Operation management system and operation management method

Abstract

To provide an operation management system and operation management method capable of accurately estimating power consumption of an electric automobile when planning a travel route for delivery.SOLUTION: An operation management system 200 includes: a load progress estimation section 224 for estimating a load change at a delivery point in consideration of a congestion degree by defining the number of stops in the delivery on a travel route of a vehicle 300 being an electric automobile as the congestion degree; and a power consumption calculation section 225 for calculating power consumption of the travel route based on the estimated load.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an operation management system and an operation management method. In particular, the present invention relates to an operation management system and the like that can be suitably used when estimating the power consumption of an electric vehicle and planning a driving route based on the estimated power consumption.

Background Art

[0002] In recent years, from the perspective of environmental consideration, electric vehicles powered by electricity have been increasingly used. And attempts have been made to perform deliveries using electric vehicles.

[0003] Patent Document 1 describes that an apparatus for estimating the power consumption amount (cost value) of an electric vehicle includes a storage unit that stores a plurality of types of driving models showing the behavior of the electric vehicle when traveling on a road link for each number of stops on the road link, a stop number estimation unit that estimates the number of stops of the electric vehicle on the road link, and a processing unit that obtains the power consumption amount (cost value). The processing unit obtains the power consumption amount required for the electric vehicle to travel on the road link by using a specific driving model that corresponds to the number of stops estimated by the stop number estimation unit and is selected from among the plurality of types of driving models.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When estimating the power consumption of an electric vehicle, if the actual power consumption exceeds the predicted power consumption, a power outage occurs and the vehicle stops. In this case, for example, costs for plan corrections due to power outages such as arranging another vehicle or reloading the cargo are incurred. On the other hand, if the actual power consumption is much lower than the predicted power consumption, the buffer of the driving plan is too large and it is necessary to perform the delivery with more vehicles than the required number. In this case, for example, an increase in the steady-state cost occurs. An object of the present invention is to provide an operation management system and an operation management method capable of accurately estimating the power consumption of an electric vehicle when planning a driving route for delivery by the electric vehicle.

Means for Solving the Problems

[0006] To solve the above problems, the present invention provides an operation management system including a load transition estimation unit that estimates a change in load at a delivery point in consideration of the density, where the density is the number of stops due to delivery in the driving route of the electric vehicle, and a power consumption calculation unit that calculates the power consumption of the driving route based on the estimated load. In this case, when planning a driving route for delivery by an electric vehicle, an operation management system capable of accurately estimating the power consumption of the electric vehicle can be provided.

[0007] Here, for example, the power consumption calculation unit calculates the power consumption including the load at the start of the electric vehicle at the stop point, which is the delivery point and the stop point where the vehicle stops due to a signal. In this case, the power consumption of the electric vehicle can be accurately estimated in consideration of the influence of the load. Also, for example, the power consumption calculation unit calculates the load by multiplying the total vehicle weight at each of the delivery point and the stop point by a predetermined power consumption coefficient. In this case, a more suitable calculation formula can be used to calculate the power consumption including the load. Furthermore, for example, the operation management system further includes a signal stop point estimation unit that estimates the stop point, which is the point where the electric vehicle stops due to a signal. In this case, the number of stops due to signals can be obtained more accurately. Furthermore, for example, the signal stop point estimation unit calculates a signal stop coefficient, which is the rate of stopping at signals, based on the history of the electric vehicle's travel and the actual record of stopping at signals, and estimates the stop point. In this case, the stop point due to signals can be estimated more accurately. And, for example, the load transition estimation unit estimates the load change based on the rate calculated based on the past delivery performance and estimated to require redelivery. In this case, the power consumption including the load can be calculated considering the deliverability of the delivered items. Also, for example, the load transition estimation unit estimates the load change based on the number of delivered items estimated to require redelivery. In this case, the load change can be estimated even if the individual weights of the delivered items are unknown. Furthermore, for example, a route planning unit that plans the travel route of the electric vehicle, and a power shortage determination unit that determines whether the electric vehicle will run out of power by comparing the power consumption calculated by the power consumption calculation unit for a single travel route planned by the route planning unit with the remaining battery level of the electric vehicle. When the power shortage determination unit determines that the electric vehicle will run out of power for a single travel route, the route planning unit replans another travel route. In this case, a more suitable travel route can be obtained.

