Vehicle charging control method and device, equipment, storage medium and program product

Abstract

The invention relates to a vehicle charging control method, device and equipment, a storage medium and a program product, and relates to the field of vehicle charging control. The method comprises the following steps: acquiring a time-of-use electricity price and the current electric quantity of a vehicle, determining a charging demand according to historical vehicle use data and the current electric quantity of the vehicle, establishing an objective function based on the charging demand and the time-of-use electricity price, solving the objective function to obtain a charging power control strategy, and charging according to the charging power control strategy. According to the charging mode, the charging cost and the charging requirement can be both considered, manual setting of a user is not needed, automatic control and dynamic adjustment of charging of the electric vehicle can be achieved, the charging operation difficulty is reduced, and the user requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle charging control, and in particular to a vehicle charging control method, device, equipment, storage medium and program product. BACKGROUND

[0002] With the rapid development of new energy vehicles, electric energy has gradually become the core power source of vehicles. For new energy vehicles, the problems of endurance mileage and battery life are very critical, and efficient charging management strategy is an important challenge to improve user experience, reduce vehicle cost and prolong battery health life.

[0003] At present, the charging of new energy vehicles mostly adopts the strategy of charging as soon as plugging in, without considering the cost difference caused by different time periods of electricity price, which cannot meet the user demand. Some models support users to set the charging time independently, but need to be set manually by users, which is cumbersome and has poor usability. SUMMARY

[0004] One of the purposes of the present application is to provide a vehicle charging control method, device, equipment, storage medium and program product to solve the problem that the charging method of new energy vehicles is difficult to meet the user demand.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] A vehicle charging control method, the method comprising:

[0007] obtaining a time-of-use electricity price and a current power of the vehicle;

[0008] determining a charging demand according to historical vehicle use data and the current power of the vehicle;

[0009] establishing a target function based on the charging demand and the time-of-use electricity price, the target function taking reducing charging cost and meeting charging demand as optimization objectives;

[0010] solving the target function to obtain a charging power control strategy, and charging according to the charging power control strategy.

[0011] Further, the determination of the charging demand according to the historical vehicle use data and the current power of the vehicle comprises:

[0012] obtaining historical vehicle use data of the vehicle;

[0013] classifying and processing the historical vehicle use data to obtain power demand of different date categories, the power demand including target power consumption and vehicle use time;

[0014] predicting a date category of next vehicle use and corresponding power demand according to the historical vehicle use data;

[0015] The charging demand includes a target charging amount and a driving time according to the current electric quantity and the electric quantity demand of the next driving.

[0016] Further, the historical driving data includes a daily electric quantity and a historical parking time, and the historical driving data is classified to obtain electric quantity demands of different date categories, including:

[0017] The daily electric quantity is clustered to obtain a plurality of cluster centers, and each cluster center corresponds to a date category;

[0018] The characteristic value of the electric quantity of each cluster center is determined, and the target electric quantity of the corresponding date category is calculated according to the characteristic value;

[0019] The driving time of the corresponding date category is determined according to the historical parking time related to each cluster center.

[0020] Further, the charging demand includes a driving time, and a target function is established based on the charging demand and a time-of-use electricity price, including:

[0021] The chargeable period is determined according to the driving time, and the chargeable period is divided into a plurality of sub-periods;

[0022] The charging power of the sub-period is taken as a decision variable to be solved, the charging cost, the charging amount and the charging power variance are taken as optimization objectives, and the target function is generated according to the charging demand, the time-of-use electricity price and the decision variable.

[0023] Further, after the charging is completed, the method further includes:

[0024] The charging feedback from the user is obtained;

[0025] The weight of the optimization objective in the target function is adjusted according to the charging feedback.

[0026] Further, the target function is solved to obtain a charging power control strategy, including:

[0027] The positions and speeds of a plurality of individuals are initialized by a particle swarm algorithm;

[0028] The fitness value of the individual is determined based on the target function;

[0029] The position of the individual is iteratively updated, and the charging power of the sub-period is determined as the charging power control strategy according to the updated optimal position.

