Energy consumption prediction method and energy consumption prediction device

Abstract

Provided is an energy consumption prediction method for predicting energy consumption in an electrically driven vehicle, wherein a controller (20) acquires a vehicle mass of the vehicle, acquires travel data indicating a travel state when the vehicle travels, estimates a rolling resistance coefficient and an air resistance coefficient on the basis of the travel data, acquires a target travel route of the vehicle, acquires a predicted vehicle speed when the vehicle travels on the target travel route and gradient information at each position of the target travel route, and predicts energy consumption when the vehicle travels on the target travel route on the basis of the vehicle mass, the rolling resistance coefficient, the air resistance coefficient, the predicted vehicle speed, and the gradient information.

Description

Energy consumption prediction method and energy consumption prediction device

[0001] The present invention relates to a method for predicting energy consumption and a device for predicting energy consumption.

[0002] In electric vehicles, a device for estimating the energy consumption of a vehicle is known (see, for example, Patent Document 1). The device in Patent Document 1 accumulates the amount of power consumed by the electric motor and air conditioner from the start to the stop of the vehicle, and the acceleration at which the vehicle reaches each speed from the start to the stop, and creates a power consumption map. It also acquires route information including the distance, time, and temperature along the planned route of the vehicle. Then, it estimates the amount of power consumed over the entire planned route using the power consumption map.

[0003] Japanese Patent Publication No. 2014-202635

[0004] In the above-mentioned Patent Document 1, power consumption (energy consumption) is estimated using a power consumption map based on actual measured values ​​(power consumption, vehicle speed, and acceleration when the vehicle reaches speed) when the vehicle is in motion. However, the rolling resistance coefficient and air resistance coefficient vary depending on the weight of the vehicle, whether or not towing is performed, and the type and size of the towed vehicle, and consequently, power consumption also changes significantly. The above-mentioned Patent Document 1 does not take such rolling resistance coefficients and air resistance coefficients into consideration, and has the problem that the accuracy of energy consumption estimation decreases depending on the weight of the vehicle, whether or not towing is performed, and the type and size of the towed vehicle.

[0005] The present invention aims to provide an energy consumption prediction method and an energy consumption prediction device that can predict energy consumption with high accuracy.

[0006] An energy consumption prediction method according to one aspect of this disclosure involves a computer acquiring the vehicle mass of a vehicle, driving data indicating the driving conditions when the vehicle is traveling, a target driving route for the vehicle, a predicted vehicle speed when the vehicle is traveling along the target driving route, and gradient information at each position along the target driving route. The computer also estimates the rolling resistance coefficient and the air resistance coefficient based on the acquired driving data. The computer then predicts the energy consumption when the vehicle is traveling along the target driving route based on the acquired vehicle mass, the predicted vehicle speed and gradient information along the target driving route, and the estimated rolling resistance coefficient and air resistance coefficient.

[0007] This allows for highly accurate prediction of energy consumption, taking into account changes in vehicle mass and towing operations, by predicting energy consumption based on the vehicle's rolling resistance coefficient and air resistance coefficient.

[0008] A diagram showing the schematic configuration of a vehicle equipped with an energy consumption prediction device according to one embodiment of the present disclosure. A schematic diagram showing a method for calculating RL resistance force when the vehicle is not sailing. A schematic diagram showing a method for calculating RL resistance force when the vehicle is sailing. A diagram showing the RL resistance force calculated for each vehicle speed and an example of an RL curve generated based on these vehicle speeds and RL resistance forces. A diagram showing an example of multiple reference RL curves. A diagram showing an example of reference RL curves due to differences in the type and size of the towed vehicle. A diagram showing an example of a notification displayed on the display when energy consumption is updated. A diagram showing another example of a notification displayed on the display when energy consumption is updated. A diagram showing an example of a driving plan updated by the driving support unit. A flowchart showing the energy consumption prediction method of this embodiment. A flowchart showing the energy consumption prediction method of this embodiment.

[0009] An embodiment of the present disclosure will be described below. Figure 1 is a schematic diagram of a vehicle equipped with the energy consumption prediction device of this embodiment. The vehicle of this embodiment is an electric vehicle driven by an electric motor, and as shown in Figure 1, the vehicle includes a measuring sensor 11, a navigation device 12, a battery unit 13, an actuator 14, a notification unit 15, a controller 20, etc. The measuring sensor 11 and the controller 20 constitute the energy consumption prediction device of the present disclosure, predict the energy consumption of the vehicle, and calculate the driving range that the vehicle can continue to drive. In particular, the energy consumption prediction device of the present disclosure is effective in towing vehicles that can tow a towed vehicle, and can predict energy consumption with high accuracy and calculate the driving range even when the vehicle mass including the towed vehicle changes, such as the type and size of the towed vehicle. The following describes such a vehicle in detail.

[0010] The measurement sensor 11 is a sensor that measures the vehicle's driving state. The measurement sensor 11 includes a torque sensor 111, a vehicle speed sensor 112, an acceleration sensor 113, a position detection sensor 114, etc. The torque sensor 111 measures the motor torque of the drive motor that constitutes the actuator 14. The measured motor torque is output to the controller 20 as a torque signal. The vehicle speed sensor 112 measures the vehicle's speed (vehicle speed) by, for example, detecting the rotational speed of the vehicle's wheels. The measured vehicle speed is output to the controller 20 as a vehicle speed signal. The acceleration sensor 113 measures the acceleration of the vehicle in the longitudinal direction (direction of travel). The measured acceleration is output to the controller 20 as an acceleration signal. The position detection sensor 114 is a sensor that detects the current position of the vehicle. An example of the position detection sensor 114 is a receiver that receives GNSS (Global Navigation Satellite System) satellite signals to determine the current position. The measured current position of the vehicle is output to the controller 20 as a current position signal. In addition, the measurement sensor 11 may also include various sensors for measuring the vehicle's azimuth angle, steering angle, yaw rate, lateral acceleration perpendicular to the direction of travel, and the temperature inside and outside the vehicle. Furthermore, although this configuration includes a measurement sensor 11 for measuring the vehicle's driving state, other ambient detection sensors such as image sensors (cameras), LiDAR, and laser radar for detecting surrounding objects may also be provided.

[0011] The navigation device 12 searches for a route to the vehicle's destination based on destination information input by the user (passenger) and map information. The map information may be stored in the memory installed in the navigation device 12, stored in a separately provided map database, or obtained from an external device using a communication line such as the internet. The route searched by the navigation device 12 is output to the controller 20 as target route information.

[0012] The battery unit 13 includes a battery 131 and a battery controller 132. The battery 131 is a secondary battery that stores power supplied to various parts of the vehicle. The battery controller 132 is a computer that manages the battery 131. The battery controller 132 detects the state of the battery 131, such as the State of Charge (SOC), and outputs the detected state of the battery 131 to the controller 20.

