Charging path selection method of new energy vehicle and related product

Abstract

The invention provides a charging path selection method of a new energy vehicle and a related product. The method comprises the following steps: acquiring a current position and a destination position of the new energy vehicle, and generating a plurality of navigation paths according to the current position and the destination position; generating an optimal charging strategy of each navigation path; obtaining the driving difficulty of each navigation path according to the historical driving data of each navigation path; acquiring a preset driving cost function; calculating the driving cost of each navigation path by adopting a preset driving cost function according to each optimal charging strategy, the driving difficulty and the optimal charging frequency; and taking the navigation path with the minimum driving cost as an optimal navigation path, and generating an optimal charging path of the new energy vehicle according to an optimal charging strategy of the optimal navigation path. According to the technical scheme, the reliability and timeliness of charging can be improved in the long-distance driving process of the new energy vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and particularly to a charging path selection method and related products for new energy vehicles. Background Art

[0002] New energy vehicles refer to vehicles that use unconventional vehicle fuels as power sources, or use conventional vehicle fuels and adopt new in-vehicle power devices, integrating advanced technologies in vehicle power control and driving, forming vehicles with advanced technical principles, new technologies, and new structures. New energy vehicles include pure electric vehicles, range-extended electric vehicles, hybrid vehicles, fuel cell electric vehicles, hydrogen engine vehicles, etc. Among them, pure electric vehicles have become the preferred choice for consumers due to their stronger environmental protection, higher energy utilization rate, better driving experience, and lower maintenance costs.

[0003] However, the driving range of pure electric vehicles is limited by the battery capacity of the power battery, causing range anxiety for vehicle owners. Especially during long-distance driving, multiple charges may be required to meet the demand. Therefore, vehicle owners need to constantly monitor the remaining power of the vehicle to charge the vehicle in a timely manner. However, new energy vehicles require a long charging time. If the charging equipment is occupied, the driver needs to wait for a long time to charge, which will affect the reliability and timeliness of charging new energy vehicles.

[0004] Therefore, how to reasonably plan the charging path of new energy vehicles during long-distance driving is of great significance for improving the reliability and timeliness of charging new energy vehicles. Summary of the Invention

[0005] An object of the present invention is to provide a charging path selection method and related products for new energy vehicles, which are used to improve the reliability and timeliness of charging during the long-distance driving of new energy vehicles, so as to achieve the purpose of improving the driving experience of drivers.

[0006] Specifically, in the first aspect, the present invention provides a charging path selection method for new energy vehicles, including:

[0007] Obtain the current position and destination position of the new energy vehicle, and generate multiple navigation paths according to the current position and the destination position;

[0008] Obtain the charging stations on each of the navigation paths, and generate an optimal charging strategy for each of the navigation paths according to each of the charging stations, including:

[0009] Obtain preset constraint conditions and a charging cost function, and obtain the optimal number of charging times for each of the navigation paths according to the preset constraint conditions, where the preset constraint conditions include a remaining power threshold of the power battery, a target power threshold for charging at each charging station, and a final power threshold when reaching the destination;

[0010] Adopt a preset particle swarm optimization algorithm, and generate an optimal charging strategy for each of the navigation paths according to the preset constraint conditions, the charging cost function, the optimal number of charging times for each, and the locations of each charging station;

[0011] Obtain the historical driving data of each of the navigation paths, and obtain the driving difficulty of each of the navigation paths according to the historical driving data of each;

[0012] Obtain a preset driving cost function, and use the preset driving cost function to calculate the driving cost of each of the navigation paths according to the optimal charging strategies, driving difficulties, and optimal number of charging times for each;

[0013] Take the navigation path with the minimum driving cost as the optimal navigation path, and generate an optimal charging path for the new energy vehicle according to the optimal charging strategy of the optimal navigation path to guide the new energy vehicle to charge during the process of driving to the destination.

[0014] Further, after the step of generating multiple navigation paths according to the current position and the destination position, it further includes:

[0015] Obtain the maximum cruising range of the new energy vehicle, and judge whether it is necessary to plan a charging path for the new energy vehicle according to the lengths of the navigation paths and the maximum cruising range;

[0016] If necessary, execute the step of obtaining the charging stations on each of the navigation paths

[0017] Further, the preset constraint conditions include:

[0018] h′ i ≤ω1×H

[0019] h i ≥ω2×H

[0020] h'≥ω3×H

[0021] t i ≤t p

[0022] Among them, H is the capacitance of the power battery, h′ is the remaining power of the power battery when reaching the destination, h′ i is the remaining power of the power battery when reaching the i-th target charging station, hi is the target power of the i-th target charging station, t i is the driving duration required to travel from the (i - 1)-th target charging station to the i-th target charging station, t p is the threshold of the driver's safe driving duration; ω1 is the first set coefficient, ω2 is the second set coefficient, ω3 is the third set coefficient, and the value ranges of ω1, ω2, and ω3 are all greater than 0 and less than 1.

[0023] Further, the step of obtaining the optimal charging times of each of the navigation paths according to the preset constraint conditions includes:

[0024] Let the optimal charging times of the i-th navigation path be N i , then:

[0025]

[0026] Among them, L i is the total path length of the i-th driving path, Δp is the power consumed by the new energy vehicle per kilometer traveled, and h0 is the current remaining power of the power battery.