[0030] A vehicle charging control device includes:

[0031] An acquisition module is configured to acquire a time-of-use electricity price and a current electric quantity of a vehicle

[0032] A demand determination module is configured to determine a charging demand according to historical driving data and the current electric quantity of the vehicle;

[0033] The function processing module is used to establish an objective function based on charging demand and time-of-use electricity pricing. The objective function aims to reduce charging costs and meet charging demand.

[0034] The control module is used to solve the objective function to obtain the charging power control strategy, and to charge according to the charging power control strategy.

[0035] An electronic device includes: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores the instructions that the computer executes;

[0037] The processor executes computer-executable instructions stored in memory to implement any of the vehicle charging control methods described above.

[0038] A computer-readable storage medium includes: computer-executable instructions stored in the computer-readable storage medium, which, when executed by a processor, are used to implement a vehicle charging control method as described above.

[0039] A computer program product includes a computer program that, when executed by a processor, implements a vehicle charging control method as described above.

[0040] The beneficial effects of the present invention are as follows: By charging in the above manner, not only can charging costs and charging demand be balanced, but also no manual settings are required from the user. It can realize automated control and dynamic adjustment of electric vehicle charging, reduce the difficulty of charging operation, and meet user needs. Attached Figure Description

[0041] Figure 1 A schematic flowchart of a vehicle charging control method provided for an exemplary embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a process for determining charging needs is provided for an exemplary embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a clustering process provided for an exemplary embodiment of the present invention;

[0044] Figure 4 A flowchart illustrating the process of solving an objective function, provided as an exemplary embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the control flow of a charging process is provided for an exemplary embodiment of the present invention;

[0046] Figure 6 A comparative schematic diagram of the charging process provided for an exemplary embodiment of the present invention;

[0047] Figure 7 A schematic diagram of the structure of a vehicle charging control device provided for an exemplary embodiment of the present invention;

[0048] Figure 8 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present invention.

[0049] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0051] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0052] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0053] The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in the process, method, product, or apparatus that includes elements is not excluded. For example, the use of terms such as "first," "second," etc., to indicate names does not imply any particular order.

[0054] New energy vehicles powered by electricity play a crucial role in promoting the low-carbon transformation of the transportation sector and the consumption of renewable energy. Continuous advancements in battery technology have gradually improved the performance of electric vehicles in terms of driving range and battery life. However, efficient charging management strategies remain a significant challenge for enhancing user experience, reducing operating costs, and extending battery lifespan. Against this backdrop, building a more intelligent, economical, and user-centric charging system has become a key focus for the industry.

[0055] Currently, electric vehicle charging strategies remain relatively simple, generally employing either a "plug-and-charge" approach or relying on users to manually set charging times. As electric vehicle battery capacities continue to increase, current charging strategies face the following challenges: Can the battery be fully charged during off-peak electricity hours? If not, will it meet the needs of users (owners and other users)? And what is the optimal charging method during off-peak hours? Due to these issues, the manual setting process is complex, unreliable, and fails to ensure that the actual charging process meets user needs.

[0056] Based on this, a technical concept is proposed to determine the user's charging needs according to user habits or user settings, and to model factors such as charging costs and charging needs into a unified objective function. By solving the objective function, the control of charging power during the charging process can be optimized, thereby realizing automated control and dynamic adjustment of electric vehicle charging, while taking into account both charging costs and charging needs.

[0057] The application scenarios described above are only partial examples. Those skilled in the art can expand the applications according to specific needs and scenarios, and the embodiments of the present invention do not impose specific limitations in this regard. The method according to an exemplary embodiment of the present invention will now be described with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart illustrating a vehicle charging control method provided as an exemplary embodiment of the present invention. Figure 1 As shown, the method may include:

[0059] Step S101: Obtain the time-of-use electricity price and the vehicle's current battery level.