[0013] The actuator 14 is a drive mechanism that drives the vehicle based on the control of the controller 20, and includes, for example, a motor actuator, a brake hydraulic actuator, and a steering angle actuator. The actuator 14 is equipped with the above-mentioned measuring sensors and measures various data related to the vehicle's movement. The motor actuator includes a drive motor, an inverter that outputs power supplied from the battery 131 to the drive motor, and a drive transmission mechanism that transmits driving force from the drive motor to the wheels. The inverter adjusts the power supplied to the drive motor based on a drive control command from the controller 20. The drive transmission mechanism transmits the driving torque of the drive motor to the wheels at a predetermined gear ratio. The brake hydraulic actuator is a hydraulic booster that controls the brake hydraulic force based on a braking control command from the controller 20. In the case of an electric vehicle that does not have a hydraulic booster, an electric booster may be used. The steering angle actuator controls the steering angle of the steering wheels based on a steering angle control command from the controller 20.

[0014] The notification unit 15 is a device that conveys various information to the passenger, and examples include a display or speaker installed inside the vehicle. In this embodiment, the notification unit 15 notifies the passenger of updates to the remaining driving range and battery level predictions, etc., according to the predicted energy consumption results.

[0015] The controller 20 is a computer that controls the vehicle. The controller 20 is composed of a storage device such as semiconductor memory and an arithmetic circuit such as a CPU (Central Processing Unit). The controller 20 realizes various functions by having the arithmetic circuit read and execute a program stored in the storage device. Specifically, as shown in Figure 1, the controller 20 functions as a mass acquisition unit 21, a driving state acquisition unit 22, a path acquisition unit 23, a towing determination unit 24, a sailing determination unit 25, a resistance coefficient calculation unit 26, an energy consumption prediction unit 27, and a driving support unit 28. Here, we show an example in which the functions of the mass acquisition unit 21, driving state acquisition unit 22, path acquisition unit 23, towing determination unit 24, sailing determination unit 25, resistance coefficient calculation unit 26, energy consumption prediction unit 27, and driving support unit 28 are realized by the arithmetic circuit of the controller 20 executing a program, but some or all of these may be realized by individual hardware configurations.

[0016] The mass acquisition unit 21 acquires the mass of the vehicle. The mass acquisition unit 21 may acquire the vehicle mass input by the passenger operating an input operation unit provided in the vehicle's cabin, or it may receive the vehicle mass transmitted from a terminal device such as a smartphone owned by the passenger. Alternatively, it may acquire the vehicle mass that has been previously stored in a storage device. Furthermore, when the vehicle is performing towing, the passenger may operate the input operation unit or terminal device to input the mass of the towed vehicle, in which case the mass acquisition unit 21 can acquire the total vehicle mass of the towed vehicle and the towing vehicle. The vehicle may also be equipped with a mass measuring device for measuring the mass of the towed vehicle, in which case the mass acquisition unit 21 can acquire the total vehicle mass of the towed vehicle and the towing vehicle measured by the mass measuring device.

[0017] The driving state acquisition unit 22 acquires vehicle driving data measured by the measurement sensor 11. Examples of driving data include the motor torque of the drive motor measured by the torque sensor 111, the vehicle speed measured by the vehicle speed sensor 112, and the vehicle acceleration measured by the acceleration sensor 113. Preferably, the driving state acquisition unit 22 synchronizes the measurement timing of the torque sensor 111, the vehicle speed sensor 112, and the acceleration sensor 113 to acquire the motor torque, vehicle speed, and acceleration at the same timing.

[0018] The route acquisition unit 23 acquires the target driving route searched by the navigation device 12. The route acquisition unit 23 also acquires gradient information regarding the road gradient along the acquired target driving route, and predicted vehicle speed information for when the vehicle is traveling along the target driving route. For example, map information is data that shows the shape of roads using nodes and links, and the road gradient and the speed of vehicles traveling on the road can be recorded in association with these nodes and links. The speed of vehicles traveling on the road may be the legal speed limit for that road, or it may be the average speed calculated based on statistical data of the actual speeds of many vehicles. Therefore, the route acquisition unit 23 acquires the road gradient and vehicle speed associated with the nodes and links corresponding to the target driving route from the map information, and uses this as gradient information and predicted vehicle speed information. In addition, as predicted vehicle speed information, the route acquisition unit 23 may acquire the average speed of other vehicles traveling on the road along the target driving route from a traffic management system (for example, VICS®, etc.) and use this as predicted vehicle speed information.

[0019] The towing determination unit 24 determines whether or not the vehicle is performing towing. Specifically, the towing determination unit 24 determines that the vehicle is performing towing if the vehicle mass (the total mass of the vehicle, including the towed vehicle) acquired by the mass acquisition unit 21 is equal to or greater than a predetermined mass threshold. The mass threshold can be exemplified by, for example, the mass of the towing vehicle plus a predetermined margin value. The mass of the towing vehicle may be acquired by input by the passenger as described above, or it may be stored in a memory device in advance.

[0020] Alternatively, the towing determination unit 24 may determine that the vehicle is performing towing based on the vehicle driving resistance force (Road Load resistance force; hereafter abbreviated as RL resistance force) calculated by the resistance coefficient calculation unit 26 described later, if the RL resistance force is equal to or greater than a predetermined vehicle driving resistance force threshold. In other words, when a vehicle is performing towing, the air resistance coefficient changes depending on the type and size of the towed vehicle, and the RL resistance force changes accordingly. Therefore, the performance of towing can be determined based on the RL resistance force. The vehicle driving resistance force threshold can be set, for example, based on the air resistance coefficient when towing a towed vehicle with the smallest size and mass.

[0021] The sailing determination unit 25 determines whether or not sailing (sailing mode) is being performed. Here, sailing means that the vehicle is traveling by coasting, that is, the brakes are not applied and the vehicle is traveling at a speed equivalent to when the vehicle's shift lever is in the neutral position. For example, if the vehicle is equipped with an accelerator position sensor (not shown) that detects the accelerator opening and a brake position sensor (not shown) that detects the brake opening, the sailing determination unit 25 determines that sailing is being performed by detecting that neither the accelerator nor the brake is being operated. Alternatively, the unit may determine whether or not the vehicle is traveling at a speed equivalent to when the vehicle's shift lever is in the neutral position based on the amount of change in vehicle speed measured by the vehicle speed sensor 112 and gradient information. For example, sailing determination map data showing the amount of change in vehicle speed in relation to the road gradient is stored in the storage device in advance for each vehicle mass. Then, by reading the road gradient corresponding to the vehicle's current position from the gradient information and comparing the actual change in vehicle speed when no braking is being performed with the change in vehicle speed relative to the road gradient at the current position in the sailing determination map data, it is possible to determine whether or not the vehicle is performing sailing maneuvers.

