| The world has paid great attention to the problem of climate deterioration and increasingly serious urban pollution.Several severe oil crises have made people realize that the contradiction between the infinite demand for energy and the limited primary energy is deepening.On the basis of bringing convenience to people’s life,cars have also caused serious problems of energy shortage and environmental deterioration.There are many environmental pollutants in automobile exhaust emissions,such as nitrogen oxide,particulate matter etc.These emissions are the main pollutants that cause haze and photochemical pollution,which will cause serious negative impact on the environment.Therefore,given the contradiction between energy supply and demand and the increase of automobile pollution,the economic development and environmental governance of various countries in the world have brought great pressure.In order to cope with this pressure,new electric vehicles which use electric energy instead of traditional fuel become the first hot spot in the automobile market.It is estimated that there will be 20 million pure electric and hybrid vehicles by 2021.However,since thermal power generation is the main form of power generation in China,large-scale development of electric vehicles is equivalent to replacing oil with coal,which represents that,if the scale of electric vehicles exceeds a certain limit,it will lead to an increase in carbon emissions.Therefore,it is of great practical significance to study the maximum number of electric vehicles in the BeijingTianjin-Hebei region under the background of low carbon.First of all,based on the characteristics of economic development level and historical power generation in Beijing-Tianjin-Hebei region,this paper selects the total regional power generation and clean energy power generation as research objects,and selects 15 influencing factors through gray correlation analysis,including: Of the Beijing-Tianjin-Hebei region,residents’ consumption level,GDP per capita GDP,total quantity of coke production,output of crude oil,gasoline,production volume,the total output of kerosene,diesel oil production,gas production volume,energy price index,the energy used in social fixed assets investment proportion,the proportion of primary industry,secondary industry,the proportion of the tertiary industry,each influence factor and the dependent variable of grey correlation degree were higher than 0.75.To eliminate the information redundancy between the data,using the factor analysis method to dimension of 15 influencing factors,extracted the three public factor macroeconomic environment factor,the other major energy output factor,total fuel oil production factor and the influence factors of five separate source use efficiency,energy price index,energy for social fixed assets investment proportion,the proportion of primary industry,secondary industry,the proportion of the tertiary industry,a total of eight factors together as the output of the Beijing-Tianjin-Hebei region prediction model of the input.On the basis of introducing the improved particle swarm optimization algorithm of simplex search method,this paper constructs the improved particle swarm optimization limit learning machine prediction model(IPSO-ELM).Secondly,based on the existing energy structure,this paper first constructs a model for the maximum number of electric vehicles under the constraint of low-carbon environment to conduct quantitative analysis on the number of electric vehicles.Finally,taking the Beijing-Tianjin-Hebei region as an example,the maximum electric vehicle PARC in the region from 2019 to 2030 is calculated.The prediction results show that IPSO has a high accuracy in predicting the total power generation and clean energy power generation in Beijing-Tianjin-Hebei region,which is suitable for power generation prediction.At the same time,it can also explain the correctness of the influencing factors selected by grey relational degree analysis and factor analysis.According to the calculation results,the maximum number of electric vehicles shall not exceed17.6347 million and 22.967 million,respectively,based on the emission reduction targets and predicted energy structure of the Beijing-Tianjin-Hebei region from 2019 to 2030.At this time,the proportion of electric vehicles in traditional vehicles is 68.99% and 70.32%,respectively.To achieve full replacement of electric vehicles,clean energy generation in the Beijing-TianjinHebei region should be increased to 37.26 percent by 2030 to achieve carbon emission reduction.Finally,based on the above research findings,this paper puts forward relevant Suggestions on how to improve the clean generation capacity and how to develop the scale of electric vehicles. |
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