[0027] Further, before the step of generating the optimal charging path of the new energy vehicle according to the optimal charging strategy of the optimal navigation path, it further includes:

[0028] Sending the optimal navigation path to the user terminal to obtain a user instruction from the user terminal;

[0029] In the case where the user instruction is to accept the optimal navigation path, execute the step of generating the optimal charging path of the new energy vehicle according to the optimal charging strategy of the optimal navigation path.

[0030] In a second aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the charging path selection method described in any one of the above are implemented.

[0031] In a third aspect, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the charging path selection method described in any one of the above are implemented.

[0032] The technical solution of the present invention can generate optimal charging strategies for multiple navigation paths, and each optimal charging strategy is restricted by preset constraints, which can prevent the starting power at each target charging station from being too high or the target power from being too low, increasing the charging frequency of new energy vehicles, and the remaining power of the power battery after reaching the destination from being too low to be used subsequently. Therefore, when controlling the new energy vehicle to charge during driving on the planned driving path according to the optimal charging strategy, it can prevent frequent charging from damaging the battery and improve the driving safety. Moreover, the technical solution of the present invention also uses the particle swarm optimization algorithm, which can generate optimal charging strategies on each navigation path before the new energy vehicle travels long distances, and select the navigation path with the smallest driving cost as the optimal navigation path according to the driving difficulty of each navigation path and the optimal charging strategy. Therefore, the optimal charging path generated according to this navigation path can ensure the reliability of vehicle charging during long-distance driving and achieve the purpose of improving the driving experience of the driver.

[0033] From the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more clear about the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Some specific embodiments of the present invention will be described in detail hereinafter with reference to the accompanying drawings in an exemplary but not restrictive manner. The same reference numerals in the drawings denote the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0035] Figure 1 is a schematic flowchart of a method for selecting a charging path of a new energy vehicle according to an embodiment of the present invention;

[0036] Figure 2 is a schematic flowchart of generating optimal charging strategies for each navigation path according to an embodiment of the present invention;

[0037] Figure 3 is a schematic flowchart of generating an optimal charging strategy for a target navigation path by using a preset particle swarm optimization algorithm according to an embodiment of the present invention;

[0038] Figure 4 is a schematic flowchart of obtaining the inertia weight and acceleration constant of each particle in the particle swarm by using a genetic algorithm according to an embodiment of the present invention;

[0039] Figure 5 is a schematic flowchart of iteratively updating the velocity and position of each particle according to an embodiment of the present invention;

[0040] Figure 6A schematic flowchart for predicting a preset charging strategy according to an embodiment of the present invention;

[0041] Figure 7 A schematic diagram of a computer program product according to an embodiment of the present invention; and

[0042] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Detailed implementation manners

[0043] Next, with reference to Figures 1 to 8 to describe a method for selecting a charging path of a new energy vehicle and related products according to an embodiment of the present invention. In the description of this embodiment, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features, that is, including one or more of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. When a certain feature "includes or contains" a certain or certain features it covers, unless otherwise specifically described, this indicates that other features are not excluded and other features may be further included.

[0044] Please refer to Figure 1 , Figure 1 shown is a schematic flowchart of a method for selecting a charging path of a new energy vehicle according to an embodiment of the present invention. This method can obtain the optimal charging path from multiple navigation paths and guide the new energy vehicle to charge during long-distance driving according to this charging path, so as to improve the reliability and timeliness of charging and achieve the purpose of improving the driving experience of the driver.

[0045] Figure 1 A schematic flowchart of a method for selecting a charging path of a new energy vehicle according to an embodiment of the present invention. Generally, this method may include the following steps:

[0046] Step S101: Obtain the current position and destination position of the new energy vehicle, and generate multiple navigation paths according to the current position and destination position;

[0047] Step S102: Obtain the charging stations on each navigation path, and generate the optimal charging strategy for each navigation path according to each charging station;

[0048] Step S103: Obtain the historical driving data of each navigation path, and obtain the driving difficulty of each navigation path according to each historical driving data;

[0049] Step S104: Obtain a preset driving cost function, and use the preset driving cost function to calculate the driving costs of each navigation path according to the driving difficulties of each optimal charging strategy.

[0050] Step S105: Take the navigation path with the minimum driving cost value as the optimal navigation path, and generate the optimal charging path of the new energy vehicle according to the optimal charging strategy of the optimal navigation path.

[0051] In the above step S101, the current position of the new energy vehicle can be obtained according to the positioning device of the new energy vehicle itself, and the current position and the destination position of the new energy vehicle are input into the navigation software. The navigation software can generate multiple navigation paths according to the reachable paths between the current position and the destination position of the new energy vehicle.

[0052] In the above step S102, taking one of the navigation paths as an example, the charging stations that can be reached by this navigation path and are less than a set distance from this navigation path can be obtained first as the charging stations on this navigation path.

[0053] In this embodiment, the method for generating the optimal charging strategy for each navigation path is as Figure 2 shown, and includes the following steps:

[0054] Step S201: Obtain preset constraint conditions, and obtain the optimal charging times of each navigation path according to the preset constraint conditions.

[0055] Step S202: Construct a charging cost function for the charging strategy according to the total driving duration required to reach the destination, the charging interval duration, and the charging duration and charging waiting duration at each target charging station.

[0056] Step S203: Use a preset particle swarm optimization algorithm to generate an optimal charging strategy according to the above preset constraint conditions, the optimal charging times, and the positions of each charging station.

