Charging control method of new energy vehicle and related product

Abstract

The invention provides a charging control method of a new energy vehicle and a related product. The method comprises the following steps: acquiring a planned driving path, and acquiring charging stations in a set range around the planned driving path and the current residual electric quantity of a power battery; acquiring a preset constraint condition of a charging strategy, and acquiring an optimal charging frequency on the planned driving path according to the planned driving path, the current residual electric quantity and the preset constraint condition; constructing a charging cost function of the charging strategy; a charging strategy optimization model based on a genetic algorithm and a particle swarm optimization algorithm is adopted, and an optimal charging strategy of the planned driving path is generated according to the preset constraint condition and the charging cost function; and according to the optimal charging strategy, the new energy vehicle is controlled to be charged in the driving process on the planned driving path. According to the technical scheme, the charging reliability and timeliness of the new energy vehicle in the long-distance driving process can be improved.

Description

Technical Field

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

[0002] New energy vehicles refer to vehicles that use unconventional vehicle fuels as the power source, 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, resulting in range anxiety among 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 battery 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 strategy of new energy vehicles during long-distance driving to improve the reliability and timeliness of charging new energy vehicles is of great significance for improving the driving experience of drivers. Summary of the Invention

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

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

[0007] Obtain a planned driving route, obtain charging stations within a set range around the planned driving route, and the current remaining power of the power battery;

[0008] Obtain preset constraint conditions for the charging strategy, and the preset constraint conditions include:

[0009] h' i ≤ω 1 ×H

[0010] h i ≥ω2 ×H

[0011] h' ≥ ω 3 ×H

[0012] t i ≤ t p

[0013] 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; ω 1 is the first set coefficient, ω 2 is the second set coefficient, ω 3 is the third set coefficient, and ω 1 、ω 2 and ω 3 all have a value range greater than 0 and less than 1;

[0014] According to the planned driving route, the current remaining power and the preset constraint conditions, obtain the optimal number of charging times on the planned driving route;

[0015] According to the total driving duration on the planned driving route, the charging interval duration, as well as the charging duration and charging waiting duration at each target charging station, construct the charging cost function of the charging strategy;

[0016] Adopt a charging strategy optimization model based on genetic algorithm and particle swarm optimization algorithm. According to the preset constraint conditions and the charging cost function, select target charging stations with a quantity matching the optimal number of charging times from each charging station, and obtain the target power of each target charging station to generate the optimal charging strategy for the planned driving route;

[0017] According to the optimal charging strategy, control the new energy vehicle to charge during the driving process on the planned driving route.

[0018] Furthermore, after obtaining the planned driving route, it further includes:

[0019] Obtain the maximum cruising range of the new energy vehicle, and according to the planned driving route and the maximum cruising range, determine whether it is necessary to plan the charging strategy of the new energy vehicle;

[0020] If necessary, perform the steps of obtaining the charging stations within a set range around the planned driving route and the remaining power of the power battery.

[0021] Further, before the step of charging while controlling the new energy vehicle to travel on the planned driving route according to the optimal charging strategy, the method further includes:

[0022] Sending the optimal charging strategy to the user terminal to obtain a user instruction from the user terminal;

[0023] In the case where the user instruction is to allow the execution of the optimal charging strategy, perform the step of charging while controlling the new energy vehicle to travel on the planned driving route according to the optimal charging strategy.

[0024] Further, the step of charging while controlling the new energy vehicle to travel on the planned driving route according to the optimal charging strategy includes:

[0025] Obtaining the exits corresponding to each target charging station on the planned driving route in the optimal charging strategy, and displaying each of the exits on the navigation map;

[0026] If it is detected that the new energy vehicle exits the planned driving route from the exit, display the navigation path between the exit and the corresponding target charging station;

[0027] After a charging behavior is detected at the target charging station, charge the new energy vehicle according to the target power of the target charging station.

[0028] Further, the step of charging while controlling the new energy vehicle to travel on the planned driving route according to the optimal charging strategy includes:

[0029] If a charging behavior that does not match the optimal charging strategy is detected, return to the step of obtaining the planned driving route to regenerate the optimal charging control strategy.

[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 control 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 control method described in any one of the above are implemented.

