JP2007312581A - Energy control system for electric car - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To highly accurately predict a drivable range given by present residual power, by exactly grasping a power consumption history and a driving history of the past. <P>SOLUTION: The data transmitted from each vehicle are received by a data center and registered in a database, and the data are totaled and analyzed to predict a drivable range given by present residual power is predicted. The prediction processing of the drivable range is executed upon a request from a display AP, and a list of ECU data to be displayed is acquired from the database (S50). If the residual power remains, average fuel consumption is acquired from a fuel consumption map (S53), and the power to be consumed is predicted (S56). Then, a driving distance is predicted, on the basis of the predicted power consumption and the average fuel consumption (S57, S58), and a display view is transmitted (S59). <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an energy management system for an electric vehicle that performs energy management by collecting and analyzing vehicle data accumulated in a database.

  In general, in an electric vehicle that is driven by a motor driven by electric power from a battery or a hybrid electric vehicle that is driven by using an engine and a motor in combination with an automobile driven by an engine that uses gasoline or the like as fuel, energy As part of the management, it is necessary to grasp the remaining power of the battery and grasp the distance that can be traveled by this remaining power.

For this reason, various techniques for predicting the distance that can be traveled with the remaining power have been proposed. For example, Patent Document 1 discloses the prior art. The technology disclosed in Patent Document 1 relates to a hybrid electric vehicle, and includes a current power amount that includes a battery dischargeable amount and a power that can be generated by an engine. When predicting at least one of the future travelable distance and travelable time of the vehicle by taking into account the travel pattern in the past predetermined period obtained from Is corrected according to the power generation mode, or at least one of the past power consumption and supply power is corrected according to the charge level of the battery, so that the future possible travel distance and travelable time of the vehicle can be accurately predicted. ·indicate.
JP 2001-231103 A

  However, in an electric vehicle that runs only with a motor supplied with power from a battery and does not have means for power generation and running by the engine, the remaining power of the battery is only supplemented by charging from the motor by regenerative running, It varies greatly depending on driving conditions and environmental conditions. Therefore, it is difficult to accurately predict the travelable distance by applying the technique of Patent Document 1.

  The present invention has been made in view of the above circumstances, and can accurately grasp past power consumption history and travel history, and can accurately predict the distance that can be traveled with the current remaining power. The purpose is to provide a system.

  In order to achieve the above object, an energy management system for an electric vehicle according to the present invention is an energy management system for an electric vehicle that performs energy management by collecting and analyzing vehicle data accumulated in a database. For each region of the map with the axis as the axis, the mileage per unit electric energy is calculated as the fuel efficiency, and the fuel consumption map registration means for registering in the fuel efficiency map of the database, the motor command torque and the motor rotation speed as the axes For each area of the map, the number of additional data for each registration in the database is calculated as a frequency representing the running state, and the frequency map registration means for registering in the frequency map of the database, the fuel consumption map, and the power by the frequency map Based on the history and travel history, predict the distance that can be traveled with the remaining power of the current vehicle, Characterized in that a travel distance predicting means for notifying the measuring traveling distance was the driver.

  The energy management system for an electric vehicle according to the present invention can accurately grasp past power consumption history and travel history using a fuel consumption map and a frequency map, and predicts a distance that can be traveled with the current remaining power with high accuracy. can do.

  Embodiments of the present invention will be described below with reference to the drawings. 1 to 7 relate to an embodiment of the present invention, FIG. 1 is a configuration diagram of an energy management system, FIG. 2 is a flowchart of instantaneous fuel consumption calculation processing, FIG. 3 is a flowchart of fuel consumption map aggregation processing, and FIG. FIG. 5 is a flowchart of frequency data summarization processing, FIG. 6 is a flowchart of average fuel consumption calculation processing, and FIG. 7 is a flowchart of travel distance predicted value calculation processing.

  The energy management system 1 shown in FIG. 1 has an in-vehicle terminal 5 and a communication line made up of electronic devices (ECUs) connected to an in-vehicle network of each EV for each user's electric vehicle (EV) registered in the system. The data center 10 is connected around the network. The data center 10 collects and analyzes ECU data remotely transmitted from the in-vehicle terminal 5 of each EV and manages the energy state of each EV. Specifically, the data center 10 performs communication processing via a communication line. The computer system includes a communication server 15 and a database server 20 that accumulates ECU data of each EV acquired through the communication server 15 and that performs aggregation / analysis processing.

