An electric heavy truck trip chain simulation method and system considering charging strategy optimization

Abstract

This invention relates to the field of electric vehicle trip chain simulation technology, specifically to a method and system for simulating the trip chain of electric heavy-duty trucks considering charging strategy optimization. The steps are as follows: Real-time updates of target station distance and battery charge; upon arrival at a loading / unloading station and when the loading status meets loading / unloading conditions, updating the loading / unloading status and executing an event; after the event, updating to a driving status; upon arrival at a charging station and when the battery charge meets charging conditions, if there is an available charging pile or the overall satisfaction level is greater than a threshold, updating the charging status and executing an event; after the event, updating to a driving status; if the overall satisfaction level is less than the threshold, directly updating to a driving status; updating the target station for the vehicle in the driving status and repeating the above steps until the entire trip chain simulation is completed. This invention has significant reference value for electric heavy-duty truck capacity demanders in terms of operational scheduling plans and station configuration, and can promote improvements in fleet operating efficiency and economy.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle trip chain simulation technology, and more specifically, to an electric heavy truck trip chain simulation method and system that takes into account charging strategy optimization. Background Technology

[0002] In the current context of my country's increasing emphasis on environmental protection and carbon emission control, heavy-duty trucks, as one of the important transportation tools in the logistics and transportation sector, are undergoing a huge green and low-carbon transformation revolution. Today, more and more green new energy transportation vehicles are being used in the logistics industry, primarily due to the rapid development of new energy-related technologies.

[0003] Taking electric heavy-duty trucks as an example, from an application scenario perspective, electric heavy-duty trucks are currently mainly used in four major scenarios. First, urban logistics: electric heavy-duty trucks are suitable for cargo delivery and transportation within cities, meeting requirements for environmental protection, low noise, and low energy consumption. Second, port logistics: electric heavy-duty trucks can be used for cargo transportation and loading / unloading operations at ports, meeting port environmental protection requirements and improving logistics efficiency. Third, short-distance freight: for short-distance freight and scenarios with relatively stable cargo sources, electric heavy-duty trucks possess energy-saving, economical, and environmentally friendly characteristics, meeting daily freight tasks. Fourth, enclosed transportation: judging from the industries that have demanded this in recent years, these mainly include large steel mills, power plants, and mining areas. It is expected that with the advancement of battery technology, electric heavy-duty trucks will be able to handle even more scenarios in the future.

[0004] Given the diverse application scenarios, high cost sensitivity, and highly organized operation and management characteristics of electric heavy-duty trucks, improving fleet operation economics by combining specific application scenarios and actual operating processes is of great significance. However, existing methods and models are mainly based on techno-economic models of total operating costs. On the one hand, they fail to consider specific scenarios and routes; on the other hand, they do not generate charging strategies based on specific fleet operation plans and real-time electricity prices, which can easily lead to inaccurate simulations of the entire electric heavy-duty truck travel chain.

[0005] Therefore, in order to address the above problems, there is an urgent need for a method and system for simulating the travel chain of electric heavy-duty trucks that takes into account the optimization of charging strategies. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for simulating the travel chain of electric heavy-duty trucks that considers charging strategy optimization. Based on a specific transportation scenario of electric heavy-duty trucks, considering fleet size and scheduling organization, the method simulates the operation, loading and unloading, queuing, and charging behavior of individual vehicles in the fleet. At the same time, upon arrival at a charging station, the method comprehensively considers economic efficiency and transportation demand to generate an optimized charging strategy and make decisions on charging behavior, thereby realizing the simulation of the entire vehicle travel chain and solving the technical problems pointed out in the background art.

[0007] This invention is achieved through the following technical solution: a method for simulating the trip chain of electric heavy-duty trucks considering charging strategy optimization, comprising the following steps:

[0008] The vehicle travel distance in the driving state is integrated, and the target station distance and vehicle battery level are calculated and updated based on the integrated travel distance. Based on the target station distance, it is determined whether the vehicle has arrived at the target station.

[0009] When it is determined that the vehicle has arrived at the target station, and the target station is a loading and unloading station and the vehicle's loading status meets the loading and unloading conditions, the vehicle status is updated to loading and unloading status and the loading and unloading event is executed. After the loading and unloading event is completed, the vehicle status is updated to driving status.