[0057] Taking one of the navigation paths as an example below, the method for generating the optimal charging strategy of the navigation path is described, and for the convenience of describing the technical solution of the present invention, this navigation path is referred to as the target navigation path in this embodiment.

[0058] In the above step S201, the preset constraint conditions of the charging strategy obtained may include the remaining power threshold of the power battery, the target power threshold for charging at each charging station, and the final power threshold when reaching the destination.

[0059] Assume that the optimal charging times of the new energy vehicle in the target navigation path is N, then the method for obtaining the optimal charging times N includes:

[0060] First, based on the historical power consumption records of new energy vehicles, calculate the power consumption Δp per kilometer traveled by the new energy vehicle, and calculate the total distance L of the navigation path;

[0061] Then, obtain the current remaining power h0 of the power battery, and calculate the optimal charging times N through the following calculation formula:

[0062]

[0063] Among them, is the ceiling symbol, H max is the target power threshold for charging at each charging station, H min is the remaining power threshold of the power battery.

[0064] In this embodiment, the current remaining power of the power battery can be obtained according to the charge and discharge records of the power battery. For example, assume that the remaining power of the power battery after the most recent charging is h, and after the most recent charging is completed, the discharge amount of the power battery is calculated as Δh according to the discharge current of the power battery using the ampere-hour integration method. Then, the current remaining power h0 of the power battery is calculated using the following calculation formula:

[0065] h0 = h - Δh

[0066] Assume that in a charging strategy for the target navigation path, the charging waiting time at the i-th target charging station is t i,1 、charging waiting time t i,2 , the interval time between the i-th charging and the (i + 1)-th charging is ΔT i , and the total driving time to reach the destination is T tol , then the charging cost function F1 of this charging strategy is:

[0067] F1 = d1Q1 + d2Q2 + d3Q3

[0068] Among them, Q1 is the charging duration index of the charging strategy, Q2 is the charging frequency index of the charging strategy, Q3 is the total driving time index of the charging strategy, d1, d2, and d3 are the weights of charging experience, charging frequency weight, and driving time weight respectively, and

[0069] d1 + d2 + d3 = 1

[0070]

[0071] Q3 = clnT tol +T ch

[0072] In the above formulas, n1 is the number of times the charging duration in the charging strategy is greater than the first set duration threshold, t j,1is the charging duration greater than the first set duration threshold for the j-th time; n2 is the number of times the charging waiting duration in the charging strategy is greater than the second set duration threshold, t m,2 is the charging waiting duration greater than the second set duration threshold for the m-th time in the charging strategy; a1 is the first weight of the charging duration, a2 is the second weight of the charging duration, a3 is the first weight of the charging waiting duration, a4 is the second weight of the charging waiting duration. ΔT i is the interval duration between the i-th charging and the (i + 1)-th charging in the charging strategy, T tol is the total driving duration on the target navigation path according to the charging strategy, b1 is the first charging frequency weight, b2 is the second charging frequency weight, c is the driving duration weight, T ch is the driving duration correction value.

[0073] In the above step S203, the method for generating the optimal charging strategy of the target navigation path by using the preset particle swarm optimization algorithm is as Figure 3 shown, and includes the following steps:

[0074] Step S211: Initialize each particle of the particle swarm according to the charging stations on the target navigation path;

[0075] Step S212: Use the genetic algorithm to generate the weighted inertia weight and acceleration constant of each particle in the particle swarm;

[0076] Step S213: Use the particle swarm optimization algorithm to iteratively update the speed and position of the corresponding particle according to each weighted inertia weight and acceleration constant until the particle swarm meets the preset iteration stop condition;

[0077] Step S214: Use the charging cost function to obtain the global optimal solution of the particle swarm, and generate the optimal charging strategy on the target navigation path according to this global optimal solution.

[0078] In the above step S211, there are multiple particles in the particle swarm, each particle has a set number of elements, and this set number is equal to the optimal charging times of the above target navigation path, that is, N. Taking one of the particles as an example, the method for initializing this particle includes:

[0079] First, number each charging station on the target navigation path according to the distance between each charging station on the target navigation path and the new energy vehicle, that is, set the number of the charging station closest to the new energy vehicle on the target navigation path to 1, and set the number of the next charging station of the charging station numbered 1 to 2; and so on, complete the numbering of all charging stations on the target navigation path.

[0080] Then, according to the above preset constraint conditions, a set number of target charging stations are selected from the charging stations on the target navigation path, and the numbers of the selected target charging stations are used as elements in the particle to complete the initialization of the particle.

[0081] In this embodiment, the method for selecting a set number of target charging stations from the charging stations on the target navigation path according to the preset constraint conditions includes:

[0082] Randomly select a set number of charging stations from the charging stations on the target navigation path as target charging stations, and randomly set the target power of each target charging station within a range greater than the target power threshold;

[0083] Then, predict the remaining power of the power battery when reaching each target charging station, as well as the driving duration between adjacent target charging stations, to determine whether each target charging station meets the above preset constraint conditions;

[0084] If not, adjust the target power of each target charging station within a range greater than the target power threshold, and determine again whether each target charging station meets the above preset constraint conditions;

[0085] If after adjusting the target power of each target charging station multiple times, each target charging station still does not meet the above preset constraint conditions, reselect the target charging stations.