[0022] The resistance coefficient calculation unit 26 estimates a rolling resistance coefficient and an air resistance coefficient when the vehicle is traveling. The resistance coefficient calculation unit 26 estimates the rolling resistance coefficient and the air resistance coefficient respectively for a case where the vehicle performs towing traveling and a case where the vehicle does not perform towing traveling. For this purpose, the resistance coefficient calculation unit 26 first calculates RL resistance force with respect to vehicle speeds for each of a plurality of vehicle speeds.

[0023] FIG. 2 is a schematic diagram showing a method for calculating RL resistance force when the vehicle is not performing sailing traveling. As shown in FIG. 2, the RL resistance force when sailing traveling is not performed can be calculated by subtracting vehicle acceleration resistance and gradient resistance from vehicle driving force. In other words, let RL resistance force be R ＲＬ (N), vehicle driving force be R ｖｅｈ (N), acceleration resistance be R ａｃｃ (N), gradient resistance be R ｇｒａｄ (N), and the RL resistance force can be calculated by the following formula (1). R ＲＬ =R ｖｅｈ -R ａｃｃ -R ｇｒａｄ ...(1)

[0024] The vehicle driving force R ｖｅｈ (N) can be calculated by the following formula (2) based on the motor torque T Ｍ (N·m) of the drive motor measured by the torque sensor 111. Note that i is the reduction gear ratio, Φ is the dynamic rolling radius of the tire, and j is the reduction gear efficiency. R ｖｅｈ =T Ｍ ×i×j / Φ ...(2)

[0025] Further, let acceleration resistance be R ａｃｃ (N) and gradient resistance be R ｇｒａｄ (N), and they are calculated by the following formula (3) using vehicle mass M (kg) and acceleration a (km / h ２ ) in the traveling direction measured by an acceleration sensor. R ａｃｃ +R ｇｒａｄ =M×a ...(3)

[0026] The resistance coefficient calculation unit 26 calculates the RL resistance force when sailing is not performed by substituting equations (2) and (3) into equation (1). In addition, the resistance coefficient calculation unit 26 calculates the RL resistance force each time the motor torque, vehicle speed, and vehicle acceleration are acquired by the driving state acquisition unit 22 and stores it in the memory device.

[0027] Figure 3 is a schematic diagram showing the method for calculating the RL resistance force when the vehicle is sailing. When the vehicle is sailing, the resistance coefficient calculation unit 26 calculates the vehicle speed when the vehicle speed is V during a predetermined coasting time T1 while coasting. ｓｐ１ ' from V ｓｐ１ If it changes to '', the vehicle speed V ｓｐ１ (=(V) ｓｐ１ '-V ｓｐ１ ´´) / 2) RL resistance (R ＲＬ (N) is calculated using the following formula (4). R ＲＬ = M × a = M × (V ｓｐ１ '-V ｓｐ１ ´´) × (1000 / 3600) / T1 ... (4) The resistance coefficient calculation unit 26 calculates the RL resistance force sequentially each time the vehicle speed is detected when the vehicle continues sailing and stores it in the memory device.

[0028] Next, the resistance coefficient calculation unit 26 estimates the rolling resistance coefficient and the air resistance coefficient from the calculated RL resistance force for each vehicle speed. The RL resistance force can be expressed using the vehicle speed V, as shown in Figure 2 and the following equation (5). ＲＬ = μ × M × g + ρ × C Ｄ ×A×V ２ = F ０ +F ２ ・V ２ …(5)

[0029] Here, F ０ The term (=μ × M × g) corresponds to the rolling resistance coefficient, F ２ term (=ρ×C Ｄ×A) is the value corresponding to the air resistance coefficient. Figure 4 shows the RL resistance force calculated for each vehicle speed V, and an example of an RL curve 50 generated based on these vehicle speeds and RL resistance forces. As shown in equation (5) above, the RL curve 50 (running resistance curve) showing the RL resistance force with respect to vehicle speed can be shown as a quadratic curve with vehicle speed V as a parameter, and the rolling resistance coefficient F can be obtained from the RL curve 50 ０ and the air resistance coefficient F ２ Therefore, the resistance coefficient calculation unit 26 calculates the RL resistance force R with respect to the vehicle speed V based on the new driving data. ＲＬ Each time the coefficient is calculated, the RL curve 50 is generated using the successive least squares method, and the rolling resistance coefficient F is calculated using equation (5). ０ and the air resistance coefficient F ２ The rolling resistance coefficient F is calculated by acquiring a large amount of driving data. ０ and the air resistance coefficient F ２ The calculation accuracy will be higher.

[0030] On the other hand, the rolling resistance coefficient F ０ and the air resistance coefficient F ２ As a method for calculating the rolling resistance coefficient, the resistance coefficient calculation unit 26 calculates the rolling resistance coefficient F based on a reference RL curve that has been stored in the memory device in advance. ０ and the air resistance coefficient F ２ You may also calculate the following. Figure 5 shows an example of multiple reference RL curves 51. The plotted points in Figure 5 represent the RL resistance force R calculated for the vehicle speed V. ＲＬ For example, as shown in Figure 5, multiple reference RL curves 51 with different vehicle masses are stored in a memory device in advance. Then, the resistance coefficient calculation unit 26 selects the reference RL curve 51 that is closest in distance from each calculated RL resistance force from among the multiple reference RL curves 51 as the RL curve for the vehicle. For each reference RL curve 51, the rolling resistance coefficient F ０ and the air resistance coefficient F ２ By pre-associating the two, the resistance coefficient calculation unit 26 can easily calculate the rolling resistance coefficient F from the selected reference RL curve. ０ and the air resistance coefficient F ２ It is possible to estimate this.

[0031] Incidentally, in towing operations where a vehicle pulls a towed vehicle, the drag coefficient varies depending on the type and size of the towed vehicle. Figure 6 shows an example of a standard RL curve 52 depending on the type and size of the towed vehicle. F ０ The rolling resistance coefficient, which is a term in the equation, is affected by the vehicle mass but does not depend on the vehicle speed V, and therefore remains constant. Consequently, if the type or size of the towed vehicle is changed, F ２ Only the air resistance coefficient term changes, and as shown in Figure 6, the RL resistance force at the intercept (V=0) remains the same. Therefore, in addition to the reference RL curves 51 shown in Figure 5, a reference RL curve 52 as shown in Figure 6 is also stored in the memory device in advance. The resistance coefficient calculation unit 26 then selects the reference RL curve 51 (or reference RL curve 52) closest to the calculated RL resistance force, thereby determining the rolling resistance coefficient F corresponding to the type and size of the towed vehicle. ０ and the air resistance coefficient F ２ It is possible to estimate this.

