A method and system for selecting charging and battery swapping time based on multi-objective optimization

Abstract

The present application relates to a kind of based on multi-objective optimization's charging time selection method and system, belong to charging time selection technical field.It includes: obtaining electricity price information, charging station resource data and the SOC data of multiple vehicles, task demand and charging power data;According to SOC data and task demand, determine the urgency of vehicle use, the priority executes the strategy of battery replacement when the urgency of vehicle use is greater than preset urgency;Optimal battery replacement time window is selected through resource data;Otherwise, execute charging strategy, determine candidate charging time window set based on SOC data and resource data;Combine basic electricity price, set window electricity price using dynamic induction mechanism;Build time window optimization model with minimum charging cost and waiting time as target;Solve optimal charging time window by optimization algorithm, and arrange vehicle charging accordingly;Synchronously update site load state, and execute cyclically until all vehicles are charged.

Description

Technical Field

[0001] This invention belongs to the field of charging and swapping time selection technology, and particularly relates to a charging and swapping time selection method and system based on multi-objective optimization. Background Technology

[0002] With the continuous growth of new energy vehicle ownership, the demand for electric vehicle charging and battery swapping is rapidly increasing, bringing challenges in energy dispatch, grid load, and user travel efficiency. How to scientifically and rationally schedule the charging and battery swapping times of electric vehicles has become a crucial issue affecting grid stability and user experience. To achieve efficient utilization of electrical resources while meeting users' personalized needs, multi-objective optimization-based methods for selecting charging and battery swapping times have gradually become a research hotspot.

[0003] Currently, common methods for selecting charging and battery swapping times mainly include rule-based scheduling strategies, time planning methods based on predictive models, and optimization of charging behavior using heuristic algorithms. These methods, by constructing user behavior models, load forecasting models, and electricity price forecasting models, enable the planning and control of vehicle charging timing, and alleviate the problem of concentrated charging during peak hours to some extent.

[0004] However, existing charging and battery swapping time selection methods are insufficient in responding to personalized user needs and cannot dynamically adjust strategies based on the urgency of vehicle usage. This can easily lead to high-priority vehicles not being scheduled in a timely manner, thus affecting the timely completion of tasks and travel efficiency. At the same time, existing charging and battery swapping time selection methods often fail to consider the load balance of charging and battery swapping stations and user experience, which can easily lead to local grid overload and excessively long waiting times, thereby reducing overall service efficiency. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the invention is to provide a charging and swapping time selection method and system based on multi-objective optimization, which can improve the global optimization of the swapping process and the ability to respond to users' personalized needs, thereby ensuring the overall efficiency of charging and swapping services.

[0006] In a first aspect, the present invention proposes a charging / swapping time selection method based on multi-objective optimization, the method comprising: S1 acquires basic electricity price, charging and battery swapping station resource data, as well as SOC data, task requirement data, and charging power data for multiple vehicles; S2, based on the SOC data and the task requirement data, calculate the vehicle urgency of each vehicle and determine whether each vehicle urgency is greater than the preset vehicle urgency; if so, proceed to S3 and execute the battery swapping strategy; otherwise, proceed to S4 and execute the charging strategy. S3, Based on the charging and battery swapping station resource data and the vehicle urgency, select the optimal battery swapping time window for each vehicle executing the battery swapping strategy; S4. Based on the SOC data and the charging / swapping station resource data, determine the set of candidate charging time windows that can be selected for each vehicle executing the charging strategy; S5. Based on the aforementioned base electricity price, a dynamic charging time window electricity price is set using a dynamic electricity price incentive mechanism; S6. Based on the charging dynamic time window electricity price, the total load of the charging and battery swapping station, and the candidate charging time window set, a battery charging time window selection model is constructed with the goal of minimizing charging cost and charging waiting time. S7. The optimal charging time window is obtained by optimizing the battery charging time window selection model through an optimization algorithm. S8, Arrange for the vehicle to charge according to the optimal charging time window; S9, update the remaining power and load of the charging and swapping station, repeat S4 to S9, and complete the charging arrangement for the remaining vehicles.

[0007] Furthermore, the specific methods for calculating vehicle urgency include: S201, Calculate the current vehicle's battery level based on the current vehicle's SOC data; S202, Calculate the time pressure of the current vehicle based on the task requirement data of the current vehicle; S203, the weighted sum of the power shortage level and the time shortage level is calculated to obtain the current vehicle urgency level.

