Electric vehicle charging path planning method based on dynamic energy consumption model and road network information

Abstract

The invention discloses an electric vehicle charging path planning method based on a dynamic energy consumption model and road network information. According to the method, the influence of different temperatures on the battery capacity is analyzed according to the charging and discharging experimental data of the lithium battery, the power consumption generated in the driving process of the electric vehicle is analyzed, and a refined air conditioner energy consumption model and a mileage energy consumption model are established. Based on an improved A * algorithm, a method for searching a reachable path with the minimum energy consumption according to the residual electric quantity of a vehicle-mounted battery, the position of a charging station and the real-time road condition is established. The A * algorithm of the model solving algorithm serves as an optimal priority search algorithm in graph theory search methods, and the optimality requirement can be met by reasonably designing a function; and meanwhile, aiming at an urban road network, a heuristic A * algorithm is adopted to be matched with the path optimization solution of a large-scale traffic network in a road-electricity coupling scene. The effectiveness of the provided model in the charging path decision is verified by respectively comparing three targets of time optimization, path optimization and comprehensive energy consumption optimization provided by the invention.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging path planning, and specifically relates to an electric vehicle charging path planning method based on a dynamic energy consumption model and road network information. Background Art

[0002] In recent years, the development of renewable energy has accelerated the research and development and exploration of new energy vehicles such as hydrogen fuel and solar energy. The development of energy storage and information technology has promoted the development of electric vehicles, electrified rail transit, aircraft and ships and other electrified transportation tools. Among them, the global production and sales of electric vehicles have grown rapidly. At present, traditional fuel vehicles have defects in energy consumption and exhaust emissions compared with new energy vehicles. In this case, it is a natural consensus that new energy electric vehicles will replace traditional fuel vehicles in the future. The electric vehicle charging service network and industry are typical cases of three-network integration, and its development form is shown in the figure. One-to-one construction of charging terminals in parking spaces integrates data acquisition, communication, charging and discharging control and monitoring terminals to achieve the integration of energy, transportation and information facilities. In the research related to electric vehicle path planning, the uniqueness of electric vehicles and charging infrastructure must be taken into account, so that related research involves the intersection of disciplines such as comprehensive electrical engineering and transportation engineering. It is mainly divided into three aspects: the first aspect starts from the electrical characteristics of electric vehicles. Regarding the interaction between electric vehicles and power grids, domestic and foreign related literature mostly starts from the impact of electric vehicle charging behavior on the power grid, with the optimal operation of the distribution network, the optimal revenue of charging stations, the optimal charging cost of users, and the optimal comprehensive benefits as charging goals, and plans the orderly charging of vehicles. However, the above literature uses static charging station information to analyze the impact of charging costs and charging station selection on electric vehicle users, and does not study the methods for users to decide charging time, choose charging locations, and plan charging routes under dynamic changes in charging station information. The second aspect starts from the traffic characteristics of electric vehicles. Considering the actual situation of the dynamic road network, a vehicle path selection model is constructed with the goal of minimizing logistics costs and time windows as constraints. The third aspect starts from the combination of electrical characteristics and traffic characteristics of electric vehicles. Considering the impact of electric vehicle charging behavior on traffic conditions and distribution network load, an electric vehicle path planning scheme is proposed. The impact of charging station status changes on the time required for users to charge is ignored.

[0003] However, there are the following shortcomings: the use of static charging station information to analyze the impact of electric vehicle users' charging costs and charging station selection, without studying the methods for users to decide charging time, choose charging locations, and plan charging routes under the dynamic changes of charging station information. At the same time, the impact of changes in the real-time power of electric vehicles on when and where to charge and the choice of charging routes in the dynamically changing actual road network is ignored, and the impact of changes in the status of charging stations on the time required for users to charge is ignored, thereby affecting the efficiency of the overall electric vehicle charging route planning. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle charging route planning method based on a dynamic energy consumption model and road network information in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for planning a charging path for an electric vehicle based on a dynamic energy consumption model and road network information, the method comprising the following steps:

[0006] S1: Obtaining the initial information of the electric vehicle;

[0007] S2: Establish a dynamic energy consumption model, which includes a battery capacity model, an onboard air conditioning start-stop energy consumption model, and a mileage energy consumption model based on traffic conditions;

[0008] S3: Calculate the battery relative capacity percentage according to the real-time temperature through the battery capacity model, and then get the current battery capacity. Calculate the air-conditioning start-stop energy consumption model according to the real-time temperature and air-conditioning usage rate curve to calculate the air-conditioning start status and air-conditioning power. Calculate the driving time correction coefficient and unit mileage energy consumption according to the real-time traffic congestion, road grade and average speed through the mileage energy consumption model based on traffic conditions.