[0032] Furthermore, when using a reference RL curve 52 as shown in Figure 6, the type and size of the towed vehicle can be identified from the calculated RL resistance force. Therefore, as described above, in determining whether towing is taking place by the towing determination unit 24, the RL resistance force may be calculated first, and then the presence or absence of towing may be determined using a reference RL curve 52 as shown in Figure 6. Alternatively, in determining whether towing is taking place by the towing determination unit 24, after calculating the RL resistance force, the difference between the reference RL curve 51 shown in Figure 5 and the RL resistance force may be calculated, and if this difference is greater than or equal to the vehicle driving resistance force threshold, it may be determined that towing is taking place.

[0033] By the way, the rolling resistance coefficient F calculated by the resistance coefficient calculation unit 26 mentioned above... ０ and the air resistance coefficient F ２The estimation process is performed when the vehicle's driving conditions meet the following calculation conditions: (I) The vehicle's speed is above a predetermined speed threshold. (II) The vehicle's acceleration is below a predetermined acceleration threshold. (III) The gradient at the road position where the vehicle is currently traveling is below a predetermined gradient threshold. (IV) The vehicle is turning, and the steering angle during that turn is below a predetermined steering angle threshold. (V) The vehicle is not braking. In other words, the system is strongly affected by disturbances when the vehicle speed is too slow, when the vehicle is accelerating rapidly, when the vehicle is traveling on a steep uphill road, when the vehicle is traveling on a curve with a large curvature, and when the vehicle is braking. In these cases, the accuracy of the RL resistance calculation decreases, making it impossible to select an appropriate reference RL curve, and as a result, the rolling resistance coefficient F ０ and the air resistance coefficient F ２ The estimation accuracy also decreases. Therefore, the resistance coefficient calculation unit 26 calculates the rolling resistance coefficient F based on the driving data measured under conditions that satisfy the calculation conditions (I) to (V) above. ０ and the air resistance coefficient F ２ This is estimated. Furthermore, the speed threshold, acceleration threshold, gradient threshold, and steering angle threshold can be set appropriately depending on the conditions under which disturbances occur, and for example, the set values ​​may differ for each type of vehicle.

[0034] Also, the rolling resistance coefficient F ０ and the air resistance coefficient F ２ The vehicle speed range in which the calculation process for estimating the rolling resistance coefficient F is performed (calculation speed range: see Figure 4) is determined. ０ and the air resistance coefficient F ２ The calculation is performed within a vehicle speed range, and this information is stored in the memory device.

[0035] The energy consumption prediction unit 27 estimates the rolling resistance coefficient F using the resistance coefficient calculation unit 26. ０ and the air resistance coefficient F ２ Based on the State of Charge (SOC) of the battery 131, predicted vehicle speed information along the target driving route, and gradient information, the energy consumption when the vehicle travels along the target driving route is predicted. For example, the energy consumption prediction unit 27 predicts the vehicle speed V at each position on the target driving route recorded in the predicted vehicle speed information, and the estimated rolling resistance coefficient F０ and the air resistance coefficient F ２ Based on this, the RL resistance force R at each position when traveling along the target route. ＲＬ The vehicle driving force R is calculated from equation (5). Also, assuming that the vehicle is traveling at a constant predicted speed (acceleration a = 0), the vehicle driving force R is calculated from equation (1). ｖｅｈ The motor torque T of the drive motor is calculated based on equation (2). Ｍ The motor torque T is then calculated. Ｍ The power consumption of the drive motor used to obtain the desired result is calculated as the energy consumption. In addition to the power consumption of the drive motor, the power consumption of the air conditioner and the power consumption of other auxiliary equipment may also be added to the energy consumption. The power consumption of the air conditioner can be calculated, for example, based on the outside temperature or the target temperature set for the vehicle. The power consumption of auxiliary equipment may be calculated by storing the average of past power consumption data of auxiliary equipment, or by using a pre-set fixed value.

[0036] In this process, the energy consumption prediction unit 27 estimates the energy consumption in the range of the target driving route in which the vehicle travels at a speed within the calculation speed range, using the estimated rolling resistance coefficient F. ０ and the air resistance coefficient F ２ The calculation is performed using [a specific method]. On the other hand, in the portion of the target route where the vehicle travels at a speed outside the calculated speed range, the energy consumption is calculated using the same method as before. For example, the energy consumption is calculated by determining the energy consumption per unit time or per unit distance at each position in the portion of the route where the vehicle travels at a speed outside the calculated speed range, based on the predicted speed and gradient information. By aggregating a large amount of data and expanding the calculated speed range, more accurate energy consumption predictions can be made.

[0037] The driving support unit 28 outputs a notification from the notification unit 15 to the vehicle driver suggesting the implementation of sailing when the vehicle switches from traveling at a speed within the calculated speed range to traveling at a speed outside the calculated speed range along the target driving route. The notification method is not particularly limited and may be displayed on a screen or announced by voice. By implementing sailing in accordance with the notification, the vehicle driver can reduce energy consumption at locations corresponding to the outside of the calculated speed range.

[0038] Furthermore, in a vehicle equipped with the energy consumption prediction device of this embodiment, the calculation speed range is expanded because actual driving data can be obtained over a wide range of vehicle speeds while the vehicle is in motion. This improves the accuracy of the predicted energy consumption. The driving support unit 28 performs more accurate energy consumption predictions as the calculation speed range expands while the vehicle is in motion, and when the energy consumption is updated due to the expansion of the calculation speed range, etc., it notifies the passenger or driver of the update in energy consumption. Figure 7 shows an example of a notification displayed on the screen when energy consumption is updated. The driving support unit 28 calculates, for example, the vehicle's remaining range and the battery level when the destination is reached, based on the energy consumption. When energy consumption is updated, these remaining ranges are also recalculated, and the notification unit 15 notifies that the remaining range has been updated using a display as shown in Figure 7.

[0039] Figure 8 shows another example of a notification displayed on the screen when energy consumption is updated. As a method of notifying when energy consumption is updated, the driving support unit 28 may notify a notification on the display or other notification unit 15, as shown in Figure 8, indicating the change in battery level relative to distance (or time).

