Method of evaluating network reliability for multistate transportation network with charging stations and the device thereof

Abstract

A method of evaluating network reliability for multistate transportation network with charging stations, which includes a processor utilized to process the following steps including constructing a network topology having network nodes accessible to electric vehicles and multiple road sections connecting each network node, calculating the travel time and power consumption required for electric vehicles to travel at different speeds on each complex road section, obtaining the upper bound speed vector and lower bound speed vector of electric vehicles that meet the travel time limit and power consumption limit, and utilizing the upper bound speed vector and lower bound speed vector to calculate network reliability.

Description

[Technical Field]

[0001] This invention relates to network reliability assessment, and in particular to a method and apparatus for assessing network reliability for multi-stage transportation networks with charging stations. [Previous Technology]

[0002] With the increasing global sales of electric vehicles and growing awareness of energy conservation and carbon reduction, electric vehicles have gradually replaced gasoline-powered vehicles as a new mode of transportation. Due to Taiwan's limited land area, dense population, and lack of natural resources, the development of new renewable energy sources is particularly challenging. Therefore, the government is currently promoting electric vehicles as one of the main means to mitigate carbon emissions. To achieve its net-zero emissions target, Taiwan has proposed exempting vehicle registration taxes and reducing commodity taxes to increase the public's willingness to purchase electric vehicles. According to estimates from the Legislative Yuan's Global Information Network, an estimated 11,000 electric vehicles will benefit annually. According to statistics from Taiwan's Ministry of Transportation, as of November 2022, the total number of electric vehicles in Taiwan was 29,370, of which 11,296 were newly registered in 2022. The total sales volume of electric vehicles in 2023 is expected to reach 29,329, representing an annual growth rate of 60%. This clearly shows a significant increase in the willingness of Taiwanese people to choose electric vehicles. To cope with the growth in the number of electric vehicles in Taiwan, it is crucial to assess the impact of electric vehicle performance on users and traffic in real time.

[0003] In this situation, the government needs to formulate relevant measures to assist in the introduction of electric vehicles. Among these measures, it is particularly important whether the Highway Authority can quickly assist electric vehicle users or make effective decisions regarding traffic conditions. The speed of vehicles on different sections of the highway may be random due to factors such as traffic jams, construction, and weather, which in turn affects travel time and the power consumption of electric vehicles.

[0004] For traffic decision-makers, assessing the performance of electric vehicles has become a new challenge, and the introduction of electric vehicles may even impact overall traffic conditions. However, in the past, no quantitative indicators for assessing the performance of electric vehicles have been developed. Highway authorities can only make decisions after traffic conditions occur, rather than making preparations in advance, resulting in low overall operational efficiency. Furthermore, the systems and methods provided by conventional technologies do not effectively take into account the randomness of driving speeds. From the perspective of decision-makers or management, adjusting actions or decisions requires clear data and indicators to help electric vehicle users and improve overall traffic conditions.

[0005] Therefore, it is necessary to propose a method to develop a quantitative indicator to efficiently and instantly assess the driving performance of electric vehicles and help decision-makers determine the probability that electric vehicles can reach their destination within the constraints of travel time and power consumption. [Summary of the Invention]

[0006] To improve upon the shortcomings of prior art, according to one aspect of the present invention, the present invention discloses a method for evaluating the network reliability of a multi-level transportation network with charging stations. The method involves a processor executing the following steps: constructing a network topology, including network nodes accessible to electric vehicles and multiple road segments connecting the individual network nodes; calculating the travel time and power consumption required for an electric vehicle to travel at different speeds on each of the multiple road segments; determining the upper and lower bound speed vectors of the electric vehicle under the conditions of satisfying the travel time and power consumption constraints; and calculating the network reliability using the upper and lower bound speed vectors.

[0007] In one embodiment, the above-mentioned method for evaluating the network reliability of a multi-level transportation network with charging stations further includes evaluating the performance of electric vehicles through the network reliability results, and planning corresponding driving routes and management strategies based on changes in network reliability.

[0008] In one embodiment, the above network topology includes a highway system, and the network nodes are all the interchanges of a selected section of the highway system. The plurality of sections includes the sections connecting the interchanges of each network node.

[0009] In one embodiment, the steps of calculating the travel time and power consumption of the electric vehicle on each road segment include: calculating the travel time of the electric vehicle at different speeds for each plurality of road segments; calculating the power consumption of the electric vehicle at different speeds for each plurality of road segments; and classifying the calculated power consumption to facilitate the solution of the continuation problem.

