Delivery route planning method

Abstract

To provide a delivery route planning method capable of lightening the burden due to heat generation of an electric vehicle and making a delivery by the electric vehicle in an optimum delivery route.SOLUTION: A delivery route planning method comprises: steps (S6, S14, S20) of estimating heat quantities of electric facilities (batteries 11, INV13a, MG13b) mounted on an electric vehicle 10 and used to drive the electric vehicle 10 based upon the loadage of the electric vehicle 10 and the altitude of the delivery route; and steps (S11, S17, S23, S26, S30, S32, etc.) of determining the delivery route so that the temperature calculated based upon the estimated heat quantities is lower than a predetermined load rate limit temperature.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to a delivery route planning method.

Background Art

[0002] International Publication No. 2020 / 090252 (Patent Document 1) discloses a delivery plan generation method that calculates the travelable distance of an electric vehicle based on the vehicle information of the electric vehicle for delivering goods and the SOC (State Of Charge) of the secondary battery mounted on the electric vehicle, and generates a delivery plan for the goods using the delivery destination information of the goods and the travelable distance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When making a delivery plan using such an electric vehicle, for example, when there is a steep slope continuously on the delivery route, there is a risk that the heat load due to the heat generation of electrical equipment such as the battery used for driving the electric vehicle will increase. When the load factor due to heat increases in this way, control is performed to reduce the driving force of the electric vehicle in order to reduce the heat load, and as a result, there may be a case where the delivery route cannot be traveled to the end.

[0005] However, in the delivery plan generation method disclosed in Patent Document 1, a method for planning a delivery route considering the load due to heat generation in an electric vehicle as described above has not been studied.

[0006] The present disclosure has been made to solve the above problems, and an object of the present disclosure is to provide a delivery route planning method that reduces the load due to heat generation in an electric vehicle and enables the electric vehicle to deliver goods on a suitable delivery route.

Means for Solving the Problem

[0007] The delivery route planning method of the present disclosure is a method for planning a delivery route for delivering goods using an electric vehicle. The delivery route planning method includes a step of estimating the heat generation amount of the electrical equipment used for driving the electric vehicle mounted on the electric vehicle based on the load capacity of the electric vehicle and the elevation on the delivery route, and a step of determining a delivery route so that an index value calculated based on the estimated heat generation amount is less than a predetermined reference value. According to such a configuration, it is possible to determine a delivery route with a small influence of the load due to the heat generation of the electrical equipment by using the heat generation amount of the electrical equipment estimated based on the load capacity of the electric vehicle and the elevation on the delivery route. Thereby, the load due to heat generation in the electric vehicle can be reduced, and the electric vehicle can deliver with a suitable delivery route.

Effect of the Invention

[0008] According to the present disclosure, the load due to heat generation in the electric vehicle can be reduced, and the electric vehicle can deliver with a suitable delivery route.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Mode for Carrying Out the Invention

[0010] Embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals and their description will not be repeated.

[0011] FIG. 1 is a diagram schematically showing the overall configuration of the delivery route planning system 1. The delivery route planning system 1 includes an electric vehicle 10 for delivering packages and a delivery route planning device 100. The electric vehicle 10 and the delivery route planning device 100 can communicate via a communication network.

[0012] The delivery route planning device 100 is a device that plans a delivery route for delivering packages using the electric vehicle 10. Hereinafter, a delivery route planning method executed by the delivery route planning device 100 will be described. The electric vehicle 10 loads packages to be delivered within a delivery area managed by a delivery company and travels along the delivery route determined by the delivery route planning device 100 to deliver the packages. There may be a plurality of electric vehicles 10. The delivery route planning device 100 allocates the packages to be loaded on each electric vehicle 10 and commands each vehicle to travel along the designated delivery route to deliver the allocated packages. Hereinafter, a method for determining a delivery route for the electric vehicle 10 after allocating a plurality of packages to one electric vehicle 10 will be described.

[0013] In this example, the delivery route planning device 100 commands the electric vehicle 10 to perform deliveries to N delivery destinations, namely, delivery destination 1, delivery destination 2, ···, delivery destination N. The delivery route from the starting point where the delivery begins to delivery destination 1 is referred to as "section 1", the delivery route from delivery destination 1 to delivery destination 2 is referred to as "section 2", and the delivery route from delivery destination N - 1 to delivery destination N is referred to as "section N". Note that the package delivery is performed up to delivery destination N - 1, and section N may be the return route to the starting point.