[0040] Furthermore, if the driving support unit 28 predicts that it will be difficult to reach the destination with the current battery level based on the updated energy consumption, it may, for example, have the navigation device 12 search for a new charging spot and guide the passenger to the found charging spot in a driving plan. Figure 9 shows an example of a driving plan updated by the driving support unit 28. The old plan in Figure 9 is a driving plan along the target driving route before the energy consumption was updated. As a large amount of driving data is collected through the vehicle's operation and the calculation speed range expands, the energy consumption prediction unit 27 updates the energy consumption, and the driving support unit 28 determines whether it is possible to reach the destination with the current battery level. If it is difficult to reach the destination with the current battery level, or if the predicted value of the battery level at the time of arrival at the destination is below a predetermined value, the driving support unit 28 searches for a charging spot on the target driving route based on map information. For example, the driving support unit 28 has the navigation device 12 search for a charging spot on the target driving route. If there are no charging stations along the target route, the system may search for charging stations near the target route. In this case, the navigation system 12 obtains a new target route. The driving support unit 28 then notifies the passenger of the driving plan along the new target route, that is, the driving plan which includes stopping at a new charging station before reaching the destination, by displaying it on the display or by other means.

[0041] [Method for Predicting Energy Consumption] Next, a method for predicting energy consumption in a vehicle equipped with the energy consumption prediction device described above, and a method for providing driving support based on the predicted energy consumption will be explained. Figures 10A and 10B are flowcharts of the energy consumption prediction method in this embodiment. In the vehicle of this embodiment, first, the mass acquisition unit 21 acquires the vehicle mass (step S1). As described above, the mass acquisition unit 21 may acquire the vehicle mass (towing vehicle and towed vehicle) by input operation by the occupant. The vehicle mass of the towing vehicle may be pre-recorded in a storage device, in which case the mass acquisition unit 21 reads the vehicle mass from the storage device. The vehicle mass of the towed vehicle may be measured by a mass measuring device provided on the vehicle.

[0042] Next, the towing determination unit 24 determines whether or not towing is being performed (step S2). The towing determination unit 24 determines that towing is being performed if the vehicle mass obtained in step S1 is equal to or greater than a predetermined mass threshold.

[0043] Next, the route acquisition unit 23 acquires the target driving route, predicted vehicle speed information, and gradient information along the target driving route (step S3). For example, when a passenger in a vehicle operates the navigation device 12 and inputs a destination, the navigation device 12 searches for the target driving route, which is the route from the vehicle's current location to the destination, based on the map information, and further acquires gradient information and predicted vehicle speed information along the target driving route. Gradient information and predicted vehicle speed information can be obtained from the map information. Predicted vehicle speed information may also be obtained by receiving traffic information transmitted from the traffic management system, in addition to the map information. The route acquisition unit 23 acquires this target driving route, predicted vehicle speed information, and gradient information from the navigation device 12.

[0044] Next, the energy consumption prediction unit 27 determines whether or not past driving data for a vehicle with the same vehicle mass as the current vehicle's driving mass (vehicle mass obtained in step S1) is recorded in the recording device (step S4). "Same vehicle mass" means that the difference in vehicle mass is within a predetermined value. For example, this can be exemplified by the case where the towed vehicle during the current vehicle's driving is the same as the towed vehicle during past vehicle driving. In this case, the rolling resistance coefficient F of the past vehicle's driving is... ０ and the air resistance coefficient F ２ This can be used. Therefore, if it is determined to be YES in step S4, the energy consumption prediction unit 27 uses the rolling resistance coefficient F estimated during the operation of past vehicles with similar vehicle mass. ０ Air resistance coefficient F ２ , and read the calculation execution vehicle speed range from the storage device (step S5).

[0045] If the result in step S4 is NO, and after step S5, the energy consumption prediction unit 27 predicts the energy consumption along the target driving route (step S6). If the result in step S4 is YES, and in step S5 the past rolling resistance coefficient F ０ Air resistance coefficient F ２ , and if the calculation is performed within the vehicle speed range, these read-out rolling resistance coefficients F ０ Air resistance coefficient F ２ The system uses the calculation speed range, the vehicle mass acquired in step S1, the target driving route, gradient information, and predicted vehicle speed information acquired in step S3 to predict the energy consumption to the destination. As described above, the energy consumption prediction unit 27 calculates the rolling resistance coefficient F for the range in the target driving route where the predicted vehicle speed is within the calculation speed range. ０ and the air resistance coefficient F ２ Energy consumption prediction is performed using the following. For ranges where the predicted vehicle speed is outside the calculation speed range, the energy consumption is calculated using the same method as in the conventional method, for example, based on the predicted vehicle speed and gradient information. On the other hand, if NO is determined in step S4, the energy consumption prediction unit 27 calculates the energy consumption using the same method as in the conventional method, for example, based on the predicted vehicle speed information and gradient information.

[0046] After this, once the vehicle starts moving, the driving state acquisition unit 22 acquires driving data based on various signals input from the measurement sensor 11 (step S7). Specifically, the driving state acquisition unit 22 acquires the motor torque of the drive motor based on the torque signal input from the torque sensor 111, the vehicle speed based on the vehicle speed signal input from the vehicle speed sensor 112, the acceleration based on the acceleration signal input from the acceleration sensor 113, and the current position of the vehicle measured by the position detection sensor 114.

[0047] Further, the sailing determination unit 25 determines whether or not the vehicle is performing sailing traveling by monitoring the acquired vehicle speed (step S8). When the vehicle is performing sailing traveling and the determination in step S8 is YES, the resistance coefficient calculation unit 26 determines whether or not a calculation execution condition is satisfied (step S9). When the determination in step S9 is NO, the process returns to step S8.

[0048] When the determination in step S9 is YES, the resistance coefficient calculation unit 26 calculates the RL resistance force based on the coasting time in sailing traveling and the amount of change in vehicle speed corresponding to the gradient of the road on which the vehicle travels (step S10). That is, based on equation (4), the RL resistance force R with respect to the vehicle speed V ＲＬ is calculated.

[0049] When the determination in step S8 is NO, the resistance coefficient calculation unit 26 determines whether or not the calculation execution condition is satisfied, similarly to step S9 (step S11). When the determination in step S11 is NO, the process returns to step S8.

[0050] When the determination in step S11 is YES, the resistance coefficient calculation unit 26 calculates the RL resistance force based on the motor torque and acceleration obtained as travel data, and the vehicle mass (step S12). That is, based on equations (1) to (3), the RL resistance force R with respect to the vehicle speed V ＲＬ is calculated.