[0008] Furthermore, S3 specifically includes: S301, Sort the vehicles implementing the battery swapping strategy in descending order of vehicle urgency, and assign each vehicle a unique battery swapping number. S302, Set the battery swapping time window; S303, predicts the dynamic battery inventory quantity for the first battery swapping time window; S304, Based on the dynamic battery inventory quantity, select the corresponding time for each vehicle according to the vehicle battery swapping number to form a battery swapping schedule for each vehicle. S305, Each of the vehicles goes to the charging and battery swapping station for battery swapping according to the battery swapping schedule; S306: Determine if there are sufficient actual battery swapping resources; if so, allocate a battery swap directly; otherwise, schedule the unallocated vehicles to the next battery swapping time window. S307, repeat steps S303 to S306 until all battery swapping vehicles are arranged.

[0009] Furthermore, S4 specifically includes: S401, Based on the SOC data, calculate the required charging time for each of the vehicles; S402 divides future rechargeable cycles into time slices of fixed duration, forming a charging time window; S403, convert each of the required charging durations into time windows, and slide each of the time windows within the charging time window to determine the set of candidate charging time windows that can be selected for the corresponding vehicle.

[0010] Furthermore, S5 specifically includes: S501, calculate the total charging power based on the charging power data of each vehicle in each charging time window; S502, determine whether the total charging power is greater than the preset total charging power; if yes, proceed to S503; otherwise, maintain the current electricity price; S503, based on the base electricity price and the total charging power, calculate the dynamic charging time window electricity price for each of the charging time windows: ; in, This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the function that takes the minimum value. Let e ​​represent the base electricity price for the t-th charging time window, e represent the exponential function, and β represent the sensitivity coefficient. This represents the total charging power during the t-th charging time window. This represents the ideal total charging power. This represents the highest electricity price during the t-th charging time window.

[0011] Furthermore, the objective function of the battery charging time window selection model is specifically: ; Where F represents the objective function, The weights represent the charging cost target, G represents the set of vehicles to be charged, and TW g This represents the charging time window for the g-th vehicle. This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the charging power of the g-th vehicle. Indicates the length of the charging time window. This represents the cost normalization coefficient. The weight representing the target charging wait time. This represents the function that takes the maximum value. This represents the actual start time of charging for the g-th vehicle. This represents the earliest rechargeable time for the g-th vehicle. This represents the normalization coefficient for the waiting time.

[0012] Furthermore, the specific constraints of the battery charging time window selection model are as follows: The total charging power of all vehicles in any given charging time window must not exceed the capacity of the charging and battery swapping station. Each vehicle must be fully charged continuously within the selected time window; Each vehicle can only select one consecutive time window for charging.

[0013] Furthermore, the optimization algorithm specifically refers to the improvement of the bat algorithm.

[0014] Furthermore, S7 specifically refers to: Based on the constraints of the battery charging time window selection model, and with the objective of minimizing the objective function of the battery charging time window selection model, the improved bat algorithm is used to optimize and solve the problem, thereby obtaining the optimal charging time window.

[0015] In a second aspect, the present invention provides a charging / swapping time selection system based on multi-objective optimization, comprising: a memory and a processor; The memory stores an application program adapted to be executed by the processor to implement the multi-objective optimization-based charging / swapping time selection method described in the first aspect.

[0016] The beneficial effects of this invention are as follows: In this embodiment of the invention, by introducing a vehicle urgency index, dynamic identification and strategy allocation based on the urgency of different vehicle trips are achieved. High-priority vehicles are given priority in battery swapping strategies and matched with the optimal battery swapping time window. This effectively enhances the system's adaptability to personalized user needs and improves the response efficiency and timeliness of task completion for high-urgency vehicles. Simultaneously, by employing candidate charging time window screening, a dynamic electricity price incentive mechanism, multi-objective optimization modeling, and optimization solution and iterative scheduling, the relationship between electricity price, power station load, and user waiting experience is coordinated, improving the balance of charging scheduling and enhancing the system's operational stability and overall service level during high-load periods. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0018] Figure 1 This is a flowchart illustrating a charging / swapping time selection method based on multi-objective optimization provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a charging / swapping time selection system based on multi-objective optimization provided in an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] 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 methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0023] This invention proposes a charging / swapping time selection method and system based on multi-objective optimization. It addresses the shortcomings of existing methods in responding to personalized user needs, failing to dynamically adjust strategies based on vehicle urgency, and thus hindering timely scheduling of high-priority vehicles, impacting task completion and travel efficiency. Furthermore, existing methods often fail to balance the load balancing of charging / swapping stations and user experience, potentially leading to local grid overload and excessively long waiting times, ultimately reducing overall service efficiency.