[0008] The present invention also provides an operation management system including a signal stop point estimation unit that estimates a stop point, which is a point where the electric vehicle stops due to a signal, on the travel route of the electric vehicle, a load transition estimation unit that estimates a load change at a delivery point, which is a point where the electric vehicle stops for delivery, on the travel route, and a power consumption calculation unit that calculates the power consumption including the load at the time of starting the electric vehicle at the estimated stop point and delivery point based on the estimated load. In this case, an operation management system capable of accurately estimating the power consumption of the electric vehicle when planning a travel route for delivery by the electric vehicle can be provided.

[0009] Furthermore, the present invention provides an operation management method in which a processor executes a program recorded in a memory to estimate the change in load at a delivery point in consideration of density, where the density is the number of stops due to delivery on the driving route of an electric vehicle, and calculates the power consumption of the driving route based on the estimated load. In this case, when planning a driving route for delivery by an electric vehicle, an operation management method capable of accurately estimating the power consumption of the electric vehicle can be provided.

Effects of the Invention

[0010] According to the present invention, it is possible to provide an operation management system and an operation management method capable of accurately estimating the power consumption of an electric vehicle when planning a driving route for delivery by the electric vehicle.

Brief Description of the Drawings

[0011]

Figure 1

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Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. <Overall Description of Vehicle Management System 1> FIG. 1 is a block diagram showing the overall configuration of the vehicle management system 1 of the present embodiment. The vehicle management system 1 according to this embodiment includes a delivery planning system 100 that manages delivery items, delivery destination points, and delivery results, and an operation management system 200 that creates a delivery route by a vehicle 300 which is an electric vehicle. The delivery planning system 100 manages the delivery destinations and delivery points of delivery items, and formulates a delivery plan for the delivery items to be delivered in one delivery as a delivery plan. In this embodiment, the delivery planning system 100 is included in the delivery backbone system. Note that the delivery plan may include the weight of the delivery items.

[0013] Based on the delivery plan formulated by the delivery planning system 100, the operation management system 200 creates a driving route when each vehicle 300 performs delivery. Note that the driving route includes, in addition to the driving route, for example, information such as the time of delivery and the vehicle information of the vehicle performing the delivery. The operation management system 200 includes a route planning unit 210 that plans a driving route of the vehicle 300, a power consumption estimation unit 220 that estimates the power consumption of the vehicle 300, and a power outage determination unit 230 that determines whether the vehicle 300 runs out of power.

[0014] The route planning unit 210 plans a driving route for performing delivery based on the delivery points of the delivery items. The route planning unit 210 plans a route from the point where the vehicle 300 departs, via each delivery point, and to the point where the vehicle 300 finally arrives, based on the delivery points of the delivery items and the map data included in the map database 240. The point where the vehicle 300 departs is, for example, a delivery center where the delivery items are collected. Also, the point where the vehicle 300 arrives corresponds to, for example, the departure delivery center or a charging facility. The map data is acquired in advance and stored in the map database 240. The map data is not particularly limited as long as it can identify the delivery points and the positions of signals on the driving route.

[0015] The power consumption estimation unit 220 estimates the power consumption of the driving route based on the density of the driving route of the vehicle 300 and the load change at the stop points. The power consumption estimation unit 220 includes a signal stop coefficient calculation unit 221, a signal stop location estimation unit 222, a delivery coefficient calculation unit 223, a load transition estimation unit 224, and a power consumption calculation unit 225.

[0016] The signal stop coefficient calculation unit 221 calculates a signal stop coefficient, which is the rate of stopping at signals, based on the driving history database 250. The driving history database 250 stores driving history data that the vehicle 300 has traveled in the past, and this driving history data includes data on the presence or absence of signal stops at the positions of signals. That is, the driving history database 250 accumulates data on whether the vehicle 300 could pass through directly when it tried to pass through each signal on the driving route (green signal) or could not pass through directly and stopped (red signal). The signal stop coefficient calculation unit 221 acquires this data as signal stop records, and for each signal existing on the driving route, calculates the rate at which the vehicle 300 stops. This rate is output as the signal stop coefficient to the signal stop location estimation unit 222.

[0017] FIG. 2 is a diagram showing driving history data and signal stop coefficients. In FIG. 2, the driving history data is composed of a signal ID (signal machine ID), an entry link indicating where the vehicle entered the signal, an exit link indicating where the vehicle exited the signal, the number of times the signal was passed through, and the number of times it could not pass through directly and stopped (stop count). The number of times passed through is the sum of the number of times the signal machine was passed through directly and the number of times it could not pass through directly and stopped (stop count). Also, the coefficient refers to the signal stop coefficient. This coefficient can be calculated by stop count / number of times passed through. Note that the driving history data and signal stop coefficients are not necessarily one for each signal, and for example, a plurality of them may be prepared according to the driving time or day of the week.

