Vehicle control system

Abstract

This system provides a vehicle control system capable of accurately predicting the amount of electricity consumed by the vehicle during operation. [Solution] The vehicle control system 100 includes a car navigation device 24 and a sensor 25 that acquire transport information regarding goods transported by the vehicle 20, an ECU 21 that predicts the amount of power consumed when the vehicle 20 travels a predetermined section, and a server 10 that stores related information in which power information regarding the power consumed when the same type of vehicle traveled the predetermined section in the past and transport information regarding goods transported by the same type of vehicle during past travels are linked together. The ECU 21 predicts the amount of power consumed from the power information based on the transport information of the vehicle 20 and the transport information of the same type of vehicle.

Description

Technical Field

[0001] This disclosure relates to a vehicle control system.

Background Art

[0002] Japanese Unexamined Patent Application Publication No. 2019-135491 (Patent Document 1) discloses a system that estimates the amount of power consumed by an electric truck when traveling on a predetermined route and selects an optimal driving route for the electric truck based on the estimated power consumption.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the system described in Patent Document 1 above, it is conceivable that the actual power consumption and the estimated power consumption deviate due to fluctuations in the power consumption depending on the weight of the cargo carried by the vehicle.

[0005] This disclosure has been made to solve the above problems, and an object thereof is to provide a vehicle control system capable of accurately predicting the amount of power consumed by the running of a vehicle.

Means for Solving the Problems

[0006] A vehicle control system relating to one aspect of this disclosure includes: an acquisition device for acquiring first transport information relating to first transported goods transported by a first vehicle; a prediction device for predicting the amount of power consumed when the first vehicle travels a predetermined section; and a storage device for storing related information relating power information relating to power consumed when a second vehicle traveled a predetermined section in the past, and second transport information relating to second transported goods transported by the second vehicle during past travel. The prediction device predicts the amount of power consumed from the power information based on the first transport information and the second transport information.

[0007] In a vehicle control system relating to one aspect of this disclosure, as described above, the prediction device predicts the amount of power consumed from power information based on the first transport information and the second transport information. This makes it possible to accurately predict the amount of power consumed by the first vehicle based on the past power consumption of the second vehicle, even when the amount of power consumed is affected due to the transport of the first transported goods.

[0008] In the vehicle control system relating to the first aspect described above, preferably, information relating to the first vehicle and the second vehicle is referred to as first vehicle information and second vehicle information, respectively, and the storage device stores the second vehicle information linked to power information and second transport information. Based on the first vehicle information and second vehicle information, the prediction device determines whether the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same, and if it determines that the vehicle types of the first vehicle and the second vehicle are the same, it predicts the amount of power consumed from the power information based on the first transport information and second transport information. With this configuration, the amount of power consumed by the first vehicle can be predicted based on the amount of power consumed by the second vehicle, which is of the same vehicle type as the first vehicle, so the amount of power consumed by the first vehicle can be predicted more accurately.

[0009] In this case, preferably, the first transport information includes first towing information relating to the first towed object towed by the first vehicle and first loading information relating to the first load loaded on the first vehicle. The second transport information includes second towing information relating to the second towed object towed by the second vehicle during past travel and second loading information relating to the second load loaded on the second vehicle during past travel. When the prediction device determines that the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same, and at least one of the cases where the first towed object and the second towed object match, and the first load and the second load match, and the related information includes at least the first related information from the first related information when the first vehicle traveled a predetermined section in the past and the second related information when a vehicle different from the first vehicle traveled a predetermined section in the past, the prediction device predicts the amount of power consumed based on the power information corresponding to the first related information. With this configuration, the amount of power consumed can be predicted based on the power information of the first vehicle itself in at least one of the cases when the first towed object and the second towed object coincide, and when the first load and the second load coincide, thus allowing for a more accurate prediction of the power consumed by the first vehicle.

[0010] In a vehicle control system that predicts power consumption based on the first and second transport information described above, preferably, when the prediction device determines that the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same, and the first towed object and the second towed object do not match, and the first load and the second load do not match, it predicts power consumption based on the average power consumption of the second vehicle when it has traveled a predetermined section in the past. With this configuration, by predicting power consumption based on the above average value, it is possible to suppress the deviation of the predicted power consumption value from the actual power consumption.

