Electric Vehicle Charging Load Forecasting Method Based on Time-Space Distribution Characteristics of User Travel
An electric vehicle, space-time distribution technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as difficulties in electric vehicle charging load prediction, and achieve the effect of avoiding randomness and uncertainty
Active Publication Date: 2022-04-15
HANGZHOU DIANZI UNIV
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Problems solved by technology
These factors have brought great difficulties to the charging load prediction of electric vehicles in the planning area, so how to accurately describe the charging load of electric vehicles is a problem worth studying
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[0071] The present invention will be further described below in conjunction with specific embodiments, but it should not be understood that the scope of the above-mentioned main body of the present invention is limited to the following embodiments. Without departing from the above-mentioned technical ideas of the present invention, various replacements and changes made according to common technical knowledge and conventional means in this field shall be included in the protection scope of the present invention.
[0072] Step 1. Establish a Bass regression analysis model to estimate the number of electric vehicles in the next N years.
[0073] Step 2. According to the nature of private car travel destinations, the planning area is divided into five categories, namely residential area (home, H), teaching area (Teaching area, T), office area (Workspace, W), and commercial area ( Businessdistrict, B) and other areas (Other areas, O), and the residential area is regarded as the use...
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The invention discloses a method for predicting the charging load of an electric vehicle based on the time-space distribution characteristics of user travel. First, the Bass regression analysis model is used to predict the number of electric vehicles in the planning area. An equivalent road model that combines road types, real-time road congestion levels and regional connections is constructed, and considering the impact of different types of days, different travel times, dwell times and real-time road conditions on user travel, the Dijkstra path optimization algorithm is used to obtain The optimal travel path and daily travel chain for users. In addition, the Monte Carlo method is used to obtain the results of multiple charging load predictions, and the corresponding charging load probability density function is obtained by using non-parametric kernel density estimation and Gaussian fitting. The charging load with the highest probability is the corresponding electric vehicle charging load prediction value. The invention can effectively describe the specific distribution range of the daily charging load in the planned area, and take the charging load with the maximum probability density as the predicted result, which is closer to reality.
Description
technical field [0001] The invention belongs to the technical field of electric vehicle charging load forecasting, and in particular relates to a method for forecasting electric vehicle charging load based on user travel time and space distribution characteristics. Background technique [0002] With the improvement of people's living standards, more and more people use cars to travel, and the continuous increase in car ownership will lead to increasingly prominent social energy conservation and environmental protection issues. According to investigations and studies, the carbon dioxide emitted by diesel vehicles accounts for about 40% of the total urban carbon dioxide emissions. Because of the advantages of energy saving and environmental protection, electric vehicles can effectively alleviate the shortage of traditional energy resources and environmental pollution, and have become the mainstream direction of automobile development. [0003] Electric vehicles have high mobi...
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IPC IPC(8): G06Q10/04G06Q50/06G06F17/18
CPCG06Q10/047G06F17/18G06Q10/04G06Q50/06
Inventor 罗平樊星驰程晟高慧敏
Owner HANGZHOU DIANZI UNIV