Travel load prediction device and electric vehicle control system

Abstract

The present invention provides a travel load prediction device comprising: a gradient acquisition unit that acquires the gradient of a target section; an average-vehicle-speed-acquisition unit that acquires the average vehicle speed of the target section; a vehicle information acquisition unit that acquires host vehicle information including the weight of a host vehicle; a load calculation unit that calculates the travel load of the host vehicle in the target section on the basis of the gradient, the average vehicle speed of the target section, and the host vehicle information; a coefficient acquisition unit that acquires an average host vehicle speed, which is the average speed of the host vehicle, and an energy consumption coefficient in a host vehicle speed variance, which is a variance of the speed of the host vehicle relative to the average host vehicle speed, on the basis of data in which a reference average vehicle speed, which is an average vehicle speed serving as a reference, a reference vehicle speed variance, which is a variance of the vehicle speed relative to the reference average vehicle speed, and the energy consumption coefficient are associated; and a correction unit that corrects the travel load calculated by the load calculation unit to calculate a corrected travel load, on the basis of the energy consumption coefficient acquired by the coefficient acquisition unit.

Description

Travel load prediction device and electric vehicle control system

[0001] The present disclosure relates to a traveling load prediction device and an electric vehicle control system.

[0002] Patent Document 1 describes an electricity consumption prediction device that predicts the electricity consumption of a vehicle running on a motor, which acquires vehicle speed data for multiple vehicles registered for each link of map information and average vehicle speeds calculated from the vehicle speed data, divides the links into vehicle speed groups according to the magnitude of the average vehicle speeds, statistically processes the vehicle speed data for the multiple vehicles for each vehicle speed group, calculates a predicted vehicle speed transition by simulating changes in vehicle speed for each vehicle speed group based on the statistical processing, and applies an electricity consumption model for the vehicle to the calculated predicted vehicle speed transition to predict the electricity consumption corresponding to the average vehicle speed for each vehicle speed group.

[0003] Japanese Patent Application Laid-Open No. 2022-140890

[0004] The electricity consumption prediction device described in Patent Document 1 predicts electricity consumption based on average vehicle speed. However, when a vehicle is actually traveling, a state known as wave-like driving, in which repeated acceleration and deceleration occur, can occur. In this case, the energy required for traveling increases significantly even though the average vehicle speed remains the same as when traveling at a constant speed. As a result, the predicted electricity consumption may significantly deviate from the actual electricity consumption. Therefore, there is a need for a more accurate prediction of the traveling load, which is the energy required for an electric vehicle to travel a target section.

[0005] Therefore, an object of the present disclosure is to provide a traveling load prediction device and an electric vehicle control system that can more accurately predict the traveling load.

[0006] According to the inventors' findings, the energy consumption coefficient increases as the variance (squared variance) of vehicle speed, which indicates the degree of wavy driving, increases. The energy consumption coefficient is a coefficient that indicates the ratio of the actual running load when the energy required for running is normalized by the vehicle weight and the running load when the vehicle runs at a constant speed at an average vehicle speed in the target section is set to 1. Furthermore, the variance of vehicle speed (hereinafter sometimes referred to as vehicle speed variance) tends to depend on the average vehicle speed. Therefore, by estimating the energy consumption coefficient based on the average vehicle speed and the vehicle speed variance relative to the average vehicle speed and using the energy consumption coefficient to correct the running load when the vehicle runs at a constant speed at the average vehicle speed in the target section, it is possible to predict an accurate running load that more closely approximates the actual running load. The running load prediction device and electric vehicle control system according to the present disclosure were developed by the inventors through extensive research based on the above findings.

[0007] That is, the traveling load prediction device according to the present disclosure is [1] "a traveling load prediction device for predicting a traveling load, which is energy required for a host vehicle, which is an electric vehicle, to travel in a target section, the traveling load prediction device including: a gradient acquisition unit that acquires a gradient of the target section; an average vehicle speed acquisition unit that acquires an average vehicle speed of the target section; a vehicle information acquisition unit that acquires host vehicle information including a weight of the host vehicle; a load calculation unit that calculates the traveling load of the host vehicle in the target section based on the gradient, the average vehicle speed of the target section, and the host vehicle information; a coefficient acquisition unit that acquires a host vehicle average vehicle speed that is an average vehicle speed of the host vehicle and an energy consumption coefficient for the host vehicle speed variance that is the variance of the vehicle speed of the host vehicle from the host vehicle average vehicle speed, based on data that associates a reference average vehicle speed that is a reference average vehicle speed, a reference vehicle speed variance that is the variance of the vehicle speed from the reference average vehicle speed, and an energy consumption coefficient; and a correction unit that corrects the traveling load calculated by the load calculation unit based on the energy consumption coefficient acquired by the coefficient acquisition unit and calculates the corrected traveling load."