[0009] S4: Determine the objective function: Minimum comprehensive energy consumption: give priority to energy consumption, taking into account driving distance and time; Minimum driving time: give priority to time, taking into account energy consumption and driving distance; Minimum driving distance: give priority to distance, taking into account energy consumption and time;

[0010] S5: Select the path planning algorithm, through the A* algorithm: combine heuristic information to improve search efficiency and path quality;

[0011] S6: construct a heuristic cost function, use the selected path planning algorithm, and search for the optimal path from the starting point to the end point according to the objective function and the heuristic cost function;

[0012] S7: Output optimal path information, including path length, travel time, energy consumption, charging stations passed, etc.;

[0013] S8: Dynamically update the energy consumption model and objective function according to the real-time road network information and environmental information; re-execute the path planning to ensure the optimality of the path, and then end the entire electric vehicle charging path planning process based on the dynamic energy consumption model and road network information.

[0014] In a preferred embodiment, in step S1, the initial information includes:

[0015] Vehicle information: including battery capacity, initial power, energy consumption per unit mileage, and air conditioning power information of electric vehicles;

[0016] Road network information: including road network topology, road grade, traffic congestion, charging station location and number of available charging piles;

[0017] Environmental information: including real-time temperature and humidity information.

[0018] In a preferred embodiment, in step S3, the battery capacity model uses a polynomial function model to fit the relationship between temperature and CR%, as shown in the formula:

[0019] C R (T) = η3T 3 +η210 -5 T 2 +η1T+η0

[0020] Where: T is the ambient temperature (℃); η3, η2, η1, η0 are the function model fitting coefficients; C R It is the relative capacity percentage of the battery.

[0021] In a preferred embodiment, in step S3, the vehicle air conditioner start-stop energy consumption model improves the air conditioner start-up decision model, and establishes a corresponding air conditioner start-up decision model by analyzing the vehicle air conditioner usage rate curve. A normal distribution fitting is performed according to the vehicle air conditioner usage rate curve, and a cold threshold Tc, a hot threshold Th and a comfortable temperature range are set. The segmented curve between the threshold and the comfortable temperature is fitted with a normal distribution according to the cftool toolbox in MATLAB to obtain the corresponding fitting parameters and obtain the corresponding air conditioner startup normal distribution function, as shown in the formula:

[0022]

[0023] Where: K open (T) is the probability of air conditioning turning on at temperature T; μ c , δ c is the mean and variance of the heating startup parameters; μ h , δ h is the mean and variance of the cooling startup parameters. When the temperature is lower than the cold threshold Tc or greater than the hot threshold Th, the probability of the user turning on the air conditioner is 1; when the temperature is within the comfortable temperature range, the probability of turning on the air conditioner is 0;

[0024] In order to quantify the impact of intraday temperature changes on air conditioning energy consumption in charging load simulation, a piecewise polynomial model can be used to fit the relationship between temperature and air conditioning power, as shown in the formula;

[0025] P ac (T) = ω3T 3 +ω210 -5 T 2 +ω1T+ω0

[0026] Where: P ac is the air conditioning power; ω0, ω1, ω2 and ω3 are the function fitting coefficients.

[0027] In a preferred embodiment, in step S3, the mileage energy consumption model based on traffic conditions divides the roads into three levels: expressways, main roads and secondary roads. The overall traffic conditions change over time through real-time traffic condition surveys, and the corresponding average speed is selected according to the TPI in different time periods and substituted into the traffic energy consumption factor model, as shown in the formula;

[0028]

[0029] The actual power consumption of electric vehicles under different ambient temperatures T and traffic conditions can be accurately calculated by the battery energy consumption model, as shown in the formula:

[0030]

[0031] Where: E all,T is the total energy consumption of electric vehicles at temperature T; K ac is the start / stop state of the air conditioner at temperature T, and its value is 1 or 0 (indicating that the car is in the air conditioner on or off state); t k , D k is the driving time and mileage during the kth trip; E o is the initial energy consumption per unit mileage of electric vehicles; t It is the driving time correction coefficient, which means that the driving time and speed of electric vehicles are corrected when they are affected by traffic congestion.

[0032] In a preferred embodiment, in step S5, the A* algorithm is a heuristic search algorithm, whose evaluation function determines the search direction, affects the search efficiency and search results, and is used to perform path search. It is generally used in path planning scenarios where the global environmental information is already known; it will use the cost estimation function to select the path for each step, starting from the initial position, and continuously search and expand to nearby child nodes; the evaluation function will select the node with the smallest cost as the next node based on the distance cost of the child node; repeat the previous steps until the final destination is found, the path planning strategy is completed, and the final path is generated.

[0033] In a preferred embodiment, in step S5, the general expression of the heuristic function of the A* algorithm is:

[0034] F n =G n +H n

[0035] The heuristic evaluation function of the A* algorithm mainly consists of two parts; Gn is the cost from the starting node to the current node n, H n is the estimated cost from the current node n to the target node; the estimated cost H n The behavior of the A* algorithm can be controlled to improve the efficiency of the search; the A* algorithm gradually finds the optimal path by selecting the node with the highest comprehensive priority. Its optimization steps mainly include:

[0036] (1) Constructing a heuristic valuation function F n The estimated cost function is selected according to actual needs. Common estimated cost functions include Manhattan distance, Euclidean distance and Chebyshev distance.