[0010] When the target station is a charging station and the vehicle battery level meets the charging requirements, it is determined whether there is an available charging pile at the charging station. If there is, the vehicle status is updated to charging status and a charging event is executed. If there is no available charging pile, it is determined whether the overall vehicle charging satisfaction is greater than a preset threshold. If it is greater, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If it is less than the threshold, the vehicle status is directly updated to driving status. The overall vehicle charging satisfaction is determined based on charging time and charging cost.

[0011] The system updates the next target station for vehicles that have entered the driving state and repeats all the above steps until the vehicle completes the simulation of the entire travel chain.

[0012] According to a preferred embodiment, the expressions for updating the target station distance and vehicle battery level are as follows:

[0013]

[0014] In the above formula, elec t elec represents the vehicle battery charge at time t. t-1 The distance represents the vehicle's battery charge at time t-1. t const represents the distance traveled at time t. elec Indicates the power consumption per kilometer, target_distance t The target distance at time t is represented by `target_distance`. t-1 This represents the distance to the target station at time t-1.

[0015] According to a preferred embodiment, the specific process for determining whether a target station is a loading / unloading station or a charging station is as follows:

[0016] The system determines whether the target station is a loading / unloading station and whether the vehicle's loading status meets the loading / unloading conditions. If the first charging condition and the second charging condition are met, the target station is determined to be a charging station. The first charging condition is that the target station is not a loading / unloading station, and the second charging condition is that the target station is a loading / unloading station and the vehicle's loading status does not meet the loading / unloading conditions.

[0017] According to a preferred embodiment, the specific process of updating the vehicle status to loading / unloading status and executing the loading / unloading event when the target station is a loading / unloading station and the vehicle loading status meets the loading / unloading conditions is as follows:

[0018] When the target station is a loading station and the vehicle loading status meets the loading conditions, the vehicle status is updated to loading status. It is then determined whether there is an empty loading port at the loading station. If not, loading waits. If so, loading begins, and after loading is completed, the vehicle loading status is updated to full load status and the vehicle status is updated to driving status.

[0019] When the target station is an unloading station and the vehicle loading status meets the unloading conditions, the vehicle status is updated to unloading status. It is then determined whether there is an empty unloading port at the unloading station. If not, unloading waits. If so, unloading begins, and after unloading is completed, the vehicle loading status is updated to empty status and the vehicle status is updated to driving status.

[0020] According to a preferred embodiment, the charging capacity condition is that the vehicle's current battery capacity is greater than or equal to the minimum capacity, where the minimum capacity is the sum of the power consumption of the vehicle traveling to the next charging station and the reserved capacity, expressed as follows:

[0021]

[0022] In the above formula, elec min Indicates the minimum battery level, bat elec This indicates battery capacity, distance indicates the distance the vehicle travels to the next charging station, and const... elec Indicates energy consumption per kilometer, factor reserved This indicates a reserved coefficient, "end" indicates that the transportation task is completed, and "continued" indicates that the transportation task is not completed.

[0023] According to a preferred embodiment, the specific process for determining the overall satisfaction with vehicle charging is as follows:

[0024] Obtain vehicle parameters and charging station configuration parameters, and calculate the estimated queuing time for vehicles based on the vehicle parameters and charging station configuration parameters;

[0025] The first charging time and the second charging time of the vehicle are determined. Based on the estimated queuing time, the first charging time and the second charging time, the estimated total charging amount of the vehicle is calculated. The first charging time is the estimated charging time for the vehicle to be charged to the minimum charge level. The second charging time is the time from the end of charging the vehicle to the end of the off-peak electricity price period or when the battery is fully charged.

[0026] Charging time satisfaction is calculated based on the estimated queuing time, the first charging time, and the second charging time. Charging cost satisfaction is calculated based on the estimated total charging amount and the real-time electricity price. Overall satisfaction is calculated based on the charging time satisfaction and the charging cost satisfaction.

[0027] According to a preferred embodiment, the calculation expression for the estimated queuing time is as follows:

[0028]

[0029] In the above formula, time queue This represents the estimated queuing time, where n represents the number of vehicles waiting to charge at the charging station, a is an adjustment factor, S represents the charging time per vehicle, m represents the number of charging piles at the charging station, and bat elec P represents the battery capacity, P represents the average charging power of the charging station's charging piles, and η represents the charging efficiency.

