CN110472354B - New energy automobile permeation environmental impact assessment method - Google Patents
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Abstract
Due to the great difference in emissions between Internal Combustion Engine Vehicles (ICEV) and new energy vehicles, including plug-in electric vehicles (PEV) and Hybrid Electric Vehicles (HEV), conventional environmental impact assessment methods are challenging. The present invention uses an absorbing Markov chain model to model the transition process between the main vehicle types in the future. In addition, it is assumed that the behavior of the traveler of the new energy automobile is the same as that of the conventional ICEV traveler. Thus, the vehicle composition of each road segment is consistent with its market penetration level. Thus, environmental impact assessment can be performed based on market penetration levels and road traffic flows for different vehicle models. For a given road network, road segment traffic flows are calculated using traffic system equalization. This is a four-stage model with feedback that can be effectively solved by a continuous average Method (MSA) with decreasing weights. Simulation studies using the Nguyen-Dupuis network verify the effectiveness of the proposed method.
Description
Technical field:
the invention provides a new energy automobile permeation environment influence assessment method, and belongs to the technical field of traffic engineering.
The background technology is as follows:
greenhouse gas emissions in the transportation field account for a significant portion of the global population, which has long been criticized by conventional internal combustion engine automobiles (ICEVs). Recently, new energy vehicles, including plug-in electric vehicles (PEV) and Hybrid Electric Vehicles (HEV), have a great impact on various fields of use in traffic. Undoubtedly, new energy automobiles are a good environmental solution. However, conventional traffic planning ignores the vast differences between ICEV, PEV, and HEV [1] . In order to investigate the potential impact of new energy vehicles as sustainable means of transportation, this new technology must be integrated into the transportation network for environmental impact assessment.
Although the emissions of conventional ICEVs have been widely studied in traffic networks, with the penetration of new energy automobiles, the evaluation of their environmental impact has not been studied intensively. With new energy automobiles becoming a ubiquitous vehicle, duell et al [2] The energy consumption of PEVs is incorporated into network design decisions. They devised a multi-objective planning problem to minimize system energy consumption and overall system travel time. Gardner et al [3] A double-layer framework is designed, and under the condition of travel demand change, the influence of PEVs on environmental pollution and energy consumption is evaluated. Jiang and Xie [4] A convex optimization model is provided for the problem of hybrid network equalization with traffic mode and route selection. Wherein, the internal combustion engine automobile and the new energy automobile are in driving mileage and driving costThe constitution is different. He or the like [5] It was found that PEV drivers' routing behavior differs from that of conventional ICEV drivers due to limited range, scarcity of charging stations, and possibly long battery charging or replacement times. Papargyri et al [6] The potential contribution of the electric automobile to greenhouse gas emission reduction in the next decade was analyzed by simulation programs. Krause et al [7] A bayesian belief network was established to evaluate possible features of german sedans in 2030, including market share of different vehicle types (including ICEV, HEV, and PEV), carbon dioxide emissions, and user costs. Javid and Nejat [8] The composition of the vehicle model and the variation of the emission level were evaluated. However, emissions are not assessed at the network level. Ma et al [9] Environmental costs of new energy automobiles are defined and a stochastic user balance model is presented to describe the driver's routing behavior. They also demonstrate the environmental benefits of introducing new energy vehicles into the traffic network and reveal the relationship between the number of new energy vehicles and the environmental cost of the overall traffic network. Duel et al [10] Methods are presented for incorporating traveler behavior and energy consumption of a motor vehicle into an assessment process to facilitate the development of new energy vehicles.
Reference is made to:
[1]Mitropoulos L K，Prevedouros P D.Incorporating sustainability assessment in transportation planning：an urban transportation vehicle-based approach[J].Transportation Planning and Technology，2016，39(5)：439-463.
[2]Duell M，Gardner L，Waller S T.Multiobjective Traffic Network Design Accounting for Plug-in Electric Vehicle Energy Consumption[C].Proceedings of Proceedings of the 92nd Annual Meeting of the Transportation Research Board，Washington，D.C.，2013.
