JP5930820B2 - Energy consumption prediction device - Google Patents

Description

  The present invention relates to an energy consumption prediction device that predicts the energy consumption of a vehicle in a predetermined section on a route.

  Conventionally, as a method for predicting the fuel consumption (energy consumption) of a vehicle with respect to a travel route, a fuel consumption prediction formula having various factors specific to each travel route as parameters, such as the average speed, distance, and road gradient of a road section is used. Thus, a method for calculating the fuel consumption has been proposed.

  Various techniques have been proposed for such a method for predicting fuel consumption. For example, Patent Document 1 describes a technique for improving the accuracy of fuel consumption predicted by a fuel consumption prediction formula. Specifically, a fuel efficiency estimation formula having parameters such as a predicted fuel efficiency calculated by a fuel efficiency prediction formula for a travel route to be traveled, and actual fuel efficiency or actual speed and acceleration obtained by past travel as parameters. The above fuel consumption prediction formula is corrected so that the error from the estimated fuel consumption calculated by the above is minimized. For this correction, for example, a method of multiplying the entire parameters constituting the formula of the fuel consumption prediction equation by the same correction coefficient, a method of multiplying each parameter by a different correction coefficient, and the like have been proposed.

International Publication No. 2010/116492

  In general, the conventional fuel consumption prediction formula uses only variables that vary depending on the travel route, such as travel time for each road link, average speed, road length, road gradient, etc., and other factors do not change. It is assumed.

  However, the actual amount of fuel consumed by the vehicle depends on factors other than the travel route, such as the total weight of the vehicle (including the weight of the load and passengers), the operating status of auxiliary equipment such as air conditioners, Fluctuates greatly depending on the amount of deceleration. Therefore, as in the technique described in Patent Document 1, even if the fuel consumption is predicted while correcting the fuel consumption prediction formula composed of the parameters of the travel route, factors other than the travel route are not taken into account, so the accuracy is high. Fuel consumption could not be predicted.

  Separately from this, the characteristics of each driver, such as the skill of the driver's driving skill, is reflected in the above fuel consumption prediction formula, and the actual fuel consumption obtained by actual vehicle travel is used to calculate the fuel consumption prediction formula. There has been proposed a technology that automatically corrects each time the vehicle travels. However, in actual vehicle travel, the above factors (number of passengers, use of auxiliary equipment such as an air conditioner, frequency of acceleration / deceleration) differ from one travel to another, so that the above coefficient correction must be performed accurately. There is a problem that can not be.

  Accordingly, the present invention has been made in view of the above-described problems, and an object thereof is to provide a technique capable of improving the accuracy of correction and, in turn, the accuracy of prediction of energy consumption.

The energy consumption prediction apparatus according to the present invention includes a first parameter corresponding to a factor of vehicle energy consumption based on predetermined route information in a predetermined section on a route, and a second parameter that weights the first parameter. The energy consumption prediction device predicts the energy consumption of the vehicle based on a predetermined mathematical formula including The energy consumption prediction device includes a movement history information management unit that manages a history of energy consumption of the vehicle and a history of movement state of the vehicle with respect to past travel of the vehicle in the predetermined section; respect past travel of the vehicle, and a vehicle characteristic information obtaining unit for obtaining vehicle characteristics information before Symbol vehicle. The energy consumption prediction apparatus is configured to control the movement state managed by the movement history information management unit and the vehicle characteristic information acquired by the vehicle characteristic information acquisition unit with respect to past travel of the vehicle in the predetermined section. And an energy consumption estimation unit that estimates the energy consumption of the vehicle based on the predetermined mathematical formula to which the derived first parameter is applied. The energy consumption prediction device is configured to generate a new second parameter based on the energy consumption managed by the movement history information management unit and the energy consumption estimated by the energy consumption estimation unit. A parameter calculation unit for calculating the energy consumption of the vehicle in the predetermined section based on the predetermined mathematical formula to which the new second parameter is applied before the vehicle travels the predetermined section next time. Predict the amount. The vehicle characteristic information acquisition unit estimates the vehicle characteristic information including a total weight of the vehicle when the vehicle travels in the predetermined section, and the vehicle characteristic information acquisition unit includes a predetermined number of pieces in the predetermined section. The vehicle characteristic information other than the total weight is acquired for a minute section, and the movement history information management unit manages the energy consumption history and the movement state history for the predetermined number of minute sections, and the energy A consumption estimation unit estimates the energy consumption for the predetermined number of minute sections, and the vehicle characteristic information acquisition unit manages the energy managed by the movement history information management unit for the predetermined number of minute sections. The total weight of the vehicle is estimated so that an error between the consumption amount and the energy consumption amount estimated by the energy consumption amount estimation unit is minimized.