[0086] In the above step S212, the method for obtaining the inertia weight and acceleration constant of each particle in the particle swarm by using the genetic algorithm is as Figure 4 shown, and includes the following steps:

[0087] Step S221: Obtain an initialized particle parameter population, where there are multiple individuals in this particle parameter population, and each individual includes the weighted inertia weight and acceleration constant of a particle;

[0088] Step S222: Calculate the fitness of each individual respectively, and select excellent individuals in the particle parameter population according to the fitness;

[0089] Step S223: Perform a crossover operation on the selected excellent individuals to generate new individuals, and add the new individuals to the particle parameter population;

[0090] Step S224: Perform a mutation operation on each individual in the particle parameter population to generate new individuals, and add the new individuals to the particle parameter population;

[0091] Step S225: Determine whether the particle parameter population meets the preset optimization criteria;

[0092] If so, execute step S226; if not, return to step S222;

[0093] Step S226: Obtain the weighted inertia weight and acceleration constant of each particle in the particle swarm according to the individuals in the particle parameter population.

[0094] Since the individuals in the population are the weighted inertia weight and acceleration constant of the particles, technicians can, according to experience, within the preset weight interval and acceleration constant interval, and set the optimal weighted inertia weight W0 and M optimal acceleration constants, where the i-th optimal acceleration constant is

[0095] In the above step S221, multiple weighted inertia weights and acceleration constants are randomly selected, and the selected weighted inertia weights and acceleration constants are used as individuals in the particle parameter population to initialize the particle parameter population.

[0096] In the above step S222, the fitness of the calculated individual is positively correlated with the quality of the individual solution, that is, the greater the fitness of the individual, the better the quality of the individual solution. Therefore, the individual with the maximum fitness is used as the excellent individual in the particle parameter population.

[0097] In this embodiment, in the particle parameter population, let the weighted inertia weight of the j-th individual be W j , and the i-th acceleration constant be Then the fitness θ j of this individual is:

[0098]

[0099] Among them, ε is the weighted inertia weight comparison coefficient, and γ i is the comparison coefficient of the i-th acceleration constant, and

[0100] In the above step S213, the hybrid crossover operation method can be used to perform crossover operations on each excellent individual in the particle parameter population to generate new individuals, specifically including:

[0101] First, select two excellent individuals as parent individuals. Let these two parent individuals be P1 and P2 respectively, where P1 is the matrix composed of the weighted inertia weight and acceleration constant in the first parent individual, and P2 is the matrix composed of the weighted inertia weight and acceleration constant in the second parent individual;

[0102] Then, determine the selection interval according to the two parent individuals; the selection interval determined in this embodiment is:

[0103] [(P1 - α(P2 - P1)), (P1 + α(P2 - P1))]

[0104] Among them, α is a constant;

[0105] Finally, randomly select two chromosomes from the above selection interval as the chromosomes of the offspring individuals, and obtain the offspring individuals according to the chromosomes of the offspring individuals.

[0106] In the above step S214, the method of mutating each individual in the particle parameter population to generate new individuals includes: obtaining the individual with the minimum fitness in the particle parameter population, and generating the weighted inertia weight of the new individual according to the weighted inertia weight and the optimal weighted inertia weight of this individual, and generating the acceleration constants of the new individual according to the acceleration constants of this individual.

[0107] For example, assume that the individual with the minimum fitness in the particle parameter population is the j-th individual, and the weighted inertia weight of this individual is W j , and the i-th acceleration constant is Then

[0108] If W j >W0, then randomly select a value in the value range greater than W0 and less than W j as the weighted inertia weight of the new individual;

[0109] If W j <W0, then randomly select a value in the value range greater than W j and less than W i as the weighted inertia weight of the new individual;

[0110] If then randomly select a value in the value range greater than and less than as the i-th acceleration constant of the new individual;

[0111] If then randomly select a value in the value range greater than and less than as the i-th acceleration constant of the new individual.

[0112] After obtaining the new individual, add this new individual to the particle parameter population, and delete the individual with the minimum fitness in the particle parameter population.

[0113] In the above step S225, the preset optimization criterion is that the number of individuals in the particle parameter population is equal to the number of particles in the particle swarm, and the fitness of each individual is greater than the set fitness threshold.

[0114] In the above step S226, the weighted inertia weight and acceleration constant in each individual of the particle parameter population can be used as the weighted inertia weight and acceleration constant of the corresponding particle in the particle swarm respectively. For example, the weighted inertia weight and acceleration constant of the i-th individual in the particle parameter population are used as the weighted inertia weight and acceleration constant of the i-th particle in the particle swarm, so as to update the velocity and position of each particle in the particle swarm optimization algorithm.

[0115] In the above step S213, the method for iteratively updating the velocity and position of each particle according to the weighted inertia weight and acceleration constant of each particle in the particle swarm is as Figure 5 shown, and it includes the following steps:

[0116] Step S231: Obtain the individual optimal solution of each particle by using the charging cost function, and obtain the global optimal solution of the particle swarm according to the individual optimal solutions of each particle;

[0117] Step S232: Update the velocity and position of each particle in the particle swarm by using the weighted inertia weight and acceleration constant of each particle, according to the global optimal solution of the particle swarm and the individual optimal solutions of each particle;

[0118] Step S233: Determine whether the particle swarm meets the preset iteration stop condition;

[0119] If not, return to step S231; if so, stop the iterative update of the particle swarm.