[0032] The technical solution of the present invention is that the generated optimal charging strategy is restricted by preset constraint conditions. It can not only prevent the starting power at each target charging station from being too high or the target power from being too low, which increases the charging frequency of new energy vehicles, but also prevent the driver from experiencing fatigue driving due to too long driving time, and prevent the remaining power of the power battery after arriving at the destination from being too low to be used subsequently. Therefore, by controlling the charging of new energy vehicles during driving on the planned driving route according to this optimal charging strategy, it is possible to prevent frequent charging from damaging the battery and improve the driving safety. In addition, in the present invention, a charging strategy optimization model based on genetic algorithm and particle swarm optimization algorithm is also adopted, which can generate the optimal charging strategy on the planned driving route before the long-distance driving of new energy vehicles, thus ensuring the reliability and timeliness of the charging of new energy vehicles during long-distance driving and achieving 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 clearly aware of 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 and non-limiting 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 flow chart of a charging control method for a new energy vehicle according to an embodiment of the present invention;

[0036] Figure 2 is a schematic flow chart of generating multiple preset charging strategies by using a charging strategy optimization model in the charging control method according to an embodiment of the present invention;

[0037] Figure 3 is a schematic flow chart of obtaining the weighted inertia weight and acceleration constant of each particle in the particle swarm by using genetic algorithm in the charging control method according to an embodiment of the present invention;

[0038] Figure 4 is a schematic flow chart of the particle optimization algorithm in the charging control method according to an embodiment of the present invention;

[0039] Figure 5 is a schematic flow chart of predicting the preset charging strategy in the charging control method according to an embodiment of the present invention;

[0040] Figure 6It is a schematic flowchart of generating an optimal charging path for a new energy vehicle according to an optimal charging strategy in a charging control method according to an embodiment of the present invention;

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

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

[0043] The following refers to Figures 1 to 8 to describe a charging control method and related products for a new energy vehicle 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, include 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 charging control method for a new energy vehicle according to an embodiment of the present invention. This charging control method can plan an optimal charging strategy based on the planned driving path of the new energy vehicle and use this optimal charging strategy to control the new energy vehicle to charge, so as to improve the reliability and timeliness of charging and achieve the purpose of improving the driver's vehicle use experience.

[0045] As Figure 1 shown, the charging control method for a new energy vehicle of the present invention generally may include:

[0046] Step S101: Obtain the planned driving path of the new energy vehicle, obtain the charging stations within a set range around the planned driving path, and the current remaining power of the power battery;

[0047] Step S102: Obtain the preset constraint conditions of the charging strategy, and obtain the optimal charging times of the new energy vehicle on the planned driving path according to the preset constraint conditions, the above-mentioned planned driving path, and the current remaining power of the power battery;

[0048] Step S103: Construct a charging cost function for the charging strategy based on the total driving duration, charging interval duration, charging duration, and charging waiting duration at each target charging station on the planned driving route.

[0049] Step S104: Use a charging strategy optimization model based on genetic algorithm and particle swarm optimization algorithm to generate an optimal charging strategy according to the above preset constraints, the optimal number of charging times, and the locations of each charging station.

[0050] Step S105: Control the new energy vehicle to charge during the driving process on the planned driving route according to the optimal charging strategy.

[0051] In the above step S101, the driver can input the destination in the navigation software, and the navigation software can generate multiple driving routes for the driver to choose according to the location of the new energy vehicle and the destination input by the driver, and use the driving route selected by the driver as the planned driving route.

[0052] In this embodiment, the charging stations that can be reached by the planned driving route and are less than the set distance from the planned driving route can be obtained first. This charging station is the charging station within the set range around the planned driving route.

[0053] 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, and then the following calculation formula is used to calculate the current remaining power h of the power battery 0 :

[0054] h 0 = h - Δh

[0055] In the above step S102, the charging strategy includes multiple target charging stations and the target power of each target charging station. The preset constraints for the obtained charging strategy include:

[0056] h' i ≤ ω 1 × H

[0057] h i ≥ ω 2 × H

[0058] h' ≥ ω 3 × H

[0059] t i ≤ t p

[0060] Among them, 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 threshold of the safe driving duration; ω 1 is the first set coefficient, and ω 2 is the second set coefficient, and ω 3 is the third set coefficient, and ω 1 and ω 2 and ω 3 all have a value range greater than 0 and less than 1.