  According to the present invention, the second parameter is calculated in consideration of the moving state and the vehicle characteristic information in addition to the route information. Therefore, since the correction for the prediction of the energy consumption can be performed in consideration of the movement state and the vehicle characteristic information, the accuracy of the correction can be improved. As a result, the influence of the movement state and vehicle characteristic information on the prediction of energy consumption can be reduced, and the accuracy of prediction of energy consumption can be improved.

It is a block diagram which shows the structure of the energy consumption prediction apparatus which concerns on Embodiment 1. FIG. 3 is a flowchart showing the operation of the energy consumption prediction apparatus according to Embodiment 1. It is a block diagram which shows the structure of the energy consumption prediction apparatus which concerns on the modification 1 of Embodiment 1. FIG.

<Embodiment 1>
FIG. 1 is a block diagram showing a configuration of an energy consumption prediction apparatus according to Embodiment 1 of the present invention. The energy consumption amount prediction device weights a first parameter corresponding to a factor of vehicle energy consumption based on predetermined route information in a predetermined section on the route (hereinafter, “prediction target section”), and the first parameter. The energy consumption amount of the vehicle is predicted based on a fuel efficiency calculation formula (predetermined formula) including the second parameter to be performed.

  In the present embodiment, the energy consumption prediction device is described as being applied to a car navigation device for a vehicle. Further, in the present embodiment, a description will be given assuming that one desired road link among a plurality of road links on the map data is set as the prediction target section.

  As shown in FIG. 1, the energy consumption prediction apparatus 1 includes a user input unit 2, a vehicle information acquisition unit 3, a movement history information management unit 4, a vehicle characteristic information acquisition unit 5, a map database 6, an energy A consumption estimation unit 7, a correction parameter calculation unit 8, and an energy consumption prediction unit 9 are provided.

  The user input unit 2 is a component that receives input for each parameter from the user. For example, an element that receives input from the user is realized by a touch panel or the like mounted on the screen of the display device.

  The vehicle information acquisition unit 3 can communicate with a sensor that measures vehicle information installed in the vehicle through a vehicle network such as a CAN (Controller Area Network), and appropriately acquires vehicle information from the sensor. The vehicle information is, for example, information including the position of the host vehicle, the traveling speed, the fuel injection amount (or the remaining amount of fuel), the operating status of the air conditioner, the outside temperature, and the like, and items that can be acquired by the sensors differ.

The movement history information management unit 4 manages movement history information including actual measurement values such as vehicle information acquired by the vehicle information acquisition unit 3 with respect to past travel of the vehicle in the prediction target section (road link). In the present embodiment, the movement history information includes the history of the energy consumption amount Qr when the vehicle has traveled in the prediction target section in the past, and the history of the movement state (here, the travel time T corresponding to the link travel time, It is assumed that a travel speed v _t and travel acceleration v _acc and v _dec history corresponding to link travel are included.

The energy consumption amount Qr managed by the movement history information management unit 4 is input to the correction parameter calculation unit 8. Further, the movement state (travel time T, travel speed v _t and travel acceleration v _acc , v _dec ) managed by the travel history information management unit 4 is input to the energy consumption estimation unit 7.

