading changes significantly over the course of a workday. Therefore, time-varying vehicle mass as a result of passenger load becomes an important factor in instantaneous energy consumption. Battery-powered electric transit buses have restricted range and longer “fueling” time compared with conventional diesel-powered buses; thus, it is critical to know how much energy they require. Our previous work has shown that instantaneous transit bus mass can be obtained by measuring the pressure in the vehicle’s airbag suspension system. This paper leverages this novel technique to determine the impact of time-varying mass on energy consumption. Sixty-five days of velocity and mass data were collected from in-use transit buses operating on routes in the Twin Cities, MN metropolitan area. The simulation tool Future Automotive Systems Technology Simulator was modified to allow both velocity and mass as time-dependent inputs. This tool was then used to model an electrified and conventional bus on the same routes and determine the energy use of each bus. Results showed that the kinetic intensity varied from 0.27 to 4.69 mi−1 and passenger loading ranged from 2 to 21 passengers. Simulation results showed that energy consumption for both buses increased with increasing vehicle mass. The simulation also indicated that passenger loading has a greater impact on energy consumption for conventional buses than for electric buses owing to the electric bus’s ability to recapture energy. This work shows that measuring and analyzing real-time passenger loading is advantageous for determining the energy used by electric and conventional diesel buses.

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.

World fisheries, already vulnerable, are under increasing pressure from the impacts of climate change. Using the Tasmanian rock lobster industry as a case study, we considered the efficacy of risk perception as a tool to inform how to communicate the science of climate change and suggestions for management in relation to development of adaptation strategies for fisheries. Fishers surveyed in this study operate in a fishery that is expected to undergo large changes as a consequence of climate change. Fishers also reported observations of similar large changes in the marine environment and lobster fishery consistent with climate change; yet most fishers surveyed expressed doubts about whether climate change was a real process. The important point for adaption of the industry to climate change is that fisher perceptions of risk tended to create barriers to acceptance of climate change as an issue. This means that there is a barrier to communication and awareness about climate change and thus a barrier to future action on the issue. Improving acceptance of climate change and thus ability to adapt will require the development of communications that are culturally appropriate and palatable to fishers. We argue that the application of social learning principles in communications about climate change may be one constructive way forward.