[0011] In this case, the storage device stores information on the ratio between the amount of power consumed by the first vehicle while it is running, corresponding to the combination of the first towing information and the first load information, and the amount of power consumed by the first vehicle while it is running when the towing state and load state are in a reference state. When the prediction device determines that the type of vehicle of the first vehicle and the type of vehicle of the second vehicle are the same, and the first towed object and the second towed object do not match, and the first load and the second load do not match, it uses the above ratio as the predicted value of power consumption by multiplying the average amount of power consumed when the second vehicle runs a predetermined section in a state based on the reference state. With this configuration, by multiplying the above average value by the above ratio, the above average value can be appropriately corrected to correspond to the first towing information and the first load information, based on the relationship between the first towing information and the first load information and the reference state. [Effects of the Invention]

[0012] According to this disclosure, it is possible to accurately predict the amount of electricity consumed by the operation of a vehicle. [Brief explanation of the drawing]

[0013] [Figure 1] This diagram shows the configuration of the vehicle control system according to this embodiment. [Figure 2] This is a diagram showing an example of a car navigation system screen. [Figure 3] This diagram shows the information stored in the server's memory. [Figure 4] This is a flowchart illustrating the control of the vehicle's ECU. [Figure 5] This figure shows the details of S7, S8, and S10 in Figure 4. [Figure 6] This is a sequence diagram illustrating the control of the vehicle control system according to this embodiment. [Modes for carrying out the invention]

[0014] Embodiments of this disclosure will be described with reference to the drawings. In the drawings referred to below, the same or equivalent components are given the same number.

[0015] FIG. 1 is a diagram showing the configuration of a vehicle control system 100 according to the present embodiment. The vehicle control system 100 includes a server 10 and a vehicle 20. The vehicle 20 is an example of the "first vehicle" of the present disclosure.

[0016] The server 10 includes a processor 11, a memory 12, and a communication unit 13. The server 10 is an example of the "storage device" of the present disclosure.

[0017] In addition to the programs executed by the processor 11, the memory 12 stores information used in the programs (for example, maps, mathematical formulas, and various parameters). The communication unit 13 is controlled by the processor 11. The communication unit 13 can communicate with a DCM (Data Communication Module) 23 described later.

[0018] The vehicle 20 includes an ECU (Electronic Control Unit) 21, a battery pack 22, a DCM 23, a car navigation device 24, and a sensor 25. The ECU 21 is an example of the "prediction device" of the present disclosure. Each of the car navigation device 24 and the sensor 25 is an example of the "acquisition device" of the present disclosure.

[0019] The ECU 21 includes a processor 21a, a memory 21b, and a communication unit 21c. In addition to the programs executed by the processor 21a, the memory 21b stores information used in the programs (for example, maps, mathematical formulas, and various parameters). The communication unit 21c is controlled by the processor 21a. The communication unit 21c can communicate with the DCM 23, the car navigation device 24, the sensor 25, etc. via CAN (Controller Area Network) communication or the like, and exchanges various information with these devices.

[0020] The battery pack 22 is a battery for driving the vehicle 20. The vehicle 20 may be a PHEV (Plug-in Hybrid Electric Vehicle), a BEV (Battery Electric Vehicle), an FCEV (Fuel Cell Electric Vehicle), or the like.

[0021] The car navigation device 24 is configured to be able to display various information and receive input operations by the user of the vehicle 20.

[0022] The sensor 25 can detect the weight of the load carried on the vehicle 20. The load includes both passengers boarding the vehicle 20 and objects (such as luggage) loaded on the vehicle 20.

[0023] The sensor 25 includes, for example, a weight sensor. The weight sensor is arranged, for example, under each seat where a passenger other than the driver sits, and can detect the weight of the passenger sitting on the seat and the load placed on the seat. Also, the weight sensor may be arranged under the trunk room and may be able to detect the weight of the load placed in the trunk room. Note that the weight sensor may also be installed under the driver's seat.