[0061] In this embodiment, assuming that the optimal number of charging times of the new energy vehicle on the planned driving route is N, the method for obtaining the optimal number of charging times N includes:

[0062] First, according to the historical power consumption record of the new energy vehicle, calculate the power consumption Δp per kilometer traveled by the new energy vehicle, and calculate the total distance L of the planned driving route;

[0063] Then, calculate the optimal number of charging times N through the following calculation formula:

[0064]

[0065] Among them, is the ceiling symbol.

[0066] In the above step S103, assuming that in the charging strategy, the charging waiting duration of the i-th target charging station is t i,1 and the charging waiting duration t i,2 , the interval duration between the i-th charging and the (i + 1)-th charging is ΔT i , and the total driving duration on the planned driving route is T tol , then the charging cost function F of this charging strategy is:

[0067] F = d 1 Q 1 + d 2 Q 2 + d 3 Q 3

[0068] Among them, Q 1 is the charging duration index of the charging strategy, Q 2 is the charging frequency index of the charging strategy, Q 3 is the total driving duration index of the charging strategy, and d1 , d 2 , d 3 are the weights of charging experience, charging frequency, and driving duration respectively, and

[0069] d 1 + d 2 + d 3 = 1

[0070]

[0071] Q 3 = clnT tol + T ch

[0072] In each of the above formulas, n 1 is the number of times the charging duration in the charging strategy is greater than the first set duration threshold, and t j,1 is the charging duration of the j-th charge greater than the first set duration threshold; n 2 is the number of times the charging waiting duration in the charging strategy is greater than the second set duration threshold, and t m,2 is the charging waiting duration of the m-th time greater than the second set duration threshold in the charging strategy; a 1 is the first weight of the charging duration, a 2 is the second weight of the charging duration, a 3 is the first weight of the charging waiting duration, a 4 is the second weight of the charging waiting duration. ΔT i is the interval duration between the i-th charge and the (i + 1)-th charge in the charging strategy, and T tol is the total driving duration on the planned driving path according to the charging strategy, b 1 is the first charging frequency weight, b 2 is the second charging frequency weight, c is the driving duration weight, and T ch is the driving duration correction value.

[0073] In the above step S104, the method of obtaining the optimal charging strategy using the charging strategy optimization model based on the genetic algorithm and the particle swarm optimization algorithm is as Figure 2 shown, and includes the following steps:

[0074] Step S201: Initialize each particle of the particle swarm according to the charging stations on the planned driving path;

[0075] Step S202: Generate the weighted inertia weight and acceleration constant of each particle in the particle swarm using the genetic algorithm;

[0076] Step S203: Use the particle swarm optimization algorithm to iteratively update the velocities and positions of the corresponding particles according to each weighted inertia weight and acceleration constant until the particle swarm meets the preset iteration stop condition;

[0077] Step S204: Use the charging cost function to obtain the global optimal solution of the particle swarm, and generate the optimal charging strategy on the planned driving route according to this global optimal solution.

[0078] In the above step S201, there are multiple particles in the particle swarm, and each particle has a set number of elements, and this set number is equal to the above-mentioned optimal charging times, 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 planned driving route according to the distances between each charging station on the planned driving route and the new energy vehicle, that is, set the number of the charging station closest to the new energy vehicle on the planned driving route to 1, and set the number of the next charging station of the charging station numbered 1 to 2; and so on, to complete the numbering of all charging stations on the planned driving route.

[0080] Then, according to the above preset constraint conditions, select a set number of target charging stations from the charging stations on the planned driving route, and use the numbers of the selected target charging stations as the elements in this particle to complete the initialization of this particle.

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

[0082] Randomly select a set number of charging stations from the charging stations on the planned driving route as target charging stations, and randomly set the target power of each target charging station within a range greater than ω 2 ×H;

[0083] Then predict the remaining power of the power battery when reaching each target charging station, and the driving duration between each adjacent target charging station, to judge 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 ω 2 ×H, and judge 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 S202, the method of using a genetic algorithm to obtain the weighted inertia weight and acceleration constant of each particle in the particle swarm is as follows: Figure 3 It includes the following steps:

[0087] Step S211: Obtain an initialized particle parameter population. 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 S212: Calculate the fitness of each individual respectively, and select the excellent individuals in the particle parameter population according to the fitness.