[0024] Method Implementation Examples Reference Figure 1 The diagram shows a flowchart of a charging / swapping time selection method based on multi-objective optimization provided by an embodiment of the present invention.

[0025] This invention provides a charging / swapping time selection method based on multi-objective optimization, the method comprising: Specifically, the method includes steps S1 to S9.

[0026] S1 acquires basic electricity price, charging and battery swapping station resource data, as well as SOC data, task requirement data, and charging power data for multiple vehicles.

[0027] Optionally, the charging / swapping station resource data specifically includes the number of fully charged batteries, standard charging time, maximum service capacity of the swapping station, maximum total power capacity of the station, and number of charging piles. Task requirement data includes a work schedule for each vehicle, containing the specific time each vehicle needs to perform a task. SOC data specifically includes each vehicle's current remaining battery power, minimum safe SOC threshold, target reference SOC value, battery rated capacity, and maximum acceptable charging power. Charging power data includes each vehicle's maximum acceptable charging power and the actual charging power used.

[0028] S2: Based on SOC data and task requirement data, calculate the urgency of each vehicle's usage and determine if each urgency exceeds the preset urgency. If so, proceed to S3 and execute the battery swapping strategy. Otherwise, proceed to S4 and execute the charging strategy.

[0029] Among them, vehicle urgency is a comprehensive indicator that measures whether a vehicle urgently needs to complete a charging / battery swapping operation, combining two key dimensions: battery level and time pressure. Battery level reflects the degree to which the vehicle's current battery level deviates from the minimum safe battery level and the target reference battery level, while time pressure measures the urgency of the vehicle's departure time before the next mission. By weighting and summing these two factors, a unified vehicle urgency index is formed, used to determine whether the vehicle should prioritize a battery swapping strategy to ensure timely mission departure and driving safety.

[0030] It should be noted that those skilled in the art can set the preset urgency level of vehicle use according to actual needs, and this invention does not limit this.

[0031] In one possible implementation, the method for calculating the urgency of vehicle use specifically includes: S201, based on the current vehicle's SOC data, calculate the current vehicle's battery level. in, This indicates the current battery level of the vehicle. Indicates the remaining battery power. Indicates the minimum safe power level. Indicates the target battery level.

[0032] S202, based on the current vehicle's task requirements data, calculate the current vehicle's time pressure level: in, This indicates the current time pressure level of the vehicle. Indicates the mission start time. Indicates the current time. Represents a constant.

[0033] S203 calculates the weighted sum of the battery and time constraints to determine the vehicle's current urgency level. in, Indicates the urgency of the current vehicle's use. This represents the weighting coefficient.

[0034] Specifically, in real-world scenarios, each vehicle has its own assigned tasks, and these tasks typically have a specific departure time. We denot this departure time as... This refers to the latest time the vehicle must depart. If the vehicle's current state of charge (SOC) is too low and there isn't enough time for a full charge, it won't be able to fully charge before hitting the road. In other words, even if a charging station is found, the vehicle might not be able to wait for a full charge before performing its mission. To avoid this situation, we introduced the concept of "vehicle urgency." This is based on two factors to determine how "urgent" the vehicle's need is, and then compared to a pre-set "urgency threshold." If the vehicle's urgency exceeds this threshold, it means it's "both out of power and in a hurry," so we directly schedule it to swap batteries. This only takes a few minutes to get a full battery and immediately start the mission. Conversely, if there's still time to charge, it follows the normal charging strategy, with further fine-tuning later.

[0035] In this embodiment of the invention, the design based on vehicle urgency enables intelligent triage of vehicle charging methods, significantly improving the overall scheduling efficiency and task assurance capabilities of the system. Simultaneously, by introducing the comprehensive indicator of "vehicle urgency," high-priority vehicles with low battery and urgent time constraints can be accurately identified, prioritizing their use of battery swapping strategies to avoid task delays or operational interruptions due to waiting for charging. Meanwhile, less urgent vehicles enter the charging process, allowing for cost optimization and load balancing scheduling in subsequent periods, effectively alleviating the concentrated pressure on charging and battery swapping station resources.

[0036] S3 selects the optimal battery swapping time window for each vehicle implementing a battery swapping strategy, based on charging and battery swapping station resource data and vehicle urgency.