  Based on the input data received by the user input unit 2 and the vehicle information acquired by the vehicle information acquisition unit 3, the vehicle characteristic information acquisition unit 5 relates to past use of the vehicle in the prediction target section, etc. Vehicle characteristic information (in this case, information including the total weight M of the vehicle when the vehicle has traveled in the prediction target section in the past, the fuel consumption per unit time when the vehicle is idling, and the air density ρ around the vehicle) ) To get.

  That is, the vehicle characteristic information acquisition unit 5 adds the weight of the vehicle to the weight when the user input unit 2 receives an approximate weight of the passenger and the load before the past travel. To get. When the user input unit 2 accepts the number of passengers instead of the weight of the passengers, the vehicle characteristic information acquisition unit 5 multiplies the number of passengers by a predetermined weight, so that the weight of the passengers described above is obtained. May be obtained.

Moreover, the vehicle characteristic information acquisition part 5 acquires the fuel consumption per unit time at the time of idling of the vehicle contained in the said vehicle information from the vehicle information acquired by the vehicle information acquisition part 3. FIG. Note that the fuel consumption per unit time during idling substantially matches the basic energy consumption q _base [cc / s or W] per unit time consumed due to factors other than traveling. Therefore, in the following description, it is assumed that the fuel consumption per unit time at idling and the basic energy consumption q _base per unit time are not distinguished.

  Further, the vehicle characteristic information acquisition unit 5 acquires the air density ρ based on the outside air temperature included in the vehicle information acquired by the vehicle information acquisition unit 3 and the known atmospheric pressure.

The total vehicle weight M, the basic energy consumption q _base, and the air density ρ acquired by the vehicle characteristic information acquisition unit 5 are input to the energy consumption estimation unit 7. Note that the two parameters of the basic energy consumption q _base and the air density ρ may be calculated as an average value during travel (hereinafter also referred to as “trip”) in the prediction target section, or at predetermined time intervals. May be calculated and stored as appropriate. In addition, here, the vehicle characteristic information acquisition unit 5 has been described to acquire the total weight M and the basic energy consumption q _base included therein from the user input and vehicle information input to the vehicle characteristic information acquisition unit 5. However, the present invention is not limited to this, and the vehicle characteristic information acquisition unit 5 may estimate the total weight M and the basic energy consumption q _base as in Modification 1 described later.

  The map database 6 is a database that stores map data used in a navigation function that executes route search and the like. In the present embodiment, the map data includes nodes, road links connecting the nodes (prediction target sections), and road types that are road attributes of the road links.

Further, the map data includes, for each road link, the length D of the road link (prediction target section), the number of _uphills n _up in the road link (prediction target section), the elevation difference H _{up_k} of the kth uphill, the road The number of downhills n _down in the link (prediction target section) and the elevation difference H _{down_k} of the _kth downhill are further included as predetermined route information. Further, the map data includes a predetermined traveling time T, a predetermined traveling speed v _t and predetermined traveling accelerations v _acc and v _dec corresponding to the link travel time for each road link (prediction target section) in a predetermined moving state. As further including.

Predetermined path information stored in the map database 6 (length D of the prediction target section, the number of uphill _{n Stay up-,} k-th uphill altitude difference _{H Up_k,} the number of downhill _{n down} and k th downlink The slope elevation difference H _{down_k} ) is input to the energy consumption estimation unit 7 and the energy consumption prediction unit 9. The predetermined movement state (predetermined travel time T, predetermined travel speed v _t and predetermined travel acceleration v _acc , v _dec ) stored in the map database 6 is input to the energy consumption prediction unit 9. .

  The energy consumption amount estimation unit 7 estimates the energy consumption amount Qs consumed by the vehicle regarding the past travel of the vehicle in the prediction target section (road link). The energy consumption estimation unit 7 performs this estimation after the vehicle travels in the prediction target section until the vehicle travels in the prediction target section next time (before the energy consumption prediction unit predicts the energy consumption). Do.