[3]Gardner L M，Duell M，Waller S T.A framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability[J].Transportation Research Part A：Policy and Practice，2013，49：76-90.
[4]Jiang N，Xie C.Computing and Analyzing Mixed Equilibrium Network Flows with Gasoline and Electric Vehicles[J].Comput.-Aided Civil Infrastruct.Eng.，2014，29(8)：626-641.
[5]He F，Yin Y，Lawphongpanich S.Network equilibrium models with battery electric vehicles[J].Transportation Research Part B：Methodological，2014，67：306-319.
[6]Papargyri E，Kanaroglou P S，Photis Y N.Electric vehicles and traffic related pollution reduction：a simulation model for Hamilton，Ontario，Canada[J].Glob.Nest.J.，2014，16(4)：753-761.
[7]Krause J，Small M J，Haas A，Jaeger C C.An expert-based bayesian assessment of 2030 German new vehicle CO2 emissions and related costs[J].Transp.Policy，2016，52：197-208.
[8]Javid R J，Nejat A.A comprehensive model of regional electric vehicle adoption and penetration[J].Transp.Policy，2017，54：30-42.
[9]Ma J，Cheng L，Li D W，Tu Q.Stochastic Electric Vehicle Network Considering Environmental Costs[J].Sustainability，2018，10(8).
[10]Duell M，Gardner L M，Waller S T.Policy implications of incorporating distance constrained electric vehicles into the traffic network design problem[J].Transportation Letters-the International Journal of Transportation Research，2018，10(3)：144-158.
the invention comprises the following steps:
technical problems: there are at least three problems with the prior art methods. First, as electric vehicles penetrate in traffic networks, their environmental impact assessment methods have not been studied. Second, dynamic changes in market penetration levels for different vehicle models are not discussed. In this approach, absorbing Markov chain models are used to predict changes in market structure, as the PEV is considered to be an absorbing state that will not transition to other vehicle types. Finally, in the past environmental impact assessment, only traffic allocation was used for traffic planning, which is very limited. Therefore, the method provides a four-stage model with feedback so as to realize the balance of the traffic system and fully reflect the decision-making behavior of the traveler.
The technical scheme is as follows: the invention provides a new energy automobile permeation environmental impact assessment method, which specifically comprises the following steps:
general technical route
Step one: the emission rates of conventional diesel locomotives (ICEVs), plug-in electric vehicles (PEVs), and Hybrid Electric Vehicles (HEVs) are defined and expressed as a function of travel speed;
step two: the market share evolution process of ICEV, PEV and HEV is represented by using an absorption Markov chain model, wherein the PEV is an absorption state, and a consumer can not select other types of automobiles after selecting the PEV;
step three: the balance of the traffic system is achieved through feedback iteration of traffic generation, traffic distribution, traffic mode division and traffic flow distribution, and road section traffic flow and traffic time in a balance state are calculated;
step four: calculating the average speed of each road section by using the length and the running time of each road section;
step five: the environmental impact of the traffic system is evaluated.