[0008] In this traveling load prediction device, the load calculation unit calculates the traveling load, which is the energy required to travel through the target section, based on host vehicle information including the gradient of the target section, the average vehicle speed of the target section, and the weight of the host vehicle. Meanwhile, the coefficient acquisition unit acquires the host vehicle's average vehicle speed and the energy consumption coefficient for the host vehicle's vehicle speed variance relative to the average vehicle speed based on data correlating a reference average vehicle speed, which is a reference average vehicle speed, a reference vehicle speed variance relative to the reference average vehicle speed, and an energy consumption coefficient. The correction unit then corrects the traveling load calculated by the load calculation unit using the energy consumption coefficient acquired by the coefficient acquisition unit to obtain the corrected traveling load. In this way, this traveling load prediction device estimates the energy consumption coefficient taking into account the vehicle speed variance relative to the average vehicle speed (i.e., the degree of undulating driving), and corrects the traveling load using the energy consumption coefficient. This enables more accurate prediction of the traveling load.

[0009] Another finding of the present inventors is that vehicle speed variance varies depending on the habits and characteristics of the driver, such as the distance between vehicles and how they accelerate and decelerate. Therefore, by matching a map generated by associating the average vehicle speed and vehicle speed variance for each of multiple drivers with a map of local average vehicle speeds and vehicle speed variances that reflects the habits and characteristics of the driver of the vehicle, it is possible to predict the vehicle speed variance of the vehicle over the entire target section.

[0010] Therefore, the traveling load prediction device according to the present disclosure may be [2] "the traveling load prediction device according to the above [1], including: a map acquisition unit that acquires, based on a driving result of the host vehicle in a local area of ​​the target section, a local average vehicle speed that is the host vehicle's average vehicle speed in the area, and a local vehicle speed variance that is the host vehicle speed variance with respect to the local average vehicle speed in the area; and a variance prediction unit that predicts the host vehicle speed variance throughout the target section based on matching between the local map and a reference map that is generated by associating the reference average vehicle speed with the reference vehicle speed variance for each of a plurality of drivers." In this case, the host vehicle speed variance throughout the entire target section can be easily acquired.

[0011] The traveling load prediction device according to the present disclosure may be [3] "the traveling load prediction device according to the above [2], including: an identification unit that identifies the driver of the host vehicle; a determination unit that determines whether the driver of the host vehicle identified by the identification unit is a registered driver for whom the reference map has been generated; and a variance acquisition unit that, when the determination unit determines that the driver of the host vehicle is the registered driver, acquires the host vehicle speed variance throughout the entire target section as the reference vehicle speed variance for the registered driver based on the reference map." In this case, the vehicle speed variance of the host vehicle throughout the entire target section can be easily acquired.

[0012] Here, an electric vehicle control system according to the present disclosure is [4] "an electric vehicle control system including the traveling load prediction device according to any one of [1] to [3] above, and a scheduling unit that schedules charging and discharging of the battery of the host vehicle and / or power generation of the host vehicle based on the corrected traveling load so that the SOC of the battery of the host vehicle does not reach the lower limit value or the upper limit value throughout the entire target section." This electric vehicle control system makes it possible to perform more optimal scheduling of battery charging and discharging and / or power generation based on the traveling load more accurately predicted by the traveling load prediction device.

[0013] The electric vehicle control system according to the present disclosure may be [5] "the electric vehicle control system described in [4] above, including: a heat generation amount prediction unit that predicts a heat generation amount of an electric vehicle unit including a motor and a battery of the host vehicle in the target section based on the corrected traveling load; and a cooling output acquisition unit that acquires a cooling output, which is electric power for cooling the electric vehicle unit in the target section, based on the heat generation amount predicted by the heat generation amount prediction unit, wherein the scheduling unit performs the scheduling further based on the cooling output." In this case, it is possible to perform more optimal scheduling of battery charging / discharging and / or power generation by taking into account the electric power required for cooling the electric vehicle unit including the motor and battery of the host vehicle.

[0014] The electric vehicle control system according to the present disclosure may be [6] "the electric vehicle control system described in [5] above, including an outside air temperature acquisition unit that acquires an outside air temperature in the target section, and the cooling output acquisition unit acquires the cooling output in consideration of the outside air temperature acquired by the outside air temperature acquisition unit." In this case, by considering the outside air temperature in the target section, it is possible to accurately acquire the power required to cool the electric vehicle unit of the host vehicle.

[0015] The electric vehicle control system according to the present disclosure may be [7] "the electric vehicle control system according to any one of [4] to [6] above, wherein the host vehicle is equipped with an attached object that uses electric power while traveling, and the scheduling unit performs the scheduling based further on the power consumption of the attached object in the target section." In this case, it is possible to perform more optimal scheduling of battery charging / discharging and / or power generation by taking into account the power consumption of the attached object of the host vehicle.