[0120] In the above step S231, taking a particle in the particle swarm as an example, the method for obtaining the individual optimal solution of this particle according to the charging cost function includes: First, obtain the individual historical solution of this particle generated by each iterative update, and generate the preset charging strategies corresponding to each individual historical solution respectively; Then, calculate the charging cost values of each preset charging strategy by using the charging cost function, and take the individual historical solution corresponding to the preset charging strategy with the minimum charging cost value as the individual optimal solution of this particle.

[0121] The method for obtaining the global optimal solution of the particle swarm according to the individual optimal solutions of each particle includes: First, generate the preset charging strategies corresponding to the individual optimal solutions of each particle respectively; Then, calculate the charging cost values of each preset charging strategy by using the charging cost function, and take the individual optimal solution corresponding to the preset charging strategy with the minimum charging cost value as the global optimal solution of the particle swarm.

[0122] Taking a particle in the particle swarm as an example, the method for generating the preset charging strategy corresponding to one of the individual historical solutions of this particle includes:

[0123] First, obtain the positions of each target charging station according to the elements in the individual historical solution of the particle;

[0124] Then, calculate the path lengths between adjacent target charging stations, and randomly set the target power of each target charging station within the range greater than the target power threshold according to each path length, so that the remaining power of the power battery when reaching each target charging station is not greater than the remaining power threshold, and the remaining power of the power battery when reaching the destination is not less than the target power threshold, so as to obtain the preset charging strategy corresponding to the historical solution of this individual.

[0125] In this embodiment, since it is necessary to predict the preset charging strategy in the process of calculating the charging cost value of the preset charging strategy using the charging cost function, in order to obtain the charging waiting time, charging time, charging interval time for each charging, and the total driving time on the target navigation path of each target charging station in the preset charging strategy, one of the preset charging strategies is taken as an example for illustration in this embodiment.

[0126] As Figure 6 shown, the method for predicting the preset charging strategy in this embodiment includes the following steps:

[0127] Step S301: Obtain the driving habit information of the driver, as well as the road conditions information and path lengths between adjacent target charging stations in the preset charging strategy;

[0128] Step S302: Predict the driving speed of the new energy vehicle between adjacent target charging stations according to the driving habit information of the driver and the road conditions information between adjacent target charging stations;

[0129] Step S303: Calculate the interval driving time of the new energy vehicle between adjacent target charging stations according to the path lengths and driving speeds between adjacent target charging stations;

[0130] Step S304: Obtain the utilization rate of charging equipment at each target charging station in each time period, and obtain the charging waiting time, charging time, charging interval time for each charging, and the total driving time on the target navigation path in the preset charging strategy according to the utilization rate of charging equipment and the above interval driving times.

[0131] In this embodiment, the driving habit information of the driver includes the driving speed of the driver under various road conditions, and the obtained road conditions information includes road width and vehicle density. Since the preset charging strategy includes the positions of multiple target charging stations, the path lengths and road conditions information between adjacent target charging stations can be obtained according to the positions of each target charging station, and then the driving speed of the new energy vehicle between each target charging station can be predicted according to the driving habit of the driver, and the interval driving time of the new energy vehicle between adjacent target charging stations can be calculated according to the path lengths and driving speeds between adjacent target charging stations.

[0132] In this embodiment, the utilization rate of charging equipment refers to the ratio of the total number of vehicles charging and waiting to charge in a charging station to the number of charging equipment. Taking one charging station as an example, the method for obtaining the utilization rate of charging equipment at each time period in this charging station includes:

[0133] First, obtain the historical usage information of the charging equipment in this charging station, including the number of charging vehicles per day for the charging equipment in the charging station for multiple consecutive days;

[0134] Then, obtain the charging vehicle change cycle of the charging station based on the number of charging vehicles per day in this charging station. The charging vehicle change cycle is one day or multiple days, and within each charging vehicle change cycle, the difference in the number of charging vehicles for the corresponding number of days is less than a set difference;

[0135] Finally, calculate the utilization rate of the charging equipment at each time period per day in the charging vehicle change cycle of the charging station, and obtain the utilization rate of the charging equipment at each time period within the day based on the number of days within the cycle on the current day.

[0136] In this embodiment, the corresponding relationship between each charging equipment utilization rate and the charging waiting duration can be preset in advance. This corresponding relationship includes:

[0137] Let the charging equipment utilization rate be δ. If the value of δ is less than 1, the charging waiting duration of this charging station is 0;

[0138] If the value of δ is greater than or equal to 1, the charging waiting duration of this charging station is δ×E, where E is the preset charging duration.

[0139] Assume that the path length between the starting position of the new energy vehicle and the first target charging station in the preset charging strategy is L0, and based on the driving habit information of the driver and the road condition information between the starting position and the first target charging station, the driving speed v0 from the starting position to the first target charging station is obtained. If the current time point is T0, then the time point T1 to reach the first target charging station is:

[0140]

[0141] Based on the time period in which the time point T1 is located and the utilization rate of the charging equipment at each time period within the day, the utilization rate of the charging equipment at the first target charging station when the new energy vehicle reaches the first target charging station can be predicted. In this embodiment, the corresponding relationship between each charging equipment utilization rate and the charging waiting duration can be preset in advance, and after predicting the utilization rate of the charging equipment at the first target charging station when the new energy vehicle reaches the first target charging station, the charging waiting duration t of the new energy vehicle at the first target charging station can be predicted based on each charging equipment utilization rate and the above corresponding relationship 1,1 .