[0010] In one embodiment, the upper bound speed vector is obtained through the following steps: generating a pseudo-energy vector representing the complete power consumption from the starting point to the ending point; converting all pseudo-energy vectors into maximum energy vectors; converting all maximum energy vectors into corresponding speed vectors, and checking whether the total travel time of each speed vector exceeds the travel time limit. If true, the speed vector is deleted; and removing duplicates and non-maximum speed vectors from the remaining speed vectors to obtain the upper bound speed vector. The lower bound speed vector is obtained through the following steps: generating a pseudo-travel time vector representing the complete travel time from the starting point to the ending point; converting all pseudo-travel time vectors into maximum travel time vectors; converting all maximum travel time vectors into corresponding speed vectors, and checking whether the total power consumption of each speed vector exceeds the power consumption limit. If true, the speed vector is deleted; and removing duplicates and non-minimum speed vectors from the remaining speed vectors to obtain the lower bound speed vector.

[0011] In one embodiment, the above-mentioned steps for calculating network reliability include: constructing a speed probability table using real speed data of the highway system; and calculating network reliability using an existing ensemble algorithm based on the obtained upper bound speed vector, lower bound speed vector, and speed probability table.

[0012] Another aspect of the present invention discloses a network reliability assessment device for a multi-level transportation network with charging stations, comprising a processor and a memory electrically connected to the processor for storing the multi-level transportation network with charging stations and an algorithm. The algorithm utilizes the processor to perform the following steps: defining a network topology for the multi-level transportation network with charging stations, wherein the network topology includes network nodes accessible to electric vehicles and multiple road segments connecting individual network nodes; calculating the travel time and power consumption required for the electric vehicle to travel at different speeds on each of the multiple road segments; obtaining the upper bound speed vector and lower bound speed vector of the electric vehicle under the conditions of satisfying the travel time and power consumption constraints; and calculating the network reliability using the upper bound speed vector and lower bound speed vector.

[0013] In one embodiment, the network topology described above includes a highway system. The network nodes are all the interchanges of a selected section of the highway system, and the plurality of sections includes the sections between the interchanges connecting the network nodes.

Implementation Method

[0014] This invention will be described in detail here with reference to specific embodiments and their viewpoints. Such descriptions are for illustrative purposes only and are not intended to limit the scope of the invention. Therefore, in addition to the specific and preferred embodiments described in the specification, the invention can also be widely implemented in other different embodiments. The following describes the implementation of the invention through specific embodiments. Those skilled in the art can easily understand the effectiveness and advantages of the invention from the content disclosed in this specification. Furthermore, the invention can also be used and implemented through other specific embodiments, and the various details set forth in this specification can be applied based on different needs, and various modifications or changes can be made without departing from the spirit of the invention.

[0015] This invention belongs to the field of "computational technology". This invention constructs the driving of a practical electric vehicle on a highway system as a multistate transportation network (MTN) topology and stores it in a readable media device. It proposes a quantitative indicator to assist electric vehicle users in real-time assessment of the vehicle's driving status and in making route-related decisions. It can also assist highway authorities in making traffic-related decisions such as electric vehicle roadside assistance and traffic flow management. In the MTN, highway interchanges are considered nodes, and the road sections between interchanges are considered transmission edges. The electric vehicle must reach its destination within a given travel time and power consumption limit. Because the driving speed on each road section may be affected by road congestion, construction, traffic accidents, etc., and under these circumstances, road users can only drive according to the current overall speed, resulting in multi-level characteristics of driving speed. Under the aforementioned randomness, the electric vehicle may not be able to reach its destination under the limited conditions or may even break down due to insufficient power. The highway authority cannot make timely traffic management decisions, such as high-occupancy vehicle (HOV) control, to alleviate road congestion. Therefore, this invention constructs an MTN (Mobile Network Node) using network analysis methods, performs network topology analysis on the MTN, and stores it in a readable media device. The device includes: a memory for storing the MTN and an algorithm; and a processor connected to the memory to execute the algorithm to calculate the network reliability of the MTN, thus calculating the probability that the electric vehicle can successfully reach its destination within travel time and battery limitations. Using network reliability as a quantitative indicator, this allows electric vehicle users to assess the feasibility of their current electric vehicle journey and adjust their routes accordingly. Highway authorities can use this information to determine the need for electric vehicle roadside assistance and traffic flow management, enabling them to respond promptly to current traffic conditions.

[0016] This invention takes into account the randomness of electric vehicle speeds when traveling on actual highway systems, due to factors such as traffic jams, construction, and traffic accidents. This speed uncertainty may affect the electric vehicle's power consumption and travel time, potentially causing users to fail to reach their destination within the allotted time and requiring roadside assistance, while also having a cascading effect on overall traffic. To address these issues, this invention stores the proposed network reliability method in a readable medium device, assessing the probability of an electric vehicle reaching its destination under speed uncertainty, travel time, and power consumption constraints. This provides electric vehicle users with quantifiable results for subsequent route planning and other decisions. Furthermore, by exploring the impact of different travel times and power consumption constraints on the feasibility of electric vehicle operation, users can pre-plan routes based on the network reliability results, avoiding breakdowns due to depleted power. Fluctuations in network reliability can also serve as a basis for highway administration managers' traffic flow management strategies. Analysis results can identify road sections where traffic conditions may be affected by random factors, allowing for the use of ramp controls, high-occupancy vehicle (HOV) regulations, or additional manpower and resources to mitigate the impact and ensure the feasibility of electric vehicle operation and overall traffic flow. By using network reliability as a quantitative indicator, drivers and decision-makers can efficiently and instantly assess the performance of electric vehicles and accordingly plan more effective routes and management strategies.