[0016] According to the present disclosure, it is possible to provide a traveling load prediction device and an electric vehicle control system that are capable of more accurately predicting a traveling load.

[0017] FIG. 1 is a schematic diagram showing a portion of an electric vehicle according to an embodiment. FIG. 2 is a block diagram showing the functional configuration of an electric vehicle control system and a traveling load prediction device configured in an energy management ECU. FIG. 1 is a schematic diagram showing a portion of an electric vehicle according to an embodiment. FIG. 4 is a graph showing an example of the relationship between average vehicle speed and actual vehicle speed during constant speed traveling. FIG. 5 is a diagram showing an example of data correlating average vehicle speed, vehicle speed variance, and energy consumption coefficient. FIG. 6 is a diagram showing a map generated by correlating average vehicle speed and vehicle speed variance. FIG. 7 is a graph showing the outside air temperature, heat generation amount, and cooling output of an electric vehicle unit in a target section. FIG. 8 is a graph showing the SOC of a drive battery and optimal charge / discharge output. FIG. 9 is a flowchart showing one step of a traveling load prediction method according to the present embodiment.

[0018] An embodiment will be described below with reference to the drawings. In the description of the drawings, the same or corresponding elements are denoted by the same reference numerals, and redundant description may be omitted.

[0019] FIG. 1 is a schematic diagram showing a portion of an electric vehicle according to one embodiment. The electric vehicle EA shown in FIG. 1 may be, for example, a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell electric vehicle (FCEV), or a battery electric vehicle (BEV). The electric vehicle EA is equipped with an attached object A0, such as a luggage compartment refrigerator, that uses electricity while traveling (see FIG. 3). The attached object A0 is not limited to a refrigerator, but may also be, for example, advertising lighting, air conditioning, a mixer, or the like.

[0020] The electric vehicle EA (host vehicle) is equipped with a drive motor A1, a drive battery A2, and an inverter A3. The drive motor A1 and the drive battery A2 are electrically connected via the inverter A3. The drive motor A1 is connected to the drive wheels A4 via a transmission A5. The drive motor A1 receives a supply of electric power from the drive battery A2 to drive the drive wheels A4. The drive motor A1 is also driven in accordance with the rotation of the drive wheels A4, and provides regenerative power to the drive battery A2. The drive motor A1, the drive battery A2, and the inverter A3 constitute an electric vehicle unit UA in the electric vehicle EA.

[0021] The electric vehicle EA has a refrigerant flow path CL through which a refrigerant W (e.g., cooling water) flows to cool the electric vehicle unit UA. The refrigerant flow path CL is provided with a pump A6 for pressurizing the refrigerant W, a radiator A7 for heat exchange between the electric vehicle unit UA and the refrigerant W, and a cooling fan A8 for supplying cooling air to the radiator A7.

[0022] The electric vehicle EA is equipped with a power plant A10 that generates electric power and provides it to a drive battery A2. The power plant A10 includes an engine A11, a generator A12, and an inverter A13. The power plant A10 may also be equipped with a fuel cell system A14. The engine A11 drives the generator A12. The generator A12 is electrically connected to the drive battery A2 via the inverter A13 and supplies electric power to the drive battery A2. If the fuel cell system A14 is provided, electric power from the fuel cell system A14 may also be provided to the drive battery A2.

[0023] The electric vehicle EA includes a plurality of ECUs (Electronic Control Units). Specifically, the electric vehicle EA includes an energy management ECUB0, a locator ECUB1, and a vehicle ECUB2.

[0024] The locator ECUB1 acquires the current position of the electric vehicle EA, for example, by receiving GPS satellite information. Based on the acquired current position, the locator ECUB1 generates gradient information for a predetermined distance ahead on the driving route of the electric vehicle EA1 and transmits the gradient information to the energy management ECUB0. The locator ECUB1 also acquires online information on the average vehicle speed (traffic flow) of the electric vehicle EA1 for the same period and time period in the past from VICS (registered trademark) statistical information and outside air temperature information from past weather information, and transmits the information to the energy management ECUB0. The locator ECUB1 can also transmit information on the travel time of the driving route of the electric vehicle EA to the energy management ECUB0.

[0025] The vehicle ECUB2 acquires the vehicle speed of the electric vehicle EA and transmits it to the energy management ECUB0. The vehicle ECUB2 also has a database B3 that stores information such as the vehicle weight and vehicle type information (dimensions, tires, mounting, etc.) of the electric vehicle EA, and can transmit this information to the energy management ECUB0 in response to a request from the energy management ECUB0, for example.

[0026] The energy management ECUB0 outputs a control signal C1 indicating a power generation request (power generation amount) to the power plant A10. The energy management ECUB0 also outputs a control signal C2 and a control signal C3 to the pump A6 and the cooling fan A8, respectively.