[0037] It should be noted that the optimal battery swapping time window is a time frame that allows the most urgent vehicles to complete their battery swapping as early, safely, and efficiently as possible, provided that resources permit. It is a balance between "task timeliness" and "site resource supply".

[0038] In one possible implementation, S3 specifically includes: S301 sorts the vehicles implementing the battery swapping strategy in descending order of urgency and assigns each vehicle a unique battery swapping number.

[0039] S302, set the battery swapping time window.

[0040] S303, predicting the dynamic battery inventory quantity for the first battery swapping window: in, This represents the predicted dynamic battery inventory quantity. This indicates the initial number of fully charged batteries within the battery swapping time window. This indicates the number of batteries that have been fully charged and returned within the battery swapping time window. Represents a binary function, when hour ,when hour , This indicates the end time of the battery swapping window. This indicates the moment when the i-th battery begins charging. Indicates the standard charging time.

[0041] S304: Based on the dynamic battery inventory, select the corresponding time for each vehicle according to the vehicle's battery swap number to form a battery swap schedule for each vehicle.

[0042] S305, each vehicle goes to the charging and battery swapping station for battery swapping according to the battery swapping schedule.

[0043] S306: Determine if there are sufficient battery swapping resources. If so, allocate a battery swap directly. Otherwise, schedule unallocated vehicles for battery swapping in the next available time window.

[0044] S307, repeat steps S303 to S306 until all battery swapping vehicles are arranged.

[0045] Specifically, after categorizing vehicles by their "urgency level," the next step is to schedule which vehicles should be swapped and when. To make the process more efficient, we first queue the vehicles according to their urgency level, assigning each vehicle a "swapping number," with smaller numbers indicating greater urgency. Next, we divide the future time into several "swapping time windows," for example, every 10 minutes. We predict how many fully-charged batteries are available at the swapping station in the first time window. This inventory is not fixed; it considers two factors: first, the number of fully-charged batteries already prepared, and second, whether batteries currently charging have a chance to be fully charged and returned within the window. If a battery can be fully charged before the window ends, it can also be used for swapping. Based on this inventory, we can begin allocating swapping time windows. We allocate time slots one by one according to the vehicle's swapping number, scheduling directly if there is enough inventory. If there is a sudden shortage of inventory, vehicles with higher swapping numbers will have to wait for the next time window. We will continue to assess and schedule in this manner.

[0046] In this embodiment of the invention, by introducing the concept of an "optimal battery swapping time window," the system effectively achieves refined scheduling and management of limited battery swapping resources, ensuring that high-urgency vehicles complete battery swapping in a timely manner before mission departure, thus guaranteeing operational efficiency. Simultaneously, by dynamically predicting battery inventory and combining it with battery swapping number sorting, the system can prioritize the needs of the most urgent vehicles, improving the fairness and rationality of resource allocation. This strategy not only avoids resource contention and queuing conflicts but also reduces vehicle waiting time and scheduling blind spots, improving overall operational efficiency and service responsiveness, and providing reliable support for efficient energy replenishment in multi-vehicle, multi-mission scenarios.

[0047] S4, based on SOC data and charging / swapping station resource data, determines the set of candidate charging time windows that can be selected for each vehicle executing a charging strategy.

[0048] The candidate charging time window set refers to a set of continuous time periods selected for each vehicle to be used for charging, assuming the vehicle meets the charging conditions and considering its battery status, charging demand, and charging / swapping station resource availability. These time windows serve as "alternative solutions" for charging scheduling optimization, from which the system will subsequently select the optimal time period for charging arrangements.

[0049] In one possible implementation, S4 specifically includes: S401, based on SOC data, calculates the required charging time for each vehicle: in, This represents the required charging time for the g-th vehicle. This indicates the battery status of the g-th vehicle. This represents the rated battery capacity of the g-th vehicle. This represents the maximum acceptable charging power for the g-th vehicle. Indicates charging efficiency. This represents the duration of the t-th charging time window. This indicates rounding up to the nearest integer.

[0050] S402 divides future rechargeable cycles into time slices of fixed duration, forming a charging time window.

[0051] S403 converts each required charging duration into a time window, and slides each time window within the charging time window to determine the set of candidate charging time windows that can be selected for the corresponding vehicle.