In the present embodiment, the energy consumption estimation unit 7 performs movement history information management on predetermined route information D, n _up , H _{up_k} , n _down , H _{down_k} stored in the map database 6 regarding the prediction target section. The first parameter E _{1 in} consideration of the movement states T, v _t , v _acc , v _dec managed by the unit 4 and the vehicle characteristic information M, q _base , ρ acquired by the vehicle characteristic information acquisition unit 5 , ..., to derive the _{E 7.} And the energy consumption estimation part 7 estimates the energy consumption Qs of a vehicle based on the above-mentioned fuel consumption calculation formula (energy consumption model) to which the derived first parameters E ₁ ,..., E ₇ are applied. .

  In the present embodiment, the fuel consumption calculation formula (energy consumption amount model) used in the energy consumption amount estimation unit 7 and the energy consumption amount prediction unit 9 is the same. This fuel consumption calculation formula (energy consumption amount model) will be described in detail later.

Based on the energy consumption Qr managed as past actual measurement data by the movement history information management unit 4 and the energy consumption Qs estimated by the energy consumption estimation unit 7, the correction parameter calculation unit 8 Second parameters C ₁ ,..., C ₇ are calculated. The correction parameter calculation unit 8 performs the above calculation after the vehicle travels in the prediction target section until the vehicle travels in the prediction target section next time (before the energy consumption prediction unit predicts the energy consumption). Do. The calculation of the new second parameters C ₁ ,..., C ₇ will be described in detail later.

The energy consumption predicting unit 9 stores predetermined vehicle characteristic information M, q _base , ρ stored in the map database 6 and predetermined stored in the map database 6 before the vehicle travels in the prediction target section next time. moving state _{T, v t, v acc,} v dec and predetermined path information _{D, n} up, _{H up_k,} based _{n down,} in the _{H down_k,} derives a first parameter in the same manner as energy consumption estimator 7 . Note that the predetermined vehicle characteristic information M, q _base and ρ used in the energy consumption prediction unit 9 may be input from other components.

The energy consumption prediction unit 9 calculates the fuel consumption to which the derived first parameters E ₁ ,..., E ₇ and the new second parameters C ₁ ,..., C ₇ calculated by the correction parameter calculation unit 8 are applied. Based on the formula, the energy consumption Qp of the vehicle in the prediction target section (road link) is predicted. Thereafter, the energy consumption amount Qp predicted by the energy consumption amount prediction unit 9 is used, for example, for notification by display to the user or route search.

<Fuel consumption calculation formula (energy consumption model)>
Next, a fuel consumption calculation formula (energy consumption amount model) used in the energy consumption amount estimation unit 7 and the energy consumption amount prediction unit 9 according to the present embodiment will be described. In the present embodiment, the fuel consumption calculation formula (energy consumption model) is defined as the following formula (1).

E _{1 in} the first term on the right side of the equation (1) is a first parameter that defines an energy consumption factor depending on the travel time T and the like, and is represented by the following equation (1-1).

The energy consumption estimation unit 7 substitutes the basic energy consumption q _base from the vehicle characteristic information acquisition unit 5 and the travel time T from the movement history information management unit 4 into Expression (1-1). estimating a first parameter _{E 1} by. On the other hand, the energy consumption amount prediction unit 9 substitutes a predetermined basic energy consumption amount q _base stored in itself and a predetermined traveling time T from the map database 6 into the equation (1-1). predicting a first parameter _{E 1} by.

E _{2 in} the second term on the right side of the equation (1) is a first parameter that defines an energy consumption factor depending on the travel distance D and the like, and is expressed by the following equation (1-2).