[0060] The vehicle charging control method of this invention can be applied to the charging control module of an electric vehicle. This module can control the charging process of the electric vehicle, which can refer to the process from the moment the electric vehicle connects to the charging pile until the end of charging. Time-of-use pricing can refer to the electricity price of the charging pile at different times in the future after the electric vehicle connects to the charging pile, for example, the hourly electricity price for the next 24 hours.

[0061] After the vehicle is connected to the charging station, the charging control module can obtain charging-related data, including the vehicle's current battery level, the vehicle's maximum charging power, the charging station's maximum charging power, and the time-of-use electricity price.

[0062] Step S102: Determine the charging demand based on the vehicle's historical usage data and current battery level.

[0063] Historical vehicle usage data may include the vehicle's mileage, power consumption, and parking time over a period of time.

[0064] In this embodiment of the invention, user habits can be analyzed based on historical vehicle usage data, such as the power consumption of users on different dates or at different times, and the times when users usually charge and use the vehicle. Based on user habits, it is possible to predict when users will use the vehicle and how much power they will need when using the vehicle. Combined with the current power level of the vehicle, the charging requirement for this time can be obtained.

[0065] Charging demand can be the amount of electricity that the user needs to obtain from the charging station before the time they use the vehicle. For example, if it is predicted that the user will use the vehicle at 7 a.m. the next day and the battery needs to have at least 30 kWh of power at that time, and the current power is 10 kWh, then the charging demand is to obtain 20 kWh of power from the charging station before 7 a.m. the next day.

[0066] Understandably, some vehicle models support expressing the vehicle's current battery level using driving range or mileage. In this case, data such as current battery level and power consumption can be expressed in mileage. For example, if user habits predict that the user will need a driving range of at least 100km, and the vehicle's current driving range is 50km, then the charging requirement could be that the electricity obtained from the charging station before use can support a driving range of 50km.

[0067] In some possible implementations, charging needs can also be manually set by the user. For example, after connecting the vehicle to a charging station, the user can set the usage time and charging amount as their charging needs through the vehicle's infotainment system or a mobile app.

[0068] Step S103: Establish an objective function based on charging demand and time-of-use pricing.

[0069] The objective function aims to reduce charging costs and meet charging demands.

[0070] In this embodiment of the invention, charging power can be used as a decision variable to model the charging process. By dynamically adjusting the charging power, user needs such as meeting charging requirements and reducing charging costs can be achieved.

[0071] For example, the rechargeable time period can be discretized in one-hour units, and the control of charging power can be transformed into a finite number of optimal powers in a discrete sequence. A sub-function representing the charging cost can be established based on the time-of-use electricity price and the charging power, a sub-function representing the charging amount can be established based on the charging duration and the charging power, and constraints can be set based on factors such as the maximum charging power and the battery capacity. The objective function can be constructed by combining these sub-functions and constraints.

[0072] Step S104: Solve the objective function to obtain the charging power control strategy, and charge according to the charging power control strategy.

[0073] In this embodiment of the invention, the objective function can be iteratively solved within the feasible interval using a heuristic algorithm to obtain a sequence of several optimal power values ​​as the optimal charging control strategy. This strategy can be used to indicate how much power to use for charging during which time period.

[0074] For example, the charging control strategy may include the sequence [0, 0, 0.5P]. max ,P max [,0,0], where P max This can represent the maximum charging power that can be used after the vehicle is connected to the charging station. This sequence can represent no charging (charging power is 0) in the first and second hours after connecting to the charging station, charging at half the maximum charging power in the third hour after connecting to the charging station, charging at the maximum charging power in the fourth hour, and no charging in the fifth and sixth hours.

[0075] In the above embodiments, by acquiring the time-of-use electricity price and the vehicle's current battery level, charging demand can be determined based on the vehicle's historical usage data and current battery level. An objective function can be established based on the charging demand and the time-of-use electricity price, and then the charging power control strategy can be obtained by solving the objective function. Charging is then performed according to the charging power control strategy. This charging method not only balances charging costs and charging demand but also eliminates the need for manual user settings, enabling automated control and dynamic adjustment of electric vehicle charging, reducing the difficulty of charging operations, and meeting user needs.