[0018] Returning to FIG. 1, the signal stop point estimation unit 222 estimates the stop point on the travel route of the vehicle 300. In the present embodiment, the stop point is defined as the point where the vehicle stops due to a signal. The signal stop point estimation unit 222 predicts the stop point based on the signal stop coefficient calculated by the signal stop coefficient calculation unit 221. In this case, it can also be said that the signal stop point estimation unit 222 calculates the signal stop coefficient, which is the rate of stopping due to a signal, based on the history of the vehicle 300 stopping due to a signal in its travel history, and estimates the stop point.

[0019] The delivery coefficient calculation unit 223 calculates a re-delivery coefficient, which is the probability of not being able to deliver the package and having to re-deliver it, based on the delivery record. The delivery record is managed by the delivery planning system 100, and the delivery coefficient calculation unit 223 acquires the delivery record from the delivery planning system 100.

[0020] The load transition estimation unit 224 estimates the load change at the delivery point considering the density. In the present embodiment, the delivery point is defined as the point where the vehicle stops due to the delivery of the goods to be delivered. The load transition estimation unit 224 estimates the load change at the delivery point based on the loading amount at the start (illustrated as the loading amount at the start in FIG. 1) and the re-delivery coefficient. In this case, the density is the frequency of stops due to deliveries on the travel route of the vehicle 300. In the present embodiment, one feature is estimating the power consumption of the vehicle 300 considering the density of the delivery area. And as the density, the frequency of stops due to deliveries on the travel route is used. That is, there is a correlation between the density and the frequency of stops due to deliveries on the travel route. When the number of stops is large, it means that there are more signals and delivery points, corresponding to a higher density. On the contrary, when the number of stops is small, it means that there are fewer signals and delivery points, corresponding to a lower density.

[0021] In this case, it can also be said that the load transition estimation unit 224 estimates the load change based on the rate calculated based on the past delivery results and estimated to require re-delivery. Also at this time, the load transition estimation unit 224 estimates the load change based on the number of deliveries estimated to require re-delivery. It is difficult to accurately grasp the weight of each delivery item. On the other hand, the number of delivery items can be clearly grasped. Therefore, for each delivery item, a predetermined weight is set, and when a delivery item is delivered, it is assumed that the load corresponding to the number of delivered items has decreased. Note that this weight can also be obtained, for example, by "loading capacity at departure / number of delivery items at departure". Note that it can also be considered that the delivery coefficient calculation unit 223 and the load transition estimation unit 224 are a density influence estimation unit that estimates the influence of the load change on the density.

[0022] The power consumption calculation unit 225 calculates the power consumption of the driving route based on the load estimated by the load transition estimation unit 224. In the present embodiment, the power consumption calculation unit 225 calculates the power consumption including the load at the start of the vehicle 300 at the delivery point and the stop point. And the load estimated at this time is used. More specifically, the estimated load is input to the power consumption model 260 to calculate the power consumption.

[0023] The delivery plan system 100 is, for example, a server computer that manages the entire vehicle management system 1. Note that although one delivery plan system 100 is shown in the figure, its functions may be realized by a plurality of server computers. Also, the delivery plan system 100 may be a virtual server device. The operation management system 200 is a computer device, for example, a PC (Personal Computer). However, it is not limited to this, and it may be a mobile computer, a smartphone, a tablet, or the like.

[0024] The delivery planning system 100 and the operation management system 200 include a processor such as a CPU (Central Processing Unit) which is an arithmetic means, and a main memory which is a storage means. Here, the processor executes various software such as an OS (basic software) and an app (application software). Also, the main memory is a storage area for storing various software and data used for its execution. Furthermore, the delivery planning system 100 and the operation management system 200 include, as an auxiliary storage device, a storage such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and a communication interface for communicating with the outside. Also, it may be provided with an input device such as a mouse or a keyboard, and an output device such as a display.

