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Abstract This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and supercapacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter-based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

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.

Current and forecasted use of air transportation by businesses in Minnesota using the Standard Industrial Classification is described. The research is based upon a study on air service and commercial and industrial activity in Minnesota required by the Minnesota legislature in 1996. Purchases from the air transportation sector that includes scheduled and nonscheduled passenger and freight services are based upon the 1993 IMPLAN input-output model for Minnesota and Bureau of Economic Analysis (BEA) and Bureau of Labor Statistics (BLS) forecasts for Minnesota. In addition to intraindustry transfers within the air transportation sector, purchases of air transportation are dominated by business associations, management and consulting services, and the U.S. Postal Service sectors. These four sectors also represent the sectors with the largest share of purchases of air transportation. BLS historical and forecast data suggest that the major group of industries that purchased air transportation in 1977 will remain intact through 2005. Under BEA forecasts for Minnesota, air transportation purchases by all industry sectors will increase more rapidly than other transportation modes. BEA also forecasts the air transportation component of gross state product growing faster than forecasted passenger originations at Minneapolis–St. Paul (MSP). The implications of the BEA forecasts will be considered in the next update of activity forecasts for MSP. This research is considered only the first step in understanding the role of air transportation to commerce and industry in Minnesota and other states.

In Bangladesh, prawn (Macrobrachium rosenbergii) farming remains dependent on the capture of wild postlarvae as hatchery production is still inadequate. However, prawn postlarvae fishing has been accompanied by concerns over recent climate change. Different climatic variables including cyclone, salinity, sea level rise, water temperature, flood, rainfall, and drought have had adverse effects on coastal ecosystem, thus determining a decline in the availability of prawn postlarvae and thereby catch. The households of postlarvae fishers also face a variety of socioeconomic constraints due to climate change. Considering extreme vulnerability to the effects of climate change, an integrated approach needs to be introduced to cope with the challenges.

The envisaged de-carbonization of power systems poses unprecedented challenges enhancing the potential of flexible demand. However, the incorporation of the latter in system planning has yet to be comprehensively investigated. This paper proposes a novel planning model that allows co-optimizing the investment and operating costs of conventional generation assets and demand flexibility, in the form of smart-charging/discharging electric vehicles (EV). The model includes a detailed representation of EV operational constraints along with the generation technical characteristics, and accounts for the costs required to enable demand flexibility. Computational tractability is achieved through clustering generation units and EV, which allows massively reducing the number of decision variables and constraints, and avoiding non-linearities. Case studies in the context of the U.K. demonstrate the economic value of EV flexibility in reducing peak demand levels and absorbing wind generation variability, and the dependence of this value on the required enabling cost and users’ traveling patterns.

AbstractResting egg production is considered the most common form of dormancy in aquatic invertebrates. Given that many taxa at least partially terminate resting egg state using environmental cues, knowledge on environmental drivers of hatching success is important, particularly within the context of climate change and environmental degradation. Fairy shrimp (anostracans) are temporary wetland specialists that are reliant on resting egg production for population persistence. Temporary wetlands are common in many arid regions projected to experience increases in temperature, and in areas often compromised by human‐mediated activities. In this study, we assessed the combined effects of light and temperature on the hatching success of Streptocephalus cafer (Anostraca) dormant eggs from temporary wetlands in an arid environment. Both temperature and light altered hatching success, with emergent effects evident. Light caused a significant threefold increase in hatching success overall, while temperature effects were non‐linear, with hatching optimised at 27°C, and especially under light conditions. These results are discussed within the context of shifting climates and disturbances to temporary wetland ecosystems.

Abstract Aluminum (Al)/air batteries have the potential to be used to produce power to operate cars and other vehicles. These batteries might be important on a long-term interim basis as the world passes through the transition from gasoline cars to hydrogen fuel cell cars. The Al/air battery system can generate enough energy and power for driving ranges and acceleration similar to gasoline powered cars. From our design analysis, it can be seen that the cost of aluminum as an anode can be as low as US$ 1.1/kg as long as the reaction product is recycled. The total fuel efficiency during the cycle process in Al/air electric vehicles (EVs) can be 15% (present stage) or 20% (projected) comparable to that of internal combustion engine vehicles (ICEs) (13%). The design battery energy density is 1300 Wh/kg (present) or 2000 Wh/kg (projected). The cost of battery system chosen to evaluate is US$ 30/kW (present) or US$ 29/kW (projected). Al/air EVs life-cycle analysis was conducted and compared to lead/acid and nickel metal hydride (NiMH) EVs. Only the Al/air EVs can be projected to have a travel range comparable to ICEs. From this analysis, Al/air EVs are the most promising candidates compared to ICEs in terms of travel range, purchase price, fuel cost, and life-cycle cost.

Abstract: The usage of multi-objective cost functions (MOCFs) in sizing and energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) has expanded due to the participation of multiple technological and economic disciplines. To better understand the impact of price fluctuation on the component size and EMS of an FCHEV, this article proposed a sensitivity analysis methodology. First, a two-step optimization approach that considers hydrogen consumption, system degradation, and trip cost is used to minimize a MOCF of the Can-Am Spyder electric motorcycle simulator. Then, an effect analysis is carried out for the cost-optimal results under two driving profiles to understand the link between cost variation and system performance. These simulations indicate that each might result in different system sizes and EMS compromise. After that, an online optimization EMS based on sequential quadratic programming is used on a reduced-scale hardware-in-the-loop configuration to evaluate the simulation results with varied weights. Experimental results indicate that when an adequate size is used for each pair of weights, the EMS results in a 6% decrease in the trip cost.