[0089] Step S213: 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 S214: 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 S215: Determine whether the particle parameter population meets the preset optimization criteria.

[0092] If so, execute step S216; if not, return to step S212.

[0093] Step S216: According to the individuals in the particle parameter population, obtain the weighted inertia weight and acceleration constant of each particle in the particle swarm.

[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 W 0 and M optimal acceleration constants, where the i-th optimal acceleration constant is

[0095] In the above step S211, randomly select multiple weighted inertia weights and acceleration constants, and use the selected weighted inertia weights and acceleration constants as the individuals in the particle parameter population to initialize the particle parameter population.

[0096] In the above step S212, the calculated fitness of the 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, assume that in the particle parameter population, the weighted inertia weight of the j-th individual is W j , and the i-th acceleration constant is Then the fitness θ j of this individual is:

[0098]

[0099] Among them, ρ 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, a 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 P 1 and P 2 , where P 1 is the matrix composed of the weighted inertia weight and the acceleration constant in the first parent individual, and P 2 is the matrix composed of the weighted inertia weight and the 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] [(P 1 -α(P 2 -P 1 )), (P 1 +α(P 2 -P 1 ))]

[0104] where 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 for performing mutation operations on 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 of this individual and the optimal weighted inertia weight, 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, the weighted inertia weight of this individual is W j , and the i-th acceleration constant is Then

[0108] If W j >W 0 , then greater than W 0 and less than W jRandomly select a value within the value range of

[0109] If W j <W 0 , then randomly select a value within 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 within 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 within 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 the 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 S215, 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 S216, the weighted inertia weight and acceleration constant in each individual in 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 to update the speed and position of each particle in the particle swarm optimization algorithm.

[0115] In the above step S203, the particle swarm optimization algorithm can be used to calculate the new speed and new position of each particle according to the weighted inertia weight and acceleration constant of each particle to iteratively update the speed and position of each particle in the particle swarm.

[0116] The method of using the particle swarm optimization algorithm to iteratively update the speed and position of each particle according to the weighted inertia weight and acceleration constant of each particle in the particle swarm is as Figure 4 shown, including the following steps:

[0117] Step S221: Use the charging cost function to obtain the individual optimal solution of each particle, and obtain the global optimal solution of the particle swarm according to the individual optimal solution of each particle;

[0118] Step S222: Update the velocities and positions of the particles in the particle swarm according to the weighted inertia weights and acceleration constants of the particles, and based on the global optimal solution of the particle swarm and the individual optimal solutions of the particles.

[0119] Step S223: Determine whether the particle swarm meets the preset iteration stop condition.

[0120] If not, return to Step S221; if so, stop the iterative update of the particle swarm.

[0121] In the above Step S221, taking a particle in the particle swarm as an example, the method for obtaining the individual optimal solution of the particle according to the charging cost function includes: First, obtain the individual historical solutions of the particle generated by each iterative update, and generate the preset charging strategies corresponding to each individual historical solution; then, calculate the charging cost values of the preset charging strategies 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 the particle.

[0122] The method for obtaining the global optimal solution of the particle swarm according to the individual optimal solutions of the particles includes: First, generate the preset charging strategies corresponding to the individual optimal solutions of the particles; then, calculate the charging cost values of the preset charging strategies 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.

[0123] 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 the particle includes:

[0124] First, obtain the positions of the target charging stations according to the elements in the individual historical solution of the particle.

[0125] Then, calculate the path lengths between adjacent target charging stations, and randomly set the target power levels of the target charging stations within the range greater than ω 2 ×H, so that the remaining power of the power battery when reaching each target charging station is not greater than ω 1 ×H, and the remaining power of the power battery when reaching the destination is not less than ω 3 ×H, to obtain the preset charging strategy corresponding to the individual historical solution.

