An electric heavy truck fleet technical configuration and operation coordination optimization method

Abstract

This invention relates to the field of logistics fleet operation management technology, specifically to a method for collaborative optimization of the technical configuration and operation of electric heavy-duty truck fleets. The steps are as follows: An integrated optimization model is established to determine decision variables; an embedded two-layer optimization architecture is established, embedding the decision variables as the inner layer optimization into the outer layer's full lifecycle unit transportation cost optimization. The outer layer optimization uses transportation task achievement and battery safety as constraints to calculate the full lifecycle unit transportation cost of the fleet. The inner layer optimization uses a fleet agent with set fleet size, set single-vehicle power battery capacity, and set a lower limit for charging thresholds to simulate operation and evaluate the expected total cost of all reachable depots; the globally optimal solution is output. This invention can effectively alleviate the disconnect between early planning and actual operation, making the technical configuration scheme more in line with actual needs, thereby reducing investment waste in vehicles and batteries and lowering the unit transportation cost of the fleet throughout its entire lifespan.

Description

Technical Field

[0001] This invention relates to the field of logistics fleet operation management technology, and more specifically, to a method for optimizing the technical configuration and operation of electric heavy truck fleets. Background Technology

[0002] With increasingly stringent environmental protection requirements and the continued implementation of carbon emission reduction policies, the traditional road freight transportation industry is moving towards green and low-carbon development. Heavy-duty trucks account for a significant proportion of carbon emissions in the transportation sector, making the promotion and use of electric heavy-duty trucks a crucial means to achieve energy conservation and emission reduction goals. In recent years, with the rapid advancement of power battery technology, the number of electric heavy-duty trucks used in specific scenarios such as ports, mines, and regional intercity logistics has been steadily increasing.

[0003] In current electric heavy truck fleet planning, operations planners typically separate the fleet's technical parameter configuration from daily operational scheduling. During the technical parameter planning phase, the number of vehicles and the battery capacity of each vehicle are generally determined based on past freight demands and work experience, without considering the actual charging situation during driving. However, during the actual operational planning phase, fixed battery standards are usually used to guide vehicle charging behavior. For example, when a vehicle's remaining battery power drops to a certain fixed value, the vehicle is driven to a nearby charging station to ensure the smooth completion of transportation tasks.

[0004] However, this approach of separating fleet technical configuration from daily operation management has obvious drawbacks. Because there is a very close relationship between the initial configuration of vehicles and their daily operating status, if the operation planners ignore the actual road conditions and various variable factors in daily operation during the planning stage, it is easy to cause the configuration plan to be mismatched with the actual operation needs. In actual use, this may not only cause waste of vehicle equipment resources, but also fail to guarantee the stability and efficiency of daily freight transportation tasks.

[0005] Therefore, how to combine the technical configuration and daily operation of electric heavy-duty truck fleets for overall optimization, so as to reduce the overall operating cost of the fleet throughout its entire service life while ensuring the smooth completion of transportation tasks, is a technical problem that urgently needs to be solved in the current heavy-duty truck logistics field. Summary of the Invention

[0006] The purpose of this invention is to provide a method for the coordinated optimization of the technical configuration and operation of electric heavy-duty truck fleets, in order to solve the technical problem of how to combine the technical configuration and daily operation of electric heavy-duty truck fleets for overall optimization, thereby reducing the overall operating cost of the fleet throughout its entire service life while ensuring the smooth completion of transportation tasks.

[0007] This invention is achieved through the following technical solution: a method for collaborative optimization of the technical configuration and operation of an electric heavy-duty truck fleet, comprising the following steps: An integrated optimization model is established to determine decision variables, which include fleet size, single-vehicle power battery capacity, and lower limit of charging threshold. An embedded two-layer optimization architecture is established, in which the decision variables are embedded as the inner layer optimization into the outer layer fleet life-cycle unit transportation cost optimization. The outer layer optimization uses the transportation task achievement degree and battery safety as constraints to calculate the fleet life-cycle unit transportation cost. The inner layer optimization uses a fleet agent with a set fleet size, set single vehicle power battery capacity and set charging threshold lower limit to simulate operation and evaluate the expected total cost of all reachable stations. Output the globally optimal solution.

[0008] According to a preferred embodiment, the calculation expression for the unit transportation cost of the fleet throughout its entire life cycle is as follows:

[0009] In the above formula, This represents the unit transportation cost over the entire lifecycle of the fleet. This represents the fleet's total annual cost. This indicates the fleet's total annual transport turnover.