[0017] The method and apparatus disclosed in this invention are applicable to system management methods and related extended management software in industries such as manufacturing, logistics, tourism, transportation, and high technology. The extended system management software of this invention can construct a multi-level transportation network for the selected highway starting and ending points of the above-mentioned industries. It takes into account the uncertainty of the speed status caused by factors such as traffic jams, construction, and traffic accidents in the road segment, assesses the probability that electric vehicles can reach the destination or meet other needs within the travel time and power consumption limits, and makes relevant decisions such as route selection based on the indicators.

[0018] This invention discloses an algorithm for calculating the network reliability of a Multi-state Transportation Network with Charging Stations (MTNCS). Network reliability is defined as the probability that an electric vehicle successfully reaches its destination within a power consumption constraint K and a travel time constraint L. The following lists the notations and related descriptions used in the network model construction and algorithm of the MTNCS mentioned in this invention: K: Energy constraint. L: Travel time constraint. N: Set of nodes. ai: The i-th transmission edge, i = 1, 2, …, n. A: Set of transmission edges, denoted by {ai | i = 1, 2, …, n}. wi: Distance to ai. W: Set of distances to ai, denoted by {wi | i = 1, 2, …, n}. vi,j: The j-th possible vehicle speed value on ai, j = 1, 2, …, n. ti,j is the j-th possible travel time value on ai, j = 1, 2, …, n. The j-th possible rounded travel time value on ai, j = 1, 2, …, n. qi,j is the j-th possible power consumption on ai, j = 1, 2, …, n. The j-th possible rounded power consumption on ai, j = 1, 2, …, n. X is the capacity vector, set as vehicle speed, denoted by (xi| i = 1, 2, …, n). U is the maximum travel time vector, the time vector denoted by (ui| i = 1, 2, …, n). vi is the number of possible time vectors on ai, i = 1, 2, …, n. ri,j is the j-th possible time vector (ui), j = 1, 2, …, vi. Z is the pseudo-time vector, denoted by (zi| i = 1, 2, …, n). D is the maximum energy vector, denoted by (di| i = 1, 2, …, n). si is the number of possible energy vectors on ai, i = 1, 2, …, n. hi,j is the j-th possible energy vector (di) value on ai, j = 1, 2, …, si. E is the pseudo-energy vector, denoted by (ei| i = 1, 2, …, n).B represents the maximal capacity (vehicle speed) vectors, denoted by (B1, B2, …, Bq). n represents the number of minimum paths. Pj represents the j-th minimum path, j = 1, 2, …, n. C represents the amount of electricity charged. T(X) represents the total travel time under the conditions of the capacity (vehicle speed) vector X. Q(X) represents the total energy consumption under the conditions of the capacity (vehicle speed) vector X. F aero represents the aerodynamic drag. F roll represents the rolling drag. F grad represents the gradient drag. F acc represents the acceleration drag. ρ represents the air density. A f represents the frontal area of ​​the vehicle. CD represents the aerodynamic drag coefficient. M represents the vehicle mass. g represents the gravitational acceleration. fr represents the rolling drag coefficient. (K, L)-UB represents the maximal upper boundary vector of the vehicle speed on each transmission edge that successfully reaches the destination within the energy consumption limit K and the travel time limit L, i.e., the maximum upper boundary velocity vector. Ω max represents the set of all (K, L)-UB. (K, L)-LB represents the minimum lower boundary vector (MPB) of the vehicle speed on each transmission edge that successfully reaches its destination within the power consumption limit K and travel time limit L. Ω is the set of all (K, L)-LB values. RK, L represents the network reliability of electric vehicles reaching their destination within the power consumption limit K and travel time limit L.

[0019] Assume that X = (x1, x2, ..., xn) and Y = (y1, y2, ..., yn) are both vehicle speed vectors. Vector operations are calculated according to the following rules: (1) X ≧ Y: (x1, x2, …, xn) ≧ (y1, y2, …, yn) if and if xi ≧ yi, for i = 1, 2, …, n. (2) X > Y: (x1, x2, …, xn) > (y1, y2, …, yn) if and if X ≧ Y and xi > yi, for i = 1, 2, …, n.

[0020] The network reliability disclosed in this invention is defined as the probability that an electric vehicle (EV) can successfully reach its destination within the power consumption limit K and the travel time limit L. Constructing a multi-level transportation network.