[0076] In one embodiment, such as Figure 2 As shown, charging needs are determined based on the vehicle's historical usage data and current battery level, including:

[0077] Step S201: Obtain the vehicle's historical usage data.

[0078] For example, user vehicle usage data can be pre-recorded over multiple periods, with a seven-day cycle. Within each cycle, the date, electricity consumption (which can be represented by mileage), and parking time are recorded as a dataset. Parking time can refer to the period from the moment the vehicle is parked until it is started.

[0079] Step S202: Classify the historical vehicle usage data to obtain the electricity demand for different date categories.

[0080] Electricity demand includes target electricity consumption and vehicle usage time.

[0081] In some possible implementations, daily electricity consumption can be clustered to obtain multiple cluster centers, each cluster center corresponding to a date category; the electricity consumption characteristic value of each cluster center can be determined, and the target electricity consumption for the corresponding date category can be calculated based on the characteristic value; the vehicle usage time for the corresponding date category can be determined according to the historical parking time associated with each cluster center.

[0082] Step S203: Predict the date category of the next vehicle use and the corresponding electricity demand based on historical vehicle usage data.

[0083] Step S204: Determine the charging requirements based on the current battery level and the next power demand for vehicle use.

[0084] Charging demand includes target charging amount and vehicle usage time.

[0085] After the vehicle is connected to the charging station, the next electricity usage date can be predicted based on the current date and user habits. The usage time and target electricity consumption can be determined according to the date category of the next electricity usage date. The target charging amount is then obtained by subtracting the current electricity consumption from the target electricity consumption.

[0086] For example, the vehicle's battery management system can record three parameters: daily mileage, vehicle start-up time, and parking time. After 1 to 3 months of recording, a sufficient dataset D can be obtained. i =(L i ,t is ,t id ), L, t s and t d These represent the driving mileage and the vehicle's start-up and parking times, respectively. Then, clustering algorithms such as K-means clustering can be used to analyze the dataset D. i The data is processed by categorizing daily electricity consumption and parking time by date, such as weekdays and weekends, and determining the target electricity consumption and usage time for each date category. For example, clusters with more and more regular data points can be labeled as weekdays, while those with fewer data points can be labeled as weekends.

[0087] Figure 3 This is a schematic diagram of a clustering process provided for an exemplary embodiment of the present invention. For example... Figure 3 As shown, the process may include:

[0088] S301, randomly select 2 data points as the initial cluster centers.

[0089] S302 assigns each data point to the cluster whose cluster center is closest to it in Euclidean distance.

[0090] S303: After allocating each data point, recalculate the average value in each cluster to obtain the new cluster center.

[0091] S304: Determine whether the cluster center point is no longer changing or whether the maximum number of iterations has been reached. If so, complete the process; otherwise, repeat S303 and S304.

[0092] After the two clusters have been assigned, they can be allocated according to mileage, with shorter mileage days designated as workdays and longer mileage days as rest days.

[0093] In the above embodiments, by classifying historical vehicle usage data, different electricity consumption habits of users under different date categories (such as weekdays and rest days) can be automatically identified, and predictions can be made accordingly. This allows for charging control strategies that better meet the user's future needs, achieving personalized charging management. Furthermore, by automatically mining user charging habits from historical vehicle usage data using clustering algorithms, the comprehensiveness and reliability of user demand predictions are ensured, and no manual settings are required from the user, reducing operational complexity.

[0094] In one embodiment, establishing an objective function based on charging demand and time-of-use pricing may include:

[0095] The available charging periods are determined based on vehicle usage time, and these periods are further divided into several sub-periods. The charging power of each sub-period is used as the decision variable to be solved. The objective function is generated based on charging demand, time-of-use pricing, and the decision variables, with charging cost, charging amount, and the variance of charging power as the optimization objectives.