[0025] Figs. 3(a) to (b) are diagrams showing a power consumption model. Among these, Fig. 3(a) is a diagram showing an example of a conventional power consumption model. The illustrated power consumption model calculates the driving force and calculates the power consumption based on the driving force. Fig. 3(b) is a diagram showing the power consumption model 260 of the present embodiment. The power consumption model 260 of the present embodiment adds a power consumption term at the time of starting to the second term on the right side compared with the conventional power consumption model in order to obtain the power consumption. After the vehicle 300 stops at the delivery point or the stop point, the vehicle 300 needs to start running again, and the power consumption term represents the power consumption due to the load at the time of starting of the vehicle 300 at this time. The power consumption term is defined by multiplying the total of the vehicle weights (load + vehicle weight) Wi at each of the delivery point and the stop point by a predetermined power consumption coefficient Cs.

[0026] Returning to FIG. 1 again, the power shortage determination unit 230 determines whether the vehicle 300 will run out of power by comparing the power consumption calculated by the power consumption calculation unit 225 for each driving route planned by the route planning unit 210 with the remaining battery level of the vehicle 300. A power shortage refers to a state in which the vehicle 300 runs out of power during driving and can no longer run. The remaining battery level is sent from the vehicle 300 to and stored in a vehicle information database 270 that stores vehicle information, which is information about the vehicle 300. The power shortage determination unit 230 acquires the remaining battery level of the vehicle 300 from the vehicle information database 270. And when the power shortage determination unit 230 determines that there is a driving route that will run out of power, the power shortage determination unit 230 makes a replanning request to the route planning unit 210. The route planning unit 210 that has received the replanning request replans another driving route.

[0027] <Operation Explanation of the Operation Management System 200> Next, the operation of the operation management system 200 will be described. FIG. 4 is a flowchart for explaining the operation of the operation management system 200. First, based on the delivery location, the route planning unit 210 plans a driving route that passes through the delivery location (S401). Next, the signal stop location estimation unit 222 estimates whether there is a stop of a signal on the driving route based on the signal stop coefficient (S402). Next, the load transition estimation unit 224 estimates the feasibility of delivery based on the re-delivery coefficient and estimates the load change when passing through the delivery location (S403). Furthermore, the power consumption calculation unit 225 estimates the power consumption of the driving route from the power consumption model 260 (S404). Then, the power shortage determination unit 230 determines whether the power consumption consumed on the driving route is greater than the remaining battery level included in the vehicle information (S405). As a result, if it is greater (Yes in S405), the process returns to S401. That is, this is the case where the power shortage determination unit 230 makes a replanning request to the route planning unit 210, and the route planning unit 210 replans another driving route. On the other hand, when it is not large (the same or smaller) (No in S405), the operation management system 200 distributes the driving route to the vehicle 300 (S406).

[0028] FIG. 5 is a flowchart for further explaining S402 in FIG. 4. First, the signal stop coefficient calculation unit 221 calculates the signal stop coefficient for each signal as "number of stops / number of passes" from the driving history (S501). Then, the signal stop location estimation unit 222 multiplies the signal stop coefficient for the signals on the driving route to estimate the presence or absence of a stop (S502).

[0029] FIG. 6 is a flowchart for further explaining S403 in FIG. 4. First, the delivery coefficient calculation unit 223 calculates the re-delivery coefficient as "number of re-deliveries / number of deliveries" from the delivery results (S601). Next, the load transfer estimation unit 224 multiplies the re-delivery coefficient for the delivery points on the driving route to estimate the possibility of delivery (S602). Furthermore, the load transfer estimation unit 224 determines whether the delivery was successful (S603). As a result, when the delivery is successful (Yes in S603), the load transfer estimation unit 224 subtracts the delivered portion from the load. The subtracted amount is calculated as "departure load / delivery point × re-delivery coefficient". On the other hand, when the delivery is not successful (No in S603), the series of processes ends.

[0030] According to the operation management system 200 described above, when planning a driving route for delivery, the power consumption of the vehicle 300 can be estimated with high accuracy. As a result, the occurrence of power shortage in the vehicle 300 is less likely, and the cost of plan modification due to power shortage in the vehicle 300 is less likely to occur. Also, delivery can be performed with an appropriate number of vehicles 300, and the steady cost can be suppressed.

[0031] Also, the operation management system 200 described above can be said to include a signal stop point estimation unit 222 that estimates a stop point, which is a point where the vehicle 300 stops due to a signal, in the travel route of the vehicle 300, a load transition estimation unit 224 that estimates a load change at a delivery point, which is a point where the vehicle stops due to delivery, in the travel route, and a power consumption calculation unit 225 that calculates power consumption including the load at the time of departure of the vehicle 300 at the estimated stop point and delivery point based on the estimated load.

