Performance of batteries for electric vehicles on short and longer term

In this work, the prospects of available and new battery technologies for battery electric vehicles (BEVs) are examined. Five selected battery technologies are assessed on battery performance and cost in the short, medium and long term. Driving cycle simulations are carried out to assess the influence of the batteries on the energetic, environmental and economic performance of BEVs in the medium term. Well-to-wheel energy consumption and emissions of BEVs are lowest for lithium-ion batteries; 314-374 Wh km -1 and 76-90 gCO 2eq km -1 (assuming 593 gCO 2 kWh -1 for European electricity mix), compared to 450-760 Wh km -1 and 150-170 gCO 2eq km -1 for petrol and diesel cars. The total driving costs are lowest for ZEBRA batteries (0.43-0.62 $km -1). But, only if ZEBRA batteries attain a very low cost of 100$ kWh -1 and driving ranges are below 200 km, BEVs become cost competitive to diesel cars. For all batteries, it remains a challenge to simultaneously meet requirements on specific energy, specific power, efficiency, cycle life, lifetime, safety and costs in the medium or even long term. Only lithium-ion batteries could possibly attain all conditions in the medium term. Batteries that do not contain lithium have best perspectives to attain low costs. © 2012 Elsevier B.V. All rights reserved

Model collaboration for the improved assessment of biomass supply, demand, and impacts

Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy-making and (ii) the linkages, feedbacks, and trade-offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade-off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land-use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy-makers and in taking better-informed decisions