[0096] The following example illustrates how to create an objective function.

[0097] In one example, the charging process can be defined as P(t), which is the function of charging power changing with time. For optimization calculations, it is discretized into P in 1-hour increments. i =k i P max , where P i Let i = 1, 2, 3... represent the charging power in the i-th hour. i The charging coefficient is [0,1]. The charging strategy can be adjusted by adjusting the set K = (k1,k2,k3...). max This is the maximum charging power of the charging station.

[0098] For example, if both the current battery level and the target battery consumption are represented by mileage, the current battery level is the current remaining driving range L.l The target electricity consumption is L, and the time-of-use electricity price is M. c =(m1,m2,m3...), m i This represents the electricity price in the i-th hour after charging begins. Using these parameters, an objective function G(K) can be established, where K = (k1, k2, k3...), and the charging control strategy K is the variable affecting the charging experience. The factors influencing G(K) may include:

[0099] (1) Cost function C

[0100] The cost function can be obtained based on the charging power coefficient and electricity price:

[0101]

[0102] Where N can be the value of rounding down the total charging time, indicating that after discretizing in 1-hour increments, the charging time can be divided into N 1-hour intervals.

[0103] (2) Vehicle usage function H

[0104] The main consideration for vehicle use the day after charging is whether the remaining range after charging meets usage needs and has a certain margin (i.e., the remaining range after use). Based on this, the usage function is:

[0105]

[0106] Where L left F represents the expected margin, and F is the electricity consumption per kilometer the vehicle travels.

[0107] (3) Battery health function B

[0108] To avoid fluctuations in charging power and maintain stable charging, which is beneficial to battery health, the battery health function is defined based on the variance of the power consumption coefficient:

[0109]

[0110] (4) Constraints

[0111] Based on the constraints of the optimal problem stipulated by charging limitations (e.g., the driving range after charging does not exceed the vehicle's maximum driving range):

[0112]

[0113] k i ∈[0,1], i=1,2,3...N

[0114] In this example, considering all the above factors, the objective function G can be obtained:

[0115] G = w1C + w2H + w3B

[0116] Among them, w1, w2 and w3 are the weight coefficients corresponding to each factor.

[0117] In the above embodiments, when modeling the charging process to construct the objective function, precise control of the charging process can be achieved by discretizing the charging period. Furthermore, by including the charging power variance, cost, and battery capacity as optimization objectives, the battery health function obtained based on the charging power variance can reduce fluctuations in charging power, helping to extend battery life. This achieves synergistic optimization of cost, demand, and battery health, improving the overall effectiveness of the charging control strategy.

[0118] In one embodiment, solving the objective function to obtain the charging power control strategy may include:

[0119] The positions and velocities of several individuals are initialized using the particle swarm optimization algorithm; the fitness values ​​of individuals are determined based on the objective function; the positions of individuals are iteratively updated; and the charging power for different time periods is determined based on the updated optimal positions as a charging power control strategy.

[0120] Figure 4 This is a schematic flowchart illustrating a method for solving an objective function, provided as an exemplary embodiment of the present invention. For example... Figure 4 As shown, in this embodiment of the invention, the individual positions X of the particle swarm can be initialized using average probability randomness. i and speed V i Substituting the values ​​into the objective function yields the individual fitness value G. i (X i The individual with the best fitness value is selected and denoted as P. best And its own best position P ibest The updated position after each iteration is obtained using the particle swarm optimization formula. This process of updating the best position and iterating is repeated until the maximum number of iterations is reached. Here, the subscript "best" indicates the optimal value.

[0121] After the iteration is complete, P can be taken. best =(p g1 ,p g2 ,p g3 ...) corresponds to p gi =k i The charging control strategy is obtained. The charging controller communicates with the charging pile to match the charging control strategy and charge according to the corresponding power.