[0052] For example, if the current time is 18:00 and the latest charging end time is 23:00, the system divides the entire schedulable charging period into multiple fixed-length charging time windows, each window being 30 minutes long, resulting in 10 consecutive time periods: 18:00–18:30, 18:30–19:00, 19:00–19:30, 19:30–20:00, 20:00–20:30, 20:30–21:00, 21:00–21:30, 21:30–22:00, 22:00–22:30, and 22:30–23:00. Assuming the calculated required charging time is 3 hours, then the sliding combinations are: Combination 1: Window 1–Window 6, actual time period 18:00–21:00. Combination 2 consists of windows 2–7, with an actual time period of 18:30–21:30. Combination 3 consists of windows 3–8, with an actual time period of 19:00–22:00. Combination 4 consists of windows 4–9, with an actual time period of 19:30–22:30. Combination 5 consists of windows 5–10, with an actual time period of 20:00–23:00. These five combinations constitute the candidate charging time window set.

[0053] In this embodiment of the invention, by precisely constructing a set of candidate charging time windows for each vehicle executing a charging strategy, flexible configuration and fine-grained management of charging time period resources are achieved. By combining the vehicle's current SOC, battery parameters, and site resource status, the system can pre-evaluate the minimum continuous time period required for each vehicle to complete charging, and slide to filter multiple feasible window combinations that meet the charging duration requirements on the global time axis. This not only improves the flexibility and adjustability of charging scheduling but also effectively avoids resource conflicts.

[0054] S5, based on the base electricity price, uses a dynamic electricity price incentive mechanism to set a dynamic charging time window electricity price.

[0055] Among them, the "dynamic electricity price inducement mechanism" is a load adjustment strategy. Its core purpose is to guide vehicles to avoid peak hours and charge during off-peak hours by adjusting the electricity price level of each time window in real time, thereby achieving the goals of peak shaving and valley filling, load balancing and improving resource utilization efficiency.

[0056] In one possible implementation, S5 specifically includes: S501 calculates the total charging power based on the charging power data of each vehicle in each charging time window.

[0057] S502, determine if the total charging power is greater than the preset total charging power. If yes, proceed to S503. Otherwise, maintain the current electricity price.

[0058] It should be noted that those skilled in the art can set the preset total charging power according to actual needs, and this invention does not limit that.

[0059] S503 calculates the dynamic charging time window price for each charging time window based on the base electricity price and total charging power: in, This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the function that takes the minimum value. Let e ​​represent the base electricity price for the t-th charging time window, e represent the exponential function, and β represent the sensitivity coefficient. This represents the total charging power during the t-th charging time window. This represents the ideal total charging power. This represents the highest electricity price during the t-th charging time window.

[0060] In this embodiment of the invention, a dynamic electricity price incentive mechanism is introduced to achieve real-time load guidance and price regulation in charging scheduling, effectively alleviating charging pressure during peak hours. By dynamically adjusting the electricity price based on the actual total charging power in each time window, the system can incentivize vehicles to actively avoid high-load periods and charge during off-peak hours, thereby achieving a staggered distribution of charging demand. Simultaneously, the dynamic electricity price incentive mechanism not only improves the utilization rate of power resources at charging and battery swapping stations and reduces the risk of grid load fluctuations, but also provides a more flexible scheduling basis for the optimization model, promoting the synergistic optimization of charging economy and system stability.

[0061] S6 constructs a battery charging time window selection model based on the dynamic charging time window electricity price, the total load of the charging and battery swapping station, and the candidate charging time window set, with the goal of minimizing charging costs and charging waiting time.

[0062] It's important to note that our objective function was designed this way primarily to address the scarcity of charging resources in the unique environment of mining areas. Electricity costs in mining areas are typically not particularly sensitive because they utilize internal industrial electricity. However, charging stations have limited capacity; if too many vehicles charge simultaneously, it can cause localized congestion or overload, potentially even affecting the stability of the entire power supply system. Therefore, we introduce a "dynamic electricity price," not to control costs, but to simulate a "queueing signal" using price. For example, if many vehicles want to charge during a certain time period, we virtually increase the electricity price for that period. This allows the scheduling system to automatically bypass this peak period and schedule charging during other, less busy times, achieving automatic peak-shifting.

[0063] In one possible implementation, the objective function of the battery charging time window selection model is specifically: Where F represents the objective function, The weights represent the charging cost target, G represents the set of vehicles to be charged, and TW g This represents the charging time window for the g-th vehicle. This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the charging power of the g-th vehicle. Indicates the length of the charging time window. This represents the cost normalization coefficient. The weight representing the target charging wait time. This represents the function that takes the maximum value. This represents the actual start time of charging for the g-th vehicle. This represents the earliest rechargeable time for the g-th vehicle. This represents the normalization coefficient for the waiting time.