[0021] To evaluate the performance of electric vehicles traveling on highway systems, this invention first establishes a network model of a multi-level transportation network (MTNCS) with charging stations, denoted as G≡(N, A, W), where N is the set of network nodes, A = {ai | i = 1, 2, …, n} represents the set of road segments (transmission edges) for electric vehicles, and W = {wi | i = 1, 2, …, n} represents the set of distances for each road segment ai. In embodiments of this invention, network nodes are highway interchanges, and transmission edges (arcs) represent road segments between any pair of network nodes (interchanges). According to embodiments of this invention, the capacity vector X is set as vehicle speed. The speed on each transmission edge may be affected by factors such as weather conditions, construction sites, and congestion, and therefore can be considered as a multi-level state. Furthermore, vehicle speed on highways is not always determined autonomously by individual drivers, but rather may have to be adjusted jointly by all drivers based on instantaneous road conditions, thus drivers may be forced to drive at undesirable speeds. At such vehicle speeds, the travel time of each transmission edge (road segment) can be calculated using the segment length, reflecting the current traffic conditions on each segment. Simply put, a multi-level transportation network (MTNCS) with charging stations must satisfy the following assumptions: (1) All network nodes are completely reliable. (2) It is assumed that the vehicle speeds on each transmission edge ai are statistically independent. (3) The vehicle speed and travel time on each transmission edge ai are integers. (4) Only the time spent charging at highway rest areas is considered.

[0022] The above assumptions (1) and (2) are necessary conditions when using network analysis techniques. For assumption (3), non-integers may affect the calculation of network reliability, so it is further limited to integers. Finally, there is assumption (4), other activities, such as time-consuming activities like resting and eating, are ignored, and only the time spent charging is considered.

[0023] Figure 1 shows an example network of a selected section of a highway. Figure 2 is a simplified multi-level transportation network diagram of Figure 1, with a starting point s and a destination point t. The method and apparatus disclosed in this invention can also be used in other practical transportation systems. The nodes in the network are all the interchanges of the selected section, and ai are the road segments between interchanges. The length of each road segment is included in Table 1. [Table 1]: Road Segment Information (Length) Transmission edge a1 a2 a3 a4 a5 a6 a7 a8 a9 a 10 a 11 a 12 a 13 a 14 a 15 a 16 a 17 a 18 a 19 a 20 Distance (km) 10.8 15.4 6.8 8.2 9.5 8.0 2.2 5.1 7.4 3.1 5.7 5.6 4.1 5.6 6.0 3.9 9.7 12.3 7.4 5.2

[0024] In one embodiment of the present invention, based on the current battery level of the electric vehicle, the power consumption and travel time limits are set to 8000 watts (K) and 55 minutes (L), respectively. To assess the feasibility of the electric vehicle reaching its destination within these limits, we will calculate network reliability through a three-stage process. First, we calculate the travel time and power consumption required for each road segment at different speeds, with the relevant parameter settings shown in Table 2. [Table 2]: Parameters for constructing the speed probability table parameter Numerical values air density ρ 1.164 kg / m 3 Rolling resistance parameter f r 0.011 gravitational acceleration g 9.81 m / sec 2 Vehicle front area A f 2.341 m 2 aerodynamic drag coefficient C D 0.23 Vehicle mass M 1988 kg