[0037] (2) Set up two lists, close and open, to store the nodes in the road network whose shortest paths to the starting point have been found and the nodes whose shortest paths have not been found;

[0038] (3) Add the starting point to the close table, add the nodes connected to it to the open table, and set the starting point as the current parent node;

[0039] (4) Find F in the open table n The point with the smallest value is added to the close table, set as the new parent node, and the F of the node connected to it in the open table is updated. n Value and G n value, and then add the nodes that are connected to the parent node and have not yet been added to the open table to the open table;

[0040] (5) Repeat step 4 until the endpoint is added to the close table;

[0041] (6) Traversing the nodes in the close table in reverse order will yield the optimal path found by the algorithm.

[0042] In a preferred embodiment, in step S6, during the process of constructing the heuristic cost function,

[0043] The cost function for minimizing comprehensive energy consumption = current energy consumption + heuristic estimated energy consumption (estimated based on information such as remaining power, distance to destination, and road conditions);

[0044] The cost function for minimizing driving time = current time + heuristic estimated time (estimated based on information such as remaining battery power, distance to destination, and road conditions);

[0045] The cost function for minimizing driving distance = current distance + heuristic estimated distance (estimated based on information such as remaining battery power and distance to destination).

[0046] In a preferred embodiment, in step S6, if charging is required, a feasible path including a charging station is searched, and the energy consumption and time to reach each charging station are calculated; and the path with the minimum energy consumption and time to reach the charging station is selected.

[0047] In a preferred embodiment, in step S7, optimizing the energy consumption model includes:

[0048] Energy consumption model optimization: Based on the test results, optimize the battery capacity model, the vehicle air conditioning start-stop energy consumption model, and the mileage energy consumption model based on traffic conditions;

[0049] Objective function optimization: Based on the test results, optimize the objective function to make it more in line with user needs;

[0050] Path planning algorithm optimization: Based on the test results, the path planning algorithm is optimized to improve search efficiency and path quality.

[0051] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0052] 1. In the present invention, a charging path planning model for electric vehicles based on a dynamic energy consumption model and road network information is proposed, taking into account the real-time traffic network, temperature and battery energy consumption. First, the influence of different temperatures on battery capacity is analyzed according to the experimental data of lithium battery charging and discharging, the power consumption generated by electric vehicles during driving is analyzed, and a refined air-conditioning energy consumption model and mileage energy consumption model are established. Then, in response to the mileage anxiety problem of electric vehicles, a method for finding a reachable minimum energy consumption path based on the remaining power of the on-board battery, the location of the charging station, and the real-time road conditions is established based on the improved A* algorithm. Finally, the effectiveness of the model in short-time scale scheduling is verified in combination with the road network. Experiments show that the proposed method can find a feasible minimum energy consumption path passing through a charging station and reduce mileage anxiety.

[0053] 2. In the present invention, the influence of different temperatures on battery capacity is analyzed based on the experimental data of lithium battery charging and discharging, the power consumption generated by electric vehicles during driving is analyzed, and a refined air conditioning energy consumption model and mileage energy consumption model are established. Then the selection of the model solving algorithm in this article must consider both the optimality of the results and the performance of the algorithm. As a best-priority search algorithm in graph theory search methods, the A* algorithm can meet the optimality requirements through reasonable function design; at the same time, for urban-level road networks, the A* algorithm introduces heuristic information in the search process as a decision-making aid for moving to the target point, which can achieve faster solution speed and lower computational complexity.

[0054] 3. In the present invention, an operating energy consumption function is established according to the energy consumption of the vehicle during operation, a new heuristic energy consumption estimation cost is designed to improve the A* algorithm, and an energy-optimal path planning method for electric vehicles considering mid-way charging is proposed. It is proved that the proposed heuristic energy consumption estimation cost meets the acceptability and consistency, and the energy-optimal path can be obtained according to the A* algorithm. This method can be used for the selection of charging stations. Simulations show that the shorter the distance, the lower the energy consumption. Therefore, it is more practical to select the optimal path according to the minimum energy consumption, which verifies the rationality of the method proposed in this article. When the battery is low, this method can be used to obtain the energy-optimal charging path passing through the charging station, saving energy and reducing mileage anxiety. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the relative capacity percentage of the battery at different temperatures of the present invention;

[0056] Figure 2 It is a schematic diagram showing the variation of air conditioning energy consumption and air conditioning utilization rate with temperature in the present invention;

[0057] Figure 3 It is the algorithm flow chart of the A* algorithm in the present invention;

[0058] Figure 4 It is a schematic diagram of the road topology and charging stations in the present invention;

[0059] Figure 5 A schematic diagram of road congestion colors in the present invention;

[0060] Figure 6 It is a schematic diagram of simulation results of the present invention;

[0061] Figure 7 It is the simulation result of the optimal distance model in the present invention;

[0062] Figure 8 It is the simulation result of the time optimal model in the present invention;