[0024] Note that the sensor 25 may include sensors other than the weight sensor. For example, the sensor 25 may include a sensor that detects the adjustment condition of the optical axis (direction) of the rear light of the vehicle 20. Specifically, when the rear suspension of the vehicle 20 sinks due to the weight of the load, the vehicle 20 adjusts the optical axis to be horizontal. In this case, the processor 21a may be able to detect the weight of the load based on the adjustment condition of the optical axis detected by the sensor. Note that the information detected by the sensor 25 is an example of the "first transportation information" and the "first loading information" of the present disclosure.

[0025] Each of the processors 11 and 21a is, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit). The processors 11 and 21a each perform various processes by reading system programs and control programs, loading them into memory 12 and 21b, and executing them. In this specification, "processor" is not limited to processors that execute processing using stored programs, but may include hardwired circuits such as ASICs (Application Specific Integrated Circuits) and FPGAs (Field-Programmable Gate Arrays). Therefore, the term "processor" can also be interpreted as processing circuitry in which processing is predefined by computer-readable code and / or hardwired circuits.

[0026] Vehicle 20 is configured to tow a towed object 200. Vehicle 20 can tow the towed object 200 by traveling while connected to the towed object 200 by a connecting member 210. The towed object 200 is an example of the "first towed object" in this disclosure.

[0027] Furthermore, Figure 1 illustrates the state in which the vehicle 20 is loaded with cargo 300. Note that cargo 300 is an example of the "first transported goods" and "first cargo" as described herein.

[0028] Figure 2 illustrates the screen displayed by the car navigation device 24. The car navigation device 24 displays an input section 24a where destination information can be entered, and an input section 24b where information about the towed object 200 can be entered. Input section 24b allows input of whether or not there is a towed object 200, and the type of towed object 200. The type of towed object 200 can be selected from a history of previously set towed objects using a pull-down menu. If the towed object 200 is not in the history, the type of towed object 200 can be directly entered into input section 24b. The information entered into input section 24b is an example of the "first transport information" and "first towing information" described herein.

[0029] The processor 21a of the ECU 21 (Figure 1) predicts the amount of power consumed when driving to the destination entered in the input unit 24a, and displays the predicted amount of power consumption on the screen of the car navigation device 24. The amount of SOC decrease corresponding to the predicted amount of power consumption may also be displayed together with the power consumption. Details will be described later. Note that the power consumption prediction (calculation) may be performed by selecting a button (not shown) on the screen shown in Figure 2. Note that power consumption prediction includes determining the amount of power consumption based on various information and calculating the amount of power consumption based on various information.

[0030] Figure 3 shows the information stored in the memory 12 (Figure 1) of the server 10. Memory 12 stores information regarding the amount of power consumed during driving for each vehicle identification number (i.e., for each vehicle). In Figure 3, the vehicle identification number is shown as a combination of vehicle type information (AAA) and vehicle number (1, 98, and 352).

[0031] Memory 12 stores information regarding the amount of power consumed when the vehicle (hereinafter referred to as "past vehicle") was driven in the past. Specifically, Memory 12 stores the ratio (see Data 1-3) between the amount of power consumed corresponding to a combination of information regarding towed objects (hereinafter referred to as "towing information") and information regarding loaded objects (hereinafter referred to as "loading information") and the amount of power consumed when the towing state and loading state are in a reference state. The above ratio is also stored in Memory 21b of ECU 21. The towing information includes information on the presence or absence of towed objects and information on the type of towed objects. The loading information includes information on the presence or absence of loaded objects and information on the weight of loaded objects. The above reference state may be, for example, a state in which there is no towed object and no loaded objects. The past vehicle is an example of the "second vehicle" in this disclosure.

[0032] Furthermore, memory 12 stores information on the amount of power consumed by each vehicle when traveling through each section (hereinafter referred to as "power consumption information"). The power consumption information is information that associates (links) the amount of power consumed during travel with the towing information and load information during travel. The above associated (linked) information is an example of the "related information" in this disclosure.

[0033] In conventional systems, the amount of electricity consumed can fluctuate depending on factors such as the weight of the goods being transported by the vehicle, which can lead to a discrepancy between the actual amount of electricity consumed and the estimated amount of electricity consumed.