[0025] Next, in the second stage, the upper bound speed vector (K, L)-UBs and the lower bound speed vector (K, L)-LBs that satisfy the travel time limit L and the power consumption limit K are obtained. Finally, the real speed data provided by the Highway Bureau is used to construct a speed probability table to present the randomness of the driving speed, and the network reliability can be calculated through the upper and lower bound speed vectors. The complete calculation process is as follows: I. Calculate the travel time and electric vehicle power consumption required for each road segment: Step (1). For each road segment, calculate the travel time ti,j (in seconds) at different speeds using (Mathematical Formula 1). , i= 1, 2, …, n, j= 1, 2, …, ui [Mathematical Formula 1] Step (2). Convert the travel time calculated in step (1) into minutes using (Mathematical Formula 2). , i= 1, 2, …, n, j= 1, 2, …, ui [Mathematical Formula 2] Step (3). For each road segment, calculate the electric vehicle power consumption at different speeds using (Mathematical Formula 3), where F aero is the aerodynamic drag, F roll is the rolling drag, and vi,j is the vehicle speed of the electric vehicle on road segment ai. , i= 1, 2, …, n, j= 1, 2, …, ui [Mathematical Formula 3] Step (4). For each road segment, classify the electric vehicle power consumption qi,j calculated in step (3) using (Mathematical Formula 4) for subsequent solutions, where b is the closest and smallest possible power consumption (qi,j) value. [Mathematical Formula 4] By performing the above four steps for each road segment, a complete travel time and electric vehicle power consumption table can be constructed, as shown in Tables 3 and 4. [Table 3]: Travel timetable for each road segment Travel time (unit: minutes) Section 110 105 100 95 90 85 80 75 70 65 60 a1 6 7 7 7 8 8 9 9 10 10 11 a2 9 9 10 10 11 11 12 13 14 15 16 a3 4 4 5 5 5 5 6 6 6 7 7 a4 5 5 5 6 6 6 7 7 8 8 9 a5 6 6 6 6 7 7 8 8 9 9 10 a6 5 5 5 6 6 6 6 7 7 8 8 a7 2 2 2 2 2 2 2 2 2 3 3 a8 3 3 4 4 4 4 4 5 5 5 6 a9 5 5 5 5 5 6 6 6 7 7 8 a 10 2 2 3 3 3 3 3 3 3 4 4 a 11 4 4 4 4 4 5 5 5 5 6 6 a 12 4 4 4 4 4 4 5 5 5 6 6 a 13 3 3 3 3 3 3 4 4 4 4 5 a 14 4 4 4 4 4 4 5 5 5 6 6 a 15 4 4 4 4 4 5 5 5 6 6 6 a 16 3 3 3 3 3 3 3 4 4 4 4 a 17 6 6 6 7 7 7 8 8 9 9 10 a 18 7 8 8 8 9 9 10 10 11 12 13 a 19 5 5 5 5 5 6 6 6 7 7 8 a 20 3 3 4 4 4 4 4 5 5 5 6 [Table 4]: Electric Vehicle Power Consumption for Different Road Sections Power consumption (unit: watts) Section 110 105 100 95 90 85 80 75 70 65 60 a1 1500 1450 1350 1300 1250 1150 1100 1050 1000 950 900 a2 2150 2050 1950 1850 1750 1650 1600 1500 1400 1350 1300 a3 950 900 850 800 800 750 700 650 650 600 550 a4 1150 1100 1050 1000 950 900 850 800 750 700 700 a5 1350 1250 1200 1150 1100 1050 950 950 900 850 800 a6 1150 1050 1000 950 900 850 800 800 750 700 650 a7 300 300 300 250 250 250 250 200 200 200 200 a8 700 700 650 600 600 550 500 500 450 450 450 a9 1050 1000 950 900 850 800 750 700 700 650 600 a 10 500 450 450 400 400 350 350 350 300 300 300 a 11 800 750 700 700 650 600 600 550 550 500 500 a 12 800 750 700 650 650 600 550 550 500 500 450 a 13 600 550 500 500 450 450 400 400 400 350 350 a 14 800 750 700 650 650 600 550 550 500 500 450 a 15 850 800 750 700 700 650 600 600 550 550 500 a 16 550 500 500 450 450 400 400 400 350 350 350 a 17 1350 1300 1250 1150 1100 1050 1000 950 900 850 800 a 18 1750 1650 1550 1500 1400 1350 1250 1200 1150 1100 1050 a 19 1050 1000 950 900 850 800 750 700 700 650 600 a 20 750 700 650 650 600 550 550 500 500 450 450 II. Obtaining the upper bound velocity vector (K, L)-UBs: Step (1) Use (Mathematical Formula 5) to generate a pseudo-energy vector E = (e1, e2, …, en) that consumes a total of K units of electricity from the starting point to the ending point. , i=1, 2, …, n [Mathematical Formula 5] Step (2) Use (Mathematical Formula 6) to convert all pseudo-energy vectors E = (e1, e2, …, en) into a maximum energy vector D = (d1, d2, …, dn). [Mathematical Formula 6] Step (3) Convert all maximum energy vectors D = (d1, d2, …, dn) into corresponding velocity vectors X u = (x1, x2, …, xn), and check whether the total travel time of each velocity vector exceeds the limit L. If true, delete the velocity vector. Step (4) Remove duplicates and non-maximum velocity vectors from the remaining velocity vectors Xu from step (3) to obtain the upper bound velocity (K, L)-UBs. Step (5) Use (Mathematical Formula 7) to generate a pseudo travel time vector Z = (z1, z2, …, zn) that consumes a total travel time L from the starting point to the ending point. [Mathematical Formula 7] Step (6) Use (Mathematical Formula 8) to convert all pseudo travel time vectors Z = (z1, z2, …, zn) into the maximum travel time vector U = (u1, u2, …, un). [Mathematical Formula 8] Step (7) Convert all maximum travel time vectors U = (u1, u2, …, un) into the corresponding velocity vector Xl = (x1, x2, …, xn), and check whether the total power consumption of each velocity vector exceeds the limit K. If true, delete the velocity vector. Step (8) Remove duplicates and non-minimum velocity vectors from the remaining velocity vector Xl from step (7) to obtain the lower bound velocity vector (K, L)-LBs. III. Constructing a speed probability table and calculating network reliability: Step (1) Construct a speed probability table using real speed data from the highway authority, as shown in Table 5. [Table 5]: Speed ​​capacity table Electric vehicle speed (unit: km / h) probability Section 110 105 100 95 90 85 80 75 70 65 60 a1 0 0.03 0.28 0.4 0.23 0.04 0.02 0 0 0 0 a2 0 0.03 0.26 0.37 0.25 0.07 0.02 0 0 0 0 a3 0 0.04 0.22 0.43 0.18 0.17 0.11 0.05 0 0.01 0 a4 0 0 0.02 0.46 0.18 0.17 0.11 0.05 0 0.01 0 a5 0 0.11 0.21 0.27 0.23 0.12 0.06 0 0 0 0 a6 0 0.09 0.24 0.22 0.21 0.16 0.05 0 0 0 0.03 a7 0 0.09 0.24 0.22 0.21 0.16 0.05 0 0 0 0.03 a8 0 0.121 0.202 0.313 0.172 0.162 0.03 0 0 0 0 a9 0 0 0.202 0.044 0.244 0.156 0.356 0.089 0.022 0 0.067 a 10 0.137 0.189 0.147 0.232 0.263 0.021 0.011 0 0 0 0 a 11 0.078 0.211 0.156 0.189 0.289 0.044 0.033 0 0 0 0 a 12 0.247 0.146 0.079 0.225 0.213 0.067 0 0.023 0 0 0 a 13 0.33 0.136 0.057 0.227 0.136 0.091 0.023 0 0 0 0 a 14 0.384 0.104 0.035 0.058 0.047 0.058 0.047 0.093 0.07 0.023 0.081 a 15 0.294 0.177 0.035 0.188 0.165 0.129 0.012 0 0 0 0 a 16 0.261 0.17 0.045 0.193 0.159 0.125 0.012 0 0 0.012 0.023 a 17 0.31 0.172 0.058 0.218 0.184 0.034 0.012 0 0.012 0 0 a 18 0.25 0.193 0.08 0.102 0.08 0.136 0.148 0.011 0 0 0 a 19 0.348 0.135 0.034 0.281 0.18 0.022 0 0 0 0 0 a 20 0.178 0.144 0.178 0.156 0.233 0.078 0.033 0 0 0 0 Step (2) Using the obtained (K, L)-UBs, (K, L)-LBs, and velocity probability table (Table 5), the network reliability is calculated using the existing ensemble algorithm (Equation 9). [Equation 9]