[0014] The electric vehicle 10 is an electric vehicle that can be charged from a charging facility (EVSE: Electric Vehicle Supply Equipment), and is a BEV (Battery Electric Vehicle) that does not have an engine (internal combustion engine) and runs using a battery 11. The electric vehicle 10 includes a battery 11, an ECU (Electronic Control Unit) 12, an inverter (INV) 13a, a motor generator (MG) 13b, a DCM (Data Communication Module) 14, an HMI (Human Machine Interface) 15, and a navigation device 17 that processes position information detected by GPS. The battery 11 stores electric power used for the running of the electric vehicle 10. The battery 11 is composed of, for example, a lithium-ion battery. A drive unit (not shown) is configured including the INV 13a and the MG 13b. The INV 13a drives the MG 13b using the electric power of the battery 11 and charges the battery 11 with the regenerative electric power generated by the MG 13b. The MG 13b is a drive source that drives the drive wheels of the electric vehicle 10. The DCM 14 is a module for communicating with an external device via a communication network, transmits data from the ECU 12 to the external device, and delivers data from the external device to the ECU 12. The HMI 15 is provided near the driver's seat of the electric vehicle 10, receives information input from the user and outputs it to the ECU 12, or displays or notifies the user of information from the ECU 12 by display or voice, and is configured to include, for example, a touch panel display.

[0015] The delivery route planning device 100 includes a CPU 110, a memory 120 that stores programs executable by the CPU 110, a communication unit 130, and a mass storage device 140. The memory 120 includes a RAM (Random Access Memory) and a ROM (Read Only Memory). The communication unit 130 can communicate with an external device via a communication network. The mass storage device 140 is composed of an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores programs and the like used by the CPU 110. The CPU 110 executes programs stored in the memory 120 or the mass storage device 140.

[0016] When the loading amount of luggage or the like on the electric vehicle 10 is large, load rate limitation may be implemented due to the heat generation of the electrical equipment used for driving the electric vehicle 10 mounted on the electric vehicle 10. In the present embodiment, the above-mentioned "electrical equipment" refers to the battery 11, the INV 13a, and the MG 13b, but may include other electrical equipment. Hereinafter, the battery 11 is also abbreviated as "battery", the INV 13a as "INV", and the MG 13b as "MG". When the load rate limitation is implemented, control is performed to reduce the driving force of the electric vehicle 10, and as a result, a case may occur where the delivery route cannot be fully traveled. For this reason, in the present embodiment, a search for a delivery route considering the influence of heat generation is performed. Specifically, in the present embodiment, the delivery route planning device 100 estimates the heat generation amount of the electrical equipment (battery, MG, INV) based on the loading amount (luggage, passengers) of the electric vehicle 10 and the elevation on the delivery route. Further, the delivery route planning device 100 determines a delivery route so that an index value (temperature) calculated based on the estimated heat generation amount is less than a predetermined reference value (load rate limitation temperature). Hereinafter, a specific explanation will be given.

[0017] The large-capacity memory device 140 stores delivery item information 141, map information 142, and vehicle information 143, which are used when determining the delivery route. The delivery item information 141 records information on the number of delivery items, delivery locations, and delivery item weights regarding the delivery items scheduled to be loaded on the electric vehicle 10. The map information 142 records elevation data, average vehicle speed data for the route, and route length regarding the delivery routes (routes) that can be assigned to the electric vehicle 10. The vehicle information 143 includes, as weight data for the electric vehicle 10, vehicle weight, weight per passenger, and, as data regarding running resistance, friction coefficient μ, CdA value, and, as data regarding the resistance of electrical equipment, battery resistance Rb, MG resistance Rm, INV resistance Rinv, and map data (battery / MG / INV, refer to Figure 2) showing the relationship between the average vehicle speed v and the temperature rise amount ΔT with respect to the heat generation amount Q are respectively recorded.

[0018] As shown in Figure 1, in section 1 (starting point ~ delivery destination 1), let the difference in elevation between the delivery destination 1 and the starting point be Δh1, the horizontal distance from the starting point to the delivery destination 1 be Δd1, and the average gradient from the starting point to the delivery destination 1 be θ[1]. In this case, the larger the elevation difference Δh1, the larger the average gradient θ[1], and the larger the heat generation amount of the electrical equipment (battery, MG, INV). The same applies to sections 2 to N.