[0034] Therefore, in this embodiment, the ECU 21 predicts the amount of power consumption required for vehicle 20 to travel a predetermined section based on the amount of power consumption when a past vehicle traveled the predetermined section, using the amount of power consumption when a past vehicle traveled the predetermined section. Details will be explained with reference to the flowchart below. Note that the information on the amount of power consumption when a past vehicle traveled the predetermined section is an example of "power information" in this disclosure. The information on the amount of power consumption when a past vehicle traveled the predetermined section is an example of "second transport information" in this disclosure.

[0035] (Control flow) Figure 4 is a flowchart showing the control performed by the ECU 21 (processor 21a) regarding the prediction of power consumption during vehicle 20 operation.

[0036] In step S1, the ECU 21 determines whether information about the destination and the towed object 200 has been entered. Specifically, the ECU 21 determines whether information has been entered into input unit 24a (Figure 2) and input unit 24b (Figure 2). If information about the destination and the towed object 200 has been entered (Yes in S1), the process proceeds to step S2. If information about the destination and the towed object 200 has not been entered (No in S1), the process ends.

[0037] In step S2, the ECU 21 obtains information on the weight of the cargo 300 loaded on the vehicle 20 from the sensor 25. Note that the processing in step S2 may be performed before or simultaneously with step S1.

[0038] In step S3, the ECU 21 determines whether it has completed predicting the power consumption for each section from the vehicle 20's current location to the destination. In this embodiment, the ECU 21 predicts the power consumption for multiple sections to the destination one by one in sequence. Note that the power consumption prediction flow is not limited to this example; for example, the predicted power consumption for each of the multiple sections may be calculated simultaneously. If the power consumption prediction for each section to the destination is completed (Yes in S3), the process proceeds to step S13. If the power consumption prediction for each section to the destination is not completed (No in S3), the process proceeds to step S4.

[0039] In step S4, the ECU 21 determines whether or not data (historical data) of the power consumption of a vehicle of the same type as vehicle 20, corresponding to a period for which the power consumption prediction is incomplete, is stored in the server 10 (memory 12). Specifically, the ECU 21 accesses the server 10 through the DCM 23 to check for the existence of the above historical data (it may also query the server 10). If the above historical data is stored in the server 10 (Yes in S4), the process proceeds to step S5. If the above historical data is not stored in the server 10 (No in S4), the process proceeds to step S12. Note that the above-mentioned "vehicle of the same type" includes not only vehicles of the same type as vehicle 20 but also vehicle 20 itself.

[0040] In step S5, the ECU 21 determines whether there is any data in step S4 that matches the towing information. Specifically, if the vehicle 20 is not towing an object, the ECU 21 determines whether there is any data in the above data that represents a state without an object towing (hereinafter referred to as "no towing"). If the vehicle 20 is towing an object 200, the ECU 21 determines whether there is any data in the above data whose type of towing matches the towing object 200. If there is data in step S4 that matches the towing information (Yes in S5), the process proceeds to step S6. If there is no data in step S4 that matches the towing information (No in S5), the process proceeds to step S9.

[0041] In step S6, the ECU 21 determines whether there is any data among the data corresponding to step S5 that matches the load information. Specifically, if the vehicle 20 is not loaded, the ECU 21 determines whether there is any data among the above data that represents a state without load (hereinafter referred to as "no load"). If the vehicle 20 is loaded with load 300, the ECU 21 determines whether there is any data among the above data in which the weight of the load matches that of load 300. Note that matching the weight of the load to that of load 300 may mean that the weight of the load in past data falls within a predetermined range (for example, ±5%) centered on the weight of load 300. If there is data among the data corresponding to step S5 that matches the load information (Yes in S6), the process proceeds to step S7. If there is no data among the data corresponding to step S5 that matches the load information (No in S6), the process proceeds to step S8.

[0042] In step S7, the ECU21 uses the data from step S6 to predict the amount of power consumption required to travel the aforementioned section. Next, the process returns to step S3.

[0043] In step S8, the ECU21 uses the data from step S5 to predict the amount of power consumed to travel the aforementioned section. Next, the process returns to step S3.