(two) step one concrete calculation procedure
Analysis of environmental impact requires the emissions rates of ICEV, PEV, and HEV. The emissions of ICEVs are related to a number of factors including the mode of transportation, the type of engine, driving habits, vehicle speed, etc. Many researchers have studied the relationship between emissions and average vehicle speed because other factors are varied and difficult to measure. Here, emissions versus speed is derived from the MOVES2010a (motor vehicle emission simulator). The emissions (g/mi) produced per mile per vehicle are typically fitted to the average speed of travel s over road segment a a A power function of (a). Note that the emission rate decreases with increasing speed. They are represented as follows:
CO 2 ICEV (s a )＝3158s a -0.56 (1)
VOC ICEV (s a )＝1.3647s a -0.679 (2)
NO x ICEV (s a )＝2.5376s a -0.42 (3)
furthermore, the average speed s of the road section a a Defined as the distance travelled per unit time. Expressed as by the formula
Wherein l a In miles, is the length of road segment a; t is t a (v a ) The unit of the hour is the travel time of the road section, and the unit of the pcu/h is the flow v of the road section a To account for congestion effects. Thus, the emissions (in grams) produced by each ICEV on segment a can be expressed as segment flow v a Is shown below:
while PEVs do not directly produce tail gas emissions, they still indirectly contaminate the atmosphere through power stations, particularly coal-fired power plants. To illustrate the effect of PEV on the environment, the total energy consumed by the vehicle is used to multiply the average plant emission rate (g/kWh). The result of the calculation is the total emission produced in order to provide the energy consumed by the electric vehicle. The method uses data obtained from tesla motor company to fit the energy consumption rate per mile (kWh/mi) of each PEV to the average speed of travel s on road segment a a Polynomial function of (c):
EC PEV (s a )＝1.79e-8s a 4 -4.073e-6s a 3 +3.654e-4s a 2 -0.0109s a +0.2372 (8)
likewise, the average speed s of the road section a May be replaced by equation (4) to account for congestion effects. Thus, the energy consumption (kWh) of each PEV on road segment a can be expressed as road segment flow v a Is shown below:
the present invention uses CO published in analysis by North America environmental Cooperation Commission 2 、NO x And SO 2 Average discharge rate of 893g/kWh, 1.66g/kWh, 3.79g/kWh, respectively [3] . Finally, the emissions in grams per PEV on segment a may be expressed as segment flow v a Is shown below:
CO 2 PEV (v a )＝893·EC PEV (v a ) (10)
NO x PEV (v a )＝1.66·EC PEV (v a ) (11)
SO 2 PEV (v a )＝3.79·EC PEV (v a ) (12)
HEVs are a type of hybrid vehicle that combines a conventional ICEV system with a PEV system. Modern HEVs utilize efficiency-enhancing techniques that result in fewer emissions than equivalent-scale ICEVs. Since HEVs are a combination of ICEVs and PEVs, a factor α is used to integrate their emissions for simplicity. Thus, the emissions in grams generated by each HEV on road segment a can also be expressed as road segment flow v a Is shown below:
CO 2 HEV (v a )＝α·[CO 2 ICEV (v a )+CO 2 PEV (v a )] (13)
NO x HEV (v a )＝α·[NO x ICEV (v a )+NO x PEV (v a )] (14)
VOC HEV (v a )＝α·VOC ICEV (v a ) (15)
SO 2 HEV (v a )＝α·SO 2 PEV (v a ) (16)
(III) specific calculation procedure of step two
In the long term, owners may replace the vehicle type from one to another. The PEV is assumed to be of an absorption type, which means that if the owner chooses a PEV, he/she will not shift to other types. The transfer process for three vehicle types is shown in fig. 1. Thus, the market penetration level evolution of three vehicle models can be represented by an Absorbing Markov Chain (AMC) model. The market penetration rate of different states is determined by researching the initial probability of the different states and the transition probability between the states, so that the purpose of predicting the future is achieved.
Consider a set of states s= { S 1 ，s 2 ，...，s r The evolution process starts from one of these states and continuously transitions from one state to the other. Each transfer is referred to as a step. If it is currently in state s i Then in the next step, the probability p is shifted ij Move to state s j And the probability does not depend on the state before the current state. The process can also keep its state, and this is with probability p ii Which occurs. Let P= [ P ] ij ]Is a transition matrix of a markov chain. Matrix P n Ij item (i)Gives the Markov chain slave state s i Initially, after n steps, in state s j Is a probability of (2). Note that P n Each element of each row vector of (c) is non-negative and sums to 1. Let u be the probability vector representing the initial distribution. Then after n steps the chain is in state s i Is the ith term in the following vector
u (n) ＝uP n (17)
For the AMC model, the states are renumbered so that the transitional states are ordered in front. If the first q states are transition states and the last r states are absorption states, the transition matrix will have the following standard form
Where I is an identity matrix of R rows and R columns, 0 is a zero matrix of R rows and Q columns, R is a non-zero matrix of Q rows and R columns, and Q is a matrix of Q rows and Q columns. For the absorbing markov chain P, matrix n= (U-Q) -1 Is the basic matrix of matrix P, where U is the identity matrix of q rows and q columns.