[0027] The energy management ECUB0 constitutes the electric vehicle control system 1 and the running load prediction device 10 according to this embodiment. The energy management ECUB0 is an electronic control unit having, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The energy management ECUB0, for example, loads a program stored in the ROM into the RAM and executes the program on the CPU, thereby realizing various functions of the electric vehicle control system 1 and the running load prediction device 10, which will be described later.

[0028] 2 is a block diagram showing the functional configuration of an electric vehicle control system and a running load prediction device configured in an energy management ECU. As shown in Fig. 2, the electric vehicle control system 1 includes a gradient acquisition unit 2, an average vehicle speed acquisition unit 3, a vehicle information acquisition unit 4, a load calculation unit 5, a coefficient acquisition unit 6, a correction unit 7, a map acquisition unit 8, a variance prediction unit 9, an identification unit 11, a determination unit 12, a variance acquisition unit 13, and a database 20. These components constitute a running load prediction device 10. In addition to the running load prediction device 10, the electric vehicle control system 1 also includes a scheduling unit 14, a heat generation amount prediction unit 15, a cooling output acquisition unit 16, and an outside air temperature acquisition unit 17.

[0029] First, each component of the traveling load prediction device 10 will be described. The traveling load prediction device 10 is for predicting the traveling load, which is the energy required for the host vehicle, which is an electric vehicle EA, to travel through a target section T0 (see FIG. 3). As shown in FIG. 3(a), the gradient acquisition unit 2 receives information from the locator ECUB1 and acquires the gradient P1 of the target section T0. The target section T0 is divided into a plurality of regions t １ , t ２ , t ３ , ..., t ｎ－２ , t ｎ－１ , tｎ (Hereinafter, the region t ｋ may be referred to as).

[0030] The average vehicle speed acquisition unit 3 receives information from the locator ECUB1 and acquires an average vehicle speed P2 of the vehicle in the same time period and time zone in the past in the target section T0, as shown in FIG. 3B. The average vehicle speed P2 here is calculated based on the average vehicle speed P2 of each area t constituting the target section T0. Ｋ The vehicle information acquisition unit 4 receives information from the vehicle ECUB 2 and acquires host vehicle information including the weight of the electric vehicle EA.

[0031] The load calculation unit 5 calculates the traveling load, which is the energy required for the electric vehicle EA to travel through the target section T0, using an equation of motion based on the gradient P1, the average vehicle speed P2, the vehicle information, the travel time through the target section T0, etc. ｋ Calculate the running load when driving at a constant speed.

[0032] In contrast, as shown in Figure 4, when a vehicle is actually traveling, a state of vehicle speed Q2, which is a so-called wave-like driving state in which acceleration and deceleration are repeated, may occur. In this case, even though the average vehicle speed remains the same as when traveling at a constant speed, the energy required for traveling increases significantly. As a result, the traveling load calculated by the load calculation unit 5 may deviate from the actual traveling load. Therefore, the traveling load prediction device 10 corrects the traveling load calculated by the load calculation unit 5 using the energy consumption coefficient, thereby predicting the traveling load more accurately.

[0033] That is, in the traveling load prediction device 10, the coefficient acquisition unit 6 acquires the host vehicle average vehicle speed, which is the average vehicle speed of the electric vehicle EA, and the energy consumption coefficient E at the host vehicle speed variance, which is the vehicle speed variance of the electric vehicle EA with respect to the host vehicle average vehicle speed, based on data correlating a reference average vehicle speed Va (reference average vehicle speed), a vehicle speed variance (vehicle speed squared variance) Vv (reference vehicle speed variance) with respect to the average vehicle speed Va, and an energy consumption coefficient E, as shown in Fig. 5. This data is, for example, stored in the database 20. Then, the correction unit 7 corrects the traveling load calculated by the load calculation unit 5 using the energy consumption coefficient E acquired by the coefficient acquisition unit 6, and calculates the corrected traveling load.

[0034] To achieve this, the traveling load prediction device 10 first predicts the vehicle speed variance of the electric vehicle EA throughout the entire target section T0. This point will be explained in more detail. As shown in FIG. 6A, the vehicle speed variance Vv varies depending on the habits and characteristics of the driver, such as the inter-vehicle distance and the manner of acceleration and deceleration. Therefore, by matching a reference map MR (e.g., stored in the database 20) generated by associating the average vehicle speed Va and the vehicle speed variance Vv for each of multiple drivers A to D with a local map ML of the local host vehicle average vehicle speed and host vehicle speed variance that reflects the habits and characteristics of the driver of the electric vehicle EA, it is possible to predict the vehicle speed variance of the electric vehicle EA throughout the entire target section T0.