[0122] For example, to solve the objective function, we can first randomly generate a swarm of particles with a quantity of Y and a dimension of N, and individual particle positions X. i =(x i1 ,x i2 ,xi3 ...), i = 1, 2, 3... N and velocity V i =(v i1 ,v i2 ,v i3 ...), i = 1, 2, 3... N, calculate the individual with the best fitness at this point as the global optimum, and the best position of each individual as the individual optimum, and then perform position updates. The update process can be represented as:

[0123] v ij (t+1)=v ij (t)+c1r1(t)[p ij (t)-x ij (t)]+c2r2(t)[p gj (t)-x ij (t)]

[0124] x ij (t+1)=x ij (t)+v ij (t+1)

[0125] The optimal position of the i-th particle is P. ibest =(p i1 ,p i2 ,p i3 ...), i = 1, 2, 3... N, the optimal position found by the entire particle swarm is P. best =(p g1 ,p g2 ,p g3 ...). c1 and c2 are learning factors. r1 and r2 are random coefficients in the range [0,1]. Iteration stops when the maximum number of iterations is reached, and the P value recorded at this point is... best To obtain the solution. Finally, make P best =(p g1 ,p g2 ,p g3 ...) corresponds to p gi =k i The charging control strategy K can be obtained.

[0126] In the above embodiments, by solving the objective function using the particle swarm optimization algorithm, the globally optimal or near-optimal charging power allocation scheme can be quickly searched in the solution space, efficiently solving optimization problems with nonlinear and multi-peak characteristics, and ensuring the feasibility and real-time performance of the entire method in practical applications.

[0127] In one embodiment, after charging is complete, the method further includes:

[0128] Obtain charging feedback from users; adjust the weights of the optimization objectives in the objective function based on the charging feedback.

[0129] In this embodiment of the invention, after charging is complete, the user can evaluate the charging status and determine if there are any dissatisfactions regarding charging costs, vehicle usage, and battery health. If so, the weights of the corresponding factors in the objective function can be adjusted based on the evaluation results. The weight adjustment process can be expressed as follows:

[0130]

[0131] In the above embodiments, by collecting users' subjective evaluations and adjusting the weights of each optimization objective in the objective function accordingly, the charging control strategy can continuously learn and adapt to users' personal preferences (e.g., whether users value cost or battery management more). This breaks the limitations of traditional algorithms that are fixed and unchanging, allowing the charging control strategy to continuously evolve and become more and more in line with the specific needs of individual users, thereby enhancing personalized service capabilities and long-term user retention.

[0132] Figure 5 This is a schematic diagram of a control flow for a charging process, provided as an exemplary embodiment of the present invention. For example... Figure 5 As shown, after the user parks the vehicle and plugs in the charging gun, the charging process begins. First, the vehicle can identify whether the current charging station is a commonly used home charging station. If not, constant voltage and constant current charging is performed. If it is a commonly used home fixed charging station, the charging strategy calculation process begins. Then, the maximum charging power of the charging station is identified, and the date category and target power consumption of the next vehicle use are predicted to obtain modeling parameters. Based on these parameters, an objective function is established and solved to obtain the charging control strategy and charging is performed.

[0133] In a specific example, taking a user's daily charging as an example, the parking time t h =19, Startup time t w =8, working days are Monday to Friday, daily mileage driven during working hours is L w =20km. Rest days are Saturday to Sunday, parking time t h =17, Startup time t w =10, Daily mileage (L) during rest period r = 300km. When I arrived home on Friday night and charged the car, the remaining range was L. l =20km, maximum charging power is 7kW, vehicle energy consumption is 18kWh / 100km, and maximum driving range is 550km. Where, t h =19 means that the user can start charging when parking the car at 19:00 in the evening.

[0134] If the time-of-use electricity price is as follows:

[0135] Peak hours (11:00-17:00, 20:00-22:00): 0.62 yuan / kWh.

[0136] Flat section (08:00-11:00, 17:00-20:00, 22:00-24:00): 0.52 yuan / kWh.

