The negative impact of the automotive industry on climate change can be tackled by changing from fossil driven vehicles towards battery electric vehicles with no tailpipe emissions. However their adoption mainly depends on the willingness to pay for the extra cost of the traction battery. The goal of this paper is to predict the cost of a battery pack in 2030 when considering two aspects: firstly a decade of research will ensure an improvement in material sciences altering a battery’s chemical composition. Secondly by considering the price erosion due to the production cost optimization, by maturing of the market and by evolving towards to a mass-manufacturing situation. The cost of a lithium Nickel Manganese Cobalt Oxide (NMC) battery (Cathode: NMC 6:2:2 ; Anode: graphite) as well as silicon based lithium-ion battery (Cathode: NMC 6:2:2 ; Anode: silicon alloy), expected to be on the market in 10 years, will be predicted to tackle the first aspect. The second aspect will be considered by combining process-based cost calculations with learning curves, which takes the increasing battery market into account. The 100 dollar/kWh sales barrier will be reached respectively between 2020-2025 for silicon based lithium-ion batteries and 2025–2030 for NMC batteries, which will give a boost to global electric vehicle adoption.

Electric vehicle (EV) energy consumption is variable and dependent on a number of external factors such as road topology, traffic, driving style, ambient temperature, etc. The goal of this paper is to detect and quantify correlations between the kinematic parameters of the vehicle and its energy consumption. Real-world data of EV energy consumption are used to construct the energy consumption calculation models. Based on the vehicle dynamics equation as underlying physical model, multiple linear regression is used to construct three models. Each model uses a different level of aggregation of the input parameters, allowing predictions using different types of available input parameters. One model uses aggregated values of the kinematic parameters of trips. This model allows prediction with basic, easily available input parameters such as travel distance, travel time, and temperature. The second model extends this by including detailed acceleration data. The third model uses the raw data of the kinematic parameters as input parameters to predict the energy consumption. Using detailed values of kinematic parameters for the prediction in theory increases the link between the statistical model and its underlying physical principles, but requires these parameters to be available as input in order to make predictions. The first two models show similar results. The third model shows a worse fit than the first two, but has a similar accuracy. This model has great potential for future improvement.

Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.