[0035] Therefore, in the traveling load prediction device 10, the map acquisition unit 8 acquires a local area t ｋ (For example, the area t １ ) based on the driving result of the electric vehicle EA in the area t ｋ the local average vehicle speed, which is the average vehicle speed of the host vehicle in the region t ｋ A local map ML is obtained that associates the local vehicle speed variance, which is the variance of the vehicle's own speed with the local average vehicle speed in the area.

[0036] The variance prediction unit 9 then predicts the vehicle speed variance for the entire target section T0 based on matching between a reference map MR generated by associating the average vehicle speed Va and the vehicle speed variance Vv for each of the drivers A to D and the local map ML acquired by the map acquisition unit 8. That is, the variance prediction unit 9 detects, through this matching, the vehicle speed variance of a driver who is close to the driver of the electric vehicle EA among the registered drivers A to D for whom the reference map MR has been generated, and sets this as the vehicle speed variance.

[0037] However, this is processing when the driver of the electric vehicle EA is not one of the registered drivers A to D. On the other hand, there are also cases where the driver of the electric vehicle EA is one of the registered drivers A to D. Therefore, in the traveling load prediction device 10, the identification unit 11 identifies the driver of the electric vehicle EA, and the determination unit 12 determines whether the driver of the electric vehicle EA identified by the identification unit 11 is one of the registered drivers A to D. Then, if the determination result of the determination unit 12 indicates that the driver of the electric vehicle EA is one of the registered drivers A to D (for example, driver C), the variance acquisition unit 13 acquires the vehicle speed variance for the entire target section T0 as the vehicle speed variance Vv of the registered driver, based on the reference map MR.

[0038] In the traveling load prediction device 10, the coefficient acquisition unit 6 acquires the energy consumption coefficient E using the host vehicle speed variance predicted by the variance prediction unit 9 or the host vehicle speed variance acquired by the variance acquisition unit 13, and the correction unit 7 corrects the traveling load calculated by the load calculation unit 5 using the energy consumption coefficient E. This makes it possible to predict an accurate traveling load that is more suited to the actual traveling of the electric vehicle EA.

[0039] The electric vehicle control system 1 uses the running load of the electric vehicle EA in the target section T0 predicted as described above to schedule the charging and discharging of the drive battery A2 and the power generation of the power plant A10. This point will be described in more detail below.

[0040] In the electric vehicle control system 1, the scheduling unit 14 schedules the charging and discharging of the driving battery A2 of the electric vehicle EA and / or the power generation of the electric vehicle EA (i.e., SOC scheduling) based on the above-described corrected running load so that the SOC of the driving battery A2 of the electric vehicle EA does not reach the lower limit or upper limit throughout the entire target section T0, as shown in Fig. 8(a). By scheduling the SOC of the driving battery A2 in this manner, it is possible to prevent the driving battery A2 from becoming fully charged by regenerative power while going downhill, or from running out of power while going uphill.

[0041] Here, in the electric vehicle control system 1, the heat generation amount prediction unit 15 predicts the heat generation amount P5 of the electric vehicle unit UA of the electric vehicle EA in the target section T0 based on the corrected running load, as shown in (b) of Figure 7.

[0042] 7C, the cooling output acquisition unit 16 acquires a cooling output P6, which is the electric power for cooling the electric vehicle unit UA in the target section T0, based on the heat generation amount P5 predicted by the heat generation amount prediction unit 15. More specifically, based on the outside air temperature P4 (see FIG. 7A) in the target section T0 acquired by the outside air temperature acquisition unit 17 via the locator ECUB1 and the heat generation amount P5 predicted by the heat generation amount prediction unit 15, the cooling output acquisition unit 16 schedules, by dynamic programming, the optimal output of the cooling system (e.g., pump A6 and cooling fan A8) that prevents the temperature of the electric vehicle unit US from exceeding a limit value and minimizes the energy used for cooling, and acquires the scheduling result as the cooling output P6.

[0043] The scheduling unit 14 then optimizes the scheduling of charging and discharging of the drive battery A2 and / or power generation of the electric vehicle EA (including the timing of power generation by the power plant A10) based on the cooling output P6 acquired by the cooling output acquisition unit 16, taking into account the efficiency of the fuel cell system A14 and the engine A11, thereby minimizing the consumption of fuel such as hydrogen and diesel while ensuring the cruising range. As a result, the scheduling unit 14 acquires the optimal charging and discharging output P8 as shown in (b) of Figure 8. Note that P9 in (b) of Figure 8 indicates the amount of power generated by the power plant A10.

[0044] The electric vehicle EA is equipped with a mounted object A0 that uses electric power while traveling. Therefore, in the electric vehicle EA, the mounted object A0 is driven by the driving battery A2, so the power consumption of the mounted object A0 may be controlled. Therefore, the scheduling unit 14 may perform the above scheduling further based on the power consumption of the mounted object A0 in the target section T0.