In an absorbing Markov chain, the probability that a process is absorbed is 1. Let the chain be in state s i Starting, let t i For the number of expected transition steps before the chain becomes absorbed and let t be the i-th element t i Is a column vector of (a). Then
t＝Nc， (18)
Where c is a column vector, all of its elements are 1.
(IV) the concrete calculation process of the third step
Step 1: inputting traffic network information, including network structure, road section length, road section traffic capacity and the like;
step 2: given traffic demand O i Obtaining an initialized traffic distribution matrix through average distributionSetting n=0, representing the number of iterations;
step 3: distributing the traffic distribution matrix to the traffic network based on user balance by Frank-Wolfe algorithm to calculate traffic flow and travel time on each road section a, after which the shortest travel time between origin i and destination j, i.eCan be calculated by Dijkstra algorithm;
step 4: based onUpdating traffic distribution matrix using destination selection model>
Step 5: traffic distribution matrix using continuous averaging (MSA) with decreasing weightsAnd->Averaging
Step 6: checking the convergence of traffic distribution matrices using Relative Root Squares Error (RRSE)
If the convergence condition is met, turning to the step 8, otherwise, turning to the step 7 by making n=n+1;
step 7: traffic distribution matrix based on user equalization by Frank-Wolfe algorithmAssigned to traffic network to calculate traffic flow and travel time on each road section a, after which the shortest travel time between origin i and destination j, i.e +.>Can be calculated by Dijkstra algorithm, and the calculation result is invertedFeeding to step 4;
step 8: output traffic distribution matrixTraffic flow v on road section a a And travel time between origin i and destination j +.>
The beneficial effects are that: traffic has a significant impact on the sustainable development of the environment, society and economy. At present, the introduction of new energy automobiles is an effective way for relieving traffic pollution. However, this presents a significant challenge for conventional environmental impact assessment, as new energy vehicles have different features than conventional diesel locomotives. Environmental impact assessment at the traffic network layer facing penetration of new energy vehicles is very useful for both short-term and long-term new energy vehicle policy formulation. The invention can predict the market share level of various types of automobiles in the future and evaluate the change of the environmental impact of traffic through the calculation example.
Description of the drawings:
FIG. 1 is a transfer process between three vehicle types
FIG. 2 is a Nguyen-Dupuis test network
FIG. 3 is a simulated transfer process for three vehicle models
FIG. 4 is a graph showing the trend of dynamic changes in market share levels for various types of vehicles
The specific embodiment is as follows:
the Nguyen-Dupuis traffic network as shown in fig. 2 is widely used in traffic research to validate various approaches. The road segment parameters include free-flow travel time, road segment traffic capacity and road segment length, as shown in table 1.
TABLE 1 road segment parameters for Nguyen-Dupuis network
There are two origin regions in the Nguyen-Dupuis networkDomains 1 and 4 and two destination areas 2 and 3. Assume that traffic at start points 1 and 4 occurs at 1800pcu/h and 1200pcu/h, respectively, during peak hours. That is, O 1 ＝1800pcu/h，O 4 =1200 pcu/h. In the traffic distribution step, it is well known that many key variables with significant explanatory power are not included in the conventional gravity model. Among these, the most influential is the traveler destination preference. For example, travelers often prefer traditional destinations over newly developed areas. Thus, the destination selection employs a polynomial logic model with traveler preferences. Although there are a variety of explanatory variables, for simplicity, the destination selection model in the feedback process is simplified to
Wherein beta is j Is the preference of the traveler on destination j, beta t Is the path travel time coefficient between the O-D pair ij. The beta can be calibrated using empirical data j And beta t Is a value of (2). Here we set beta 2 ＝0、β 3 =1 and β t -0.1. That is, the traveler preference on destination area 2 is 0 and destination area 3 is 1, which means that the traveler generally prefers destination 3. The travel time coefficient is-0.1, which means that travel time is negative utility.
