Electric mobility is the answer to the current problems associated with transport as inefficiency and pollution; however autonomy presents difficulties due to the low energy storage capacity of batteries. In that way, it is necessary to optimize the performance of electric vehicles in a city considering the factors that could affect it. In road tests of EVs in other cities it has been established that the range of the vehicle depends on the temperature, the road type, the acceleration, velocity, topography and the usage of auxiliary services (heating, air conditioning). These variables are defined by the urban conditions of each city. In this paper, road tests are performed in order to evaluate the efficiency and performance of the I-MiEV electric vehicle during operating condition in the city of Bogotá, which is 2600 meters above sea level and an average temperature of 15 o C. Initially road tests were performed with GPS measurements to determine the typical road for corporate fleet. The definition of the typical road was performed by analysing rates of speed, acceleration, inclination and travel time. 100 road tests for evaluating the performance of electric vehicles which contain local and arterial roads, intermediate, different conductor types and topography were performed. Measurements analyses of road tests were performed, which allowed finding operating efficiencies of 80% due to the regeneration of energy from the vehicle.
This paper sets out features of traditional Energy Key Performance Indicators (KPIs) employed in energy management programs; then, new indicators are proposed based on Advanced Metering Infrastructure (AMI) usage. These indicators make it possible to directly relate the amount of energy, type of end use and user consumption patterns. Analysis of AMI system information enables planning for differentiated Demand-Side Management (DSM) strategies. A case study developed at Universidad Nacional de Colombia - Bogotá campus is presented, which proposes new Energy Key Performance Indicators in Real Time. These indicators enable information analysis and DSM strategies that are appropriate for new technologies and that are aimed at increasing energy efficiency. Additionally, this paper presents the factors that have to be taken into account when implementing KPIs (Key Performance Indicators) and the decision-making process. This results in variable overall energy savings between 5 and 40%, according to the DSM strategy implemented.
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