[0051] Further, the acquisition of travel data in step S8 is performed, for example, at a predetermined cycle, and the processes from step S8 to step S12 are performed on the obtained vehicle speed V. After step S10 or step S12, the resistance coefficient calculation unit 26 obtains the RL resistance force R with respect to the vehicle speed V ＲＬ each time a new value is calculated, the rolling resistance coefficient F ０ and the air resistance coefficient F ２ are estimated by the recursive least squares method (step S13). Further, when the determination in step S4 is YES, the rolling resistance coefficient F ０ and the air resistance coefficient F ２may be estimated. Specifically, the resistance coefficient calculation unit 26 calculates the RL resistance R calculated for the vehicle speed V ＲＬ , generates the RL curve 50 by the sequential least squares method, and obtains the rolling resistance coefficient F from the generated RL curve 50 according to Equation (5) ０ and the aerodynamic drag coefficient F ２ are calculated. Alternatively, from among a plurality of reference RL curves 51 and 52 as shown in FIG. 5 and FIG. 6, the closest reference RL curve 51 (or reference RL curve 52) to the RL curve 50 generated based on the RL resistance R ＲＬ is selected, and the rolling resistance coefficient F associated with the selected reference RL curve 51 (or reference RL curve 52) ０ and the aerodynamic drag coefficient F ２ are acquired.

[0052] Furthermore, in step S13, the resistance coefficient calculation unit 26 specifies the range of vehicle speeds used for estimating the rolling resistance coefficient F ０ and the aerodynamic drag coefficient F ２ as the calculation execution vehicle speed range (step S14). Then, the detected calculation execution vehicle speed range, the rolling resistance coefficient F estimated in step S13 ０ and the aerodynamic drag coefficient F ２ are stored (updated) in the storage device in association with the vehicle mass (step S15).

[0053] After the above processing, the energy consumption prediction unit 27 determines whether or not the calculation execution vehicle speed range obtained in step S14 is equal to or greater than a preset predetermined range (step S16). If NO is determined in step S16, the process returns to step S8 to repeat acquisition of travel data and estimation processing of the rolling resistance coefficient F ０ and the aerodynamic drag coefficient F ２ . This makes it possible to estimate the rolling resistance coefficient F ０ and the aerodynamic drag coefficient F ２ for a wider vehicle speed range.

[0054] On the other hand, if YES is determined in step S16, the energy consumption prediction unit 27 uses the vehicle mass acquired in step S1, the target travel route, gradient information, and predicted vehicle speed information acquired in step S3, and the rolling resistance coefficient F estimated in step S13０ and the air resistance coefficient F ２ Based on the SOC of the battery 131, the energy consumption when the vehicle travels along the target route is predicted (step S17). In this case, as described above, for the range of the target route in which the vehicle speed of the predicted vehicle speed information is within the calculation speed range, the rolling resistance coefficient F estimated in step S13 is used. ０ and the air resistance coefficient F ２ The energy consumption is calculated using the following, and for the range where the predicted vehicle speed information is outside the calculated vehicle speed range, the rolling resistance coefficient F is used. ０ and the air resistance coefficient F ２ Energy consumption calculations are performed using predicted vehicle speed and gradient information, without using [a specific method / tool].

[0055] After this, the driving support unit 28 provides driving support to the driver based on the energy consumption predicted in step S16 (step S18). That is, if the energy consumption calculated in step S6 or step S7 differs from the energy consumption predicted (updated) in step S16 by a predetermined value or more, the driving support unit 28 displays a display screen, such as those shown in Figures 7 and 8, on the display to notify the passenger of the updated energy consumption. In addition, if the battery level at the time of reaching the destination falls below a predetermined value, the driving support unit 28 may have the navigation device 12 search for charging spots on the target driving route or within a predetermined range from the target driving route, and generate a new driving plan that includes stopping at the searched charging spots. In this case, the new driving plan is notified to the passenger by displaying a display screen, such as those shown in Figure 9, on the display.

[0056] Furthermore, the driving support unit 28 may notify the driver to perform sailing if the vehicle's current position is outside the calculated speed range for the predicted speed information. This suppresses the increase in energy consumption when driving at speeds outside the calculated speed range. In Figure 10B, the process is terminated after step S18, but in reality, the process returns to step S8 to acquire driving data and the rolling resistance coefficient F ０ and the air resistance coefficient F ２We will continue to estimate and predict energy consumption.

[0057] [Effects of this Embodiment] The vehicle of this embodiment is equipped with an energy consumption prediction device consisting of a measurement sensor 11 that measures driving data indicating the driving state of the vehicle when it is in motion, and a controller 20. The controller 20 acquires the vehicle mass and acquires driving data indicating the driving state of the vehicle. Based on the driving data, it estimates the rolling resistance coefficient and the air resistance coefficient. It also acquires the target driving route, predicted vehicle speed information, and gradient information from the navigation device 12, and predicts the energy consumption when the vehicle is traveling along the target driving route based on this vehicle mass, rolling resistance coefficient, air resistance coefficient, predicted vehicle speed information, and gradient information. In this embodiment, by predicting energy consumption based on the rolling resistance coefficient and air resistance coefficient of the vehicle, it is possible to make highly accurate predictions of energy consumption based on appropriate rolling resistance coefficients and air resistance coefficients even when towing is performed or when the towed vehicle is changed.

[0058] In this embodiment, the controller 20 acquires vehicle speed, vehicle acceleration, and motor torque of the drive motor as driving data, calculates the RL resistance force by subtracting the vehicle acceleration resistance and gradient resistance calculated based on the vehicle mass and acceleration from the vehicle driving force calculated based on the motor torque, and calculates the rolling resistance coefficient F based on the vehicle speed and RL resistance force. ０ and the air resistance coefficient F ２ The following is estimated. When the vehicle is not sailing, the vehicle driving force R is calculated based on the motor torque using equation (2). ｖｅｈ Calculate the acceleration resistance R using equation (3). ａｃｃ and gradient resistance R ｇｒａｄ By calculating the sum of the values, the RL resistance force can be calculated using equation (1). Therefore, by calculating the RL resistance force for multiple vehicle speeds V, an RL curve can be generated, and the rolling resistance coefficient F can be obtained from the RL curve. ０ and the air resistance coefficient F ２ It is possible to estimate this.

[0059] In this embodiment, the controller 20 acquires vehicle speed as driving data, and when the vehicle is performing sailing, it calculates the RL resistance force based on the coasting time during which sailing was performed continuously, the vehicle mass, and gradient information. Then, based on the vehicle speed and RL resistance force, it calculates the rolling resistance coefficient F ０ and the air resistance coefficient F ２ The following is estimated. When the vehicle is sailing, the vehicle driving force R is calculated based on the motor torque using equation (4). ｖｅｈ Calculate the acceleration resistance R using equation (3). ａｃｃ and gradient resistance R ｇｒａｄ By calculating the sum of the values, the RL resistance force can be calculated using equation (1). Therefore, by calculating the RL resistance force for multiple vehicle speeds V, an RL curve can be generated, and the rolling resistance coefficient F can be obtained from the RL curve. ０ and the air resistance coefficient F ２ It is possible to estimate this.