[0126] In this embodiment, since in the process of calculating the charging cost value of the preset charging strategy using the charging cost function, it is necessary to predict the preset charging strategy to obtain the charging waiting time and charging time of each target charging station in the preset charging strategy, the charging interval time of each charging, and the total driving time on the planned driving path, therefore, one of the preset charging strategies is taken as an example for illustration in this embodiment.

[0127] As Figure 5 shown, the method for predicting a preset charging strategy in this embodiment includes the following steps:

[0128] 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;

[0129] 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;

[0130] Step S303: Calculate the driving duration of the new energy vehicle between adjacent target charging stations according to the path lengths and driving speeds between adjacent target charging stations;

[0131] Step S304: Obtain the utilization rate of charging devices at each target charging station in each time period, and according to the utilization rate of the charging devices and the above driving durations of each interval, obtain the charging waiting duration and charging duration of each target charging station in the preset charging strategy, the charging interval duration for each charging, and the total driving duration on the planned driving path.

[0132] 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. The preset charging strategy includes the locations of multiple target charging stations. Therefore, according to the locations of each target charging station, the path lengths and road conditions information between adjacent target charging stations are obtained, 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 according to the path lengths and driving speeds between adjacent target charging stations, the driving duration of the new energy vehicle between adjacent target charging stations can be calculated.

[0133] In this embodiment, the charging device utilization rate refers to the ratio of the total number of vehicles being charged and waiting to be charged in the charging station to the number of charging devices. Taking one of the charging stations as an example, the method for obtaining the utilization rate of the charging devices at this charging station in each time period includes:

[0134] First, obtain the historical usage information of the charging devices in this charging station, including the number of charging vehicles of the charging devices in the charging station every day for consecutive days;

[0135] Then, obtain the charging vehicle change cycle of the charging station according to the number of charging vehicles in the charging station every day. The charging vehicle change cycle is one day or multiple days, and in each charging vehicle change cycle, the difference in the number of charging vehicles in the corresponding days is less than the set difference;

[0136] Finally, calculate the utilization rate of the charging equipment at each time period of the charging station during its charging vehicle change cycle, and obtain the utilization rate of the charging equipment at each time period of the day according to the number of days in the current cycle of the day.

[0137] In this embodiment, the corresponding relationship between each charging equipment utilization rate and the charging waiting duration can be preset, and the corresponding relationship includes:

[0138] Let the charging equipment utilization rate be δ. If the value of δ is less than 1, the charging waiting duration of the charging station is 0;

[0139] If the value of δ is greater than or equal to 1, the charging waiting duration of the charging station is δ×E, where E is the preset charging duration.

[0140] 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 L 0 , and according to 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 from the starting position to the first target charging station is v 0 , and the current time point is T 0 , then the time point T 1 to reach the first target charging station is:

[0141]

[0142] According to the time period where the time point T 1 is located, and the utilization rate of the charging equipment at each time period of 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, 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, according to each charging equipment usage rate and the above corresponding relationship, the charging waiting duration t 1,1 of the new energy vehicle at the first target charging station can be predicted.

[0143] In this embodiment, according to the path length L 0 between the starting position and the first target charging station, the power consumption Δh 1 of the power battery during the process of the new energy vehicle reaching the first target charging station from the starting position can be predicted, and then the remaining power h' 1 of the power battery of the new energy vehicle when the new energy reaches the first target charging station can be predicted, that is, h' 1 = h 0 -Δh 1 . Then obtain the target power h of the first target charging station1 and predict the remaining power h' 1 required to charge to the target power h 1 The required duration is the charging duration t of the first target charging station 1,2 .

[0144] Correspondingly, let the time point when the new energy vehicle arrives at the i-th target charging station be T i , then

[0145]

[0146] where t j,1 is the charging waiting duration at the j-th target charging station in the preset charging strategy, and 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.

[0147] In this embodiment, the charging waiting duration t i at the i-th target charging station at time point T can be predicted according to the charging equipment utilization rate at the i-th target charging station, and according to the path length L i,1 , the remaining power h' i of the power battery when arriving at the i-th target charging station can be predicted, and according to the remaining power h' i and the target power h i+1 of the i-th target charging station i , the charging duration t i,2 at the i-th target charging station can be predicted.