The energy consumption estimation unit 7 substitutes the total weight M from the vehicle characteristic information acquisition unit 5 and the length D of the prediction target section from the map database 6 into the formula (1-2). estimating a one-parameter _{E 2.} On the other hand, the energy consumption prediction unit 9 substitutes a predetermined total weight M stored in itself and the length D of the prediction target section from the map database 6 into the equation (1-2). predicting a first parameter _{E 2.} For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

E _{3 in} the third term on the right side of the above equation (1) is a first parameter that prescribes an energy consumption factor depending on the above-described uphill altitude difference H _{up_k} and is expressed by the following equation (1-3). The

The energy consumption amount estimation unit 7 substitutes the number n _up of the _uphills from the map database 6 and the elevation difference H _{up_k} of the _kth uphill into the first parameter E _3. Is estimated. Similarly, the energy consumption prediction unit 9 also substitutes the first parameter E by substituting the number of _uphills n _up and the elevation difference H _{up_k} of the kth uphill from the map database 6 into the equation (1-3). ₃ is predicted. For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

E _{4 in} the fourth term on the right side of the above equation (1) is a first parameter that defines an energy consumption factor depending on the above-described downhill elevation difference H _{down_k and the} like, and is expressed by the following equation (1-4): The

The energy consumption estimation unit 7 substitutes the number of downhills n _down and the altitude difference H _{down_k} of the k-th downhill from the map database 6 into the first parameter E _4. Is estimated. Similarly, the energy consumption amount prediction unit 9 also substitutes the first parameter E by substituting the number of downhills n _down and the altitude difference H _{down_k} of the kth downhill from the map database 6 into the equation (1-4). ₄ is predicted. For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

E _{5 in} the fifth term on the right side of the above equation (1) is a first parameter that defines an energy consumption factor depending on the air resistance received by the host vehicle, and is represented by the following equation (1-5).

The energy consumption estimation unit 7 substitutes the air density ρ from the vehicle characteristic information acquisition unit 5 and the traveling speed v _t from the movement history information management unit 4 into the equation (1-5). to estimate the one-parameter _{E 5.} On the other hand, the energy consumption predicting unit 9 substitutes the predetermined air density ρ stored in itself and the predetermined traveling speed v _t from the map database 6 into the equation (1-5). to predict the one-parameter _{E 5.} For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

E _{6 in} the sixth term on the right side of the above equation (1) is a first parameter that defines an energy consumption factor depending on the acceleration of the host vehicle, and is represented by the following equation (1-6).

Note that the energy consumption amount estimation unit 7 _calculates the travel acceleration v _acc (here, corresponding to the final speed and the initial speed of the acceleration section and the kth acceleration section) from the movement history information management section 4 using the formula (1-6). the first parameter E ₆ is estimated by substituting the). On the other hand, the energy consumption prediction unit 9 uses a predetermined travel acceleration v _acc (corresponding to the final speed and the initial speed of the acceleration section and the kth acceleration section) from the map database 6 according to the formula (1-6). predicting a first parameter E ₆ by substituting the). For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

E _{7 in} the seventh term on the right side of the equation (1) is a first parameter that defines an energy consumption factor depending on the deceleration of the host vehicle, and is represented by the following equation (1-7).

The energy consumption estimation unit 7 _calculates the travel acceleration v _dec (here, corresponding to the final speed and the initial speed of the deceleration section and the k-th deceleration section) from the movement history information management section 4 from the equation (1-7). the first parameter E ₇ is estimated by substituting the). On the other hand, the energy consumption prediction unit 9 uses a predetermined travel acceleration v _dec (corresponding to the final speed and the initial speed of the deceleration section and the kth deceleration section) from the map database 6 according to the formula (1-7 predicting a first parameter E ₇ by substituting the). For other values, constant values common to the energy consumption estimation unit 7 and the energy consumption prediction unit 9 are used.

<Second Parameter (Correction Parameter) Calculation Procedure>
Next, the process of calculating new second parameters (correction parameters) C ₁ ,..., C ₇ by the energy consumption prediction apparatus 1 according to the present embodiment will be described using the flowchart shown in FIG. To do. As a premise, it is assumed that the process shown in FIG. 2 is started at the timing when the travel (trip) of the prediction target section is completed (for example, when the route guidance of the car navigation function is completed).

First, in step S1, the vehicle characteristic information acquisition unit 5 relates to vehicle characteristics information (total weight M, basic energy consumption q _base ) regarding the current trip i (i: a natural number assigned in order for each trip). Obtain the air density ρ).