In the traffic flow allocation step, the present invention adopts a classical user balancing method that applies the road segment performance function in the balanced state. A common road segment performance function was developed by the united states highway office (BPR), which is expressed as follows:
wherein t is a (v a ) Is provided with traffic flow v a An impedance function of a given road segment a; c a Is the traffic capacity of the road section;is the free flow impedance of road segment a; alpha and beta are delay coefficients that can be empirically calibrated. For α and β, the conventional BPR values were 0.15 and 4.0, respectively, which were also used in our simulation studies. Therefore, the road traffic flow v can be obtained by the Frank-Wolfe algorithm a 。
The convergence criterion RRSE is set to 0.01, i.e., epsilon=0.01. By using the above parameters, a single stable solution can be converged with consistent travel time/cost and traffic distribution matrices. In addition, the shortest path travel time between i and j can be calculated using Dijkstra's algorithm. The link performance for a given traffic network is shown in table 2.
TABLE 2 road segment Performance under a given traffic network
The market penetration level for the three vehicle models is dynamic and changes over time. The owner may continue to use one type of vehicle or transfer to another type of vehicle. Note that PEV is an absorption state. This can be represented by an absorbing markov chain model. The transition probability may be affected by traffic policies, such as subsidizing new energy automobiles. For ICEV, HEV and PEV in turn, it is assumed that the one-step transfer matrix P is
The one-step transition process assumed in the absorbing markov chain model is shown in fig. 3. Thus, the functional matrix N is
The desired number of transfer steps (i.e., years) before the chain becomes absorbent is according to equation (18)
This means that current ICEV owners are expected to become absorbent after 14.4 years and current HEV owners are expected to become absorbent after 13.3 years. Note that the expectation here does not mean that there will be no ICEV and HEV after about fifteen years.
Furthermore, it is assumed that the current market share is u= (0.7,0.2,0.1), which means that ICEV currently occupies most of the automobile market. Dynamic changes in market penetration levels for the next 20 years are shown in FIG. 4 according to equation (17).
Note that the HEV and PEV travelers are assumed to behave the same as conventional ICEV travelers. Currently, they are not distinguished from ICEV travelers in terms of behavior. They are identical to the behavior of travelers of conventional vehicles in terms of destination selection, route selection, etc. Thus, it is assumed that the proportion of vehicles on each road segment is consistent with their market penetration level. However, the three vehicle types, ICEV, PEV, and HEV, are distinguished by their previously defined emissions rates. Although HEV has been reported to reduce smoke pollutant emissions by up to 90% and reduce carbon dioxide emissions by half, the factor α is set to 0.6 here. Further, the link traffic amount and the link travel time as shown in table 2 can be obtained by the traffic system equalization. Thus, environmental impact can be assessed over time in a traffic network, as shown in table 3.
TABLE 3 strategic environmental impact over time
It is evident that as conventional ICEVs are increasingly abandoned, CO 2 、NO x And VOC emissions continue to decrease. Their emissions in the last year account for 43.8%,53.9% and 19.8% of the baseline year, respectively. However, as electric vehicles become mainstream, SO 2 Is increasing. SO in last year 2 Is 3.913 times as much as the reference year. Although tremendous changes occur over the first few years, changes in environmental impact begin to stabilize over the last few years, which is known as steady state. Eventually, ICEV and HEV will be absorbed by the PEV, and the environmental impact will not change further unless traffic demand changes.