[0064] Optionally, .

[0065] In this embodiment of the invention, by introducing "dynamic electricity price" as the objective function for load guidance signal design, the system adaptively avoids peak charging periods when scheduling charging windows, balances the distribution of charging load, and reduces the risk of local resource congestion. Simultaneously, incorporating charging waiting time into the optimization objective helps ensure that vehicles complete refueling operations without delaying tasks, improving the timeliness and operational efficiency of overall scheduling.

[0066] In one possible implementation, the constraints of the battery charging time window selection model are specifically as follows: The total charging power of all vehicles within any given charging time window must not exceed the capacity of the charging / swapping station. Where g represents the vehicle number, G t This represents the set of all vehicles in the t-th charging time window. This represents the charging power of the g-th vehicle. This represents the remaining available power of the charging station during the t-th charging time window. Represents any symbol, Indicates that it belongs to the symbol. This represents the union of the charging time windows for all vehicles.

[0067] Each vehicle must be continuously charged within the selected time window.

[0068] Each vehicle can only select one consecutive time window for charging: Among them, TW g This represents the charging time window for the g-th vehicle. Represents a binary decision variable. This indicates that the g-th vehicle chooses to charge during the t-th charging time window. This indicates that the g-th vehicle did not choose to charge during the t-th charging time window.

[0069] S7. By optimizing the battery charging time window selection model through an optimization algorithm, the optimal charging time window is obtained.

[0070] In one possible implementation, the optimization algorithm is specifically an improved bat algorithm.

[0071] In one possible implementation, S7 specifically refers to: Based on the constraints of the battery charging time window selection model, and with the objective of minimizing the objective function of the battery charging time window selection model, the optimal charging time window is obtained by optimizing the solution through an improved bat algorithm.

[0072] Optionally, the specific process for optimizing the battery charging time window selection model through the improved bat algorithm includes: The reciprocal of the objective function is used as the fitness function of the improved bat algorithm.

[0073] Initialize the population, which consists of multiple individuals, each representing a feasible battery charging time window selection scheme.

[0074] Calculate the fitness value of each individual, and select the bat corresponding to the individual with the highest fitness value, recording it as the optimal bat.

[0075] Update the position of each individual bat based on the optimal bat: in, This represents the frequency used in the current iteration for the j-th bat individual. Represents the minimum frequency. This represents the maximum value of the frequency. Represents a random number. This represents the velocity of the j-th bat in the (k+1)th iteration. This represents the velocity of the j-th bat individual in the k-th iteration. This indicates the position of the optimal bat in the current iteration. This represents the position of the j-th bat in the k-th iteration. This indicates the position of the j-th bat individual in the (k+1)-th iteration.

[0076] Generate a random number. If the random number is greater than the pulse emission rate, then perform perturbation using the loudness of the current solution. in, This represents the random disturbance factor. Let represent the loudness of the j-th bat individual in the k-th iteration.

[0077] Otherwise, generate a new random solution and determine whether to accept it according to the following criteria: in, Represents a random number. This represents the loudness of the j-th individual bat. This represents the fitness value corresponding to the new solution generated by the j-th bat in a certain round. This represents the best individual in the current iteration.

[0078] For each bat, three different bats are randomly selected to perform differential mutation: in, Represents the mutation vector. , Let each represent a random individual in the k-th iteration.

[0079] Two candidate test individuals are generated by performing two different crossover operations respectively: in, Indicates the first individual tested. This represents the value of the mutation vector in the v-th dimension. This represents the value of the v-th dimension in the current bat individual. Indicates the second test individual. , Both represent the control coefficients of the crossover operator.

[0080] Choose the individual with the highest fitness value from the individuals obtained by differential mutation and the two candidate test individuals.

[0081] After each update, the loudness and pulse emission rate of the bats are updated as follows: in, This represents the loudness of the j-th bat individual in the (k+1)-th iteration. The attenuation coefficient represents the loudness. This represents the pulse emission rate of the j-th bat individual in the (k+1)-th iteration. Indicates the initial pulse emission rate. Represents an exponential function. This represents the pulse emission rate growth control factor.

[0082] Repeat the above iterations until the fitness value is greater than the preset fitness value, then stop the iteration.

[0083] It should be noted that those skilled in the art can set the preset fitness value according to actual needs, and this invention does not limit this.