[0142] In this embodiment, the power consumption Δh1 of the power battery can be predicted according to the path length L0 between the starting position and the first target charging station during the process of the new energy vehicle reaching the first target charging station from the starting position. Then, when the new energy vehicle reaches the first target charging station, the remaining power h'1 of the power battery of the new energy vehicle can be predicted, that is, h'1 = h0 - Δh1. Then, the target power h1 of the first target charging station is obtained, and the duration required to charge from the remaining power h'1 to the target power h1 is predicted, and this duration is the charging duration t of the first target charging station. 1,2 .

[0143] Correspondingly, let the time point when the new energy vehicle reaches the i-th target charging station be T i , then

[0144]

[0145] where t j,1 is the charging waiting duration at the j-th target charging station in the preset charging strategy, t j,2 is the charging duration at the j-th target charging station in the preset charging strategy, L j is the path length between the (j - 1)-th target charging station and the j-th target charging station, and v j is the driving speed from the (j - 1)-th target charging station to the j-th target charging station.

[0146] In this embodiment, the charging waiting duration t i at the i-th target charging station can be predicted according to the charging equipment utilization rate at the time point T i,1 of the i-th target charging station. The remaining power h' i of the power battery when reaching the i-th target charging station can be predicted according to the path length L i , and the charging duration t i+1 at the i-th target charging station can be predicted according to the remaining power h' i and the target power h i,2 of the i-th target charging station.

[0147] Therefore, in the charging cost value data of the preset charging strategy predicted in this embodiment, the charging waiting duration at the i-th target charging station is t i,1 , the charging waiting duration t i,2 , and the interval duration ΔT i between the i-th charging and the (i + 1)-th charging is

[0148] ΔT i = L i / v i + t i,1

[0149] Total driving duration T on the target navigation path tol is

[0150]

[0151] where L n is the distance between the last target charging station and the destination in the preset charging strategy, and v n is the driving speed of the new energy vehicle from the last target charging station to the destination.

[0152] In the above step S232, the method of updating the velocity and position of each particle in the particle swarm according to the weighted inertia weight and acceleration constant of each particle, and the global optimal solution of the particle swarm and the individual optimal solution of each particle includes:

[0153] First, perform a weighted operation on the weighted inertia weight and acceleration constant of the particles within the neighborhood of each particle to obtain the weighted inertia weight and weighted acceleration constant of each particle;

[0154] Then, update the velocity and position of each particle according to the weighted inertia weight and weighted acceleration constant of each particle, and the individual optimal solution of each particle and the global optimal solution of the particle swarm.

[0155] In this embodiment, let W i be the weighted inertia weight of the i-th particle, and be the first weighted acceleration constant and the second weighted acceleration constant of the i-th particle respectively, then

[0156]

[0157] where g i is the neighborhood of the i-th particle, is the weighted inertia weight of the j-th particle in the neighborhood of the i-th particle, the first acceleration constant of the j-th particle in the neighborhood of the i-th particle, the second acceleration constant of the j-th particle in the neighborhood of the i-th particle, is the weight of the j-th particle in the neighborhood of the i-th particle.

[0158] In this embodiment, let the velocity of the d-th target charging station number in the i-th particle after the t-th iteration update be and the position be That is, the d-th target charging station number of the i-th particle after the t-th iteration update is Then, the velocity and position of each particle in the particle swarm can be updated through the following calculation formula:

[0159]

[0160] where p id is the d-th target charging station number in the individual optimal solution of the i-th particle, and p gd is the d-th target charging station number in the global optimal solution of the particle swarm. and are the first acceleration constant and the second acceleration constant of the i-th particle respectively, and both r1 and r2 are random numbers greater than or equal to 0 and less than 1.

[0161] Since the elements in each particle are the numbers of charging stations, and the numbers of charging stations are integers and will not be greater than the total number of charging stations, but the numbers may not be the numbers of charging stations after being updated according to the above method. Therefore, in this embodiment, after updating the velocity and position of each particle, if the value of the element in the particle is not an integer, the value of the element is corrected by rounding, and if the value of an element in the updated particle is greater than the total number of charging stations, the value of the element is set to the total number of charging stations.

[0162] In the above step S233, the preset iteration stop condition can be that the number of iterative updates of the particle swarm reaches the preset update number, that is, after performing iterative updates on the particle swarm for the preset update number of times, the iterative update of the particle swarm is stopped.

[0163] In the above step S214, after stopping the iterative update of the particle swarm, the individual optimal solutions of the particles in the particle swarm are obtained by using the charging cost function, the global optimal solution of the particle swarm is obtained according to each individual optimal solution, and a charging strategy is generated according to the global optimal solution, and this charging strategy is the optimal charging strategy on the target navigation path.

[0164] In the above step S103, taking the target navigation path as an example, the method for obtaining the driving difficulty of the target navigation path according to the historical driving data of the navigation path includes:

[0165] According to the historical driving data of the target navigation path, the traffic flow density ε of the target navigation path is obtained;

[0166] The number of lanes K and the number of maintenance sections Y of the target navigation path are obtained, where the length of the i-th maintenance section is s i , then the driving difficulty F2 of the target navigation path is:

[0167]

[0168] where θ1, θ2, and θ3 are the first difficulty coefficient, the second difficulty coefficient, and the third difficulty coefficient respectively, and CH is the difficulty correction value, and this difficulty correction value is a constant.

[0169] The method for calculating the target navigation path using a preset driving cost function includes: setting the driving difficulty of the target navigation path as Hard, then

[0170] Hard = μ1F2 + μ2F2

[0171] where μ1 is the first evaluation coefficient, μ2 is the second evaluation coefficient, and μ1 + μ2 = 1.

