Travel load prediction device and electric vehicle control system

Abstract

To provide a travel load prediction device which can more accurately predict a travel load, and an electric vehicle control system.SOLUTION: A travel load prediction device 10 includes: a gradient acquisition section 2 which acquires gradient of an object section T0; an average vehicle speed acquisition section 3 which acquires average vehicle speed of the object section T0; a vehicle information acquisition section 4 which acquires own vehicle information; a load calculation section 5 which calculates a travel load in the object section T0; a coefficient acquisition section 6 which acquires an energy consumption coefficient E in own vehicle average vehicle speed and own vehicle speed dispersion on the basis of data that average vehicle speed Va to be a reference, vehicle speed dispersion Vv with respect to the average vehicle speed Va and the energy consumption coefficient E are associated with each other; and a correction section 7 which corrects a travel load on the basis of the energy consumption efficient E and calculates the travel load after the correction.SELECTED DRAWING: Figure 2

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-140890 A Summary of the Invention [Problem to be solved by the invention]

[0004] The electricity consumption prediction device described in the above Patent Document 1 predicts electricity consumption based on average vehicle speed. However, when a vehicle is actually traveling, a state such as so-called wavy driving in which acceleration and deceleration are repeated may occur. In this case, although 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 predicted electricity consumption may deviate significantly from the actual electricity consumption. Therefore, there is a demand for more accurate prediction of the traveling load, which is the energy required for a vehicle, which is 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 are capable of predicting the traveling load more accurately. [Means for solving the problem]

[0006] Here, according to the findings of the present inventors, the energy consumption coefficient increases as the variance (squared variance) of the 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 target section is run at a constant speed at the average vehicle speed is set to 1. In addition, the variance of the vehicle speed (hereinafter, sometimes referred to as the 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 correcting the running load when the target section is run at a constant speed at the average vehicle speed using the energy consumption coefficient, it becomes possible to predict an accurate running load that is closer to the actual running load. The running load prediction device and the electric vehicle control system according to the present disclosure have been made by the present inventors through intensive research based on the findings described above.

[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, comprising: 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 the 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 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 that corrects the traveling load calculated by the load calculation unit based on the energy consumption coefficient acquired by the coefficient acquisition unit to calculate 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 the target section, based on the 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 average vehicle speed of the host vehicle and the energy consumption coefficient in the vehicle speed variance of the host vehicle relative to the average vehicle speed of the host vehicle, based on data in which the reference average vehicle speed, which is the reference average vehicle speed, the reference vehicle speed variance, which is the vehicle speed variance relative to the reference average vehicle speed, and the energy consumption coefficient are associated with each other. Then, the correction unit corrects the traveling load calculated by the load calculation unit using the energy consumption coefficient acquired by the coefficient acquisition unit, thereby acquiring the corrected traveling load. In this way, in this traveling load prediction device, the energy consumption coefficient is estimated taking into account the vehicle speed variance relative to the average vehicle speed (i.e., the degree of wavy driving), and the traveling load is corrected using the energy consumption coefficient. This makes it possible to predict the traveling load more accurately.

[0009] Here, as another finding of the present inventors, the vehicle speed variance varies depending on the driver's habits and characteristics such as the vehicle distance and the way of acceleration and deceleration. Therefore, by matching a map generated by associating the average vehicle speed and the vehicle speed variance for each of a plurality of drivers with a map of local average vehicle speed and vehicle speed variance that reflects the habits and characteristics of the driver of the vehicle, it is possible to predict the vehicle speed variance of the vehicle in the entire target section.

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

[0011] The traveling load prediction device according to the present disclosure may be the traveling load prediction device described in [2] above, which includes: [3] 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 which the reference map is generated; and a variance acquisition unit that acquires the host vehicle speed variance in the entire target section as the reference vehicle speed variance of the registered driver based on the reference map when the driver of the host vehicle is the registered driver as a result of the determination by the determination unit. In this case, the vehicle speed variance of the host vehicle in 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 the above [1] to [3], and a scheduling unit that schedules charging / discharging of the battery of the vehicle and / or power generation of the vehicle based on the corrected traveling 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." With this electric vehicle control system, it is possible to perform more optimal scheduling of charging / discharging of the battery and / or power generation based on the traveling load more accurately predicted by the above traveling load prediction device.

[0013] The electric vehicle control system according to the present disclosure may be the electric vehicle control system described in [4] above, [5] 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 vehicle in the target section based on the corrected running 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, and the scheduling unit performs the scheduling further based on the cooling output." In this case, it is possible to perform more optimal battery charging / discharging and / or power generation scheduling in consideration of the electric power required for cooling the electric vehicle unit including the motor and the battery of the vehicle.