[0063] Fig. 9 It is the simulation result of the optimal model of comprehensive energy consumption in the present invention;

[0064] Fig.10 Comparison of parameters solved by different algorithms in this invention Figure 1 ;

[0065] Fig.11 Comparison of parameters solved by different algorithms of the present invention Figure 2 . DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] Reference Figure 1-11 ,

[0068] An electric vehicle charging path planning method based on a dynamic energy consumption model and road network information comprises the following steps:

[0069] S1: Obtaining the initial information of the electric vehicle;

[0070] S2: Establish a dynamic energy consumption model, which includes a battery capacity model, an onboard air conditioning start-stop energy consumption model, and a mileage energy consumption model based on traffic conditions;

[0071] S3: Calculate the battery relative capacity percentage according to the real-time temperature through the battery capacity model, and then get the current battery capacity. Calculate the air-conditioning start-stop energy consumption model according to the real-time temperature and air-conditioning usage rate curve to calculate the air-conditioning start status and air-conditioning power. Calculate the driving time correction coefficient and unit mileage energy consumption according to the real-time traffic congestion, road grade and average speed through the mileage energy consumption model based on traffic conditions.

[0072] S4: Determine the objective function: Minimum comprehensive energy consumption: give priority to energy consumption, taking into account driving distance and time. Minimum driving time: give priority to time, taking into account energy consumption and driving distance. Minimum driving distance: give priority to distance, taking into account energy consumption and time;

[0073] S5: Select the path planning algorithm, through the A* algorithm: combine heuristic information to improve search efficiency and path quality;

[0074] S6: construct a heuristic cost function, use the selected path planning algorithm, and search for the optimal path from the starting point to the end point according to the objective function and the heuristic cost function;

[0075] S7: Output optimal path information, including path length, travel time, energy consumption, charging stations passed, etc.;

[0076] S8: Dynamically update the energy consumption model and objective function according to the real-time road network information and environmental information. Re-execute the path planning to ensure the optimality of the path, and then the entire electric vehicle charging path planning process based on the dynamic energy consumption model and road network information can be completed.

[0077] In step S1, the initial information includes:

[0078] Vehicle information: including the battery capacity, initial power, energy consumption per unit mileage, and air conditioning power information of electric vehicles.

[0079] Road network information: including road network topology, road grade, traffic congestion, charging station locations and the number of available charging piles.

[0080] Environmental information: including real-time temperature and humidity information.

[0081] In step S3, a battery capacity model is established:

[0082] During normal driving, electric vehicles are affected by external objective factors such as road grade, road traffic, vehicle speed, and temperature, which in turn affect the charging load demand and its daily conditions. By coupling the impact of external objective information on the energy consumption of electric vehicle travel mileage, a refined electric vehicle unit mileage energy consumption model is established. The capacity and charge and discharge characteristics of the same battery at different temperatures vary greatly, so the experimental data of the current mainstream lithium battery is used as an example to establish the relationship between temperature and battery capacity.

[0083] The lithium battery test data of NASA's Ames Research Center is used to analyze the effects of different temperatures and charge and discharge cycles on battery capacity. In the test of lithium iron phosphate batteries, the battery capacity at 25°C is used as the benchmark to analyze the relative capacity percentage (CR%) of the battery at different temperatures. In high temperature environments, CR% is slightly improved, but the increase is not obvious. At 40°C, CR% is 106%. When the ambient temperature is greater than 55°C, the CR% upward trend turns to a decrease. In low temperature environments, CR% decreases significantly relative to the baseline capacity. When the temperature reaches -20°C, CR% is only 43%. In order to quantify the effect of temperature on battery capacity during charging load simulation, a polynomial function model is used to fit the relationship between temperature and CR%, as shown in the formula.

[0084] C R (T) = η3T 3 +η210 -5 T 2 +η1T+η0

[0085] Where: T is the ambient temperature (℃); η3, η2, η1, η0 are the function model fitting coefficients; C R Relative capacity percentage of the battery

[0086] In step S3, the vehicle air conditioner start-stop energy consumption model is established:

[0087] As the main energy-consuming appliance of electric vehicles, the vehicle air conditioning system (including cooling and heating) directly affects the charging demand and driving range of electric vehicles. The start and stop and energy consumption values ​​of the air conditioning system will also be different under different weather conditions such as ambient temperature and humidity. Based on the experimental data, the energy consumption of the vehicle air conditioning at different temperatures T is analyzed, and the energy consumption data when the vehicle air conditioning is turned on is fitted to obtain the air conditioning energy consumption and air conditioning usage rate with temperature. Figure 2 shown.