[0030] According to a preferred embodiment, the specific process for calculating the vehicle's expected total charging amount is as follows:

[0031] The estimated charging time to the minimum charge level of a vehicle is calculated using the following expression:

[0032]

[0033] elec charge1 =max{elec min -elec,0}

[0034] In the above formula, time charge1 Indicates the estimated charging time to the minimum charge level of the vehicle, elec charge1 The estimated charge level to the minimum charge level is indicated by 'elec', while 'elec' indicates the current battery level of the vehicle.

[0035] The time it takes for a vehicle to finish charging from its lowest charge level until the end of a low electricity price period or when the battery is fully charged is calculated using the following expression:

[0036]

[0037] time charge2_start =time present +time queue +timecharge1

[0038] In the above formula, time charge2 This indicates the time elapsed from the end of charging to the end of a low electricity price period or when the battery is fully charged. nearest_end Indicates the end of the electricity price trough, time charge2_start Indicates the time when charging ends at the minimum charge level. present Indicates the current moment;

[0039] The estimated total charging time for the vehicle is calculated based on the estimated queuing time, the first charging time, and the second charging time, as shown in the following expression:

[0040] elec total =elec charge1 +ele charge2

[0041] elec charge2 =time charge2 *P

[0042] In the above formula, elec total Indicates the expected total charging amount, elec charge2 This indicates the estimated charge amount from the lowest available charge level to the end of a low electricity price period or when the battery is fully charged.

[0043] According to a preferred embodiment, the specific process for calculating the overall satisfaction level is as follows:

[0044] The charging time satisfaction is calculated based on the estimated queuing time, the first charging time, and the second charging time, as expressed below:

[0045]

[0046] value oc,t =time charge1 +time charge2 +time queue

[0047] value max,t =value min,t *(1+factor t )

[0048] value min,t =time charge1 +time charge2

[0049] In the above formula, sat time Indicates satisfaction with charging time, value oc,t Value represents the estimated charging time.max,t Indicates the maximum tolerable charging time, value min,t This indicates the minimum charging time, factor. t This indicates the charging time tolerance factor;

[0050] Based on the estimated total charging volume and real-time electricity price, the charging cost satisfaction is calculated as follows:

[0051] value oc,c =elec total *Price true

[0052] value max,c =value min,c *(1+factor c )

[0053] value min,c =elec total *Price vally

[0054] In the above formula, sat cost Indicates satisfaction with charging costs, value oc,c Price represents the estimated charging cost. true Indicates the real-time peak-valley electricity price for the region, value max,c Indicates the maximum tolerable charging cost, value min,c Price represents the minimum charging cost. vally Indicates off-peak electricity price, factor c This indicates the tolerance factor for charging costs;

[0055] Based on the satisfaction levels regarding charging time and charging cost, the overall satisfaction level is calculated as follows:

[0056] sat overall =sat time *W time +sat cost *W cost

[0057] In the above formula, sat overall Indicates overall satisfaction, sat time Indicating satisfaction with charging time, W time This indicates the weighting of charging time, sat cost Indicating satisfaction with charging costs, W cost This indicates the weight of the charging cost.

[0058] This invention also provides an electric heavy-duty truck trip chain simulation system considering charging strategy optimization, comprising:

[0059] The driving distance integration module is used to integrate the driving distance of the vehicle in the driving state, calculate and update the target station distance and vehicle battery power based on the driving distance integration, and determine whether the vehicle has arrived at the target station based on the target station distance.

[0060] The status update module is used to update the vehicle status to loading / unloading status and execute a loading / unloading event when it is determined that the vehicle has arrived at the target station. If the target station is a loading / unloading station and the vehicle loading status meets the loading / unloading conditions, the module will update the vehicle status to driving status.

[0061] When the target station is a charging station and the vehicle battery level meets the charging requirements, it is determined whether there is an available charging pile at the charging station. If there is, the vehicle status is updated to charging status and a charging event is executed. If there is no available charging pile, it is determined whether the overall vehicle charging satisfaction is greater than a preset threshold. If it is greater, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If it is less than the threshold, the vehicle status is directly updated to driving status. The overall vehicle charging satisfaction is determined based on charging time and charging cost.

[0062] The loop module is used to update the next target station for vehicles that have entered the driving state, and to repeatedly execute all the steps of the driving distance integration module and the status update module until the vehicle completes the simulation of the entire travel chain.