[0026] The network reliability calculated through the above steps is 0.724, which means that the probability of an electric vehicle successfully reaching its destination within the power consumption limit K and travel time limit L while traveling on this network is 0.724. Network reliability allows electric vehicle users to assess the current driving status of their electric vehicles and determine whether route adjustments are necessary; it also provides information for highway administration managers to pre-arrange roadside assistance for electric vehicles and manage real-time traffic flow. Utilizing data-driven management methods effectively reduces the risk costs associated with the uncertainty of driving speed and further assists in making appropriate decisions.

[0027] Based on the above description of the application of the algorithm to actual numerical examples, and referring to Figure 3, a flowchart of the method for evaluating the network reliability of a multi-level transportation network with charging stations proposed in this invention is summarized. This invention considers the uncertainty of driving speeds on different road segments in a multi-level transportation network with charging stations (MTNCS). Based on the current battery level of the electric vehicle, a power consumption limit K and a travel time limit L are set to evaluate the feasibility of the electric vehicle reaching its destination within these limits. Using the aforementioned algorithm, the processor 405 (refer to Figure 4) accesses and executes the algorithm to calculate network reliability and provide performance indicators for measuring the current transportation network. The method for evaluating the network reliability of a multi-level transportation network with charging stations includes the following steps performed using the processor 405 (refer to Figure 4): Step S30: Constructing a multi-level transportation network with charging stations as a network topology, including network nodes accessible to electric vehicles and multiple road segments connecting each individual network node. Step S31: Calculate the travel time and power consumption of the electric vehicle on each road segment; this step includes (1) calculating the travel time at different speeds for each road segment; (2) converting the calculated travel time into minutes; (3) calculating the power consumption of the electric vehicle at different speeds for each road segment; and (4) classifying the power consumption of the electric vehicle calculated in step (3) for subsequent solutions. Step S32: Calculate the upper bound speed vector (K, L)-UBs and the lower bound speed vector (K, L)-LBs; this step includes (1) generating a pseudo energy vector E that consumes K units of electricity from the starting point to the end point; (2) converting all pseudo energy vectors E into the maximum energy vector D; (3) converting all maximum energy vectors D into the corresponding speed vectors X u, and checking whether the total travel time of each speed vector exceeds the travel time limit L. If true, delete the speed vector; (4) removing duplicates and non-maximum speed vectors from the remaining speed vectors X u in step (3), and the upper bound vector (K, L)-UBs can be obtained; (5) generating a pseudo travel time vector Z that consumes L travel time from the starting point to the end point; (6) converting all pseudo travel time vectors Z into the maximum travel time vector U; (7) converting all maximum travel time vectors U into the corresponding speed vectors X l, and checking whether the total power consumption of each speed vector exceeds the power consumption limit K. If true, then delete the velocity vector; and remove duplicates and non-minimum velocity vectors from the remaining velocity vector X l in step (7) to obtain the lower bound vector (K, L)-LBs.Step S33: Calculate network reliability, which represents the probability that an electric vehicle will reach its destination within the power consumption limit K and travel time limit L when traveling on a multi-level transportation network (MTNCS) with charging stations. This step includes (1) constructing a speed probability table using real speed data from highways; (2) calculating network reliability RK,L using the obtained upper bound speed vector (K, L)-UBs, lower bound speed vector (K, L)-LBs and speed probability table, and using existing ensemble algorithms. Step S34: Based on the above network reliability results, evaluate the performance of electric vehicle driving, and make corresponding driving route planning and management strategies based on the changes in network reliability.