[0088] On this basis, the air-conditioning start-up decision model is improved. The corresponding air-conditioning start-up decision model is established by analyzing the vehicle air-conditioning usage rate curve. The normal distribution is fitted according to the vehicle air-conditioning usage rate curve, and the cold threshold Tc, hot threshold Th and comfortable temperature range are set. The segmented curve between the threshold and the comfortable temperature is fitted with the normal distribution according to the cftool toolbox in MATLAB to obtain the corresponding fitting parameters and obtain the corresponding air-conditioning startup normal distribution function, as shown in the formula:

[0089]

[0090] Where: K open (T) is the probability of air conditioning turning on at temperature T; μ c , δ c is the mean and variance of the heating startup parameters; μ h , δ h is the mean and variance of the cooling startup parameters. When the temperature is lower than the cold threshold Tc or greater than the hot threshold Th, the probability of the user turning on the air conditioner is 1; when the temperature is within the comfortable temperature range, the probability of turning on the air conditioner is 0. The parameter value settings are shown in the table

[0091]

[0092]

[0093] In order to quantify the impact of intraday temperature changes on air conditioning energy consumption in charging load simulation, a piecewise polynomial model can be used to fit the relationship between temperature and air conditioning power, as shown in Eq.

[0094] P ac (T) = ω3T 3 +ω210 -5 T 2 +ω1T+ω0

[0095] Where: P ac is the air conditioning power; ω0, ω1, ω2 and ω3 are the function fitting coefficients.

[0096] In step S3, a mileage energy consumption model based on traffic conditions is established:

[0097] The energy consumption per unit mileage of electric vehicles during normal driving is most significantly affected by different road grades and traffic conditions. In order to eliminate the problem of inconsistency between the speed and feeling of each road network, and fully absorb people's tolerance for different levels of road congestion, the traffic congestion index (TPI) is introduced to quantitatively describe the road traffic operation status in a numerical way to improve the accuracy of the expression of road traffic operation status. Since vehicles travel at non-economic speeds, traffic congestion increases driving duration and air conditioning service time, and reduces driving efficiency, which will significantly affect energy consumption and charging requirements. The driving time correction coefficient ζt is introduced to adjust the driving time under different traffic conditions. When choosing a road, users give priority to sections with unobstructed roads. For sections with higher traffic congestion index values, the lower the driving speed, the longer the driving time, and the lower the user's willingness to choose, as shown in the table.

[0098]

[0099] The roads are divided into three levels: expressways, main roads and secondary roads. The changes in the overall traffic conditions over time are obtained through real-time traffic condition surveys, and the corresponding average speed is selected according to the TPI in different time periods and substituted into the traffic energy consumption factor model, as shown in the formula.

[0100]

[0101] The selection of driving speeds for different levels of roads under different levels of traffic congestion is shown in the table.

[0102]

[0103] In summary, the actual power consumption of electric vehicles under different ambient temperatures T and traffic conditions can be accurately calculated by the battery energy consumption model, as shown in the formula:

[0104]

[0105] Where: E all,T is the total energy consumption of electric vehicles at temperature T; K ac is the start / stop state of the air conditioner at temperature T, and its value is 1 or 0 (indicating that the car is in the air conditioner on or off state); t k , D k is the driving time and mileage during the kth trip; E o is the initial energy consumption per unit mileage of electric vehicles; t It is the driving time correction coefficient, which means that the driving time and speed of electric vehicles are corrected when they are affected by traffic congestion.

[0106] In step S5, the A* algorithm is a heuristic search algorithm. Its evaluation function determines the search direction, affects the search efficiency and search results, and is used to search for paths. It is generally used in path planning scenarios where global environmental information is already known. It uses a cost estimation function to select paths for each step, starting from the initial position and continuously searching and expanding to nearby child nodes. The evaluation function selects the node with the smallest cost value as the next node based on the distance cost of the child node. Repeat the previous steps until the final destination is found, the path planning strategy is completed, and the final path is generated.

[0107] In step S5, the general expression of the heuristic function of the A* algorithm is:

[0108] F n =G n +H n

[0109] The heuristic evaluation function of the A* algorithm mainly consists of two parts. n is the cost from the starting node to the current node n, H n is the estimated cost from the current node n to the target node. n You can control the behavior of the A* algorithm and improve the efficiency of the search. The A* algorithm gradually finds the optimal path by selecting the node with the highest comprehensive priority. Its optimization steps mainly include:

[0110] (1) Constructing a heuristic valuation function F n The estimated cost function is selected according to actual needs. Common estimated cost functions include Manhattan distance, Euclidean distance and Chebyshev distance.

[0111] (2) Set up two lists, close and open, to store the nodes in the road network whose shortest paths to the starting point have been found and the nodes whose shortest paths have not been found;

[0112] (3) Add the starting point to the close table, add the nodes connected to it to the open table, and set the starting point as the current parent node;

[0113] (4) Find F in the open table n The point with the smallest value is added to the close table, set as the new parent node, and the F of the node connected to it in the open table is updated. n Value and G n value, and then add the nodes that are connected to the parent node and have not yet been added to the open table to the open table;

[0114] (5) Repeat step 4 until the endpoint is added to the close table;

[0115] (6) Traversing the nodes in the close table in reverse order will yield the optimal path found by the algorithm.

[0116] In step S6, during the process of constructing the heuristic cost function,

[0117] The cost function with the minimum comprehensive energy consumption = current energy consumption + heuristic estimated energy consumption (estimated based on information such as remaining power, destination distance and road conditions).