[0014] The electric vehicle control system according to the present disclosure may be the electric vehicle control system described in [6] above, which includes 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 taking the outside air temperature in the target section into consideration, it is possible to acquire an accurate amount of 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 the above [4] to [6], in which 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 battery charging / discharging and / or power generation scheduling by taking into account the power consumption of the attached object of the host vehicle. Effect of the Invention

[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 predicting a traveling load more accurately. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic diagram showing a part of an electric vehicle according to one embodiment. [Diagram 2] FIG. 2 is a block diagram showing a functional configuration of an electric vehicle control system and a running load prediction device configured in an energy management ECU. [Diagram 3] FIG. 1 is a schematic diagram showing a part of an electric vehicle according to one embodiment. [Figure 4] FIG. 4 is a graph showing an example of the relationship between the average vehicle speed and the actual vehicle speed during constant speed driving. [Diagram 5] FIG. 5 is a diagram showing an example of data in which the average vehicle speed, the vehicle speed variance, and the energy consumption coefficient are associated with each other. [Figure 6] FIG. 6 is a diagram showing a map generated by associating the average vehicle speed with the vehicle speed variance. [Figure 7] FIG. 7 is a graph showing the outside air temperature, heat generation amount of the electric vehicle unit, and cooling output in the target section. [Figure 8] FIG. 8 is a graph showing the SOC and optimal charge / discharge output of the driving battery. [Figure 9] FIG. 9 is a flowchart showing one step of the road load prediction method according to this embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0019] Fig. 1 is a schematic diagram showing a part of an electric vehicle according to an embodiment. The electric vehicle EA shown in Fig. 1 is, for example, a hybrid vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell electric vehicle (FCEV), an electric vehicle (BEV), or the like. The electric vehicle EA is equipped with an attached object A0 that uses electric power while traveling, such as a luggage compartment refrigerator (see Fig. 3). The attached object A0 is not limited to a refrigerator, and various examples include advertising lighting, air conditioning, a mixer, and 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 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 response to the rotation of the drive wheels A4, and provides regenerative electric 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 includes a coolant flow path CL through which a coolant W (e.g., cooling water) flows for cooling the electric vehicle unit UA. The coolant flow path CL includes a pump A6 for pumping the coolant W, a radiator A7 for heat exchange between the electric vehicle unit UA and the coolant W, and a cooling fan A8 for supplying cooling air to the radiator A7.

[0022] The electric vehicle EA includes a power plant A10 for generating electric power and providing it to a driving battery A2. The power plant A10 includes an engine A11, a generator A12, and an inverter A13. The power plant A10 may include a fuel cell system A14. The engine A11 drives the generator A12. The generator A12 is electrically connected to the driving battery A2 via the inverter A13 and supplies electric power to the driving battery A2. When the fuel cell system A14 is provided, electric power from the fuel cell system A14 may also be provided to the driving 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 travel route of the electric vehicle EA1, and transmits the gradient information to the energy management ECUB0. The locator ECUB1 also acquires the average vehicle speed (traffic flow) of vehicles in the same period and time zone in the past on the travel route of the electric vehicle EA1 from VICS (registered trademark) statistical information or the like, and outside temperature information from past weather information, online, and transmits each of them to the energy management ECUB0. Furthermore, the locator ECUB1 can transmit information related to the travel time of the travel 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 holds information such as the vehicle weight and vehicle type information (dimensions, tires, mounting, etc.) of the electric vehicle EA, and can transmit such 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 by 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] Fig. 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 configure a running load prediction device 10. In addition to the running load prediction device 10, the electric vehicle control system 1 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 a traveling load, which is energy required for a 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 a locator ECUB1 and acquires a gradient P1 of the target section T0. The target section T0 is divided into a plurality of regions t1, t2, t3, ..., t n-2 ,t n-1 ,t n (Hereinafter, the region t will be used to represent the multiple regions.) k Sometimes 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 target section T0 in the past, as shown in FIG. 3B. The average vehicle speed P2 here is the average vehicle speed P2 of the vehicle in each area t 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 in the target section T0, by using an equation of motion based on the gradient P1, the average vehicle speed P2, the vehicle information, the travel time in the target section T0, etc. In this way, the load calculation unit 5 calculates the traveling load, which is the energy required for the electric vehicle EA to travel in the target section T0, by using an equation of motion. k The running load when traveling at a constant speed is calculated.