[0172] In the above step S105, the path from the optimal navigation path to the target charging station of its optimal charging strategy can be obtained, and this path can be added to the optimal navigation path to generate a new navigation path. Since this navigation path passes through the target charging station of the optimal charging strategy of the optimal navigation path, this navigation path is also the optimal charging path for the new energy vehicle.

[0173] According to the above content, it can be known that the technical solution of this embodiment can generate the optimal charging strategies for multiple navigation paths, and each optimal charging strategy is restricted by preset constraint conditions, which can prevent the starting power at each target charging station from being too high or the target power from being too low, thus increasing the charging frequency of the new energy vehicle, and the remaining power of the power battery after reaching the destination is too low to be used subsequently. Therefore, controlling the new energy vehicle to charge during the driving process on the planned driving path according to this optimal charging strategy can prevent frequent charging from damaging the battery and improve the driving safety. And in this embodiment, the particle swarm optimization algorithm is also used, which can generate the optimal charging strategies on each navigation path before the new energy vehicle travels long distances, and select the navigation path with the minimum driving cost as the optimal navigation path according to the driving difficulty and the optimal charging strategy of each navigation path. Therefore, the optimal charging path generated according to this navigation path can ensure the reliability of charging during the long-distance driving of the vehicle and achieve the purpose of improving the driving experience of the driver.

[0174] In some embodiments of the present invention, after generating multiple navigation paths according to the current position and the destination position of the new energy vehicle in the above step S101, it further includes:

[0175] Obtaining the maximum cruising range of the new energy vehicle, and judging whether it is necessary to plan the charging path of the new energy vehicle according to the lengths of each navigation path and the maximum cruising range of the new energy vehicle;

[0176] If it is necessary, continue to execute the steps of obtaining the charging stations on each navigation path and generating the optimal charging strategies for each navigation path according to each charging station

[0177] In this embodiment, if the maximum cruising range of the new energy vehicle is greater than the length of the navigation route, then at most only one charging is required for the new energy vehicle. Even if the optimal charging route is not planned, it will not have too much impact on the driver's driving experience. Therefore, if the maximum cruising range of the new energy vehicle is greater than the length of the navigation route, it is determined that there is no need to plan the charging route of the new energy vehicle; conversely, if the maximum cruising range of the new energy vehicle is not greater than the length of the navigation route, during the process of driving along the navigation route, the new energy vehicle may need to be charged more than once, so it is necessary to plan the charging route of the new energy vehicle.

[0178] Through the technical solution of this embodiment, it is possible to determine whether it is necessary to plan the charging route of the new energy vehicle according to the maximum cruising range of the new energy vehicle and the lengths of each navigation route, thereby reducing the workload of the new energy vehicle charging control and improving the reliability of the new energy vehicle charging control.

[0179] In some embodiments of the present invention, the obtained preset constraint conditions are:

[0180] h' i ≤ω1×H

[0181] h i ≥ω2×H

[0182] h'≥ω3×H

[0183] t i ≤t p

[0184] Wherein, H is the capacitance of the power battery, n is the total number of target charging stations in the charging strategy, h′ i is the power of the power battery when arriving at the i-th target charging station, h i is the target power of the power battery at the i-th target charging station, h′ is the target remaining power of the power battery when arriving at the destination, t i is the driving duration required to drive from the (i - 1)-th target charging station to the i-th target charging station, t p is the safety driving duration threshold of the driver; ω1 is the first set coefficient, ω2 is the second set coefficient, ω3 is the third set coefficient, and the value ranges of ω1, ω2, and ω3 are all greater than 0 and less than 1.

[0185] The technical solution of this embodiment can limit the generated optimal charging strategy by preset constraint conditions, which can not only prevent the starting power from being too high or the target power from being too low when charging at each target charging station, thereby increasing the charging frequency of new energy vehicles, but also prevent the driver from experiencing fatigue driving due to excessive driving duration, and prevent the remaining power of the power battery from being too low after arriving at the destination, making it impossible to use the vehicle subsequently. Therefore, when controlling the new energy vehicle to charge during driving on the optimal navigation path according to this optimal charging strategy, it can prevent frequent charging from damaging the battery and improve the driving safety.

[0186] In some embodiments of the present invention, assume that the optimal charging times of the i-th navigation path is N i , then:

[0187]

[0188] Wherein, L i is the total path length of the i-th driving path.

[0189] Through the technical solution of this embodiment, the charging times can be increased during the process of planning the optimal charging strategy of the charging path, so as to ensure the endurance of the new energy vehicle and improve the reliability of the new energy vehicle driving.

[0190] In some embodiments of the present invention, before generating the optimal charging path of the new energy vehicle according to the optimal charging strategy of the optimal navigation path in step S104, it further includes:

[0191] Sending the obtained optimal navigation path to the user terminal to obtain a user instruction from the user terminal;

[0192] In the case of receiving the user instruction to accept the optimal navigation path, execute the step of generating the optimal charging path of the new energy vehicle according to the optimal charging strategy of the optimal navigation path.

[0193] In this embodiment, after receiving the optimal navigation path, the user terminal can display the optimal navigation path on the screen and display a button on the screen to indicate whether to accept and execute the optimal navigation path for the user to select. If the user terminal detects that the user selects the button to accept the optimal navigation path, it sends a user instruction to accept the optimal navigation path to the new energy vehicle; if the user terminal detects that the user selects the button to reject the optimal navigation path, or does not detect the user's selection operation within the set duration, it sends a user instruction not to accept the optimal navigation path to the new energy vehicle.