[0118] The cost function for minimizing driving time = current time + heuristic estimated time (estimated based on information such as remaining battery power, distance to destination, and road conditions).

[0119] The cost function for minimizing driving distance = current distance + heuristic estimated distance (estimated based on information such as remaining battery power and distance to destination).

[0120] In step S6, if charging is required, feasible paths that include charging stations are searched, and the energy consumption and time to reach each charging station are calculated, and the path with the minimum energy consumption and time to reach the charging station is selected.

[0121] In step S7, optimizing the energy consumption model includes:

[0122] Energy consumption model optimization: Based on the test results, optimize the battery capacity model, vehicle air conditioning start-stop energy consumption model, and mileage energy consumption model based on traffic conditions.

[0123] Objective function optimization: Based on the test results, optimize the objective function to make it more in line with user needs.

[0124] Path planning algorithm optimization: Based on the test results, the path planning algorithm is optimized to improve search efficiency and path quality.

[0125] Example:

[0126] A certain area of ​​a city in Hebei Province downloaded from Gaode Map was used as the underlying simulation road network of the experiment. After supplementing relevant road information and charging station data, a real road network mapping map was obtained.

[0127] Figure 4 The four star nodes of the road network, 4, 27, 30, and 33, are set as charging station nodes, and ten public charging piles are set in each charging station node. The simulation vehicle model is set to BYD-Tang EV, the vehicle battery capacity is 90.3kWh, and the power consumption per 100 kilometers is about 15.05kW.

[0128] Figure 5 The lines of different colors represent the predicted congestion conditions of different road sections at different times. Among them, dark red road sections represent serious congestion, light red represents congestion, yellow represents slow traffic, and green represents smooth traffic.

[0129] Simulation results under different objectives:

[0130] In order to verify the effectiveness of the model proposed in this paper, the following three schemes are designed to compare the optimization results of the model under different objective functions. Scheme 1 uses the shortest distance path model, Scheme 2 uses the minimum time path model, and Scheme 3 uses the optimal comprehensive energy consumption model in this paper. All three schemes are solved using the A* algorithm, setting the vehicle to not need to charge during the trip, and selecting the charging pile with the vehicle departure node numbered 1 and the destination node numbered 30.

[0131] like Figure 6 In the simulation, the results are composed of three layers: the bottom layer is the underlying road network, the middle layer is the predicted road congestion obtained from the Internet of Vehicles, and the top layer is the comprehensive optimal path found by the algorithm based on the objective function.

[0132] Comparison of results of different objective functions:

[0133] Table 1 Comparison of results of different objective functions

[0134]

[0135] From the experimental data in the chart, we can see that in this case, the planning results of the distance optimal model and the comprehensive optimal model mentioned in this article are the same. Although the time optimal model has a shorter travel time, its two travel evaluation parameters, driving distance and vehicle energy consumption, are worse than the comprehensive optimal path planning model with multi-agent participation in this article. This shows that distance is not the only factor that determines the user's travel time. Compared with the simple time optimal model, the model proposed in this article can comprehensively select the optimal path according to the user's travel time and road congestion conditions, which can effectively shorten the user's travel distance and save the user's travel energy consumption, and performs more prominently in terms of energy consumption and distance indicators.

[0136] Output performance of different solving algorithms

[0137] Dijkstra algorithm is another common algorithm for solving the shortest distance between two points and is widely used in path planning. In order to verify the influence of different algorithms on the model solving speed and solving performance, a planning function with the shortest distance as the goal is constructed. The algorithm is used twice to solve the path planning model that needs to be charged. The simulation results are as follows:

[0138] Table 2 Comparison of solution performance of different algorithms

[0139]

[0140] By analyzing the experimental data of the algorithm in road networks of different sizes, it can be found that the solution performance of the Dijkstra algorithm and the A* algorithm in a small-scale road network is not much different. When the scale of the road network expands, the A* algorithm has more advantages than the Dijkstra algorithm in both time complexity and space complexity. Although the Dijkstra algorithm can also find the optimal path quickly, the results show that the number of nodes marked by the Dijkstra algorithm exceeds the maximum value of the road network nodes in road networks of different sizes. This is because the model uses two algorithms to explore the optimal charging path, so the maximum number of marked nodes of the algorithm can reach up to twice the number of road network nodes during the solution process. It also shows that the Dijkstra algorithm is essentially an undirected breadth-first algorithm. In comparison, the heuristic A* algorithm has better applicability in the model proposed in this article.

[0141] in conclusion:

[0142] In the present invention, an operation energy consumption function is established according to the energy consumption of the vehicle during operation, a new heuristic energy consumption estimation cost is designed to improve the A* algorithm, and an optimal path planning method for electric vehicle energy consumption considering mid-way charging is proposed.

[0143] In the present invention, it is proved that the proposed heuristic energy consumption estimation cost satisfies the acceptability and consistency, and the energy-optimal path can be obtained according to the A* algorithm.