In step S2, the energy consumption estimation unit 7 relates to the current trip i, the movement state T, v _t , v _acc , v _dec managed by the movement history information management unit 4 and vehicle characteristic information according to step S1. The vehicle characteristic information M, q _base , ρ acquired by the acquisition unit 5 and predetermined route information D, n _up , H _{up_k} , n _down , H _{down_k} stored in the map database 6 are _expressed by the above formula ( 1-1) to (1-7) are substituted to derive the first parameters E ₁ ,..., E ₇ .

In step S3, the energy consumption estimation unit 7 substitutes the first parameters E ₁ ,..., E ₇ derived in step S2 for the current trip i into the fuel consumption calculation formula shown in (1). Is estimated. As the second parameters C ₁ ,..., C ₇ used here, those previously calculated by the correction parameter calculation unit 8 are used.

  In step S4, the correction parameter calculation unit 8 determines the energy consumption Qr managed by the movement history information management unit 4 for the current trip i and the energy consumption estimated by the energy consumption estimation unit 7 in step S3. It is determined whether or not the difference from the quantity Qs is larger than a predetermined threshold value.

If it is not determined that the value is larger, the process proceeds to step S5, and new second parameters C ₁ ,..., C ₇ are calculated using the energy consumption Qs estimated this time in step S3. On the other hand, the if it is determined in large, assumes that such vehicle characteristics information can not be accurately estimated, the second parameter C _1, ..., ends the processing shown in FIG. 2 without calculation of C ₇ To do.

That is, in the present embodiment, when the correction parameter calculation unit 8 determines that the difference between the energy consumption amount Qr and the energy consumption amount Qs is larger than the predetermined threshold, the energy consumption amount Qs estimated this time is The new second parameters C ₁ ,..., C ₇ are excluded from the energy consumption used to calculate them. Note that the determination is not limited to the difference between the energy consumption amount Qr and the energy consumption amount Qs, and the determination may be made based on the ratio of the energy consumption amount Qr and the energy consumption amount Qs.

In step S5, the correction parameter calculation unit 8 sets a plurality of energy consumption amounts Qs (hereinafter referred to as “Q ₁ s,..., Q _i-1 s” estimated for the past trips 1,. also) of the plurality of fuel economy calculation formula be referred, the second parameter C _1, ..., a plurality of previously used to calculate the C ₇ energy consumption _{Q 1 s, ..., Q m} s ( provided that , M ≦ i−1) is obtained. Then, the correction parameter calculating unit 8, a plurality of energy consumption Q _{1 s} with the acquired, ..., the fuel consumption calculation formula of Q m _s, estimated in step S3 with respect to the current trip i energy consumption Qs (hereinafter " Add a fuel consumption formula for calculating the Q i _s "and sometimes referred to), to produce the following equation (2).

In this equation (2), E ₁₁ ,..., E ₁₇ are the first parameters of the energy consumption Q ₁ s, and ..., E _m1 , ..., E _m7 are the first parameters of the energy consumption Q _m s. in _{and, E i1, ...,} E _i7 is the first parameter of the energy consumption _{Q i} s.

The correction parameter calculation unit 8 solves the generated equation (2) for the second parameters C ₁ ,..., C ₇ . Thus, new second parameters C ₁ ,..., C ₇ with a small estimation error with the actually measured energy consumption Qr managed by the movement history information management unit 4 are calculated.

In step S6, the correction parameter calculation unit 8 outputs the new second parameters C ₁ ,..., C ₇ to the energy consumption amount estimation unit 7 and the energy consumption amount prediction unit 9. Then, in step S7, this resulting _E i1, _..., save the _{E i7} and energy consumption _{Q i} s, and ends the processing shown in FIG. Note that step S6 and step S7 may be performed simultaneously.