[0028] This invention primarily proposes a quantitative indicator for multi-level transportation networks (MTNCS) with charging stations to assist electric vehicle users in real-time assessment of their electric vehicle's driving status and in making route-related decisions. It can also assist highway authorities in making traffic-related decisions, including electric vehicle roadside assistance and traffic flow management. This invention mainly covers a performance evaluation method for electric vehicles traveling on highway systems, taking into account the uncertainty of highway speeds. The highway system is constructed as a multi-level transportation network, whose topology includes all interchanges of selected road sections at nodes, road sections connecting the interchanges between nodes, and is stored in a readable media device. The processor in the device then performs network reliability calculations, calculating the upper and lower bound speed vectors that allow electric vehicles to meet travel time and power consumption limits while traveling on the network. The network reliability is then calculated using these upper and lower bound speed vectors, representing the probability that the electric vehicle can successfully reach its destination within the limits. Network reliability can provide electric vehicle users with a basis for route selection and highway authorities with a basis for making decisions regarding roadside assistance and traffic flow management.

[0029] In this invention, network reliability is obtained using the Recursive Disjoint Summation Method (RSDP). Please refer to FIG4, which shows a schematic diagram of a computing device for evaluating the network reliability of a multi-level transport network with charging stations, i.e., a network reliability computing device 400, in an embodiment of the present invention. As shown in FIG4, the network reliability computing device 400 applied to the present invention may include at least an input device 401, a memory 403, a processor 405, and an output device 407. The input device 401 is electrically connected to the memory 403. The input device 401 may include various input interfaces of a computer device or a file receiving device. It can receive information about the architecture of nodes and transmission edges of the multi-level transport network (MTNCS) 4031 with charging stations (i.e., the MTNCS network topology 4031) through the input device 401 and store it in the memory 403. The memory 403 can store the algorithm for the network reliability calculation method developed for the above-mentioned multi-level transportation network (MTNCS) 4031 with charging stations, and calculate the accurate network reliability (RK,L) using the process steps disclosed in the foregoing embodiments.

[0030] In a preferred embodiment, memory 403 may include read-only memory, flash memory, disk or cloud database, etc.

[0031] In a preferred embodiment, the processor 405 is electrically connected to the memory 403. The processor 405 includes a central processing unit, a video processor, a microprocessor, etc., and may include a multi-core processing unit or a combination of multiple processing units. The processor 405 can access the multi-level transport network (MTNCS) 4031 with charging stations in the memory and the algorithm 4032 for reliability calculation method to perform network reliability estimation.

[0032] In a preferred embodiment, the result of the calculation by the processor 405 can be output by the output device 407. The output device 407 can be a display that presents the calculation result, such as an LCD, LED or OLED display screen, or a wired / wireless network transmission device that transmits the calculation result to a remote user.

[0033] This invention takes into account the uncertainty of speed during travel caused by factors such as traffic jams, construction, and traffic accidents from the perspectives of both electric vehicle users and highway administration managers. Network reliability is used as a performance indicator to assess the probability that an electric vehicle can reach its destination. The advantages of this invention compared to prior art are summarized below: (1) The highway system is constructed as a multi-level transportation network (MTN), taking into account the different speeds that may occur on each road segment due to random factors, thus reflecting speed uncertainty. Therefore, the speeds on each road segment are in a multi-level state. (2) After incorporating speed uncertainty, it is extended to the electric vehicle's power consumption and travel time, so that the travel time and power consumption on each road segment are affected by speed. (3) A network reliability performance indicator is proposed to quantify the probability that an electric vehicle can reach its destination within the limits of travel time and power consumption. Based on this result, electric vehicle users can determine whether they need to change routes or go to charging stations, while highway administration managers can determine whether they need to implement traffic control or electric vehicle roadside assistance, effectively mitigating the negative impact of speed uncertainty.

[0034] In general, compared with other previous documents or prior art, this invention takes into account the feasibility of electric vehicles reaching their destination within travel time and power consumption limits under the multi-level state of travel speed due to random factors, from the perspectives of both electric vehicle users and highway administration managers. It provides electric vehicle users with a reference for travel route planning and can also explore the changes in network reliability under different travel time and power consumption conditions, providing highway administration management with a basis for decision-making and responding more quickly to traffic conditions and electric vehicle-related needs.