[0084] Output the optimal individual and output the window selection scheme represented by the optimal individual as the optimal charging time window.

[0085] S8 schedules vehicle charging based on the optimal charging time window.

[0086] Specifically, after we calculate the optimal charging time window for each vehicle using the optimization model, the next step is "implementation." Simply put, according to the time schedule provided by the model, the system pre-determines when each vehicle should charge during which time slot. Everyone follows the "schedule," avoiding both competition for charging stations and overcrowding. The system also checks the current load of the charging stations to ensure that power limits are not exceeded during actual operation, preventing charging failures due to insufficient power.

[0087] S9 updates the remaining power and load of the charging and battery swapping station, repeats S4 to S9, and completes the charging arrangement for the remaining vehicles.

[0088] Specifically, instead of scheduling charging times for all vehicles at once, we use a "rolling" approach. For example, we schedule the first vehicle, select the most suitable charging time window, and immediately deduct the power used during that time slot from the station resources, updating the current available power. When it's the second vehicle's turn, the system, based on the updated resource situation, reassess which time slots it can choose to charge, regenerates candidate windows and dynamic electricity prices, and then uses an optimization algorithm to schedule an optimal time slot for it.

[0089] It's important to note that in industrial areas, the number of vehicles needing charging each day isn't particularly high, unlike in cities where dozens or even hundreds of vehicles compete for charging stations simultaneously. Therefore, when scheduling charging, it's unnecessary to plan the charging times for all vehicles uniformly at once. Instead, a rolling scheduling approach is used, arranging charging for each vehicle individually. Because the initial calculations are quick, and the number of vehicles is relatively small, it doesn't create too much pressure later on, improving the stability and practicality of the scheduling.

[0090] The beneficial effects of this invention are as follows: In this embodiment of the invention, by introducing a vehicle urgency index, dynamic identification and strategy allocation based on the urgency of different vehicle trips are achieved. High-priority vehicles are given priority in battery swapping strategies and matched with the optimal battery swapping time window. This effectively enhances the system's adaptability to personalized user needs and improves the response efficiency and timeliness of task completion for high-urgency vehicles. Simultaneously, by employing candidate charging time window screening, a dynamic electricity price incentive mechanism, multi-objective optimization modeling, and optimization solution and iterative scheduling, the relationship between electricity price, power station load, and user waiting experience is coordinated, improving the balance of charging scheduling and enhancing the system's operational stability and overall service level during high-load periods.

[0091] System Implementation Examples Reference manual attached Figure 2 The diagram shows a schematic of a charging / swapping time selection system based on multi-objective optimization provided by an embodiment of the present invention.

[0092] The present invention proposes a charging / swapping time selection system 30 based on multi-objective optimization, comprising: a memory 303 and a processor 301.

[0093] The memory 303 stores an application program adapted to be executed by the processor 301 to implement the multi-objective optimization-based charging / swapping time selection method of the method embodiment.

[0094] The charging / swapping time selection system 30 based on multi-objective optimization includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302.

[0095] The structure of the charging / swapping time selection system 30 based on multi-objective optimization does not constitute a limitation on the embodiments of the present invention.

[0096] Processor 301 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0097] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI bus or an EISA bus, etc. Bus 302 may be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not mean that there is only one bus or one type of bus.

[0098] The memory 303 may be a ROM or other type of static storage device capable of storing static information and instructions, RAM or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0099] Computer-readable storage medium embodiments The present invention proposes a computer-readable storage medium having a computer program stored thereon, the computer program being loadable and executed by a processor for a charging / swapping time selection method based on multi-objective optimization, as described in the first aspect.

[0100] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A charging / swapping time selection method based on multi-objective optimization, characterized in that, The charging / swapping time selection method based on multi-objective optimization includes: S1 acquires basic electricity price, charging and battery swapping station resource data, as well as SOC data, task requirement data, and charging power data for multiple vehicles; S2, based on the SOC data and the task requirement data, calculate the vehicle urgency of each vehicle and determine whether each vehicle urgency is greater than the preset vehicle urgency; if so, proceed to S3 and execute the battery swapping strategy; otherwise, proceed to S4 and execute the charging strategy. S3, Based on the charging and battery swapping station resource data and the vehicle urgency, select the optimal battery swapping time window for each vehicle executing the battery swapping strategy; S4. Based on the SOC data and the charging / swapping station resource data, determine the set of candidate charging time windows that can be selected for each vehicle executing the charging strategy; S5. Based on the aforementioned base electricity price, a dynamic charging time window electricity price is set using a dynamic electricity price incentive mechanism; S6. Based on the charging dynamic time window electricity price, the total load of the charging and battery swapping station, and the candidate charging time window set, a battery charging time window selection model is constructed with the goal of minimizing charging cost and charging waiting time. S7. The optimal charging time window is obtained by optimizing the battery charging time window selection model through an optimization algorithm. S8, Arrange for the vehicle to charge according to the optimal charging time window; S9, update the remaining power and load of the charging and swapping station, repeat S4 to S9, and complete the charging arrangement for the remaining vehicles.

2. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, The calculation method for the urgency of vehicle use specifically includes: S201, Calculate the current vehicle's battery level based on the current vehicle's SOC data; S202, Calculate the time pressure of the current vehicle based on the task requirement data of the current vehicle; S203, the weighted sum of the power shortage level and the time shortage level is calculated to obtain the current vehicle urgency level.

3. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, S3 specifically includes: S301, Sort the vehicles implementing the battery swapping strategy in descending order of vehicle urgency, and assign each vehicle a unique battery swapping number. S302, Set the battery swapping time window; S303, predicts the dynamic battery inventory quantity for the first battery swapping time window; S304, Based on the dynamic battery inventory quantity, select the corresponding time for each vehicle according to the vehicle battery swapping number to form a battery swapping schedule for each vehicle. S305, Each of the vehicles goes to the charging and battery swapping station for battery swapping according to the battery swapping schedule; S306: Determine if there are sufficient actual battery swapping resources; if so, allocate a battery swap directly; otherwise, schedule unallocated vehicles for the next battery swapping window. S307, repeat steps S303 to S306 until all battery swapping vehicles are arranged.

4. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, S4 specifically includes: S401, Based on the SOC data, calculate the required charging time for each of the vehicles; S402 divides future rechargeable cycles into time slices of fixed duration, forming a charging time window; S403, convert each of the required charging durations into time windows, and slide each of the time windows within the charging time window to determine the set of candidate charging time windows that can be selected for the corresponding vehicle.

5. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, S5 specifically includes: S501, calculate the total charging power based on the charging power data of each vehicle in each charging time window; S502, determine whether the total charging power is greater than the preset total charging power; if yes, proceed to S503; otherwise, maintain the current electricity price; S503, based on the base electricity price and the total charging power, calculate the dynamic charging time window electricity price for each of the charging time windows: ； in, This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the function that takes the minimum value. Let e ​​represent the base electricity price for the t-th charging time window, e represent the exponential function, and β represent the sensitivity coefficient. This represents the total charging power during the t-th charging time window. This represents the ideal total charging power. This represents the highest electricity price during the t-th charging time window.

6. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, The objective function of the battery charging time window selection model is as follows: ； Where F represents the objective function, The weights represent the charging cost target, G represents the set of vehicles to be charged, and TW g This represents the charging time window for the g-th vehicle. This represents the dynamic charging time window electricity price for the t-th charging time window. This represents the charging power of the g-th vehicle. Indicates the length of the charging time window. This represents the cost normalization coefficient. The weight representing the target charging wait time. This represents the function that takes the maximum value. This represents the actual start time of charging for the g-th vehicle. This represents the earliest charging time for the g-th vehicle. This represents the normalization coefficient for the waiting time.

7. The charging / swapping time selection method based on multi-objective optimization according to claim 6, characterized in that, The specific constraints of the battery charging time window selection model are as follows: The total charging power of all vehicles in any given charging time window must not exceed the capacity of the charging and battery swapping station. Each vehicle must be fully charged continuously within the selected time window; Each vehicle can only select one consecutive time window for charging.

8. The charging / swapping time selection method based on multi-objective optimization according to claim 1, characterized in that, The optimization algorithm is specifically the improved bat algorithm.

9. The charging / swapping time selection method based on multi-objective optimization according to claim 8, characterized in that, Specifically, S7 is: Based on the constraints of the battery charging time window selection model, and with the objective of minimizing the objective function of the battery charging time window selection model, the improved bat algorithm is used to optimize and solve the problem, thereby obtaining the optimal charging time window.

10. A charging / swapping time selection system based on multi-objective optimization, characterized in that, include: Memory and processor; The memory stores an application program adapted to be executed by the processor to implement the charging / swapping time selection method based on multi-objective optimization as described in any one of claims 1 to 9.

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