[0137] Valley segment (00:00-08:00): 0.34 yuan / kWh.

[0138] The vehicle charging control method provided by this invention is used to determine a charging control strategy, and charging is performed according to this strategy. This charging process is compared with two other methods: plug-in charging and charging only during off-peak hours. The comparison of the charging processes is as follows: Figure 6 As shown.

[0139] exist Figure 6 In the diagram, the horizontal axis represents time, specifically the hour since the vehicle was connected to the charging station. For example, 5 on the horizontal axis represents the 5th hour since the vehicle was connected to the charging station. The vertical axis represents the charging power coefficient value. For example, the charging power coefficient is always 1 when the vehicle is plugged in, indicating that the charging power is at the maximum charging power (1×P). max (Charge)

[0140] The plug-in charging method consumes a total of 105 kWh of electricity. With a 10% loss, the remaining range after charging is 545 km, and the cost is 45.92 yuan.

[0141] Setting the charging mode to only charge during off-peak hours consumes a total of 56 kWh of electricity. Assuming a 10% loss, the remaining range after charging is 300 km, costing 19.04 yuan. Although this method is low-cost, the remaining range after charging cannot exceed 300 km, which does not meet user needs.

[0142] The method of this invention consumes a total of 80.22 kWh of electricity. With a 10% loss, the remaining range after charging is 421 km, and the cost is 31.93 yuan.

[0143] The comparison shows that the method provided by the present invention can not only meet the user's electricity needs, but also reduce charging costs.

[0144] Figure 7 This is a schematic diagram of a vehicle charging control device provided as an exemplary embodiment of the present invention. (See diagram below.) Figure 7 As shown, the vehicle charging control device 700 may include:

[0145] Module 701 is used to obtain the time-of-use electricity price and the vehicle's current battery level.

[0146] The demand determination module 702 is used to determine charging demand based on the vehicle's historical usage data and current battery level.

[0147] The function processing module 703 is used to establish an objective function based on charging demand and time-of-use electricity pricing. The objective function aims to reduce charging costs and meet charging demand.

[0148] The control module 704 is used to solve the objective function to obtain the charging power control strategy, and to charge according to the charging power control strategy.

[0149] In some possible implementations, the demand determination module 702 can also be used to: acquire historical vehicle usage data; classify the historical usage data to obtain electricity demand for different date categories, including target electricity consumption and usage time; predict the date category of the next vehicle use and the corresponding electricity demand based on the historical usage data; and determine the charging demand based on the current electricity level and the electricity demand for the next vehicle use, including target charging amount and usage time.

[0150] In some possible implementations, the demand determination module 702 can also be used to: cluster the daily electricity consumption to obtain multiple cluster centers, each cluster center corresponding to a date category; determine the electricity consumption characteristic value of each cluster center, and calculate the target electricity consumption of the corresponding date category based on the characteristic value; and determine the vehicle usage time of the corresponding date category according to the historical parking time associated with each cluster center.

[0151] In some possible implementations, the function processing module 703 can also be used to: determine the charging period based on the vehicle usage time, and divide the charging period into several sub-periods; use the charging power of the sub-periods as the decision variable to be solved, and use the charging cost, charging amount and charging power variance as optimization objectives to generate an objective function based on charging demand, time-of-use electricity price and decision variables.

[0152] In some possible implementations, the control module 704 can also be used to: obtain charging feedback from the user; and adjust the weights of the optimization objective in the objective function based on the charging feedback.

[0153] In some possible implementations, the control module 704 can also be used to: initialize the position and velocity of several individuals using a particle swarm optimization algorithm; determine the fitness value of an individual based on an objective function; iteratively update the position of an individual; and determine the charging power for different time periods as a charging power control strategy based on the updated optimal position.

[0154] The vehicle charging control device provided in this embodiment is used to execute the technical solution in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0155] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present invention can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0156] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of the present invention can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0157] Figure 8 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 includes:

[0158] Processor 81, memory 82, and communication interface 83;

[0159] The memory 82 is used to store the executable instructions of the processor 81; the executable instructions can be instructions that the computer can execute.