[0144] In the present invention, this method can be used for the selection of charging stations. Simulation shows that the shorter the distance, the lower the energy consumption. Therefore, it is more practical to select the optimal path according to the minimum energy consumption, which verifies the rationality of the method proposed in this article.

[0145] In the present invention, when the battery power is insufficient, the method can be used to obtain the energy-optimal charging path through the charging station, thereby saving energy and reducing mileage anxiety.

[0146] In the present invention, a charging path planning model for electric vehicles based on a dynamic energy consumption model and road network information is proposed, taking into account the real-time traffic road network, temperature and battery energy consumption. First, the influence of different temperatures on the battery capacity is analyzed according to the lithium battery charging and discharging experimental data, the power consumption generated by the electric vehicle during driving is analyzed, and a refined air conditioning energy consumption model and mileage energy consumption model are established. Then, in response to the mileage anxiety problem of electric vehicles, based on the improved A* algorithm, a method for finding a reachable minimum energy consumption path based on the remaining power of the on-board battery, the location of the charging station, and the real-time road conditions is established. Finally, the effectiveness of the model in short-time scale scheduling is verified in combination with the road network. Experiments show that the proposed method can find a feasible minimum energy consumption path passing through a charging station and reduce mileage anxiety.

[0147] In the present invention, the influence of different temperatures on battery capacity is analyzed based on the experimental data of lithium battery charging and discharging, the power consumption generated by electric vehicles during driving is analyzed, and a refined air conditioning energy consumption model and mileage energy consumption model are established. Then the selection of the model solving algorithm in this paper should consider both the optimality of the results and the performance of the algorithm. As a best priority search algorithm in graph theory search methods, the A* algorithm can meet the optimality requirements through reasonable function design; at the same time, for urban-level road networks, the A* algorithm introduces heuristic information in the search process as a decision-making aid for moving to the target point, which can obtain faster solution speed and lower computational complexity.

[0148] In the present invention, an operating energy consumption function is established according to the energy consumption of the vehicle during operation, a new heuristic energy consumption estimation cost is designed to improve the A* algorithm, and an energy-optimal path planning method for electric vehicles considering mid-way charging is proposed. It is proved that the proposed heuristic energy consumption estimation cost meets the acceptability and consistency, and the energy-optimal path can be obtained according to the A* algorithm. This method can be used for the selection of charging stations. Simulations show that the energy consumption is not low if the distance is short. Therefore, it is more practical to select the optimal path according to the minimum energy consumption, which verifies the rationality of the method proposed in this article. When the battery is low, this method can be used to obtain the energy-optimal charging path through the charging station, saving energy and reducing mileage anxiety.

[0149] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electric vehicle charging path planning method based on a dynamic energy consumption model and road network information, characterized in that: The method comprises the following steps: S1: Obtaining the initial information of the electric vehicle; S2: Establish a dynamic energy consumption model, which includes a battery capacity model, an onboard air conditioning start-stop energy consumption model, and a mileage energy consumption model based on traffic conditions; S3: Calculate the battery relative capacity percentage according to the real-time temperature through the battery capacity model, and then get the current battery capacity. Calculate the air-conditioning start-stop energy consumption model according to the real-time temperature and air-conditioning usage rate curve to calculate the air-conditioning start status and air-conditioning power. Calculate the driving time correction coefficient and unit mileage energy consumption according to the real-time traffic congestion, road grade and average speed through the mileage energy consumption model based on traffic conditions. S4: Determine the objective function: Minimum comprehensive energy consumption: give priority to energy consumption, taking into account driving distance and time; Minimum driving time: give priority to time, taking into account energy consumption and driving distance; Minimum driving distance: give priority to distance, taking into account energy consumption and time; S5: Select the path planning algorithm, through the A* algorithm: combine heuristic information to improve search efficiency and path quality; S6: construct a heuristic cost function, use the selected path planning algorithm, and search for the optimal path from the starting point to the end point according to the objective function and the heuristic cost function; S7: Output optimal path information, including path length, travel time, energy consumption, and information of charging stations passed; S8: Dynamically update the energy consumption model and objective function according to the real-time road network information and environmental information; re-execute the path planning to ensure the optimality of the path, and then end the entire electric vehicle charging path planning process based on the dynamic energy consumption model and road network information.

2. The electric vehicle charging path planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S1, the initial information includes: Vehicle information: including battery capacity, initial power, energy consumption per unit mileage, and air conditioning power information of electric vehicles; Road network information: including road network topology, road grade, traffic congestion, charging station location and number of available charging piles; Environmental information: including real-time temperature and humidity information.

3. The electric vehicle charging path planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S3, the battery capacity model uses a polynomial function model to fit the relationship between temperature and CR%, as shown in the formula; C R (T)=η3T 3 +η210 -5 T 2 +η1T+η0 Where: T is the ambient temperature (℃); η3, η2, η1, η0 are the function model fitting coefficients; C R It is the relative capacity percentage of the battery.

4. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S3, the vehicle air conditioner start-stop energy consumption model improves the air conditioner start-up decision model, and establishes a corresponding air conditioner start-up decision model by analyzing the vehicle air conditioner usage rate curve. A normal distribution fitting is performed according to the vehicle air conditioner usage rate curve, and a cold threshold Tc, a hot threshold Th and a comfortable temperature range are set. The segmented curve between the threshold and the comfortable temperature is fitted with a normal distribution according to the cftool toolbox in MATLAB to obtain the corresponding fitting parameters and obtain the corresponding air conditioner startup normal distribution function, as shown in the formula: Where: K open (T) is the probability of air conditioning turning on at temperature T; μ c , δ c is the mean and variance of the heating startup parameters; μ h , δ h is the mean and variance of the cooling startup parameters. When the temperature is lower than the cold threshold Tc or greater than the hot threshold Th, the probability of the user turning on the air conditioner is 1; when the temperature is within the comfortable temperature range, the probability of turning on the air conditioner is 0; In order to quantify the impact of intraday temperature changes on air conditioning energy consumption in charging load simulation, a piecewise polynomial model can be used to fit the relationship between temperature and air conditioning power, as shown in the formula; P ac (T)=ω3T 3 +ω210 -5 T 2 +ω1T+ω0 Where: P ac is the air conditioning power; ω0, ω1, ω2 and ω3 are the function fitting coefficients.

5. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S3, the mileage energy consumption model based on traffic conditions divides the roads into three levels: expressways, main roads and secondary roads. The overall traffic conditions change over time through real-time traffic condition surveys, and the corresponding average speed is selected according to the TPI in different time periods and substituted into the traffic energy consumption factor model, as shown in the formula; The actual power consumption of electric vehicles under different ambient temperatures T and traffic conditions can be accurately calculated by the battery energy consumption model, as shown in the formula: Where: E all,T is the total energy consumption of electric vehicles at temperature T; K ac is the start / stop state of the air conditioner at temperature T, and its value is 1 or 0; t k , D k is the driving time and mileage during the kth trip; E o is the initial energy consumption per unit mileage of electric vehicles; t It is the driving time correction coefficient, which means that the driving time and speed of electric vehicles are corrected when they are affected by traffic congestion.

6. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S5, the A* algorithm is a heuristic search algorithm, and its evaluation function determines the search direction, affects the search efficiency and search results, and is used to perform path search. It is generally used in path planning scenarios where global environmental information is already known; It will use the cost estimation function to select the path at each step, starting from the initial position and continuously searching and expanding to nearby child nodes; the evaluation function will select the node with the smallest cost as the next node based on the distance cost of the child node; repeat the previous steps until the final destination is found, the path planning strategy is completed, and the final path is generated.

7. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S5, the general expression of the heuristic function of the A* algorithm is: F n =G n +H n The heuristic evaluation function of the A* algorithm mainly consists of two parts; G n is the cost from the starting node to the current node n, H n is the estimated cost from the current node n to the target node; the estimated cost H n The behavior of the A* algorithm can be controlled to improve the efficiency of the search; the A* algorithm gradually finds the optimal path by selecting the node with the highest comprehensive priority. Its optimization steps mainly include: (1) Constructing a heuristic valuation function F n The estimated cost function is selected according to actual needs. Common estimated cost functions include Manhattan distance, Euclidean distance and Chebyshev distance. (2) Set up two lists, close and open, to store the nodes in the road network whose shortest paths to the starting point have been found and the nodes whose shortest paths have not been found; (3) Add the starting point to the close table, add the nodes connected to it to the open table, and set the starting point as the current parent node; (4) Find F in the open table n The point with the smallest value is added to the close table, set as the new parent node, and the F of the node connected to it in the open table is updated. n Value and G n value, and then add the nodes that are connected to the parent node and have not yet been added to the open table to the open table; (5) Repeat step 4 until the endpoint is added to the close table; (6) Traversing the nodes in the close table in reverse order will yield the optimal path found by the algorithm.

8. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S6, during the process of constructing the heuristic cost function, The cost function with the minimum comprehensive energy consumption = current energy consumption + heuristic estimated energy consumption; The cost function for minimizing driving time = current time + heuristic estimated time; The cost function for minimum driving distance = current distance + heuristic estimated distance.

9. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S6, if charging is required, feasible paths that include charging stations are searched, and the energy consumption and time to reach each charging station are calculated; and the path with the minimum energy consumption and time to reach the charging station is selected.

10. The electric vehicle charging route planning method based on a dynamic energy consumption model and road network information as claimed in claim 1, characterized in that: In step S7, optimizing the energy consumption model includes: Energy consumption model optimization: Based on the test results, optimize the battery capacity model, the vehicle air conditioning start-stop energy consumption model, and the mileage energy consumption model based on traffic conditions; Objective function optimization: Based on the test results, optimize the objective function to make it more in line with user needs; Path planning algorithm optimization: Based on the test results, the path planning algorithm is optimized to improve search efficiency and path quality.

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