[0045] Next, an embodiment of a method for predicting a traveling load by the traveling load prediction device 10 according to the present embodiment will be described. FIG. 9 is a flowchart showing one step of the method for predicting a traveling load according to the present embodiment. As shown in FIG. 9 , first, the identification unit 11 identifies the driver of the electric vehicle EA (step S101). As an example, the identification unit 11 can identify the driver of the electric vehicle EA based on an image taken by a camera or the like, or identify the driver of the electric vehicle EA by acquiring an operation plan for the electric vehicle EA, or can perform the identification in various ways.

[0046] Next, the determination unit 12 determines whether the identification unit 11 has been able to identify the driver of the electric vehicle EA, and whether the driver of the electric vehicle EA identified by the identification unit 11 is one of the registered drivers A to D for which the reference map MR shown in Figure 6 has been generated (step S102).

[0047] As a result of the judgment in step S102, if the identification unit 11 can identify the driver of the electric vehicle EA and the driver of the electric vehicle EA identified by the identification unit 11 is a registered driver A to D (step S102: Yes), the variance acquisition unit 13 compares the reference map MR stored in the database 20 and acquires the vehicle speed variance for the entire target section T0 as the vehicle speed variance Vv of the registered driver (step S103).

[0048] Next, the coefficient acquisition unit 6 acquires the vehicle's average vehicle speed and the energy consumption coefficient E at the vehicle speed variance acquired in step S103 based on data (see Figure 5) that associates the reference average vehicle speed Va, the vehicle speed variance Vv for the average vehicle speed Va, and the energy consumption coefficient E (step S104).

[0049] Then, the correction unit 7 corrects the running load calculated by the load calculation unit 5 using the energy consumption coefficient E (step S105). This makes it possible to predict an accurate running load that is more suited to the actual running of the electric vehicle EA.

[0050] On the other hand, as a result of the determination in step S102, if the identification unit 11 cannot identify the driver of the electric vehicle EA, or if the driver of the electric vehicle EA identified by the identification unit 11 is not one of the registered drivers A to D (step S102: No), the map acquisition unit 8 determines whether the electric vehicle EA is in a local area t ｋ Based on the vehicle speed average when traveling, the local vehicle speed variance is calculated (step S106), and a local map ML (see FIG. 6(b)) of the local vehicle speed average and vehicle speed variance is generated (step S107).

[0051] Next, the traveling load prediction device 10 determines whether the local map ML satisfies the conditions for later matching with the reference map MR (step S108). If the result of this determination is that the local map ML does not satisfy the matching conditions, for example, because the traveling distance is short (step S108: No), the process returns to step S106, and the variance prediction unit 9 calculates the host vehicle speed variance again.

[0052] On the other hand, if the result of the determination in step S108 is that the local map ML satisfies the matching conditions (step S108: Yes), the traveling load prediction device 10 determines whether the identification unit 11 can identify the driver of the electric vehicle EA and whether the vehicle speed variance calculated in step S106 can be added to the database 20 (step S109).

[0053] If the determination result in step S109 indicates that the driver of the electric vehicle EA can be identified and that the vehicle speed variance calculated in step S106 can be added to the database 20 (step S109: Yes), the traveling load prediction device 10 associates the driver of the electric vehicle EA with the vehicle speed variance and registers the identified new driver in the database 20 (step S110).

[0054] Next, the variance prediction unit 9 predicts the vehicle speed variance for the entire target section T0 based on matching between the reference map MR and the local map ML (step 111).Then, the coefficient acquisition unit 6 acquires the vehicle average speed and the energy consumption coefficient E for the vehicle speed variance predicted in step S111 based on data correlating the reference average vehicle speed Va, the vehicle speed variance Vv for the average vehicle speed Va, and the energy consumption coefficient E (see FIG. 5) (step S104).Then, the correction unit 7 corrects the running load calculated by the load calculation unit 5 using the energy consumption coefficient E (step S105).

[0055] On the other hand, if the result of the determination in step S109 is that the driver of the electric vehicle EA cannot be identified or that the vehicle speed variance calculated in step S106 cannot be added to the database 20 (step S109: No), the process proceeds to step S111 without performing step S110. As a result, a more accurate prediction of the running load is possible whether the driver of the electric vehicle EA is a registered driver or an unregistered driver.

[0056] As described above, in the traveling load prediction device 10 according to this embodiment, the load calculation unit 5 calculates the traveling load, which is the energy required to travel through the target section T0, based on host vehicle information including the gradient P1 of the target section T0, the average vehicle speed P2 of the target section T0, and the weight of the electric vehicle EA. Meanwhile, the coefficient acquisition unit 6 acquires the average vehicle speed of the electric vehicle EA (host vehicle speed average) and the energy consumption coefficient E in the vehicle speed variance Vv (host vehicle speed variance) of the electric vehicle EA relative to the average vehicle speed of the electric vehicle EA, based on data in which a reference average vehicle speed Va (reference average vehicle speed), a vehicle speed variance Vv (reference vehicle speed variance) relative to the average vehicle speed Va, and the energy consumption coefficient E are associated with each other.