[0148] 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 charge and the (i + 1)-th charge is

[0149] ΔT i = L i / v i + t i,1

[0150] The total driving duration T tol on the planned driving path is

[0151]

[0152] where Ln is the distance between the last target charging station and the destination in the preset charging strategy, v n is the driving speed of the new energy vehicle from the last target charging station to the destination.

[0153] In the above step S222, 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:

[0154] First, perform a weighted operation on the weighted inertia weight and acceleration constant of the particles in the neighborhood of each particle to obtain the weighted inertia weight and weighted acceleration constant of each particle;

[0155] 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.

[0156] 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

[0157]

[0158] 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.

[0159] 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 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:

[0160]

[0161] where, p id is the d-th target charging station number in the individual optimal solution of the i-th particle, 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 r 1 and r 2 are both random numbers greater than or equal to 0 and less than 1.

[0162] Since the numbers of charging stations are integers and do not exceed the total number of charging stations, and the numbers updated according to the above method may not be the numbers of charging stations. Therefore, in this embodiment, after updating the speeds and positions of each particle, if the value of an 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.

[0163] In the above step S223, the preset iteration stop condition may be that the number of iterative updates of the particle swarm reaches the preset number of updates, that is, after performing iterative updates of the particle swarm for the preset number of updates, the iterative updates of the particle swarm are stopped.

[0164] In the above step S204, after stopping the iterative updates of the particle swarm, the individual optimal solutions of each particle 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 planned driving route.

[0165] The optimal charging strategy obtained in this embodiment includes the selected target charging stations and the target battery levels at each target charging station. Therefore, in the above step S104, the method for controlling the charging of a new energy vehicle during driving on the planned driving route according to the optimal charging strategy includes:

[0166] When the new energy vehicle drives to the target charging station in the optimal charging strategy, a prompt message is sent to guide the driver to drive to the target charging station for charging, and the battery of the power battery is charged to the corresponding target battery level at the target charging station.

[0167] According to the above content, the optimal charging strategy generated by the technical solution of this embodiment is restricted by preset constraint conditions. It can not only prevent the starting power at each target charging station from being too high or the target power from being too low, which increases the charging frequency of new energy vehicles, but also prevent the driver from experiencing fatigue driving due to excessive driving time, and prevent the remaining power of the power battery after arriving at the destination from being too low to be used subsequently. Therefore, when controlling the new energy vehicle to charge during the driving process on the planned driving route according to this optimal charging strategy, it can prevent frequent charging from damaging the battery and improve the driving safety. In addition, in this embodiment, a charging strategy optimization model based on the genetic algorithm and the particle swarm optimization algorithm is also adopted, which can generate the optimal charging strategy on the planned driving route before the new energy vehicle travels long distances. Therefore, it ensures the reliability and timeliness of the new energy vehicle charging during the long-distance driving process, achieving the purpose of improving the driver's vehicle use experience.

[0168] In some embodiments of the present invention, in the above step S101, after obtaining the planned driving route of the new energy vehicle, it further includes:

[0169] Obtain the maximum cruising range of the new energy vehicle, and judge whether it is necessary to plan the charging strategy of the new energy vehicle according to the maximum cruising range of the new energy vehicle and the planned driving route;

[0170] If so, continue to execute the steps of obtaining the charging stations within a set range around the planned driving route and the current remaining power of the power battery.

[0171] In this embodiment, if the maximum cruising range of the new energy vehicle is greater than the length of the planned driving route, at most only one charge is required for the new energy vehicle. Even if the optimal charging path is not planned, it will not have too much impact on the driver's vehicle use experience. Therefore, if the maximum cruising range of the new energy vehicle is greater than the length of the planned driving route, it is determined that there is no need to plan the charging path of the new energy vehicle; on the contrary, if the maximum cruising range of the new energy vehicle is not greater than the length of the planned driving route, during the driving process along the planned driving route, the new energy vehicle may need to be charged more than once. Therefore, it is necessary to plan the charging path of the new energy vehicle.

[0172] Through the technical solution of this embodiment, it is possible to judge whether it is necessary to plan the charging strategy of the new energy vehicle according to the maximum cruising range of the new energy vehicle and the planned driving route, thereby reducing the workload of the new energy vehicle charging control and improving the reliability of the new energy vehicle charging control.