[0010] According to a preferred embodiment, the fleet's total annual cost The calculation expression is as follows:

[0011] In the above formula, This indicates the annual diluted cost of vehicle investment. This indicates the annual diluted cost of battery investment. Indicates the cost of energy and charging services. This represents the driver's labor and time costs; Among them, the annual diluted cost of vehicle investment The calculation expression is as follows:

[0012] In the above formula, Indicates the vehicle's lifespan. This indicates the purchase price of a single bicycle. Indicates the size of the fleet. This represents the vehicle capital recovery factor. , Indicates the discount rate; Battery investment annual diluted cost The calculation expression is as follows:

[0013] In the above formula, Indicates the lifespan of the power battery. This indicates the purchase price per kilowatt-hour of power batteries. Indicates the battery capacity of a single vehicle. Indicates the capital recovery factor of power batteries; Energy and charging service costs The calculation expression is as follows:

[0014] In the above formula, Indicates vehicle In the Number of times to charge per day Indicates vehicle In the Heavenly The amount of electricity on the first charge Indicates vehicle In the Heavenly The electricity price corresponding to the first charge. Indicates vehicle In the Heavenly The unit price of the service fee at the charging station during the first charging; Driver labor and time costs The calculation expression is as follows:

[0015] In the above formula, This indicates the driver's wage per unit of time. This indicates the average daily travel time of a single bicycle. This indicates the average daily queuing time for a single bicycle. This indicates the average daily charging time for a single vehicle.

[0016] According to a preferred embodiment, the fleet's total annual transport turnover The calculation expression is as follows:

[0017] In the above formula, This indicates the average number of trips completed per day by a single bicycle. Indicates the transportation distance. Indicates the full load mass.

[0018] According to a preferred embodiment, the transportation task achievement constraint is that the fleet's daily transportation capacity meets the daily transportation task requirements, as expressed below:

[0019] In the above formula, This indicates the planned average daily freight volume; The battery safety constraint is that the charging-triggered state-of-charge threshold is within a safety margin, as expressed below:

[0020] In the above formula, This indicates the lower limit of the charging threshold. This indicates the minimum safe charge level allowed by the power battery. Indicates the safety margin.

[0021] According to a preferred embodiment, the formula for calculating the expected total cost is as follows:

[0022] In the above formula, Indicates that the vehicle has arrived at the depot. The expected total cost, This represents the weighting coefficient between time cost and energy cost. Indicates the journey from the current location to the station. The expected time, Indicates at the station The expected average queuing time Indicates at the station The expected cost to complete this charging session. Indicates at the station The battery depreciation cost for completing this charge.

[0023] According to a preferred embodiment, the expected average queuing time is estimated by a queuing theory model, as shown in the following expression:

[0024] In the above formula, Indicates station The number of charging facilities, Indicates system busy level. This represents the system's average service rate. This represents the square of the coefficient of variation of service time. , This represents the variance of the single-vehicle charging service time. This represents the expected average service time for charging a single vehicle.

[0025] According to a preferred embodiment, the expected cost The calculation expression is as follows:

[0026] In the above formula, This indicates the amount of electricity required to reach the upper limit of the charging threshold. Indicates the expected start time of charging. Electricity price, Indicates the expected arrival time at the station. Indicates station The unit price of the service fee.

[0027] According to a preferred embodiment, the battery depreciation cost The calculation expression is as follows:

[0028] In the above formula, This indicates the average depth of discharge of the power battery in this test. and station charging power The number of cycles under the specified lifespan, This indicates the unit price of the power battery.

[0029] According to a preferred embodiment, the inner layer optimization process is as follows: The fleet is simulated by setting the fleet size, the battery capacity of each vehicle, and the lower limit of the charging threshold, and random scenarios are set. A daily cycle simulation is performed, and energy consumption is calculated in real time based on the vehicle dynamics model while the vehicle is traveling along a preset route. The SOC is obtained by integration. SOC determination is performed. When the SOC is lower than the set charging threshold, the expected total cost of all reachable stations is evaluated, and the station with the lowest expected total cost is selected as the target station. Add to the target site queue and update SOC and site status in real time; Output the simulation results.