[0060] In this embodiment, the controller 20 calculates an RL curve 50 based on the RL resistance force calculated for each of the multiple vehicle speeds, and calculates the rolling resistance coefficient F based on the RL curve 50. ０ and the air resistance coefficient F ２ This is used to calculate the rolling resistance coefficient F by applying equation (5) to the generated RL curve 50. ０ and the air resistance coefficient F ２ It is possible to estimate this with high accuracy.

[0061] In this embodiment, the controller 20 determines whether the vehicle is performing towing, and if it is determined that towing is being performed, it selects a reference RL curve 52 from a plurality of reference RL curves 52 with different types and sizes of towed vehicles that corresponds to the estimated RL resistance force, and calculates the rolling resistance coefficient F based on the selected reference RL curve 52. ０ and the air resistance coefficient F ２The rolling resistance coefficient F is calculated. In towing operations where a vehicle pulls a towed vehicle, the air resistance coefficient changes depending on the type and size of the towed vehicle. In this embodiment, by pre-storing multiple reference RL curves corresponding to multiple types and sizes of towed vehicles in a memory device, it is possible to select a reference RL curve that corresponds to the calculated RL resistance force. This allows for the selection of an appropriate rolling resistance coefficient F according to the type and size of the towed vehicle. ０ and the air resistance coefficient F ２ Furthermore, if multiple reference RL curves 52 are stored in advance, the rolling resistance coefficient F can be obtained even if the type and size of the towed vehicle are unknown. ０ and the air resistance coefficient F ２ It can be calculated.

[0062] In this embodiment, the controller 20 determines that the vehicle is performing towing when the vehicle mass is equal to or greater than a predetermined mass threshold. This makes it easy to determine whether or not towing is occurring based on the vehicle mass.

[0063] In this embodiment, the controller 20 may determine that the vehicle is performing towing when the RL resistance is equal to or greater than a predetermined vehicle driving resistance threshold. As described above, when a vehicle tows a towed vehicle by towing, the air resistance coefficient changes depending on the type and size of the towed vehicle. Therefore, the RL resistance can be calculated, and if the RL resistance is greater than the RL resistance shown in the reference RL curve 51, it can be determined that the vehicle is towing a towed vehicle.

[0064] In this embodiment, the controller 20 calculates the rolling resistance coefficient F when the calculation conditions (I) to (V) are met. ０ and the air resistance coefficient F ２ The rolling resistance coefficient F is calculated. If the above calculation conditions are not met under the vehicle's driving conditions, the vehicle is more susceptible to disturbances. ０ and the air resistance coefficient F ２ The estimation accuracy of the rolling resistance coefficient F decreases. In this embodiment, if the above calculation conditions are not met, ０ and the air resistance coefficient F ２This process does not involve estimating the rolling resistance coefficient F. ０ and the air resistance coefficient F ２ This can suppress the decrease in estimation accuracy.

[0065] In this embodiment, the controller 20 controls the rolling resistance coefficient F ０ and the air resistance coefficient F ２ The calculation speed range used for estimation is identified, and if a calculation speed range of a predetermined range or higher is obtained, the estimated rolling resistance coefficient F is determined. ０ and the air resistance coefficient F ２ Energy consumption is predicted based on this. If the vehicle speed range in which the calculation is performed is below a predetermined range, the reliability of the RL curve 50 is insufficient, and the appropriate rolling resistance coefficient F ０ and the air resistance coefficient F ２ There is a possibility that this cannot be estimated. In contrast, if the calculation is performed within a certain vehicle speed range, an appropriate RL curve 50 can be generated based on the RL resistance force estimated for a wide vehicle speed range. This allows the rolling resistance coefficient F to be calculated. ０ and the air resistance coefficient F ２ This allows for improved estimation accuracy and more precise prediction of energy consumption.

[0066] In this embodiment, the controller 20 predicts energy consumption for the range in the target driving route where the predicted vehicle speed falls within the calculation speed range. In the target driving region where the predicted vehicle speed is outside the calculation speed range, the rolling resistance force at that vehicle speed has not been calculated, and it is unclear whether it is close to the value of the rolling resistance curve 50 calculated at vehicle speeds within the calculation speed range. In this embodiment, the rolling resistance coefficient F is calculated for the range in which the vehicle is scheduled to travel at speeds outside the calculation speed range. ０ and the air resistance coefficient F ２ Energy consumption is not predicted using [a specific method], but rather based on predicted vehicle speed and gradient information. On the other hand, for the range within the target driving area where the predicted vehicle speed is within the calculated vehicle speed range, the rolling resistance coefficient F is calculated based on the RL resistance force at that vehicle speed. ０ and the air resistance coefficient F ２ Since the rolling resistance coefficient F has been calculated, ０ and the air resistance coefficient F ２Its reliability is high. Therefore, the rolling resistance coefficient F is within the range of the vehicle speed range in which the calculation is performed. ０ and the air resistance coefficient F ２ By using this method, highly accurate predictions of energy consumption can be made.

[0067] In this embodiment, the controller 20 notifies the driver to perform sailing when the vehicle is traveling at a speed outside the calculated speed range. As described above, in the range where the vehicle is scheduled to travel at a speed outside the calculated speed range, accurate energy consumption predictions cannot be made, and energy consumption may change significantly while traveling in that range. In contrast, if the above notification is given for the range where the vehicle is scheduled to travel at a speed outside the calculated speed range, and the driver performs sailing accordingly, energy consumption can be suppressed, fluctuations in energy consumption can be suppressed, and the recalculation of energy consumption can be omitted.

[0068] In this embodiment, the controller 20 updates the vehicle's remaining range, the predicted battery level at the destination, the driving plan to the destination, and the charging plan based on the predicted energy consumption, and notifies the vehicle's occupants. This allows passengers to recognize the updates to the driving range and battery level that accompany the energy consumption updates. Furthermore, if it is difficult to reach the destination with the current battery level, or if the battery level will be low upon arrival at the destination, the controller can suggest a driving plan that includes stopping at a charging station, thereby reducing the risk of running out of battery.

[0069] [Modifications] The present invention is not limited to the embodiments described above, but also includes the following modifications to the extent that the objectives of the present invention can be achieved. For example, in the above embodiments, in the flowcharts of Figures 10A and 10B, the towing determination unit 24 determined in step S2 whether or not towing driving was performed based on the vehicle mass obtained in step S1. In contrast, as described above, the towing determination unit 24 determined whether or not the RL resistance force R estimated in step S10 or step S12 ＲＬHowever, if the value is higher than the reference value shown by the reference RL curve 51, it may be determined that towing is being performed.