[0173] In some embodiments of the present invention, in the above step S104, before controlling the new energy vehicle to charge during the driving process on the planned driving route according to this charging strategy, it further includes:

[0174] Send the generated optimal charging strategy to the user terminal to obtain a user instruction from the user terminal;

[0175] In the case where the received user instruction is to allow the execution of the optimal charging strategy, perform the step of charging the new energy vehicle during driving on the planned driving route according to the optimal charging strategy.

[0176] In this embodiment, after receiving the optimal charging strategy, the user terminal can display the optimal charging strategy on the screen and display a button on the screen to indicate whether to accept the execution of the optimal charging strategy for the user to select. If the user terminal detects that the user selects the button to accept the execution of the optimal charging strategy, it sends a user instruction allowing the execution of the optimal charging strategy to the new energy vehicle; if the user terminal detects that the user selects the button to reject the execution of the optimal charging strategy, or does not detect the user's selection operation within the set duration, it sends a user instruction not allowing the execution of the optimal charging strategy to the new energy vehicle.

[0177] The technical solution of this embodiment adds a link of interaction with the user after generating the optimal charging strategy, so that the user can independently choose whether to execute the optimal charging strategy, thereby improving the flexibility of charging control of new energy vehicles and further improving the user experience.

[0178] In some embodiments of the present invention, the method of charging the new energy vehicle during driving on the planned driving route according to the charging strategy in step S104 above is as Figure 6 shown, and includes the following steps:

[0179] Step S311: Obtain the exits corresponding to each target charging station on the planned driving route in the optimal charging strategy, and display each exit on the navigation map;

[0180] Step S312: Detect whether the new energy vehicle drives out of the planned driving route from the above-mentioned exit;

[0181] If so, execute step S313;

[0182] Step S313: Display the navigation path between the driven-out exit and the corresponding target charging station, and detect whether a charging behavior occurs at the target charging station;

[0183] If so, execute step S314;

[0184] Step S314: Charge the new energy vehicle according to the target power of the target charging station.

[0185] In this embodiment, based on the locations of the target charging stations in the optimal charging strategy, the paths from the planned driving route to the target charging stations in the optimal charging strategy can be obtained, and the locations where the paths are connected to the planned driving route are used as the exits corresponding to the target charging stations.

[0186] After obtaining the exits of the target charging stations in the optimal charging strategy, the locations of the exits can be marked on the navigation map. For example, the words "Best Charging Station Exit" can be marked at the locations of the exits on the planned driving route to provide charging guidance for the driver.

[0187] After detecting that the new energy vehicle drives out of the planned driving route from the exit corresponding to the target charging station in the optimal charging strategy, it can be determined that the new energy vehicle is going to charge at the corresponding target charging station. Therefore, the navigation path between the exit and the corresponding target charging station is turned on to provide navigation guidance for the driver.

[0188] The technical solution of this embodiment does not display the optimal charging path of the new energy vehicle on the navigation map after obtaining it to avoid interfering with the driving of the new energy vehicle; by marking the exits of the target charging stations on the planned driving route and displaying the navigation path between the charging exit and the corresponding target charging station after the new energy vehicle drives out of the exit, the reliability of the charging guidance for the new energy vehicle can be improved.

[0189] In some embodiments of the present invention, the method of controlling the new energy vehicle to charge during driving on the planned driving route according to the charging strategy in step S104 further includes:

[0190] Detect whether the new energy vehicle has a charging behavior;

[0191] If so, determine whether the charging behavior matches the optimal charging strategy;

[0192] If they do not match, return to step S101 after the charging behavior ends to regenerate the optimal charging strategy.

[0193] Through the technical solution of this embodiment, the optimal charging strategy can be regenerated after a charging behavior outside the optimal charging strategy occurs, so that the optimal charging strategy matches the state of the new energy vehicle and improves the reliability of the charging control of the new energy vehicle.

[0194] 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 idea provided by the methods in this embodiment, additional changes can be made to the above methods.

[0195] 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.

[0196] 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. When the computer program 11 is executed by a processor 32, it implements the steps of any one of the above-mentioned charging control methods for new energy vehicles. The computer-readable storage medium 20 stores the above-mentioned computer program 11. When the computer program 11 is executed by a processor 32, it implements the steps of the charging control 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.