[0063] The technical solution of the electric heavy-duty truck trip chain simulation method and system considering charging strategy optimization provided by this invention has at least the following advantages and beneficial effects: Based on a specific transportation scenario of electric heavy-duty trucks, this invention simulates the operation, loading and unloading, queuing, and charging behavior of individual vehicles in the fleet, taking into account fleet size and scheduling organization. At the same time, upon arrival at the charging station, it comprehensively considers economic efficiency and transportation demand to generate an optimized charging strategy and make decisions on charging behavior, realizing the simulation of the entire vehicle trip chain. This has important reference significance for electric heavy-duty truck capacity demanders in terms of layout, operation scheduling plans, and station configuration, and can promote the improvement of fleet operation efficiency and economy. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating the electric heavy-duty truck trip chain simulation method considering charging strategy optimization provided in Embodiment 1 of the present invention.

[0065] Figure 2 This is a schematic diagram of the loading determination provided in Embodiment 1 of the present invention;

[0066] Figure 3 This is a logical diagram of the unloading judgment provided in Embodiment 1 of the present invention;

[0067] Figure 4 This is a schematic diagram of the charging determination provided in Embodiment 1 of the present invention;

[0068] Figure 5 This is a logical diagram of the queuing determination provided in Embodiment 1 of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0070] Example 1

[0071] Figure 1 This is a flowchart illustrating the electric heavy-duty truck trip chain simulation method considering charging strategy optimization provided in an embodiment of the present invention. See also... Figure 1 As shown, the electric heavy-duty truck trip chain simulation method considering charging strategy optimization includes the following detailed steps in its implementation process:

[0072] Step 1: Update vehicle location and vehicle battery level in real time.

[0073] For the vehicle's location and battery level, this embodiment integrates the vehicle's travel distance while in motion, calculates and updates the target station distance and vehicle battery level based on the integrated travel distance, determines whether the vehicle has arrived at the target station based on the target station distance, and simulates the electric heavy truck's travel location based on the vehicle battery level, and simulates the electric heavy truck's charging decision process.

[0074] The expressions for updating the target station distance and vehicle battery level are as follows:

[0075]

[0076] In the above formula, elec t elec represents the vehicle battery charge at time t. t-1 The distance represents the vehicle's battery charge at time t-1. t const represents the distance traveled at time t. elec Indicates the power consumption per kilometer, target_distance tThe target distance at time t is represented by `target_distance`. t-1 This represents the distance to the target station at time t-1.

[0077] Step 2: Update the vehicle's status during operation.

[0078] This includes driving status, loading / unloading status, queuing status, and charging status. For a vehicle in motion, only step 1 needs to update the vehicle's location and battery level in real time. For the loading / unloading, queuing, and charging statuses, the status update logic in this embodiment is as follows:

[0079] Step 2.1: Determine the target site type. The specific process is as follows:

[0080] Determine whether the target station is a loading / unloading station and whether the vehicle's loading status meets the loading / unloading conditions; when the first charging condition and the second charging condition are met, determine that the target station is a charging station. The first charging condition is that the target station is not a loading / unloading station, and the second charging condition is that the target station is a loading / unloading station and the vehicle's loading status does not meet the loading / unloading conditions.

[0081] Step 2.2: Update the vehicle status at the loading and unloading site. The specific process is as follows:

[0082] When it is determined that the vehicle has arrived at the target station, and the target station is a loading and unloading station and the vehicle's loading status meets the loading and unloading conditions, the vehicle status is updated to loading and unloading status and the loading and unloading event is executed; after the loading and unloading event is completed, the vehicle status is updated to driving status.

[0083] Specifically, in this embodiment, the process of step 2.2 is as follows:

[0084] Step 2.2.1, see Figure 2 As shown, when the target station is a loading station and the vehicle loading status meets the loading conditions, the vehicle status is updated to loading status; further, it is determined whether there is an empty loading port at the loading station. If not, loading waits; if so, loading begins, and after loading is completed, the vehicle loading status is updated to full load status and the vehicle status is updated to driving status.

[0085] Step 2.2.2, see [link / reference] Figure 3 As shown, when the target station is an unloading station and the vehicle loading status meets the unloading conditions, the vehicle status is updated to unloading status; further, it is determined whether there is an empty unloading port at the unloading station. If not, unloading waits; if so, unloading begins, and after unloading is completed, the vehicle loading status is updated to empty status and the vehicle status is updated to driving status.