[0194] The technical solution of this embodiment adds a link for interaction with the user after generating the optimal navigation path, so that the user can independently choose whether to execute the generation of the optimal charging path, thereby improving the flexibility of the charging control of new energy vehicles and further enhancing the user experience.

[0195] The flowcharts provided in this embodiment are not intended to indicate that the operations of the method will be performed in any specific order, or that all operations of the method are included in every case. In addition, the above methods may include additional operations. Within the scope of the technical concept provided by the method of this embodiment, additional changes can be made to the above methods.

[0196] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0197] This embodiment also provides a computer program product 10 and a computer-readable storage medium 20. Figure 7 is a schematic diagram of a computer program product 10 according to an embodiment of the present invention, Figure 8 is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, and when the computer program 11 is executed by a processor 32, it implements the steps of any one of the above charging path selection methods for new energy vehicles. The computer-readable storage medium 20 stores the above computer program 11, and when the computer program 11 is executed by a processor 32, it implements the steps of the charging path selection method for new energy vehicles in any one of the above embodiments. The computer device 30 may include a memory 31, a processor 32, and a computer program 11 stored on the memory 31 and running on the processor 32.

[0198] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages and procedural programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using the Internet through an Internet service provider). In some embodiments, in order to perform aspects of the present invention, an electronic circuit, including for example a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit.

[0199] For the description of this embodiment, the computer program product 10 is a related product that includes the computer program 11.

[0200] For the description of this embodiment, the computer-readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, which may be any device that can contain, store, communicate, propagate, or use the computer program 11 for an instruction execution system, apparatus, or device or in combination with these instruction execution systems, apparatuses, or devices. More specific examples (non-exhaustive list) of the computer-readable storage medium 20 include the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, and any suitable combination of the above.

[0201] At this point, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived from the disclosed content of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized to cover all such other variations or modifications.

Claims

1. A method for selecting a charging path of a new energy vehicle, characterized in that, Including: Obtain the current position and the destination position of the new energy vehicle, and generate multiple navigation paths based on the current position and the destination position; Obtain the charging stations on each of the navigation paths, and generate an optimal charging strategy for each of the navigation paths according to each of the charging stations, including: Obtain a preset constraint condition and a charging cost function, and obtain the optimal charging times for each of the navigation paths according to the preset constraint condition, where the preset constraint condition includes a remaining power threshold of the power battery, a target power threshold for charging at each charging station, and a final power threshold when arriving at the destination; Adopt a preset particle swarm optimization algorithm, and generate an optimal charging strategy for each of the navigation paths according to the preset constraint condition, the charging cost function, the optimal charging times, and the positions of each of the charging stations; Obtain the historical driving data of each of the navigation paths, and obtain the driving difficulty of each of the navigation paths according to each of the historical driving data; Obtain a preset driving cost function, and adopt the preset driving cost function to calculate the driving cost of each of the navigation paths according to each of the optimal charging strategies, the driving difficulty, and the optimal charging times; Take the navigation path with the minimum driving cost as the optimal navigation path, and generate an optimal charging path for the new energy vehicle according to the optimal charging strategy of the optimal navigation path, so as to guide the new energy vehicle to charge during the process of driving to the destination.

2. The charging path selection method according to claim 1, wherein After the step of generating multiple navigation paths based on the current position and the destination position, it further includes: Obtain the maximum cruising range of the new energy vehicle, and judge whether it is necessary to plan the charging path of the new energy vehicle according to the lengths of each of the navigation paths and the maximum cruising range; If necessary, execute the step of obtaining the charging stations on each of the navigation paths.

3. The charging path selection method according to claim 1, wherein The preset constraint condition includes: h' i ≤ ω1 × H h i ≥ω2×H h'≥ω3×H t i ≤t p Wherein, H is the capacitance of the power battery, h′ is the remaining power of the power battery when reaching the destination, and h′ i is the remaining power of the power battery when reaching the i-th target charging station, and h i is the target power of the i-th target charging station, and t i is the driving duration required to drive from the (i - 1)-th target charging station to the i-th target charging station, and t p is the safety driving duration threshold of the driver; ω1 is the first set coefficient, ω2 is the second set coefficient, ω3 is the third set coefficient, and the value ranges of ω1, ω2, and ω3 are all greater than 0 and less than 1.

4. The charging path selection method according to claim 3, wherein The step of obtaining the optimal charging times for each of the navigation paths according to the preset constraint condition includes: Let the optimal number of charging times for the i-th navigation path be N i , then: Among them, L i is the total path length of the i-th driving path, Δp is the power consumption per kilometer traveled by the new energy vehicle, and h0 is the current remaining power of the power battery.

5. The charging path selection method according to claim 1, wherein Before the step of generating an optimal charging path for the new energy vehicle according to the optimal charging strategy of the optimal navigation path, it further includes: Send the optimal navigation path to the user terminal to obtain a user instruction from the user terminal; When the user instruction is to accept the optimal navigation path, execute the step of generating an optimal charging path for the new energy vehicle according to the optimal charging strategy of the optimal navigation path.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the charging path selection method according to any one of claims 1 to 5 are implemented.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the charging path selection method according to any one of claims 1 to 5 are implemented.

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