  According to the energy consumption prediction apparatus 1 according to the present embodiment as described above, in addition to the route information, the moving state such as the speed and the vehicle characteristic information such as the total weight of the vehicle and the basic energy consumption are added. To calculate a second parameter (correction parameter). Therefore, since the correction for the prediction of the energy consumption can be performed in consideration of the movement state and the vehicle characteristic information, the accuracy of the correction can be improved. As a result, the influence of the movement state and vehicle characteristic information on the prediction of energy consumption can be reduced, and the accuracy of prediction of energy consumption can be improved. In addition, when comprised so that the above-mentioned correction | amendment process and prediction process may be performed for every driver | operator, appropriate correction | amendment and prediction can be performed for every driver | operator.

Further, according to the energy consumption prediction apparatus 1 according to the present embodiment, when the difference (or ratio) between the managed energy consumption Qr and the estimated energy consumption Qs is larger than a predetermined threshold value. Excludes the estimated energy consumption Qs from the energy consumption used to calculate the new second parameters C ₁ ,..., C ₇ . That is, since the energy consumption Qs that cannot be accurately estimated is excluded, it is possible to avoid performing an incorrect correction. Therefore, the accuracy of correction can be increased, and as a result, the accuracy of energy consumption prediction can be improved.

  In the above description, the case where the energy consumption prediction device 1 is applied to a car navigation device has been described. However, the present invention is not limited to a vehicle-mounted device, but a mobile phone terminal or a portable information terminal (for example, a PDA (Personal) Digital Assistance)). Alternatively, the server can perform energy consumption prediction and parameter correction by transmitting movement history, travel route, etc. using communication, and the device mounted on the vehicle displays and uses only the result. It is good also as a form to do. Further, in the present embodiment, it has been described that one desired road link is set as a prediction target section. However, the present invention is not limited to this, and some desired road links are set as prediction target sections. It may be set.

<Modification 1>
FIG. 3 is a block diagram showing the configuration of the energy consumption prediction apparatus 1 according to this modification. In this modification, the vehicle characteristic information acquisition unit 5 estimates vehicle characteristic information including the total weight M of the vehicle when the vehicle has traveled in the past in the prediction target section in a form different from the first embodiment. That is, the vehicle characteristic information acquisition unit 5 acquires vehicle characteristic information other than the total weight M for N (predetermined number) minute sections in the prediction target section. For example, 40 minute intervals defined every minute time (for example, 1 second) are applied to the N minute intervals.

The movement history information management unit 4 manages the history of energy consumption Qr and the history of movement states T, v _t , v _acc , v _dec similar to those described above for N minute sections. Then, the energy consumption amount estimation unit 7 estimates the energy consumption amount Qs similar to that described above with respect to the N minute sections.

  Then, the vehicle characteristic information acquisition unit 5 calculates the energy consumption amount Qr managed by the movement history information management unit 4 and the energy consumption amount Qs estimated by the energy consumption amount estimation unit 7 for N minute sections. The total weight M of the vehicle is estimated so that the error is minimized.

  According to the energy consumption prediction apparatus 1 according to the present modification as described above, as in the energy consumption prediction apparatus 1 according to the first embodiment, the correction for the prediction of the energy consumption is performed using the movement state and vehicle characteristic information. Therefore, the accuracy of energy consumption prediction can be improved. Moreover, since it is not necessary to provide a special sensor for measuring the total weight of the vehicle, it can be expected to reduce the number of elements of the device.

  In addition, it is preferable that the predetermined number of minute sections be applied only to an ascending minute section or only a minute section where the vehicle is accelerated. In such a configuration, data of a minute section that has a large influence on the fuel consumption is used, and an error in estimation of the total weight can be reduced.

<Modification 2>
In this modification, the vehicle characteristic information acquisition unit 5 is different from the first embodiment, and consumes energy per unit time when the vehicle is idling when the vehicle travels in a predetermined section (substantially, The vehicle characteristic information including the above basic energy consumption) is estimated.