[0035] The above description is a preferred embodiment of the present invention. Those skilled in the art should understand that it is used to illustrate the present invention and not to limit the scope of the patent rights claimed by the present invention. The scope of patent protection shall be determined by the appended claims and their equivalent fields. Any modifications or refinements made by those skilled in the art without departing from the spirit or scope of this patent are equivalent changes or designs made under the spirit disclosed in the present invention and should be included in the scope of the following claims. [Simplified Explanation of the Diagram]

[0036] [Figure 1] shows an example network of selected sections of a highway.

[0037] [Figure 2] shows a simplified multi-level transportation network diagram constructed based on [Figure 1].

[0038] [Figure 3] shows a flowchart of the present invention for evaluating the network reliability of a multi-stage transportation network with charging stations.

[0039] [Figure 4] shows a schematic diagram of the computing device of the present invention for evaluating the network reliability of a multi-level transportation network with charging stations.

Claims

1. A method for evaluating the reliability of a multi-level transportation network with charging stations, the multi-level transportation network being constructed as a network topology and stored in a readable medium, and the method performing the following steps via processor computation: constructing the network topology, including network nodes accessible to electric vehicles and multiple road segments connecting each of the network nodes; calculating the travel time and power consumption required for the electric vehicle to travel on each of the multiple road segments at different speeds; The upper and lower bound speed vectors of the electric vehicle under the constraints of travel time and power consumption are obtained by the processor through the following steps: generating a pseudo-energy vector representing the complete power consumption from the starting point to the destination, converting all pseudo-energy vectors into maximum energy vectors, and converting all maximum energy vectors into corresponding speed vectors to obtain the upper bound speed vector; generating a pseudo-travel time vector representing the complete travel time from the starting point to the destination, converting all pseudo-travel time vectors into maximum travel time vectors, and converting all maximum travel time vectors into corresponding speed vectors to obtain the lower bound speed vector; and using the upper and lower bound speed vectors, the network reliability is calculated using an algorithm.

2. The method for assessing the network reliability of a multi-level transportation network with charging stations as described in claim 1 further includes assessing the driving performance of the electric vehicle through the network reliability results, and planning corresponding driving routes and management strategies based on changes in network reliability.

3. The method for evaluating the network reliability of a multi-level transportation network with charging stations as described in claim 2, wherein the network topology includes a highway system, the network node is all the interchanges of selected sections in the highway system, and the plurality of sections includes the sections connecting the interchanges of each network node.

4. The method for evaluating the network reliability of a multi-tiered transportation network with charging stations as described in claim 3, wherein the steps of calculating the travel time and power consumption of the electric vehicle on each road segment include: For each of the multiple road segments, calculate the travel time of the electric vehicle at different speeds; For each of the multiple road segments, calculate the power consumption of the electric vehicle at different speeds; and classify the calculated power consumption to facilitate the solution of the problem.

5. The method for evaluating the network reliability of a multi-stage transportation network with charging stations as described in claim 1, wherein the total travel time of each speed vector is checked to see if it exceeds the travel time limit, and if so, the speed vector is deleted; and the upper bound speed vector is obtained by removing duplicates and non-maximum speed vectors from the remaining speed vectors.

6. The method for evaluating the network reliability of a multi-stage transportation network with charging stations as described in claim 1, wherein the total power consumption of each speed vector exceeds the power consumption limit; if true, the speed vector is deleted; and duplicates and non-minimum speed vectors are removed from the remaining speed vectors to obtain the lower bound speed vector.

7. The method for evaluating the network reliability of a multi-tiered transportation network with charging stations as described in claim 6, wherein the steps for calculating network reliability include: A speed probability table is constructed using real speed data from the highway system; and network reliability is calculated using existing ensemble algorithms based on the derived upper bound speed vector, lower bound speed vector, and speed probability table.

8. An apparatus for evaluating the reliability of a multi-tiered transportation network with charging stations, comprising: One processor; A memory, electrically connected to the processor, is used to store the multi-level transportation network with charging stations and an algorithm. The algorithm uses the processor to perform the following steps: defining a network topology for the multi-level transportation network with charging stations, wherein the network topology includes network nodes accessible to electric vehicles and multiple road segments connecting each of the network nodes; calculating the travel time and power consumption required for the electric vehicle to travel on each of the multiple road segments at different speeds; determining the upper and lower bound speed vectors of the electric vehicle under the travel time and power consumption constraints; and calculating the network reliability using the upper and lower bound speed vectors.

9. The network reliability assessment apparatus for a multi-stage transportation network with charging stations as described in claim 8, wherein the network topology includes a highway system.

10. The apparatus for evaluating network reliability of a multi-stage transportation network with charging stations as described in claim 9, wherein the network nodes are all interchanges of selected road segments in the highway system, and the plurality of road segments includes road segments connecting the interchanges of each network node.

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