[0032] <Description of the operation management method> The processing performed by the operation management system 200 described above is realized by the cooperation of software and hardware resources. That is, a processor such as a CPU provided in the operation management system 200 loads a program that realizes each function of the operation management system 200 into the main memory and executes it to realize these functions.

[0033] Therefore, the processing performed by the operation management system 200 described above can be regarded as an operation management method in which a processor executes a program recorded in a memory, estimates a load change at a delivery point in consideration of the density, with the frequency of stops due to delivery in the travel route of the vehicle 300 as the density, and calculates the power consumption of the travel route based on the estimated load.

[0034] Note that the program for realizing this embodiment can be provided not only by communication means but also by storing it in a recording medium such as a CD-ROM.

[0035] As described above, this embodiment has been described, but the technical scope of the present invention is not limited to the scope described in the above embodiment. It is clear from the description of the claims that various modifications or improvements added to the above embodiment are also included in the technical scope of the present invention.

Explanation of reference numerals

[0036] 1... Vehicle management system, 100... Delivery planning system, 200... Operation management system, 210... Route planning section, 220... Power consumption estimation section, 221... Signal stop coefficient calculation section, 222... Signal stop location estimation section, 223... Delivery coefficient calculation section, 224... Load transition estimation section, 225... Power consumption calculation section, 230... Power failure determination section

Claims

1. a load transition estimation unit that estimates a load change at a delivery point by taking into consideration the density, the density being determined by the number of stops made by the electric vehicle for delivery along its travel route; a power consumption calculation unit that calculates the power consumption of the travel route based on the estimated load; a traffic light stop point estimation unit that estimates a stop point where the electric vehicle stops due to a traffic light; Equipped with The traffic light stop point estimation unit is a traffic control system that calculates a traffic light stop coefficient, which is the rate at which the electric vehicle stops at traffic lights, based on the electric vehicle's driving history and the results of stopping at traffic lights, and estimates the stopping point.

2. The traffic management system according to claim 1 , wherein the power consumption calculation unit calculates the power consumption including the load when the electric vehicle departs from the delivery point and a stop point where the electric vehicle stops due to a traffic light.

3. The traffic management system according to claim 2 , wherein the power consumption calculation unit calculates the load by multiplying a total vehicle weight at each of the delivery point and the stopping point by a predetermined power consumption coefficient.

4. The operation management system according to claim 1 , wherein the load transition estimation unit estimates the load change based on a rate at which redelivery is estimated to be necessary, calculated based on past delivery records.

5. The traffic management system according to claim 4, wherein the load transition estimation unit estimates the load change based on the number of deliveries estimated to require redelivery.

6. a route planning unit that plans a driving route for the electric vehicle; a power shortage determination unit that determines whether the electric vehicle will run out of power by comparing the power consumption calculated by the power consumption calculation unit with the remaining battery charge of the electric vehicle for one travel route planned by the route planning unit; Furthermore, The traffic management system according to claim 1 , wherein when the power shortage determination unit determines that the one travel route will run out of power, the route planning unit re-plans another travel route.

7. a traffic light stop point estimation unit that estimates a stop point, which is a point where the electric vehicle stops due to a traffic light, on a travel route of the electric vehicle; a load transition estimation unit that estimates a load change at a delivery point on the travel route where the vehicle stops for delivery; a power consumption calculation unit that calculates power consumption including loads at the estimated stop points and delivery points when the electric vehicle departs based on the estimated load; Equipped with The traffic light stop point estimation unit is a traffic control system that calculates a traffic light stop coefficient, which is the rate at which the electric vehicle stops at traffic lights, based on the electric vehicle's driving history and the results of stopping at traffic lights, and estimates the stopping point.

8. The processor executes the program stored in the memory. The number of stops for delivery on the electric vehicle's travel route is used as a density, and a load change at the delivery point is estimated taking the density into consideration; Calculate the power consumption of the travel route based on the estimated load, Estimating a stop point where the electric vehicle will stop due to a traffic light; A stop point where the electric vehicle stops due to a traffic light is estimated by calculating a traffic light stop coefficient, which is a rate at which the electric vehicle stops due to a traffic light, based on a record of stopping at traffic lights in the driving history of the electric vehicle. Operation management method.

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