[0086] Step 2.3: Update the vehicle status at the charging station. The specific process is as follows:

[0087] When the target station is a charging station and the vehicle's battery level meets the charging requirements, it is determined whether there is an available charging pile at the charging station. If there is, the vehicle status is updated to charging status and a charging event is executed. If not, it is determined whether the vehicle's overall charging satisfaction is greater than a preset threshold. If it is greater, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If it is less than the threshold, the vehicle status is directly updated to driving status. The overall charging satisfaction is determined based on charging time and charging cost.

[0088] Specifically, in this embodiment, the process of step 2.3 is as follows:

[0089] 2.3.1 Perform charging judgment, see [link / reference] Figure 4 As shown, it determines whether the vehicle's current battery level meets the charging requirements. In this embodiment, the charging requirements are that the vehicle's current battery level is greater than or equal to the minimum charge level and the current electricity price is during off-peak hours. The minimum charge level is the sum of the electricity consumed by the vehicle to reach the next charging station and the reserved charge level, expressed as follows:

[0090]

[0091] In the above formula, elec min Indicates the minimum battery level, bat elec This indicates battery capacity, distance indicates the distance the vehicle travels to the next charging station, and const... elec Indicates energy consumption per kilometer, factor reserved This indicates a reserve coefficient, "end" indicates that the transportation task is completed, and "continued" indicates that the transportation task is not completed.

[0092] 2.3.2 Execute queuing judgment, see [link / reference] Figure 5 As shown, the overall charging satisfaction is calculated, and the specific process is as follows:

[0093] 2.3.2.1 Obtain vehicle parameters and charging station configuration parameters, and calculate the estimated queuing time for vehicles based on the vehicle parameters and charging station configuration parameters; wherein, the vehicle parameters include the number of vehicles waiting to be charged at the charging station, the charging time for each vehicle, and the battery capacity of the vehicle model waiting to be charged; the charging station configuration parameters include the number of charging piles at the charging station, the average charging power of the charging piles at the charging station, and the charging efficiency.

[0094] The expression for calculating the estimated queuing time is as follows:

[0095]

[0096] In the above formula, time queue η represents the estimated queuing time, n represents the number of vehicles waiting to be charged at the charging station, a is an adjustment factor, S represents the charging time per vehicle, m represents the number of charging piles at the charging station, P represents the average charging power of the charging piles at the charging station, and η represents the charging efficiency.

[0097] 2.3.2.2 The vehicle charging process is divided into two parts. The first part is charging to the minimum charge level, and the second part is charging from the minimum charge level to the full charge level or the end of the low electricity price period during which the low electricity price period ends. The charging time for the two parts is the first charging time and the second charging time, respectively. Specifically, in this embodiment, considering the charging economy, the first charging time is the estimated charging time for the vehicle to reach the minimum charge level, and the second charging time is the time from the end of charging from the minimum charge level to the end of the low electricity price period or the full charge of the battery during the low electricity price period.

[0098] Furthermore, the first charging time and the second charging time of the vehicle are determined. Based on the estimated queuing time, the first charging time, and the second charging time, the estimated total charging amount of the vehicle is calculated. The specific process for calculating the estimated total charging amount of the vehicle is as follows:

[0099] The estimated charging time to bring a vehicle to its minimum charge level is calculated using the following expression:

[0100]

[0101] elec charge1 =max{elec min -elec,0}

[0102] In the above formula, time charge1 Indicates the estimated charging time to the minimum charge level of the vehicle, elec charge1 This indicates the estimated charge level to which the vehicle will reach its minimum charge level, while elec indicates the current battery charge level.

[0103] The time it takes for a vehicle to finish charging from its lowest charge level until the end of a low electricity price period or when the battery is fully charged is calculated using the following expression:

[0104]

[0105] time charge2_start =time present +time queue +time charge1

[0106] In the above formula, time charge2 This indicates the time elapsed from the end of charging to the end of a low electricity price period or when the battery is fully charged. nearest_endIndicates the end of the electricity price trough, time charge2_start Indicates the time when charging ends at the minimum charge level. present Indicates the current moment.

[0107] The estimated total charging time for the vehicle is calculated based on the estimated queuing time, the first charging time, and the second charging time, as shown in the following expression:

[0108] elec total =elec charge1 +elec charge2

[0109] elec charge2 =time charge2 *P

[0110] In the above formula, elec total Indicates the expected total charging amount, elec charge2 This indicates the estimated charge amount from the lowest available charge level to the end of a low electricity price period or when the battery is fully charged.