[0057] Then, the correction unit 7 corrects the road load calculated by the load calculation unit 5 using the energy consumption coefficient E acquired by the coefficient acquisition unit 6, thereby acquiring the corrected road load. In this way, the road load prediction device 10 estimates the energy consumption coefficient E in consideration of the vehicle speed variance with respect to the average vehicle speed (i.e., the degree of wavy driving), and corrects the road load using the energy consumption coefficient E. This makes it possible to more accurately predict the road load.

[0058] In addition, the traveling load prediction device 10 according to the present embodiment predicts the local area t ｋ Based on the driving results of the electric vehicle EA in the area t ｋ the local average vehicle speed, which is the average vehicle speed of the host vehicle in the region t ｋ The system includes a map acquisition unit 8 that acquires a local map ML that associates a local vehicle speed variance, which is the variance of the host vehicle's speed with a local average vehicle speed in a target section T0, and a variance prediction unit 9 that predicts the host vehicle speed variance in the entire target section T0 based on matching between the local map ML and a reference map MR that is generated by associating the reference average vehicle speed with the reference vehicle speed variance for each of a plurality of drivers A to D. This makes it possible to easily acquire the host vehicle speed variance of the electric vehicle EA in the entire target section T0.

[0059] Moreover, the traveling load prediction device 10 according to this embodiment includes an identification unit 11 that identifies the driver of the electric vehicle EA, a determination unit 12 that determines whether the driver of the electric vehicle EA identified by the identification unit 11 is a registered driver A to D for which a reference map MR has been generated, and a variance acquisition unit that, when the determination result of the determination unit 12 indicates that the driver of the electric vehicle EA is a registered driver A to D, acquires the vehicle speed variance for the entire target section T0 as the vehicle speed variance Vv for the registered driver A to D based on the reference map MR. This makes it possible to easily acquire the vehicle speed variance for the entire target section T0.

[0060] The electric vehicle control system 1 according to this embodiment also includes a traveling load prediction device 10 and a scheduling unit 14 that schedules the charging and discharging of the driving battery A2 and / or the power generation at the power plant A10 of the electric vehicle EA based on the corrected traveling load so that the SOC of the driving battery A2 of the electric vehicle EA does not reach the lower limit value or the upper limit value throughout the entire target section T0. The electric vehicle control system makes it possible to more optimally schedule the charging and discharging of the driving battery A2 and / or the power generation at the power plant A10 based on the traveling load more accurately predicted by the traveling load prediction device 10.

[0061] The electric vehicle control system 1 according to this embodiment also includes a heat generation amount prediction unit 15 that predicts a heat generation amount P5 of the electric vehicle unit UA of the electric vehicle EA in the target section T0 based on the corrected traveling load, and a cooling output acquisition unit 16 that acquires a cooling output P6, which is the electric power required to cool the electric vehicle unit UA in the target section T0, based on the heat generation amount P5 predicted by the heat generation amount prediction unit 15. The scheduling unit 14 then performs the scheduling based further on the cooling output P6. This makes it possible to more optimally schedule the charging and discharging of the drive battery A2 and / or the power generation by the power plant A10, taking into account the electric power required to cool the electric vehicle unit UA of the electric vehicle EA.

[0062] The electric vehicle control system 1 according to this embodiment also includes an outside air temperature acquisition unit 17 that acquires an outside air temperature P4 in the target section T0. The cooling output acquisition unit 16 acquires the cooling output P6 in consideration of the outside air temperature P4 acquired by the outside air temperature acquisition unit 17. In this way, by taking the outside air temperature P4 in the target section T0 into consideration, it is possible to accurately acquire the electric power required to cool the electric vehicle unit US.

[0063] Furthermore, in the electric vehicle control system 1 according to this embodiment, the scheduling unit 14 may perform the scheduling based on the power consumption of the mounted object A0 in the target section T0. In this case, it is possible to perform more optimal scheduling of charging and discharging of the drive battery A2 and / or power generation by the power plant A10, taking into account the power consumption of the mounted object A0 of the electric vehicle EA.

[0064] The above embodiment has described one form of the traveling load prediction device and electric vehicle control system according to the present invention. Therefore, the traveling load prediction device and electric vehicle control system according to the present invention are not limited to the traveling load prediction device 10 and electric vehicle control system 1 according to the above embodiment, and may be any modified version thereof.