[0044] In step S9, the ECU21 determines, in the same way as in step S6, whether there is any data among the data corresponding to step S4 that matches the loading information. If there is data among the data corresponding to step S4 that matches the loading information (Yes in S9), the process proceeds to step S10. If there is no data among the data corresponding to step S9 that matches the loading information (No in S9), the process proceeds to step S11.

[0045] In step S10, the ECU21 uses the data corresponding to step S9 to predict the amount of power consumption required to travel the above section. Next, the process returns to step S3.

[0046] In step S11, the ECU21 calculates a predicted value for power consumption by multiplying the average value of the data corresponding to step S4 by the above ratio relative to the standard driving conditions. Next, the process returns to step S3.

[0047] Specifically, let's assume that vehicle 20 has a vehicle identification number of AAA-352, and ECU 21 is predicting the power consumption for the ○~X section. In this case, ECU 21 calculates the average power consumption value when a vehicle of type AAA travels the ○~X section under the above-mentioned standard conditions. In the example shown in Figure 3, the above average value is the average of the power consumption value (1.9kWh) when an AAA-1 vehicle (no towing / 100kg load) travels the ○~X section and the power consumption value (2.1kWh) when an AAA-98 vehicle (no towing / 100kg load) travels the ○~X section (2.0kWh). Note that the conditions based on the standard conditions (no towing / no load) mean conditions close to the standard conditions. For example, conditions close to the standard conditions may mean no towing and a load weight of 150kg or less. Note that the criteria for determining whether a condition is close to the standard conditions are not limited to the above example. Also, the above average value may take into account the data of vehicle 20 itself. For example, the data of the vehicle 20 itself may take precedence (may be given a higher weight), or the value may be based solely on the data of the vehicle 20 itself.

[0048] Then, ECU21 uses the calculated average value multiplied by the ratio as the predicted value for power consumption. For example, suppose vehicle 20 is not towing object 200, and the weight of the load 300 is 400 kg. In this case, ECU21 uses the average value (2.0) multiplied by the ratio (1.2) corresponding to no towing / 400 kg load for vehicle 20 (2.4 kWh) as the predicted value for power consumption in the ○~X section. Next, the process returns to step S3.

[0049] In step S12, the ECU 21 predicts the amount of power consumed based on the average power consumption rate (electricity efficiency) of the vehicle 20. For example, if the distance of the above section is 2 km and the average power consumption rate (electricity efficiency) is 0.1 kWh / km, the ECU 21 calculates the predicted amount of power consumed as 0.2 kWh. The average power consumption rate used in step S12 may be a value corresponding to the combination of the towing state and the loading state of the vehicle 20. The average power consumption rate for each of the above combinations may be stored in the memory 21b of the ECU 21. Next, the process returns to step S3.

[0050] In step S13, the ECU 21 calculates the total predicted power consumption for each section and displays the total on the screen of the car navigation system 24 (see Figure 2). After that, the process ends. If multiple driving routes to the destination are expected, the ECU 21 may calculate the total for each driving route using the control shown in Figure 4 and display the total for each driving route on the screen of the car navigation system 24.

[0051] Figure 5 shows the details of steps S7, S8, and S10 in Figure 4. Step S7 includes steps S71 to S73. Step S8 includes steps S81 to S83. Step S10 includes steps S101 to S103.

[0052] In step S71, the ECU 21 determines whether the data for the vehicle 20 corresponding to step S6 is stored in the server 10. If the data for the vehicle 20 corresponding to step S6 is stored in the server 10 (Yes in S71), the process proceeds to step S72. If the data for the vehicle 20 corresponding to step S6 is not stored in the server 10 (No in S71), the process proceeds to step S73.

[0053] In step S72, the ECU 21 uses the power consumption of the vehicle 20 corresponding to step S6 as the predicted power consumption value. If there are multiple data points for the vehicle 20 corresponding to step S6, the average of the multiple data points (power consumption) may be used as the predicted power consumption value.

[0054] In step S73, the ECU21 uses the average value of the power consumption of other vehicles, different from the vehicle 20 corresponding to step S6, as the predicted power consumption value.