[0197] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (InstructionSet Architecture, abbreviated as ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status 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 an independent 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 (Local AreaNetwork, abbreviated as LAN) or a wide area network (Wide Area Network, abbreviated as WAN)), or may be connected to an external computer (for example, using an Internet service provider through the Internet). In some embodiments, in order to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field-programmable gate array (Field-Programmable Gate Array, abbreviated as FPGA), or a programmable logic array (ProgrammableLogic Array, abbreviated as PLA) may execute computer-readable program instructions by using the status information of the computer-readable program instructions to personalize the electronic circuit.

[0198] For the description of this embodiment, the computer program product 10 is a related product containing the computer program 11.

[0199] 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 can 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.

[0200] At this point, those skilled in the art should recognize that although many 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 content disclosed in 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 as covering all these other variations or modifications.

Claims

1. A charging control method for a new energy vehicle, characterized in that: include: Obtaining a planned driving route, and obtaining charging stations within a set range around the planned driving route, and the current remaining power of the power battery; Obtaining preset constraints of the charging strategy, wherein the preset constraints include: h' i ≤ω1×H h i ≥ω2×H h'≥ω3×H ti≤t p Wherein, H is the capacity of the power battery, h' is the remaining power of the power battery when arriving at the destination, and h' i is the remaining power of the power battery when arriving at the i-th target charging station, h i is the target power of the i-th target charging station, t i is the driving time required to travel from the i-1th target charging station to the i-th target charging station, t p is the driver's safe driving time threshold; ω1 is the first setting coefficient, ω2 is the second setting coefficient, ω3 is the third setting coefficient, and the value ranges of ω1, ω2 and ω3 are all greater than 0 and less than 1; According to the planned driving route, the current remaining power and the preset constraint condition, obtaining the optimal number of charging times on the planned driving route; Constructing a charging cost function of the charging strategy according to the total driving time, charging interval time, and charging time and charging waiting time at each target charging station on the planned driving route; A charging strategy optimization model based on a genetic algorithm and a particle swarm optimization algorithm is adopted to select target charging stations whose number matches the optimal number of charging times from the charging stations according to the preset constraints and the charging cost function, and obtain the target power of each target charging station to generate an optimal charging strategy for the planned driving route; According to the optimal charging strategy, the new energy vehicle is controlled to charge during driving on the planned driving route.

2. The charging control method according to claim 1, characterized in that: After obtaining the planned driving path, the method further includes: Obtaining the maximum cruising range of the new energy vehicle, and determining whether it is necessary to plan a charging strategy for the new energy vehicle according to the planned driving route and the maximum cruising range; If necessary, the step of obtaining the charging stations within a set range around the planned driving route and the remaining power of the power battery is performed.

3. The charging control method according to claim 1, characterized in that: Before the step of controlling the charging of the new energy vehicle during driving on the planned driving route according to the optimal charging strategy, the method further includes: Sending the optimal charging strategy to a user terminal to obtain a user instruction from the user terminal; In the case where the user instruction is to allow the optimal charging strategy to be executed, the step of controlling the charging of the new energy vehicle during the driving on the planned driving route according to the optimal charging strategy is executed.

4. The charging control method according to claim 1, characterized in that: The step of controlling the charging of the new energy vehicle during driving on the planned driving route according to the optimal charging strategy includes: Obtaining the exit corresponding to each target charging station in the optimal charging strategy on the planned driving route, and displaying each of the exits on the navigation map; If it is detected that the new energy vehicle drives out of the planned driving path from the exit, the navigation path between the exit and the corresponding target charging station is displayed; After the charging behavior is detected in the target charging station, the new energy vehicle is charged according to the target power of the target charging station.

5. The charging control method according to claim 1, characterized in that: The step of controlling the charging of the new energy vehicle during driving on the planned driving route according to the optimal charging strategy includes: If a charging behavior that does not match the optimal charging strategy is detected, the method returns to the step of obtaining the planned driving route to regenerate the optimal charging control strategy.

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 control 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 control method according to any one of claims 1 to 5 are implemented.

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