[0030] The technical solution of the electric heavy-duty truck fleet technical configuration and operation collaborative optimization method provided by this invention has at least the following advantages and beneficial effects: By combining the technical parameter configuration of the fleet with the daily operation plan, this invention can effectively alleviate the disconnect between the initial planning and actual operation; the inner-layer operation simulation can take into account various changes such as vehicle queuing, battery wear and tear, and electricity price fluctuations during actual driving, so the method can make the technical configuration scheme more in line with daily usage needs; on this basis, the outer-layer optimization, under the premise of ensuring the smooth completion of cargo transportation tasks and the safety of power batteries, can find the optimal configuration of fleet size, single-vehicle battery capacity, and charging lower limit, which can not only reduce the waste of initial investment in vehicles and batteries, but also reduce the unit transportation cost of the fleet throughout its entire service life. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall process of the electric heavy truck fleet technology configuration and operation collaborative optimization method provided in Embodiment 1 of the present invention. Detailed Implementation

[0032] 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.

[0033] Example 1 This invention provides a method for the collaborative optimization of technical configuration and operation of electric heavy truck fleets. Figure 1 See the overall flowchart of this collaborative optimization method. Figure 1 As shown, the collaborative optimization method includes the following steps: Step S1: Construction of the integrated optimization model; This embodiment constructs an integrated optimization model with the objective of minimizing the unit transportation cost of the fleet throughout its entire lifecycle, i.e., the cost per ton-kilometer. The objective function is expressed as:

[0034] In the above formula, This represents the unit transportation cost over the entire lifecycle of the fleet. This represents the fleet's total annual cost. This represents the fleet's total annual transport turnover; the decision variables of this integrated optimization model include fleet size, single-vehicle power battery capacity, and lower limit of charging threshold.

[0035] In some preferred embodiments, the fleet's total annual cost The calculation expression is as follows:

[0036] In the above formula, This indicates the annual diluted cost of vehicle investment. This indicates the annual diluted cost of battery investment. Indicates the cost of energy and charging services. This represents the driver's labor and time costs; Among them, the annual diluted cost of vehicle investment The calculation expression is as follows:

[0037] In the above formula, Indicates the vehicle's lifespan. This indicates the purchase price of a single bicycle. Indicates the size of the fleet. This represents the vehicle capital recovery factor. , Indicates the discount rate; Battery investment annual diluted cost The calculation expression is as follows:

[0038] In the above formula, Indicates the lifespan of the power battery. This indicates the purchase price per kilowatt-hour of power batteries. Indicates the battery capacity of a single vehicle. Indicates the capital recovery factor of power batteries; Energy and charging service costs The calculation expression is as follows:

[0039] In the above formula, Indicates vehicle In the Number of times to charge per day Indicates vehicle In the Heavenly The amount of electricity on the first charge Indicates vehicle In the Heavenly The electricity price corresponding to the first charge. Indicates vehicle In the Heavenly The unit price of the service fee at the charging station during the first charging; Driver labor and time costs The calculation expression is as follows:

[0040] In the above formula, This indicates the driver's wage per unit of time. This indicates the average daily travel time of a single bicycle. This indicates the average daily queuing time for a single bicycle. This indicates the average daily charging time for a single vehicle.

[0041] In addition, the fleet's total annual transport turnover The calculation expression is as follows:

[0042] In the above formula, This indicates the average number of trips completed per day by a single bicycle. Indicates the transportation distance. Indicates the full load mass.

[0043] Step S2: Simulation optimization solution; This embodiment establishes an embedded two-layer optimization architecture, embedding the decision variables as the inner layer optimization into the outer layer fleet lifecycle unit transportation cost optimization; Among them, the outer layer optimization uses the achievement of transportation tasks and battery safety as constraints to calculate the unit transportation cost of the fleet throughout its entire life cycle.

[0044] In some embodiments of this example, the transportation task achievement constraint is that the fleet's daily transportation capacity meets the daily transportation task requirements, as expressed below:

[0045] In the above formula, This indicates the planned average daily freight volume; The battery safety constraint is that the charging-triggered state-of-charge threshold is within a safety margin, as expressed below:

[0046] In the above formula, This indicates the lower limit of the charging threshold. This indicates the minimum safe charge level allowed by the power battery. Indicates the safety margin.

[0047] In some preferred embodiments, the outer layer employs an improved genetic algorithm for global optimization, the specific process of which is as follows: Step S201: Encode the decision variables as chromosomes; Step S202: Call the inner layer to simulate each individual in the population, obtain the corresponding unit transportation cost of the fleet's full life cycle, and perform fitness evaluation; Step S203: Perform genetic operations to generate a new generation population; Step S204: Repeat steps S202 to S203 until the maximum number of iterations is reached or convergence is achieved, and output the optimal chromosome, which is the decision variable.