[0019] Figure 2 is a diagram for explaining the outline of the method for planning a delivery route. In the present embodiment, when delivering the goods assigned to the electric vehicle 10, the delivery route planning device 100 first searches for the shortest delivery route (the shortest route) to complete the delivery. Then, as shown in Figure 2, the heat generation amount for each electrical equipment (battery, MG, INV) in each section is calculated. The weight M of the electric vehicle 10 is calculated as the sum of the vehicle weight, the weight of one delivery person, and the weight m of the delivered goods. The total weight m of the delivered goods decreases every time the delivery location is reached. Here, it is assumed that the vehicle is traveling at the average vehicle speed v of the section, and it is considered that the driving force F for overcoming the running resistance is output, and the power P required by the vehicle is calculated by the following formula (1). The average gradient θ is calculated by the following formula (2) from the elevation difference Δh and the distance Δd of the section. At this time, since the required power P is expressed as P = iv in terms of the battery voltage V and the current i, the current i is calculated by the following formula (3). Since the heat generation amount Q = i 2 R, the heat generation amount Q is calculated by the following formula (4).

[0020]

Equation

[0021] By inputting the battery resistance Rb as the resistance R, the battery heat generation amount Qb is obtained. By inputting the MG resistance Rm, the MG heat generation amount Qm is obtained. By inputting the INV resistance Rinv, the INV heat generation amount Qinv is obtained.

[0022] Next, based on the calculated heat generation amount Q, the temperature of the electrical equipment after the section travel is simply predicted. The temperature rise amount ΔT changes based on the average vehicle speed v and the heat generation amount Q. Therefore, a map associating the relationship between the average vehicle speed v and the heat generation amount Q and the temperature rise amount ΔT is prepared, and the temperature rise amount ΔT is calculated from the correspondence between the average vehicle speed v and the heat generation amount Q and the map (calculation by map lookup). Then, the temperature rise amount ΔT is added to the current temperature T to calculate the predicted temperature T.

[0023] Next, a load factor intervention determination is performed to determine whether the predicted temperature is within the load factor limit temperature. These processes are executed for the entire section (section 1 to N). If the determination conditions for the entire section are satisfied, the route is determined as the delivery route. If the determination conditions are not satisfied, route search is repeated until the determination conditions are satisfied. Hereinafter, it will be specifically described using a flowchart.

[0024] Figure 3 is a flowchart showing the processing procedure of the processing executed by the delivery route planning system 1. First, in S1, the delivery route planning device 100 sets the route search variable i = 1. In S2, the delivery route planning device 100 searches for the shortest route of the delivery route. In S3, the delivery route planning device 100 selects a route that can travel at the i-th shortest distance. When i = 1, a candidate for the delivery route that travels the shortest distance is set. When i = 2, a candidate for the delivery route that travels the second shortest distance is set. The larger i is, the longer the travel distance becomes, but a route with a smaller average gradient is set. Note that it is not limited to setting candidates for the delivery route in ascending order of travel distance, and candidates for the delivery route may be set in ascending order of travel time.

[0025] In S4, the delivery route planning device 100 sets the section loop variable j = 1. When j ≤ the number of delivery points N (YES determination in S5), the process proceeds to S6. When j > the number of delivery points N (NO determination in S5), the process proceeds to S30. In S6, the delivery route planning device 100 calculates the average gradient θ by the following formula (5). In the following, for example, when i = 1, in the example of FIG. 1, Δh1 = h[1] - h[0] (the difference between the elevation of the delivery destination 1 and the elevation of the starting point), and Δd1 = d[1] - d[0] (the horizontal distance from the starting point to the delivery destination 1).

[0026]

Equation

[0027] In S7, the delivery route planning device 100 calculates the weight M[j] of the electric vehicle 10 according to the following formula (6). For example, when i = 2, M[2] (weight in section 2) = M[1] - m[1] (weight in section 1 - weight of the goods delivered to delivery destination 1), and the weight of the electric vehicle 10 in section 1 (M[1]) is the weight when all the goods are loaded.

[0028]

Number

[0029] The delivery route planning device 100 calculates the battery heat generation amount Qb (S8, formula (7)), calculates the battery temperature rise ΔTb[j] by map lookup from Qb[j] and v[j] (S9), and calculates the battery temperature Tb[j] (section maximum temperature) (S10, formula (8)) (for details, refer to Figure 2).

[0030]

Number

[0031] In the battery load factor limit determination, when the section maximum temperature Tb[j] < the load factor limit temperature Tb (YES determination in S11), the delivery route planning device 100 sets the battery load factor limit intervention flag Fb[j] = 0 (S12), and when Tb[j] ≥ Tb (NO determination in S11), it sets Fb[j] = 1 (S13). Thus, it is determined whether the load factor limit is implemented based on the predicted temperature.