[0065] For example, the traveling load prediction device 10 may not include at least one of the map acquisition unit 8, the variance prediction unit 9, the identification unit 11, the determination unit 12, and the variance acquisition unit 13. In this case, as an example, the traveling load prediction device 10 may implement a model trained using machine learning such as a neural network on past data of local driving by the driver and data of driving over the entire section. In this case, by performing machine learning offline on a PC or the like and loading the resulting learning weight matrix into the energy management ECUB0, it is possible to accurately predict the host vehicle speed variance by extrapolation while reducing the computational load.

[0066] Furthermore, the electric vehicle control system 1 does not necessarily have to include at least one of the heat generation amount prediction unit 15, the cooling output acquisition unit 16, and the outside air temperature acquisition unit 17. Furthermore, the electric vehicle EA is not limited to a vehicle equipped with an attached object A0 that consumes power while traveling, and may be any vehicle.

[0067] 1...electric vehicle control system, 10...traveling load prediction device, 2...gradient acquisition unit, 3...average vehicle speed acquisition unit, 4...vehicle information acquisition unit, 5...load calculation unit, 6...coefficient acquisition unit, 7...correction unit, 8...map acquisition unit, 9...variance prediction unit, 11...identification unit, 12...determination unit, 13...variance acquisition unit, 14...scheduling unit, 15...heat generation prediction unit, 16...cooling output acquisition unit, 17...outside air temperature acquisition unit, A0...mounted object, EA...electric vehicle, T0...target section, UA...electric vehicle unit.

Claims

1. A driving load prediction device for predicting a driving load, which is energy required for a host vehicle, which is an electric vehicle, to travel through a target section, comprising: a gradient acquisition unit for acquiring a gradient of the target section; an average vehicle speed acquisition unit for acquiring an average vehicle speed of the target section; a vehicle information acquisition unit for acquiring host vehicle information including a weight of the host vehicle; a load calculation unit for calculating the driving load of the host vehicle in the target section based on the gradient, the average vehicle speed of the target section, and the host vehicle information; a coefficient acquisition unit for acquiring an energy consumption coefficient for a host vehicle average vehicle speed that is an average vehicle speed of the host vehicle and a host vehicle speed variance that is the variance of the vehicle speed of the host vehicle relative to the host vehicle average vehicle speed, based on data in which a reference average vehicle speed that is a reference average vehicle speed, a reference vehicle speed variance that is the variance of the vehicle speed relative to the reference average vehicle speed, and an energy consumption coefficient are associated with each other; and a correction unit for correcting the driving load calculated by the load calculation unit based on the energy consumption coefficient acquired by the coefficient acquisition unit, and calculating the corrected driving load.

2. A driving load prediction device as described in claim 1, comprising: a map acquisition unit that acquires a local map that associates a local average vehicle speed, which is the vehicle's average vehicle speed in a local area of ​​the target section, with a local vehicle speed variance, which is the vehicle speed variance relative to the local average vehicle speed in the local area, based on the driving results of the vehicle in that area; and a variance prediction unit that predicts the vehicle speed variance in the entire target section based on matching between the local map and a reference map generated by associating the reference average vehicle speed with the reference vehicle speed variance for each of a plurality of drivers.

3. A traveling load prediction device as described in claim 2, comprising: an identification unit that identifies a driver of the host vehicle; a judgment unit that determines whether the driver of the host vehicle identified by the identification unit is a registered driver for whom the reference map has been generated; and a variance acquisition unit that, when the judgment unit determines that the driver of the host vehicle is the registered driver, acquires the host vehicle speed variance for the entire target section based on the reference map as the reference vehicle speed variance for the registered driver.

4. An electric vehicle control system comprising: a driving load prediction device according to any one of claims 1 to 3; and a scheduling unit that schedules charging and discharging of the battery of the vehicle and / or power generation of the vehicle based on the corrected driving load so that the SOC of the battery of the vehicle does not reach the lower limit value or the upper limit value throughout the entire target section.

5. An electric vehicle control system as described in claim 4, comprising: a heat amount prediction unit that predicts the amount of heat generated by an electric vehicle unit including a motor and a battery of the vehicle in the target section based on the corrected driving load; and a cooling output acquisition unit that acquires cooling output, which is electric power for cooling the electric vehicle unit in the target section, based on the amount of heat generated predicted by the heat amount prediction unit, wherein the scheduling unit performs the scheduling further based on the cooling output.

6. An electric vehicle control system as described in claim 5, further comprising an outside air temperature acquisition unit that acquires the outside air temperature in the target section, and the cooling output acquisition unit acquires the cooling output taking into account the outside air temperature acquired by the outside air temperature acquisition unit.

7. An electric vehicle control system as claimed in any one of claims 4 to 6, wherein the host vehicle is equipped with an equipment that uses electric power while traveling, and the scheduling unit performs the scheduling further based on the power consumption of the equipment in the target section.

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