  That is, when the vehicle characteristic information acquisition unit 5 acquires from the vehicle information from the vehicle information acquisition unit 3 the operating status of the air conditioner mounted on the vehicle and the presence or absence of the operation of the wiper and headlight mounted on the vehicle. Estimates the energy consumption per unit time described above based on such information. In addition, when the vehicle characteristic information acquisition part 5 also acquires the information of travel time, external temperature, and weather from the vehicle information from the vehicle information acquisition part 3, based on these information and the above-mentioned information, the above-mentioned information is obtained. Estimate energy consumption per unit time.

  According to the energy consumption prediction apparatus 1 according to the present modification as described above, as in the energy consumption prediction apparatus 1 according to the first embodiment, the correction for the prediction of the energy consumption is performed using the movement state and vehicle characteristic information. Therefore, the accuracy of energy consumption prediction can be improved. Further, since it is not necessary to provide a special sensor for measuring the energy consumption per unit time described above, it can be expected to reduce the number of elements of the apparatus.

  In the present invention, the embodiments can be appropriately modified and omitted within the scope of the invention.

  DESCRIPTION OF SYMBOLS 1 Energy consumption prediction apparatus, 4 Movement history information management part, 5 Vehicle characteristic information acquisition part, 7 Energy consumption estimation part, 8 Correction parameter calculation part.

Claims (4)

Based on a predetermined mathematical formula including a first parameter corresponding to a factor of vehicle energy consumption based on predetermined route information in a predetermined section on the route, and a second parameter for weighting the first parameter, An energy consumption prediction device for predicting the energy consumption of a vehicle,
A movement history information management unit that manages a history of energy consumption of the vehicle and a history of the movement state of the vehicle regarding past travel of the vehicle in the predetermined section;
Respect past travel of the vehicle in the predetermined interval, and the vehicle characteristic information obtaining unit for obtaining vehicle characteristics information before Symbol vehicle,
Regarding the past travel of the vehicle in the predetermined section, the first state in consideration of the movement state managed by the movement history information management unit and the vehicle characteristic information acquired by the vehicle characteristic information acquisition unit An energy consumption estimation unit that derives a parameter and estimates an energy consumption of the vehicle based on the predetermined mathematical formula to which the derived first parameter is applied;
A parameter calculation unit for calculating the new second parameter based on the energy consumption managed by the movement history information management unit and the energy consumption estimated by the energy consumption estimation unit; Prepared,
Before the vehicle travels the predetermined section next time, predicting the energy consumption of the vehicle in the predetermined section based on the predetermined mathematical formula to which the new second parameter is applied ,
The vehicle characteristic information acquisition unit
Estimating the vehicle characteristic information including the total weight of the vehicle when the vehicle travels in the predetermined section;
The vehicle characteristic information acquisition unit acquires the vehicle characteristic information other than the total weight with respect to a predetermined number of minute sections in the predetermined section,
The movement history information management unit manages the energy consumption history and the movement state history for the predetermined number of minute sections,
The energy consumption estimation unit estimates the energy consumption with respect to the predetermined number of minute sections,
The vehicle characteristic information acquisition unit
With respect to the predetermined number of minute sections, the vehicle has a minimum error between the energy consumption managed by the movement history information management unit and the energy consumption estimated by the energy consumption estimation unit. An energy consumption forecasting device that estimates the total weight of the machine. It is an energy consumption prediction apparatus of Claim 1, Comprising:
The small section is only small sections of the ascending slope, or, Ru small sections Nomidea which the vehicle is accelerated, energy consumption prediction apparatus. The energy consumption prediction apparatus according to claim 1 or 2 , wherein
The vehicle characteristic information acquisition unit
An energy consumption amount prediction apparatus that estimates the vehicle characteristic information including energy consumption per unit time when the vehicle is idling when the vehicle travels in the predetermined section . It is an energy consumption prediction apparatus of any one of Claims 1-3, Comprising :
The vehicle characteristic information acquisition unit
Examples vehicle characteristic information, you acquire information including the outside air temperature when the vehicle has traveled the predetermined interval, energy consumption prediction apparatus.

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