[0032] On the other hand, as shown in Fig. 4, when a vehicle is actually traveling, a state of vehicle speed Q2, which is a so-called wavy driving state in which acceleration and deceleration are repeated, may occur. In this case, although 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 a more accurate traveling load.

[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 vehicles EA, and the energy consumption coefficient E at the host vehicle speed variance, which is the vehicle speed variance of the electric vehicles EA relative to the host vehicle average vehicle speed, based on data in which a reference average vehicle speed Va (reference average vehicle speed), a vehicle speed variance (vehicle speed squared variance) Vv (reference vehicle speed variance) relative to the average vehicle speed Va, and an energy consumption coefficient E are associated as shown in Fig. 5. The data is held in the database 20, for example. 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] For this purpose, 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. 6(a), the vehicle speed variance Vv varies depending on the habits and characteristics of the driver, such as the vehicle distance and the manner of acceleration and deceleration. Therefore, by matching a reference map MR (e.g., stored in the database 20) that is generated by associating the average vehicle speed Va and the vehicle speed variance Vv for each of a plurality of 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 Based on the driving result of the electric vehicle EA in the area t1 (for example, the area t1 including the start point of the target section T0), k The local average vehicle speed, which is the average vehicle speed of the own 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 speed with the local average vehicle speed in the area.

[0036] Then, the variance prediction unit 9 predicts the vehicle speed variance in 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, by this matching, the vehicle speed variance of a driver close to the driver of the electric vehicle EA among the registered drivers A to D for which the reference map MR is generated, and sets it 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 is also a case 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 or not the driver of the electric vehicle EA identified by the identification unit 11 is one of the registered drivers A to D. Then, when the result of the determination by 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 in 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 schedules the charging and discharging of the driving battery A2 and the power generation of the power plant A10, using the traveling load of the electric vehicle EA in the target section T0 predicted as described above. 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., scheduling the SOC) based on the above-mentioned corrected running 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 as shown in (a) of Fig. 8. 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 in the middle of a downhill slope, for example, or the driving battery A2 from running out of power in the middle of an uphill slope.

[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 traveling load as shown in FIG. 7(b).

[0042] 7(c), the cooling output acquisition unit 16 acquires a cooling output P6, which is 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, the cooling output acquisition unit 16 schedules, by dynamic programming, an optimal output of a 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, based on the outside air temperature P4 (see FIG. 7(a)) 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, and acquires the cooling output P6.

[0043] Then, the scheduling unit 14 optimizes the scheduling of charging and discharging the driving 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 distance. As a result, the scheduling unit 14 acquires the optimal charging and discharging output P8 as shown in (b) of Fig. 8. Note that P9 in (b) of Fig. 8 indicates the amount of power generation in the power plant A10.

[0044] The electric vehicle EA is equipped with an attachment A0 that uses electric power while traveling. Therefore, in the electric vehicle EA, it is desirable to control the power consumption of the attachment A0 in order to drive the attachment A0 from the driving battery A2. Therefore, the scheduling unit 14 may perform the above scheduling further based on the power consumption of the attachment 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 one example, the identification unit 11 can identify the driver of the electric vehicle EA based on an image from a camera or the like, or identify the driver of the electric vehicle EA by acquiring an operation plan of the electric vehicle EA, or can perform the identification in various ways.

[0046] Next, the judgment unit 12 judges whether or not the driver of the electric vehicle EA has been identified by the identification unit 11, and whether or not 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 is able to 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 vehicle speed variance with the reference map MR stored in the database 20 and acquires the vehicle speed variance throughout the 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 average speed and the energy consumption coefficient E at the vehicle speed variance acquired in step S103 based on data (see FIG. 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 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 the vehicle speed variance is generated (step S107).

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

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

[0053] If the determination result in step S109 is that the driver of the electric vehicle EA can be identified and 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 driver in the database 20 as the identified new driver (step S110).

[0054] Next, the variance prediction unit 9 predicts the vehicle speed variance in 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 (see FIG. 5) in which the reference average vehicle speed Va, the vehicle speed variance Vv for the average vehicle speed Va, and the energy consumption coefficient E are associated (step S104), and 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 traveling 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 in 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 (host vehicle speed average) of the electric vehicle EA and the energy consumption coefficient E in the vehicle speed variance (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 an 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 with the energy consumption coefficient E acquired by the coefficient acquisition unit 6, thereby acquiring the corrected road load. In this manner, 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 is configured to predict a local area t k 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 own vehicle in the region t k and a variance prediction unit 9 that predicts the 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 the drivers A to D. This makes it possible to easily obtain the vehicle speed variance of the electric vehicle EA in the entire target section T0.