[0111] 2.3.2.3 Calculate charging time satisfaction based on the estimated queuing time, the first charging time, and the second charging time; calculate charging cost satisfaction based on the estimated total charging amount and the real-time electricity price.

[0112] The expression for calculating charging time satisfaction is as follows:

[0113]

[0114] value oc,t =time charge1 +time charge2 +time queue

[0115] value max,t =value min,t *(1+factor t )

[0116] value min,t =time charge1 +time charge2

[0117] In the above formula, sat time Indicates satisfaction with charging time, value oc,t Value represents the estimated charging time. max,t Indicates the maximum tolerable charging time, value min,t This indicates the minimum charging time, factor. t This indicates the charging time tolerance factor.

[0118] The expression for calculating charging cost satisfaction is as follows:

[0119]

[0120] value oc,c =elec total *Price true

[0121] value max,c =value min,c *(1+factor c )

[0122] value min,c =elec total *Price vally

[0123] In the above formula, sat cost Indicates satisfaction with charging costs, value oc,c Price represents the estimated charging cost. true Indicates real-time electricity price, value max,c Indicates the maximum tolerable charging cost, value min,c Price represents the minimum charging cost. vally Indicates off-peak electricity price, factor c This indicates the tolerance factor for charging costs.

[0124] 2.3.2.4. Assign corresponding weights to charging time and charging cost, and calculate the overall satisfaction based on the satisfaction with charging time and charging cost. The specific expression is as follows:

[0125] sat overall =sat time *W time +sat cost *W cost

[0126] In the above formula, sat overall Indicates overall satisfaction, sat time Indicating satisfaction with charging time, W time This indicates the weighting of charging time, sat cost Indicating satisfaction with charging costs, W cost This indicates the weight of the charging cost.

[0127] Step 2.3.3: Determine whether the overall satisfaction with vehicle charging is greater than the preset threshold.

[0128] In this embodiment, the preset threshold is set to 0.8. When the overall satisfaction is greater than or equal to 0.8, the vehicle status is updated to queuing status or directly to charging status. When the vehicle status is updated to queuing status, the vehicle switches to charging status when an empty charging pile appears at the charging station. When the overall satisfaction is less than 0.8, the vehicle status is updated to driving status, and the vehicle leaves the charging station to continue driving.

[0129] Step 3: Update continuously.

[0130] In this embodiment, the next target station is updated for vehicles that have entered the driving state, and steps 1 to 3 above are executed repeatedly until the vehicle completes the simulation of the entire travel chain.

[0131] In summary, this invention, based on a specific transportation scenario of electric heavy-duty trucks, considers fleet size and scheduling organization to simulate the operation, loading and unloading, queuing, and charging behavior of individual vehicles in the fleet. Simultaneously, upon arrival at a charging station, it comprehensively considers economic efficiency and transportation demand to generate an optimized charging strategy and make decisions regarding charging behavior. This achieves full-process simulation of the vehicle travel chain, providing significant reference for electric heavy-duty truck capacity demanders in planning operational scheduling and station configuration, and can promote improvements in fleet operating efficiency and economy.

[0132] Example 2

[0133] Based on the technical solution provided in Embodiment 1, this invention provides an electric heavy-duty truck trip chain simulation system that considers charging strategy optimization. The system includes a driving distance integration module, a status update module, and a loop module.

[0134] The system includes a driving distance integration module, which integrates the vehicle's driving distance while in motion, calculates and updates the target station distance and vehicle battery level based on the integrated driving distance, and determines whether the vehicle has arrived at the target station based on the target station distance. A status update module, when determining that the vehicle has arrived at the target station, updates the vehicle status to loading / unloading status and executes a loading / unloading event if the target station is a loading / unloading station and the vehicle's loading status meets the loading / unloading conditions. After the loading / unloading event is completed, the vehicle status is updated back to driving status. If the target station is a charging station and the vehicle's battery level meets the charging conditions, the module determines whether a charging station exists. If an empty charging station exists, the vehicle status is updated to charging status and a charging event is executed. If no charging station exists, it is determined whether the vehicle's overall charging satisfaction is greater than a preset threshold. If it is greater, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If it is less than the threshold, the vehicle status is directly updated to driving status. The overall charging satisfaction is determined based on charging time and charging cost. The loop module is used to update the next target station for vehicles entering the driving state and loop through all steps of the driving distance integration module and the status update module until the vehicle completes the simulation of the entire travel chain.