[0055] In step S81, the ECU 21 determines whether the data for the vehicle 20 corresponding to step S5 is stored in the server 10. If the data for the vehicle 20 corresponding to step S5 is stored in the server 10 (Yes in S81), the process proceeds to step S82. If the data for the vehicle 20 corresponding to step S5 is not stored in the server 10 (No in S81), the process proceeds to step S83.

[0056] In step S82, the ECU 21 uses the power consumption of the vehicle 20 corresponding to step S5 as the predicted power consumption value. If there are multiple data points for the vehicle 20 corresponding to step S5, the average of the multiple data points (power consumption) may be used as the predicted power consumption value.

[0057] In step S83, the ECU21 uses the average value of the power consumption of other vehicles, different from the vehicle 20 corresponding to step S5, as the predicted power consumption value.

[0058] In step S101, the ECU 21 determines whether the data for the vehicle 20 corresponding to step S9 is stored in the server 10. If the data for the vehicle 20 corresponding to step S9 is stored in the server 10 (Yes in S101), the process proceeds to step S102. If the data for the vehicle 20 corresponding to step S9 is not stored in the server 10 (No in S101), the process proceeds to step S103.

[0059] In step S102, the ECU 21 uses the power consumption of the vehicle 20 corresponding to step S9 as the predicted power consumption value. If there are multiple data points for the vehicle 20 corresponding to step S9, the average of the multiple data points (power consumption) may be used as the predicted power consumption value.

[0060] In step S103, the ECU21 uses the average value of the power consumption of other vehicles, different from the vehicle 20 corresponding to step S9, as the predicted power consumption value.

[0061] Figure 6 is a sequence diagram showing how the vehicle control system 100 learns the amount of power consumed in each section. Figure 6 shows a learning method based on the amount of power consumed when the vehicle 20 travels from a set location to the destination. The sequence in Figure 6 may be executed, for example, each time the vehicle 20 arrives at the destination.

[0062] In step S21, the ECU 21 determines whether or not the user has entered information about the towed object 200 (towing information). Specifically, when a destination is set in the vehicle 20, the ECU 21 determines whether or not information about the towed object 200 has been entered into the input unit 24b (Figure 2). If information about the towed object 200 has been entered (Yes in S21), the process proceeds to step S22. If information about the towed object 200 has not been entered (No in S21), the process proceeds to step S24.

[0063] In step S22, the ECU 21 determines whether the power consumption of the vehicle 20 during normal driving is greater than the power consumption of the vehicle 20 when it is not towed and unloaded (reference state). At this time, the ECU 21 (processor 21a) determines whether the server 10 (memory 12) or memory 21b has stored information on the power consumption of the vehicle 20 in the reference state. If the server 10 (memory 12) or memory 21b does not have information on the power consumption of the vehicle 20 in the reference state, the ECU 21 may determine whether the server 10 (memory 12) or memory 21b has stored information on the power consumption of a state close to the reference state (for example, no towing and a load of 100 kg). Note that the power consumption during normal driving mentioned above may be the power consumption during driving other than deceleration driving and driving downhill in this driving test.

[0064] If the power consumption during normal driving is greater than the power consumption in the reference state (Yes in S22), the process proceeds to step S23. If the power consumption during normal driving is less than or equal to the power consumption in the reference state (No in S22), the process proceeds to step S24. Note that the reference value (threshold) in step S22 may be the power consumption in a state close to the reference state, rather than the power consumption in the reference state.

[0065] By performing the determination in step S22, it is possible to prevent inaccurate information (abnormal values) regarding the towed object 200 from being registered in the server 10, such as when the vehicle 20 is not towing the towed object 200 despite the user having entered the towed object 200 into the input unit 24a (Figure 2), or when the vehicle 20 stops towing the towed object 200 midway.

[0066] In step S23, the ECU 21 determines whether the weight of the load 300 can be detected by the vehicle 20's sensor 25 (Figure 1). If the weight of the load 300 can be detected (Yes in S23), the process proceeds to step S25. If the weight of the load 300 cannot be detected (No in S23), the process proceeds to step S26. The statement that the weight of the load 300 can be detected means that the ECU 21 has received a detected value for the weight of the load 300 from the sensor 25 (including a value of 0 if the load 300 is not placed). The statement that the weight of the load 300 cannot be detected means that the ECU 21 has not received a detected value for the weight of the load 300 from the sensor 25, or that it has received an abnormal value (for example, an excessively large value) or an error signal from the sensor 25.