[0032] The delivery route planning device 100 calculates the MG heat generation amount Qm (S14, formula (9)), calculates the MG temperature rise ΔTm[j] by map lookup from Qm[j] and v[j] (S15), and calculates the MG temperature Tm[j] (section maximum temperature) (S16, formula (10)).

[0033]

Number

[0034] When the distribution route planning device 100 determines in the MG load factor limit determination that the section maximum temperature Tm[j] < the load factor limit temperature Tm (YES determination in S17), it sets the MG load factor limit intervention flag Fm[j] = 0 (S18). When Tm[j] ≥ Tm (NO determination in S17), it sets Fm[j] = 1 (S19).

[0035] The distribution route planning device 100 calculates the INV heat generation amount Qinv (S20, Equation (11)), calculates the INV temperature rise ΔTinv[j] by map lookup from Qinv[j] and v[j] (S21), and calculates the INV temperature Tinv[j] (section maximum temperature) (S22, Equation (12)).

[0036]

Equation

[0037] When the distribution route planning device 100 determines in the INV load factor limit determination that the section maximum temperature Tinv[j] < the load factor limit temperature Tinv (YES determination in S23), it sets the INV load factor limit intervention flag Finv[j] = 0 (S24). When Tinv[j] ≥ Tinv (NO determination in S23), it sets Finv[j] = 1 (S25).

[0038] When all of Fb[j], Fm[j], and Finv[j] are 0 (YES determination in S26), the distribution route planning device 100 sets the section load factor limit intervention flag F[j] = 0 (S27). When any one of Fb[j], Fm[j], and Finv[j] is 1 (NO determination in S26), it sets Finv[j] = 1 (S28).

[0039] The delivery route planning device 100 adds 1 to j (S29) and returns the process to S5. When there is an interval in which F[j] becomes 1 in S30 (judgment is YES in S30), the delivery route planning device 100 adds 1 to i (S31) and returns the process to S2. On the other hand, when F[j] is 0 in any interval in S30 (judgment is NO in S30), the delivery route planning device 100 sets the currently selected route as the delivery route (S32) and ends this process. Note that in S26, it may be determined as YES when "any one of Fb[j], Fm[j], and Finv[j] is 0", and in S30, it may be determined as YES when "F[j] is 1 in all intervals".

[0040] In this way, the delivery route planning method is a method for the delivery route planning device 100 to plan the delivery route of the goods using the electric vehicle 10. The delivery route planning method includes steps of estimating the calorific value of the electrical equipment (battery 11, INV13a, MG13b) used for driving the electric vehicle 10 mounted on the electric vehicle 10 based on the loading capacity (goods, passengers) of the electric vehicle 10 and the elevation on the delivery route (S6, S14, S20), and determining the delivery route so that the index value (temperature) calculated based on the estimated calorific value is less than a predetermined reference value (load rate limit temperature) (steps such as S11, S17, S23, S26, S30, S32, etc.). According to such a configuration, it is possible to determine a delivery route with a small influence of the load due to the heat generation of the electrical equipment by using the calorific value of the electrical equipment estimated based on the loading capacity of the electric vehicle 10 and the elevation on the delivery route. Thereby, the load due to heat generation in the electric vehicle 10 can be reduced, and the electric vehicle 10 can deliver with a suitable delivery route.

[0041] The embodiments disclosed this time should be considered as illustrative in all respects and not restrictive. The scope of the present disclosure is shown by the claims rather than the description of the above embodiments, and it is intended that all modifications within the meaning and scope equivalent to the claims are included.

Explanation of Signs

[0042] 1 Delivery route planning system, 10 Electric vehicle, 11 Battery, 12 ECU, 13a INV, 13b MG, 14 DCM, 15 HMI, 17 Navigation device, 100 Delivery route planning device, 110 CPU, 120 Memory, 130 Communication unit, 140 Mass storage device, 141 Delivery item information, 142 Map information, 143 Vehicle information.

Claims

Claim 1 A delivery route planning method for planning a delivery route for delivering goods using an electric vehicle, comprising: estimating the calorific value of electrical equipment used for driving the electric vehicle mounted on the electric vehicle based on the load capacity of the electric vehicle and the elevation on the delivery route; and determining the delivery route so that an index value calculated based on the estimated calorific value is less than a predetermined reference value. A delivery route planning method.

Images