[0160] The processor 81 is configured to execute the technical solutions in any of the foregoing method embodiments by executing executable instructions.

[0161] Optionally, the memory 82 can be either standalone or integrated with the processor 81.

[0162] Optionally, when the memory 82 is a device independent of the processor 81, the electronic device 80 may further include:

[0163] Bus 84, memory 82 and communication interface 83 are connected to processor 81 through bus 84 and complete communication with each other. Communication interface 83 is used to communicate with other devices.

[0164] Optionally, the communication interface 83 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0165] Bus 84 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one line is used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0166] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0167] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0168] This invention also provides a readable storage medium, which can be a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the technical solution provided in any of the foregoing method embodiments.

[0169] This invention also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0170] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0171] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0172] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0173] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A vehicle charging control method, characterized in that, include: Obtain time-of-use electricity pricing and the vehicle's current battery level; The charging requirement is determined based on the vehicle's historical usage data and the current battery level. An objective function is established based on the charging demand and the time-of-use electricity price, with the objective function aiming to reduce charging costs and meet the charging demand. Solving the objective function yields the charging power control strategy, and charging is performed according to the charging power control strategy.

2. The vehicle charging control method according to claim 1, characterized in that, The step of determining the charging demand based on the vehicle's historical usage data and the current battery level includes: Obtain the historical usage data of the vehicle; The historical vehicle usage data is classified and processed to obtain the electricity demand for different date categories. The electricity demand includes the target electricity consumption and vehicle usage time. Based on the historical vehicle usage data, predict the date category of the next vehicle usage and the corresponding electricity demand; The charging requirement is determined based on the current battery level and the power demand for the next vehicle use. The charging requirement includes the target charging amount and the vehicle use time.

3. The vehicle charging control method according to claim 2, characterized in that, The historical vehicle usage data includes daily electricity consumption and historical parking time. The process of classifying the historical vehicle usage data to obtain electricity demand for different date categories includes: The daily electricity consumption is clustered to obtain multiple cluster centers, and each cluster center corresponds to a date category; Determine the electricity consumption characteristic value for each cluster center, and calculate the target electricity consumption for the corresponding date category based on the characteristic value; The vehicle usage time for the corresponding date category is determined based on the historical parking time associated with each cluster center.

4. The vehicle charging control method according to any one of claims 1 to 3, characterized in that, The charging demand includes vehicle usage time, and the objective function established based on the charging demand and the time-of-use electricity price includes: The available charging periods are determined based on the vehicle usage time, and the available charging periods are divided into several sub-periods. The charging power of the sub-period is used as the decision variable to be solved, and the charging cost, charging amount and charging power variance are used as optimization objectives. An objective function is generated based on the charging demand, the time-of-use electricity price and the decision variables.

5. The vehicle charging control method according to claim 4, characterized in that, After charging is complete, it also includes: Get charging feedback from users; The weights of the optimization objective in the objective function are adjusted based on the charging feedback.

6. The vehicle charging control method according to any one of claims 1 to 3, characterized in that, The process of solving the objective function to obtain the charging power control strategy includes: The position and velocity of several individuals are initialized using the particle swarm optimization algorithm; The fitness value of the individual is determined based on the objective function; The position of the individual is iteratively updated, and the charging power for different time periods is determined based on the updated optimal position as the charging power control strategy.

7. A vehicle charging control device, characterized in that, include: The acquisition module is used to obtain the time-of-use electricity price and the vehicle's current battery level. The demand determination module is used to determine the charging demand based on the vehicle's historical usage data and the current battery level. The function processing module is used to establish an objective function based on the charging demand and the time-of-use electricity price, wherein the objective function aims to reduce charging costs and meet the charging demand. The control module is used to solve the objective function to obtain the charging power control strategy, and to charge according to the charging power control strategy.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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