[0067] In step S24, similar to step S23, the ECU 21 determines whether the weight of the load 300 can be detected by the vehicle 20's sensor 25 (Figure 1). If the weight of the load 300 can be detected (Yes in S24), the process proceeds to step S27. If the weight of the load 300 cannot be detected (No in S24), the process proceeds to step S28.

[0068] In step S25, the ECU21 sends information to the server10 that associates vehicle information (vehicle identification number), towing information, and load information with the amount of power consumed for each section.

[0069] In step S26, the ECU 21 transmits information to the server 10 that associates vehicle information (vehicle identification number) and towing information with the amount of power consumed for each section. The information from step S26 is stored in the server 10 as data without load information (unknown).

[0070] In step S27, the ECU 21 transmits information to the server 10 that associates vehicle information (vehicle identification number) and load information with the amount of power consumed for each section. The information from step S27 is stored in the server 10 as data without (unknown) towing information.

[0071] In step S28, the ECU 21 sends information to the server 10 that associates vehicle information (vehicle identification number) with data on power consumption for each section. The information from step S28 is sent to the server 10 as data without towing information and load information (unknown).

[0072] The process of associating the power consumption in steps S25 to S28 with each piece of information (vehicle information, towing information, and load information) may be performed on the server 10.

[0073] In step S29, the server 10 (processor 11) calculates the ratio of the power consumption in steps S25 to S28 to the average power consumption of a vehicle (same type as vehicle 20) in a state of no towing and no load (reference state), for each interval. Note that the above average power consumption may be the average value of the power consumption of vehicle 20.

[0074] In step S30, the server 10 calculates the average value of the above ratios for each section calculated in step S29. The server 10 then associates the calculated average value of the ratios (hereinafter referred to as the average ratio) with the towing information and loading information corresponding to steps S25 to S28 and stores it in memory 12. If the average ratio data is already stored in memory 12 before processing S30, the server 10 may calculate the average of the average ratio obtained in step S29 and the existing average ratio. The average ratio is an example of the "ratio" in this disclosure.

[0075] In step S31, the server 10 transmits the information stored in the server 10 in step S30 to the vehicle 20. After that, the process ends. The vehicle 20 uses the information received in step S31 in step S11. At this time, the ECU 21 (processor 21a) calculates a predicted value of power consumption using the above ratio average value stored in the memory 21b of the ECU 21. Alternatively, the ECU 21 may calculate the predicted value of power consumption using the ratio average value stored in the server 10. In this case, the data transmission process in step S31 does not need to be executed.

[0076] As described above, in this embodiment, the ECU 21 uses information about the goods (200, 300) transported by the vehicle 20 and information about goods transported by the vehicle in the past to predict the amount of power consumed when the vehicle 20 travels the predetermined section, based on the amount of power consumed when the vehicle traveled the predetermined section in the past. This makes it possible to predict the amount of power consumed while also considering the power required to transport the goods. As a result, it is possible to accurately predict the amount of power consumed by the vehicle 20 during its operation.

[0077] <Variation> The above embodiment shows an example of inputting destination and towing information into the car navigation device 24, but the disclosure is not limited thereto. For example, the destination and towing information may be input into a terminal owned by the user (e.g., a smartphone and a PC). In this case, the user inputs the weight of the cargo into the terminal, and the terminal may predict the amount of power consumption based on the towing information and the cargo information. Furthermore, after the vehicle is started, the calculation of power consumption may be performed again based on the amount of cargo detected by the sensor 25. For example, the amount of power consumption may be calculated using a correction value based on the ratio of the amount of cargo input by the user and the amount of cargo detected by the sensor 25. In this case, the information of the amount of cargo detected after the vehicle is started is registered on the server in association with the towing information, power information, and vehicle information. Also, when predicting power consumption by the terminal, the amount of power consumption may be predicted assuming that no cargo is loaded.

