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. OPEN ACCESSEnergies 2015, 8 8574
Abstract:In this paper, the performances of various lithium-ion chemistries for use in plug-in hybrid electric vehicles have been investigated and compared to several other rechargeable energy storage systems technologies such as lead-acid, nickel-metal hydride and electrical-double layer capacitors. The analysis has shown the beneficial properties of lithium-ion in the terms of energy density, power density and rate capabilities. Particularly, the nickel manganese cobalt oxide cathode stands out with the high energy density up to 160 Wh/kg, compared to 70-110, 90 and 71 Wh/kg for lithium iron phosphate cathode, lithium nickel cobalt aluminum cathode and, lithium titanate oxide anode battery cells, respectively. These values are considerably higher than the lead-acid (23-28 Wh/kg) and nickel-metal hydride (44-53 Wh/kg) battery technologies. The dynamic discharge performance test shows that the energy efficiency of the lithium-ion batteries is significantly higher than the lead-acid and nickel-metal hydride technologies. The efficiency varies between 86% and 98%, with the best values obtained by pouch battery cells, ahead of cylindrical and prismatic battery design concepts. Also the power capacity of lithium-ion technology is superior compared to other technologies. The power density is in the range of 300-2400 W/kg against 200-400 and 90-120 W/kg for lead-acid and nickel-metal hydride, respectively. However, considering the influence of energy efficiency, the power density is in the range of 100-1150 W/kg. Lithium-ion batteries optimized for high energy are at the OPEN ACCESSEnergies 2012, 5 2953 lower end of this range and are challenged to meet the United States Advanced Battery Consortium, SuperLIB and Massachusetts Institute of Technology goals. Their association with electric-double layer capacitors, which have low energy density (4-6 Wh/kg) but outstanding power capabilities, could be very interesting. The study of the rate capability of the lithium-ion batteries has allowed for a new state of charge estimation, encompassing all essential performance parameters. The rate capabilities tests are reflected by Peukert constants, which are significantly lower for lithium-ion batteries than for nickel-metal hydride and lead-acid. Furthermore, rate capabilities during charging have been investigated. Lithium-ion batteries are able to store about 80% of the capacity at current rate 2I t , with high power cells accepting over 90%. At higher charging rates of 5I t or more, the internal resistance impedes charge acceptance by high energy cells. The lithium titanate anode, due to its high surface area (100 m 2 /g compared to 3 m 2 /g for the graphite based anode) performs much better in this respect. The behavior of lithium-ion batteries has been investigated at different conditions. The analysis has leaded us to a new lithium ion battery model. This model will be compared to existing battery models in future research contributions.
How to compare the environmental performance of different vehicle technologies? Vehicles with lower tailpipe emissions are perceived as cleaner. However, does it make sense to look only to tailpipe emissions? Limiting the comparison only to these emissions denies the fact that there are emissions involved during the production of a fuel and this approach gives too much advantage to zero-tailpipe vehicles like battery electric vehicles (BEV) and fuel cell electric vehicle (FCEV). Would it be enough to combine fuel production and tailpipe emissions? Especially when comparing the environmental performance of alternative vehicle technologies, the emissions during production of the specific components and their appropriate end-of-life treatment processes should also be taken into account. Therefore, the complete life cycle of the vehicle should be included in order to avoid problem shifting from one life stage to another. In this article, a full life cycle assessment (LCA) of petrol, diesel, fuel cell electric (FCEV), compressed natural gas (CNG), liquefied petroleum gas (LPG), hybrid electric, battery electric (BEV), bio-diesel and bio-ethanol vehicles has been performed. The aim of the manuscript is to investigate the impact of the different vehicle technologies on the environment and to OPEN ACCESSEnergies 2014, 7 1468 develop a range-based modeling system that enables a more robust interpretation of the LCA results for a group of vehicles. Results are shown for climate change, respiratory effects, acidification and mineral extraction damage of the different vehicle technologies. A broad range of results is obtained due to the variability within the car market. It is concluded that it is essential to take into account the influence of all the vehicle parameters on the LCA results.
Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12-14% of the average trip consumption, of which 7-9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.
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