[0059] Moreover, the traveling load prediction device 10 according to the present embodiment includes an identification unit 11 that identifies the driver of the electric vehicle EA, a determination unit 12 that determines whether or not 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 acquires the vehicle speed variance in the entire target section T0 as the vehicle speed variance Vv of the registered driver A to D based on the reference map MR when the driver of the electric vehicle EA is a registered driver A to D as a result of the determination by the determination unit 12. Therefore, the vehicle speed variance in the entire target section T0 can be easily acquired.

[0060] Moreover, the electric vehicle control system 1 according to this embodiment includes a traveling load prediction device 10, and a scheduling unit 14 that schedules charging and discharging of the driving battery A2 and / or 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 charging and discharging of the driving battery A2 and / or power generation at the power plant A10 based on the traveling load more accurately predicted by the above-mentioned 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 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. The scheduling unit 14 performs the scheduling further based on the cooling output P6. This makes it possible to perform more optimal scheduling of charging and discharging the driving battery A2 and / or power generation in the power plant A10, taking into account the electric power required for cooling 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 a 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 into consideration the outside air temperature P4 in the target section T0, 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 further based on the power consumption of the mounted object A0 in the target section T0. In this case, it becomes possible to perform more optimal scheduling of charging and discharging the driving battery A2 and / or power generation in 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 embodiment 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 any 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, the traveling load prediction device 10 may implement, as an example, a model trained using machine learning such as a Neural Network on past data of a local driving time of a driver and data of driving time of an entire section. In that 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 vehicle speed variance by extrapolation while suppressing the computational load.

[0066] Furthermore, the electric vehicle control system 1 does not necessarily have to include at least any 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 provided with an attached object A0 that consumes electric power during traveling, and may be any vehicle. [Explanation of symbols]

[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...judgment 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 equipment, EA...electric vehicle, T0...target section, UA...electric vehicle unit.

Claims

1. A traveling load prediction device for predicting a traveling load that is energy required for a host vehicle, which is an electric vehicle, to travel a target section, comprising: A gradient acquisition unit that acquires a gradient of the target section; an average vehicle speed acquisition unit that acquires an average vehicle speed in 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 a traveling load of the host vehicle in the target section based on the gradient, an average vehicle speed of the target section, and the host vehicle information; a coefficient acquisition unit that acquires an energy consumption coefficient for a host vehicle average speed that is an average vehicle speed of the host vehicle and a host vehicle speed variance that is the variance of the host vehicle's speed with respect to the host vehicle average 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 with respect to the reference average vehicle speed, and an energy consumption coefficient are associated with each other; a correction unit that corrects the road load calculated by the load calculation unit based on the energy consumption coefficient acquired by the coefficient acquisition unit and calculates the corrected road load; A traveling load prediction device comprising:

2. a map acquisition unit that acquires a local map that associates a local average vehicle speed, which is an average vehicle speed of the host vehicle in a local area of ​​the target section, with a local vehicle speed variance, which is a variance of the host vehicle speed with respect to the local average vehicle speed in the local area, based on a driving result of the host vehicle in the local area of ​​the target section; a variance prediction unit that predicts the vehicle speed variance in the entire target section based on matching between a reference map generated by associating the reference average vehicle speed with the reference vehicle speed variance for each of a plurality of drivers and the local map; Equipped with The traveling load prediction device according to claim 1.

3. an identification unit for identifying a driver of the vehicle; a determination unit that determines whether or not the driver of the host vehicle identified by the identification unit is a registered driver for which the reference map is generated; a variance acquisition unit that acquires the vehicle speed variance in the entire target section as the reference vehicle speed variance of the registered driver based on the reference map when the driver of the vehicle is the registered driver as a result of the judgment by the judgment unit; Equipped with The traveling load prediction device according to claim 2.

4. A traveling load prediction device according to any one of claims 1 to 3, 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 running load so that the SOC of the battery of the host vehicle does not reach a lower limit value or an upper limit value throughout the entire target section; An electric vehicle control system comprising:

5. 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; 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; Equipped with The scheduling unit performs the scheduling further based on the cooling output.

5. An electric vehicle control system according to claim 4.

6. An outside air temperature acquisition unit that acquires an outside air temperature in the target section, The cooling output acquisition unit acquires the cooling output in consideration of the outside air temperature acquired by the outside air temperature acquisition unit.

6. An electric vehicle control system according to claim 5.

7. The vehicle is equipped with an attachment that uses electric power while traveling, The scheduling unit performs the scheduling further based on the power consumption of the mounting object in the target section.

5. An electric vehicle control system according to claim 4.

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