[0078] In the above embodiment, an example is shown in step S4 of Figure 4 in which the control flow is divided based on whether or not past data on the power consumption of the vehicle 20 and similar vehicles is stored in the server 10. However, the disclosure is not limited to this. Instead of step S4, a step may be provided in which the control flow is divided based on whether or not past data on the power consumption of the vehicle 20 is stored in the server 10. In this case, the processing in steps S71, S73, S81, S83, S101, and S103 of Figure 5 is unnecessary.

[0079] In the above embodiment, an example was shown in which past data relating power consumption and transportation information (towing information, loading information) is stored in the server 10, but the disclosure is not limited to this. The above past data may be stored in a storage device (for example, memory 21b) within the vehicle.

[0080] The above embodiment shows an example in which power consumption is predicted based on towing information and load information, but the disclosure is not limited thereto. Power consumption may be predicted based on either towing information or load information alone.

[0081] The above embodiment shows an example in which power consumption is predicted based on towing information entered by the user, but the disclosure is not limited thereto. For example, power consumption may be predicted based on information about the towed object identified by a camera or the like installed on the vehicle.

[0082] The configurations (controls) of each of the above embodiments and each modified example may be combined with each other.

[0083] It should be noted that the embodiments disclosed herein are illustrative in all respects and not restrictive. The scope of this disclosure is defined by the claims rather than the description of the embodiments above, and includes all modifications within the meaning and scope equivalent to the claims. [Explanation of Symbols]

[0084] 10 Server (storage device), 20 Vehicle (first vehicle), 21 ECU (prediction device), 24 Car navigation device (acquisition device), 25 Sensor (acquisition device), 100 Vehicle control system.

Claims

1. An acquisition device for acquiring first transportation information relating to the first transported goods transported by the first vehicle, A prediction device that predicts the amount of power consumed when the first vehicle travels a predetermined section, The system includes a storage device that stores related information, which is a combination of power information relating to the power consumed by the second vehicle when it previously traveled the predetermined section, and second transport information relating to the second transported goods that were transported by the second vehicle during the previous travel. The prediction device is a vehicle control system that predicts the amount of power consumed from the power information based on the first transport information and the second transport information.

2. If the information relating to the first vehicle and the second vehicle is referred to as the first vehicle information and the second vehicle information, respectively, The storage device stores the second vehicle information linked to the power information and the second transportation information. The prediction device is Based on the first vehicle information and the second vehicle information, it is determined whether the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same. The vehicle control system according to claim 1, which, when it is determined that the type of vehicle of the first vehicle and the type of vehicle of the second vehicle are the same, predicts the amount of power consumed from the power information based on the first transport information and the second transport information.

3. The first transport information includes first towing information relating to a first towed object towed by the first vehicle, and first loading information relating to a first load loaded onto the first vehicle. The second transport information includes second towing information relating to the second towed object that was towed by the second vehicle during the past trip, and second loading information relating to the second loaded object that was loaded onto the second vehicle during the past trip. The prediction device is When it is determined that the type of vehicle of the first vehicle and the type of vehicle of the second vehicle are the same, and the first towed object and the second towed object match, and at least one of the cases in which the first load and the second load match, the related information includes at least the first related information from the first related information when the first vehicle traveled the predetermined section in the past and the second related information when a vehicle other than the first vehicle traveled the predetermined section in the past, A vehicle control system according to claim 2, which predicts the amount of power consumption based on the power information corresponding to the first related information.

4. The prediction device is When it is determined that the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same, and the first towed object and the second towed object do not match, and the first load and the second load do not match, The vehicle control system according to claim 3, which predicts the amount of power consumption based on the average value of the amount of power consumption when the second vehicle has traveled the predetermined section in the past.

5. The storage device stores information on the ratio of the amount of power consumed by the first vehicle while it is running, corresponding to the combination of the first towing information and the first loading information, to the amount of power consumed by the first vehicle while it is running, when the towing state and loading state are in a standard state. The prediction device is If it is determined that the vehicle type of the first vehicle and the vehicle type of the second vehicle are the same, and the first towed object and the second towed object do not match, and the first load and the second load do not match, The vehicle control system according to claim 4, wherein the predicted value of the power consumption is obtained by multiplying the average value of the power consumption when the second vehicle travels the predetermined section in a state based on the reference state by the ratio.

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