[0135] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating the trip chain of electric heavy-duty trucks considering charging strategy optimization, characterized in that, Includes the following steps: The distance to the target station and the vehicle's battery level are updated in real time while the vehicle is in motion; When it is determined that the vehicle has arrived at the target station, if the target station is a loading and unloading station and the vehicle's loading status meets the loading and unloading conditions, the vehicle status is updated to loading and unloading status and a loading and unloading event is executed. After the loading and unloading event is completed, the vehicle status is updated to driving status. If the target station is a charging station and the vehicle's battery level meets the charging conditions, it is determined whether there are any vacant charging piles at the charging station and whether the vehicle's overall charging satisfaction is greater than a preset threshold. If there are any vacant charging piles or the overall charging satisfaction is greater than the preset threshold, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If there are no vacant charging piles or the overall charging satisfaction is less than the preset threshold, the vehicle status is directly updated to driving status. The overall charging satisfaction is determined based on charging time and charging cost. The target station is updated for vehicles entering driving status, and the above steps are repeated until the simulation is completed; The specific process for determining the overall satisfaction with vehicle charging is as follows: The charging time satisfaction is calculated based on the estimated queuing time, the first charging time, and the second charging time, as expressed below: In the above formula, This indicates satisfaction with the charging time. This indicates the estimated charging time. This indicates the maximum tolerable charging time. This indicates the minimum charging time. The first charging time indicates the estimated charging time to bring the vehicle to its minimum charge level. The second charging duration refers to the time it takes for the vehicle to finish charging from its lowest battery level until the end of a low electricity price period or when the battery is fully charged. Indicates the estimated waiting time. This indicates the charging time tolerance factor; Based on the estimated total charging volume and real-time electricity price, the charging cost satisfaction is calculated as follows: In the above formula, This indicates satisfaction with the charging costs. This indicates the estimated charging cost. This represents the minimum charging cost. This indicates the average charging power of the charging piles at the charging station. This indicates the tolerance factor for charging costs. This indicates the maximum tolerable charging cost. This indicates the expected total charging amount. This indicates the real-time peak-valley electricity price for the region. Indicates off-peak electricity prices; Based on the satisfaction levels regarding charging time and charging cost, the overall satisfaction level is calculated as follows: In the above formula, Indicates overall satisfaction. This indicates satisfaction with the charging time. Indicates the weight of charging time. This indicates satisfaction with the charging costs. This indicates the weight of the charging cost.

2. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 1, characterized in that, The expressions for updating the target station distance and vehicle battery level are as follows: In the above formula, express Vehicle battery level at any given time. express Vehicle battery level at any given time. express Distance traveled at any given time Indicates the power consumption per kilometer. express Distance to the target station at any given time express Distance to the target station at any given time.

3. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 1, characterized in that, The specific process for determining whether a target site is a loading / unloading site or a charging site is as follows: The system determines whether the target station is a loading / unloading station and whether the vehicle's loading status meets the loading / unloading conditions. If the first charging condition and the second charging condition are met, the target station is determined to be a charging station. The first charging condition is that the target station is not a loading / unloading station, and the second charging condition is that the target station is a loading / unloading station and the vehicle's loading status does not meet the loading / unloading conditions.

4. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 1, characterized in that, The specific process of updating the vehicle status to loading / unloading status and executing the loading / unloading event when the target station is a loading / unloading station and the vehicle loading status meets the loading / unloading conditions is as follows: When the target station is a loading station and the vehicle loading status meets the loading conditions, the vehicle status is updated to loading status. It is then determined whether there is an empty loading port at the loading station. If not, loading waits. If so, loading begins, and after loading is completed, the vehicle loading status is updated to full load status and the vehicle status is updated to driving status. When the target station is an unloading station and the vehicle loading status meets the unloading conditions, the vehicle status is updated to unloading status. It is then determined whether there is an empty unloading port at the unloading station. If not, unloading waits. If so, unloading begins, and after unloading is completed, the vehicle loading status is updated to empty status and the vehicle status is updated to driving status.

5. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 1, characterized in that, The charging capacity condition is that the vehicle's current battery capacity is greater than or equal to the minimum capacity, where the minimum capacity is the sum of the power consumption during the journey to the next charging station and the reserved capacity, expressed as follows: In the above formula, Indicates the minimum battery level. Indicates battery capacity, This indicates the distance the vehicle has traveled to the next charging station. Indicates the power consumption per kilometer. Indicates the reserve coefficient. This indicates that the transportation task has been completed. This indicates that the transportation task has not been completed.

6. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in any one of claims 1 to 5, characterized in that, The specific process for determining the overall satisfaction with vehicle charging also includes: Obtain vehicle parameters and charging station configuration parameters, and calculate the estimated queuing time for vehicles based on the vehicle parameters and charging station configuration parameters; The first charging time and the second charging time of the vehicle are determined, and the total expected charging amount of the vehicle is calculated based on the expected queuing time, the first charging time and the second charging time.

7. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 6, characterized in that, The formula for calculating the estimated queuing time is as follows: In the above formula, This indicates the number of vehicles waiting to be charged at the charging station. To adjust the factor, This indicates the charging time for each vehicle. This indicates the number of charging stations at a charging station. Indicates battery capacity, This indicates charging efficiency.

8. The electric heavy-duty truck trip chain simulation method considering charging strategy optimization as described in claim 7, characterized in that, The specific process for calculating the vehicle's expected total charging amount is as follows: The estimated charging time to bring a vehicle to its minimum charge level is calculated using the following expression: In the above formula, This indicates the estimated amount of charge required to bring the vehicle to its minimum charge level. Indicates the vehicle's current battery level; The time it takes for a vehicle to finish charging from its lowest charge level until the end of a low electricity price period or when the battery is fully charged is calculated using the following expression: In the above formula, Indicates the end of the electricity price trough. Indicates the point at which charging ends at the minimum charge level. Indicates the current time; The estimated total charging time for the vehicle is calculated based on the estimated queuing time, the first charging time, and the second charging time, as shown in the following expression: In the above formula, This indicates the estimated charge amount from the lowest available charge level to the end of a low electricity price period or when the battery is fully charged.

9. An electric heavy-duty truck trip chain simulation system considering charging strategy optimization, characterized in that, include: The driving distance integration module is used to integrate the driving distance of the vehicle in the driving state, calculate and update the target station distance and vehicle battery power based on the driving distance integration, and determine whether the vehicle has arrived at the target station based on the target station distance. The status update module is used to update the vehicle status to loading / unloading status and execute a loading / unloading event when it is determined that the vehicle has arrived at the target station. If the target station is a loading / unloading station and the vehicle loading status meets the loading / unloading conditions, the module will update the vehicle status to driving status. When the target station is a charging station and the vehicle battery level meets the charging requirements, it is determined whether there is an available charging pile at the charging station. If there is, the vehicle status is updated to charging status and a charging event is executed. If there is no available charging pile, it is determined whether the overall vehicle charging satisfaction is greater than a preset threshold. If it is greater, the vehicle status is updated to charging status and a charging event is executed. After the charging event is completed, the vehicle status is updated to driving status. If it is less than the threshold, the vehicle status is directly updated to driving status. The overall vehicle charging satisfaction is determined based on charging time and charging cost. The loop module is used to update the next target station for vehicles that have entered the driving state, and to repeatedly execute all the steps of the driving distance integration module and the status update module until the vehicle completes the simulation of the entire travel chain. The specific process for determining the overall satisfaction with vehicle charging is as follows: The charging time satisfaction is calculated based on the estimated queuing time, the first charging time, and the second charging time, as expressed below: In the above formula, This indicates satisfaction with the charging time. This indicates the estimated charging time. This indicates the maximum tolerable charging time. This indicates the minimum charging time. The first charging time indicates the estimated charging time to bring the vehicle to its minimum charge level. The second charging duration refers to the time it takes for the vehicle to finish charging from its lowest battery level until the end of a low electricity price period or when the battery is fully charged. Indicates the estimated waiting time. This indicates the charging time tolerance factor; Based on the estimated total charging volume and real-time electricity price, the charging cost satisfaction is calculated as follows: In the above formula, This indicates satisfaction with the charging costs. This indicates the estimated charging cost. This represents the minimum charging cost. This indicates the average charging power of the charging piles at the charging station. This indicates the tolerance factor for charging costs. This indicates the maximum tolerable charging cost. This indicates the expected total charging amount. This indicates the real-time peak-valley electricity price for the region. Indicates off-peak electricity prices; Based on the satisfaction levels regarding charging time and charging cost, the overall satisfaction level is calculated as follows: In the above formula, Indicates overall satisfaction. This indicates satisfaction with the charging time. Indicates the weight of charging time. This indicates satisfaction with the charging costs. This indicates the weight of the charging cost.

Images