Claims (2)
1. The method for evaluating the environmental impact of the permeation of the new energy automobile comprises the following steps:
step one: emission rates of a conventional diesel locomotive (ICEV), a plug-in electric vehicle (PEV), and a Hybrid Electric Vehicle (HEV) are defined, respectively, as a function of travel speed:
(1) The emissions versus speed relationship for a conventional diesel locomotive (ICEV) is derived from MOVES2010a (automotive vehicle emission simulator), and the emissions per mile produced by each vehicle are fitted to the average travel speed s over road segment a a Is expressed as follows:
wherein l a In miles, is the length of road segment a, t a (v a ) In hours, the travel time of the road section is pcu +.Road section flow v in h a To account for congestion effects;
(2) Emission of plug-in electric vehicles (PEV) using data obtained from tesla motor company, fitting the energy consumption rate per mile of each PEV to the average travel speed s on road segment a a Polynomial function of (c):
CO published in analytics using North America environmental Cooperation Commission 2 、NO x And SO 2 Is 893g/kWh, 1.66g/kWh, 3.79g/kWh, respectively, and finally, the emissions in grams per PEV on segment a may be expressed as segment flow v a Is shown below:
CO 2 PEV (v a )＝893·EC PEV (v a )
NO x PEV (v a )＝1.66·EC PEV (v a )
SO 2 PEV (v a )＝3.79·EC PEV (v a )；
(3) Hybrid Electric Vehicles (HEVs) are a combination of ICEV and PEV, which for simplicity use a factor α to integrate their emissions:
CO 2 HEV (v a )＝α·[CO 2 ICEV (v a )+CO 2 PEV (v a )]
NO x HEV (v a )＝α·[NO x ICEV (v a )+NO x PEV (v a )]
VOC HEV (v a )＝α·VOC ICEV (v a )
SO 2 HEV (v a )＝α·SO 2 PEV (v a )；
step two: the market share evolution process of ICEV, PEV and HEV is represented by using an absorbing Markov chain model, wherein the PEV is an absorbing state, and a consumer can not select other types of automobiles after selecting the PEV, and the specific calculation process is as follows:
(1) Consider a set of states s= { S 1 ，s 2 ，...，s r The evolution process starts from one of these states and transitions continuously from one state to the other, each transition being called a step, if currently in state s i Then in the next step, the probability p is shifted ij Move to state s j And the probability is not dependent on the state before the current state;
(2) Let u be the probability vector representing the initial distribution, then after n steps the chain is in state s i Is the ith term in the following vector
u (n) ＝uP n ；
(3) The states are renumbered so that the transition states are ordered in front, and if the first q states are transition states and the last r states are absorption states, the transition matrix will have the following standard form
Wherein I is an identity matrix of R rows and R columns, 0 is a zero matrix of R rows and Q columns, R is a non-zero matrix of Q rows and R columns, Q is a matrix of Q rows and Q columns, and matrix n= (U-Q) -1 Is the basic matrix of matrix P, where U is the identity matrix of q rows and q columns;
step three: the balance of the traffic system is achieved through feedback iteration of traffic generation, traffic distribution, traffic mode division and traffic flow distribution, and road section traffic flow and traffic time in a balance state are calculated;
step four: calculating the average speed of each road section by using the length and the running time of each road section;
step five: the environmental impact of the traffic system is evaluated.
2. The method for evaluating the environmental impact of new energy automobile permeation according to claim 1, wherein the third step adopts the following specific calculation process:
step 1: inputting traffic network information, including network structure, road section length and road section traffic capacity;
step 2: given traffic demand O i Obtaining an initialized traffic distribution matrix through average distributionSetting n=0, representing the number of iterations;
step 3: distributing the traffic distribution matrix to the traffic network based on user balance by Frank-Wolfe algorithm to calculate traffic flow and travel time on each road section a, after which the shortest travel time between origin i and destination j, i.eCan be calculated by Dijkstra algorithm;
step 4: based onUpdating traffic distribution matrix using destination selection model>
Step 5: traffic distribution matrix using continuous averaging (MSA) with decreasing weightsAnd->Averaging
Step 6: checking the convergence of traffic distribution matrices using Relative Root Squares Error (RRSE)
If the convergence condition is met, turning to the step 8, otherwise, turning to the step 7 by making n=n+1;
step 7: traffic distribution matrix based on user equalization by Frank-Wolfe algorithmAssigned to traffic network to calculate traffic flow and travel time on each road section a, after which the shortest travel time between origin i and destination j, i.e +.>The calculation result can be fed back to the step 4 through Dijkstra algorithm calculation;
step 8: output traffic distribution matrixTraffic flow v on road section a a And travel time between origin i and destination j +.>
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