[0070] Furthermore, the passenger may input the vehicle mass, type, and size of the towed vehicle by operating the input control unit. In this case, the towing determination unit 24 may determine that towing is being performed based on the input of the towed vehicle information by the passenger. In this case, the passenger may input the type and size of the towed vehicle, allowing the system to select a reference RL curve 52 from a plurality of reference RL curves 52, as shown in Figure 6, that corresponds to the input type and size of the towed vehicle.

[0071] Furthermore, in the above embodiment, the energy consumption prediction unit 27 estimates the rolling resistance coefficient F for the range in which the predicted vehicle speed is within the calculation execution vehicle speed range. ０ and the air resistance coefficient F ２ An example was shown in which energy consumption prediction processing is performed using the predicted vehicle speed and gradient information, and the predicted vehicle speed is within the calculation speed range, and the conventional energy consumption prediction processing is performed using the predicted vehicle speed and gradient information. In contrast, the energy consumption prediction unit 27 estimates the rolling resistance coefficient F regardless of whether it is inside or outside the calculation speed range. ０ and the air resistance coefficient F ２ Energy consumption prediction processing may be performed using [this method].

[0072] 11... Measuring sensor, 12... Navigation device, 13... Battery unit, 14... Actuator, 15... Notification unit, 20... Controller, 21... Mass acquisition unit, 22... Driving state acquisition unit, 23... Path acquisition unit, 24... Towing determination unit, 25... Sailing determination unit, 26... Resistance coefficient calculation unit, 27... Energy consumption prediction unit, 28... Driving support unit, 50... RL curve, 51, 52... Reference RL curve, 111... Torque sensor, 112... Vehicle speed sensor, 113... Acceleration sensor, 114... Position detection sensor, 131... Battery, 132... Battery controller.

Claims

1. A method for predicting energy consumption in an electrically driven vehicle, wherein the computer obtains the vehicle mass of the vehicle, obtains driving data indicating the driving state when the vehicle is in motion, estimates the rolling resistance coefficient and the air resistance coefficient based on the driving data, obtains the target driving route of the vehicle, obtains the predicted vehicle speed when the vehicle is traveling along the target driving route and gradient information which is the gradient at each position along the target driving route, and predicts the energy consumption when the vehicle is traveling along the target driving route based on the vehicle mass, the rolling resistance coefficient, the air resistance coefficient, the predicted vehicle speed, and the gradient information.

2. The method for predicting energy consumption according to claim 1, wherein the computer acquires the vehicle's speed, the vehicle's acceleration, and the driving force in the vehicle's drive mechanism as the driving data; calculates the vehicle's running resistance force by subtracting the vehicle's acceleration resistance and gradient resistance calculated based on the vehicle's mass and acceleration from the driving force; and estimates the rolling resistance coefficient and the air resistance coefficient based on the vehicle's speed and the vehicle's running resistance force.

3. The method for predicting energy consumption according to claim 1, wherein the computer obtains the speed of the vehicle as the driving data, determines whether or not the vehicle is coasting, measures the coasting time during which the sailing is continuously performed if it is determined to be coasting, calculates the vehicle's resistance force based on the vehicle's mass, the coasting time, and the gradient information of the road on which the vehicle is currently traveling, and estimates the rolling resistance coefficient and the air resistance coefficient based on the vehicle's speed and the vehicle's resistance force.

4. The method for predicting energy consumption according to claim 2 or 3, wherein the computer calculates a running resistance curve showing the relationship between the speed of the vehicles and the running resistance force based on the vehicle running resistance force calculated in accordance with each of the speeds of the plurality of vehicles, and calculates the rolling resistance coefficient and the air resistance coefficient based on the running resistance curve.

5. The method for predicting energy consumption according to claim 2 or 3, wherein the computer determines whether the vehicle is performing towing, and if it is determined that the vehicle is performing towing, it selects a running resistance curve from a plurality of running resistance curves showing the relationship between the vehicle's speed and the vehicle's running resistance, where the running resistance curves are of different types and sizes of towed vehicles, and the running resistance curve corresponding to the vehicle's running resistance estimated based on the running data, and calculates the rolling resistance coefficient and the air resistance coefficient based on the selected running resistance curve.

6. The energy consumption prediction method according to claim 5, wherein the computer determines that the vehicle is performing towing when the vehicle mass is equal to or greater than a predetermined mass threshold.

7. The energy consumption prediction method according to claim 5, wherein the computer determines that the vehicle is performing towing when the vehicle's driving resistance is equal to or greater than a predetermined vehicle driving resistance threshold.

8. The method for predicting energy consumption according to any one of claims 1 to 7, wherein the computer performs estimation of the rolling resistance coefficient and the air resistance coefficient when the following conditions are met: the speed of the vehicle is greater than or equal to a predetermined speed threshold; the acceleration of the vehicle is less than or equal to a predetermined acceleration threshold; the gradient of the gradient information at the position where the vehicle is currently traveling is less than or equal to a predetermined gradient threshold; the vehicle is turning and the steering angle during said turning is less than or equal to a predetermined steering angle threshold; and the vehicle is not braking.

9. The method for predicting energy consumption according to any one of claims 1 to 8, wherein the computer identifies a calculation speed range which is a range of vehicle speeds used to estimate the rolling resistance coefficient and the air resistance coefficient, and when the calculation speed range is obtained to a predetermined range or higher, predicts the energy consumption based on the estimated rolling resistance coefficient and the air resistance coefficient.

10. The energy consumption prediction method according to claim 9, wherein the computer predicts the energy consumption based on the rolling resistance coefficient and the air resistance coefficient for the range in which the predicted vehicle speed is included in the calculation vehicle speed range along the target driving route.

11. The energy consumption prediction method according to claim 9, wherein the computer requests the vehicle to perform sailing, which involves coasting, when the vehicle is traveling in a vehicle speed range outside the calculation speed range.

12. The energy consumption prediction method according to any one of claims 1 to 11, wherein the computer updates the vehicle's remaining range, the predicted battery level of the vehicle when it reaches its destination, the driving plan and charging plan until it reaches its destination, based on the predicted energy consumption, and notifies the occupants of the vehicle.

13. Energy consumption prediction device for predicting energy consumption in an electrically driven vehicle, comprising: a measuring sensor for measuring driving data indicating the driving state of the vehicle when it is running; and a controller, wherein the controller acquires the vehicle mass of the vehicle, acquires the driving data from the measuring sensor, estimates the rolling resistance coefficient and the air resistance coefficient based on the driving data, acquires the target driving route of the vehicle, acquires the predicted vehicle speed when the vehicle is traveling along the target driving route, and gradient information which is the gradient at each position along the target driving route, and predicts the energy consumption when the vehicle is traveling along the target driving route based on the vehicle mass, the rolling resistance coefficient, the air resistance coefficient, the predicted vehicle speed, and the gradient information.

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