[0048] The inner layer optimization simulates fleet operation using a fleet agent with set fleet size, set single-vehicle battery capacity, and set lower limit for charging threshold, and evaluates the expected total cost of all reachable depots; in some embodiments of this example, the calculation expression for the expected total cost is as follows:

[0049] In the above formula, Indicates that the vehicle has arrived at the depot. The expected total cost, This represents the weighting coefficient between time cost and energy cost. Indicates the journey from the current location to the station. The expected time, Indicates at the station The expected average queuing time Indicates at the station The expected cost to complete this charging session. Indicates at the station The battery depreciation cost for completing this charge.

[0050] The expected average queuing time is estimated by a queuing theory model, as shown in the following expression:

[0051] In the above formula, Indicates station The number of charging facilities, Indicates system busy level. This represents the system's average service rate. This represents the square of the coefficient of variation of service time. , This represents the variance of the single-vehicle charging service time. This represents the expected average service time for charging a single vehicle.

[0052] The expected costs The calculation expression is as follows:

[0053] In the above formula, This indicates the amount of electricity required to reach the upper limit of the charging threshold. Indicates the expected start time of charging. Electricity price, Indicates the expected arrival time at the station. Indicates station The unit price of the service fee.

[0054] The battery depreciation cost The calculation expression is as follows:

[0055] In the above formula, This indicates the average depth of discharge of the power battery in this test. and station charging power The number of cycles under the specified lifespan, This indicates the unit price of the power battery.

[0056] In some preferred embodiments, the inner layer optimization process is as follows: Step S205: Simulate operation using a fleet intelligent agent with set fleet size, set single vehicle power battery capacity and set charging threshold lower limit, and set random scenarios; Step S206: Perform daily cycle simulation. Calculate energy consumption in real time based on the vehicle dynamics model while the vehicle is traveling along a preset route, and obtain the SOC through integration. Step S207: Perform SOC determination. When the SOC is lower than the set charging threshold, evaluate the expected total cost of all reachable stations and select the station with the lowest expected total cost as the target station. Step S208: Add to the target site queue and update the SOC and site status in real time; Step S209: Output simulation results for outer layer calculation of the fleet's total lifecycle unit transportation cost. .

[0057] Step S3: Output the globally optimal solution.

[0058] In this embodiment, the globally optimal solution includes the optimal technical configuration and operation strategy, expected economic indicators, and expected operational performance indicators. Among them, the optimal technical configuration and operation strategy includes the optimal fleet size, the optimal power battery capacity per vehicle, and the optimal lower limit of the charging threshold. The expected economic indicators include the minimum ton-kilometer cost and its detailed cost composition, such as the proportion of vehicle, power battery, energy, and labor costs. The expected operational performance indicators include the average daily operating time per vehicle, the average daily queuing time, and the distribution of charging costs among the average daily number of power battery cycles.

[0059] In summary, this invention, by combining the technical parameter configuration of the fleet with daily operation plans, can effectively alleviate the disconnect between initial planning and actual operation. The inner-layer operation simulation can take into account various changes such as vehicle queuing, battery wear, and electricity price fluctuations during actual driving, thus making the technical configuration plan more in line with daily usage needs. On this basis, the outer-layer optimization, under the premise of ensuring the smooth completion of cargo transportation tasks and the safety of power batteries, can find the optimal configuration of fleet size, single-vehicle battery capacity, and charging minimum. This not only reduces the waste of initial investment in vehicles and batteries but also reduces the unit transportation cost of the fleet throughout its entire service life.

[0060] 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 collaborative optimization of technology configuration and operation of electric heavy-duty truck fleets, characterized in that, Includes the following steps: An integrated optimization model is established to determine decision variables, which include fleet size, single-vehicle power battery capacity, and lower limit of charging threshold. An embedded two-layer optimization architecture is established, in which the decision variables are embedded as the inner layer optimization into the outer layer fleet life-cycle unit transportation cost optimization. The outer layer optimization uses the transportation task achievement degree and battery safety as constraints to calculate the fleet life-cycle unit transportation cost. The inner layer optimization uses a fleet agent with a set fleet size, set single vehicle power battery capacity and set charging threshold lower limit to simulate operation and evaluate the expected total cost of all reachable stations. Output the globally optimal solution.

2. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 1, characterized in that, The formula for calculating the unit transportation cost of the fleet throughout its entire lifecycle is as follows: In the above formula, This represents the unit transportation cost over the entire lifecycle of the fleet. This represents the fleet's total annual cost. This indicates the fleet's total annual transport turnover.

3. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 2, characterized in that, Total annual cost of the fleet The calculation expression is as follows: In the above formula, This indicates the annual diluted cost of vehicle investment. This indicates the annual diluted cost of battery investment. Indicates the cost of energy and charging services. This represents the driver's labor and time costs; Among them, the annual diluted cost of vehicle investment The calculation expression is as follows: In the above formula, Indicates the vehicle's lifespan. This indicates the purchase price of a single bicycle. Indicates the size of the fleet. This represents the vehicle capital recovery factor. , Indicates the discount rate; Battery investment annual diluted cost The calculation expression is as follows: In the above formula, Indicates the lifespan of the power battery. This indicates the purchase price per kilowatt-hour of power batteries. Indicates the capacity of the vehicle's power battery. Indicates the capital recovery factor of power batteries; Energy and charging service costs The calculation expression is as follows: In the above formula, Indicates vehicle In the Number of times to charge per day Indicates vehicle In the Heavenly The amount of electricity on the first charge Indicates vehicle In the Heavenly The electricity price corresponding to the first charge. Indicates vehicle In the Heavenly The unit price of the service fee at the charging station during the first charging; Driver labor and time costs The calculation expression is as follows: In the above formula, This indicates the driver's wage per unit of time. This indicates the average daily travel time of a single bicycle. This indicates the average daily queuing time for a single bicycle. This indicates the average daily charging time for a single vehicle.

4. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 3, characterized in that, Fleet's annual total transport turnover The calculation expression is as follows: In the above formula, This indicates the average number of trips completed per day by a single bicycle. Indicates the transportation distance. Indicates the full load mass.

5. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 4, characterized in that, The constraint on the achievement of transportation tasks is that the daily transportation capacity of the fleet meets the daily transportation task requirements, as expressed below: In the above formula, This indicates the planned average daily freight volume; The battery safety constraint is that the charging-triggered state-of-charge threshold is within a safety margin, as expressed below: In the above formula, This indicates the lower limit of the charging threshold. This indicates the minimum safe charge level allowed by the power battery. Indicates the safety margin.

6. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 2, characterized in that, The formula for calculating the expected total cost is as follows: In the above formula, Indicates that the vehicle has arrived at the depot. The expected total cost, This represents the weighting coefficient between time cost and energy cost. Indicates the journey from the current location to the station. The expected time, Indicates at the station The expected average queuing time Indicates at the station The expected cost to complete this charging session. Indicates at the station The battery depreciation cost for completing this charge.

7. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 6, characterized in that, The expected average queuing time is estimated by a queuing theory model, as shown in the following expression: In the above formula, Indicates station The number of charging facilities, Indicates system busy level. This represents the system's average service rate. This represents the square of the coefficient of variation of service time. , This represents the variance of the single-vehicle charging service time. This represents the expected average service time for charging a single vehicle.

8. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 6, characterized in that, The expected costs The calculation expression is as follows: In the above formula, This indicates the amount of electricity required to reach the upper limit of the charging threshold. Indicates the expected start time of charging. Electricity price, Indicates the expected arrival time at the station. Indicates station The unit price of the service fee.

9. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in claim 6, characterized in that, The battery depreciation cost The calculation expression is as follows: In the above formula, This indicates the average depth of discharge of the power battery in this test. and station charging power The number of cycles under the specified lifespan, This indicates the unit price of the power battery.

10. The method for collaborative optimization of electric heavy-duty truck fleet technology configuration and operation as described in any one of claims 1 to 9, characterized in that, The inner layer optimization process is as follows: The fleet is simulated by setting the fleet size, the battery capacity of each vehicle, and the lower limit of the charging threshold, and random scenarios are set. A daily cycle simulation is performed, and energy consumption is calculated in real time based on the vehicle dynamics model while the vehicle is traveling along a preset route. The SOC is obtained by integration. SOC determination is performed. When the SOC is lower than the set charging threshold, the expected total cost of all reachable stations is evaluated, and the station with the lowest expected total cost is selected as the target station. Add to the target site queue and update SOC and site status in real time; Output the simulation results.

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