  The communication server 15 controls data communication via a communication line with the in-vehicle terminal 5 of each EV, receives ECU data transmitted from the in-vehicle terminal 5, and registers it in the database server 20. As the communication line, a dedicated line or an existing line can be used. These lines are preferably wireless communication networks that can transmit the data of each EV in real time. However, if the necessary amount of data can be stored in the in-vehicle terminal 5 of each EV, a wired line can be used. is there.

  Further, the communication server 15 controls communication with the display device 50 for displaying various information from the data center 10 and notifying each user. As the display device 50, various devices such as a navigation device mounted on each EV, a personal computer (personal computer), a personal digital assistant (PDA), and a mobile phone of each user can be used. By installing a display application (display AP) for displaying information from the data center 10 in these devices, the device can be operated as the display device 50 of the present system.

  On the other hand, the database server 20 constructs a database management system (DBMS) under a predetermined operating system (OS) that manages the computer system. The DBMS mainly includes a database (DB) 25 that stores vehicle data of each EV, and a database analysis unit (DB analysis unit) 30 that performs various processes on input / output data of the database 25.

  The DB analysis unit 30 is formed by various APs that operate in the server, and is mainly formed by a database analysis application (DB analysis AP) that performs totalization / analysis processing of input / output data of the database 20. Fuel consumption map registration means, frequency map registration means, travelable distance prediction means, and driving support information generation means. As one of the DB analysis APs, a travel distance prediction AP is provided that predicts a distance that can be traveled with the current remaining power of each EV based on the energy consumption trend of each EV up to the present.

The ECU data transmitted from each EV is, for example, the following data, and other data such as battery / inverter voltage / current, motor / inverter temperature, battery temperature, and the like are transmitted as appropriate. The DB analysis unit 30 predicts the travelable distance of each EV based on these data.
-Vehicle ID: Identification information for identifying the user's vehicle registered in the system-Cumulative travel distance ... Cumulative value of distance traveled by each EV-Collection date / time ... Date / time when ECU data was acquired-Expected remaining power ... Each EV Remaining electric energy of the on-vehicle battery and motor command torque calculated in step ... Moving average value of motor command torque at the time of ECU data transmission (for example, moving average value for one second)
・ Motor rotation speed: Moving average value of motor rotation speed when sending ECU data (for example, moving average value for 1 second)
The remaining power of the battery is a value calculated by the in-vehicle ECU of each EV, but may be calculated on the data center 10 side from the voltage / current data of the battery.

  Here, when predicting the travelable distance of each EV, it is possible to simplify the process by introducing a parameter corresponding to “fuel consumption rate (fuel consumption)” in an automobile traveling with an engine. Since the fuel consumption rate in the engine vehicle indicates the travel distance per unit fuel amount, in this embodiment, the travel distance per unit power amount is defined as “fuel consumption”. When the distance is expressed in units [m] and the power is expressed in units [Wh], the fuel consumption in the present embodiment is an amount expressed in [m / Wh], and “fuel consumption” is used as an index of the travelable distance of the engine vehicle. In contrast to “rate”, it can also be expressed as “power consumption rate” as an index representing the travelable distance of an electric vehicle.

  In this embodiment, a fuel consumption map storing fuel consumption data for each setting region with the motor command torque and the motor rotation speed as the grid axis, and similarly for each setting region with the motor command torque and the motor rotation speed as the lattice axis, A frequency map storing the number of data is used. From these maps, energy consumption and traveling trends are grasped, and the distance that can be traveled with the current remaining power is predicted. The travelable distance is predicted through the processes shown in the following (1) to (6) in the DB analysis unit 30.

(1) Instantaneous fuel consumption calculation processing The difference between the accumulated travel distance and the expected remaining electric energy transmitted from each EV is calculated, and the ratio of these differences is calculated as the instantaneous fuel consumption (fuel consumption during sampling).
At the same time, based on the difference in the travel distance and the difference in the remaining power, the EV travel state travels while consuming the battery power, “consumption travel”, and “regenerative travel” that travels while regenerating the battery power, The vehicle is classified into four types of travel: “inertia travel” in which the battery power consumption and power regeneration are offset, and “idling” in the vehicle stop (motor stop) state.

(2) Fuel consumption map totaling process The fuel consumption is totaled for each set range of motor command torque and motor rotation speed during the specified period, and the averaged values are “consumed travel” and “regenerative travel” among the four travel types. And stored in the fuel consumption map created for.

(3) Frequency map totaling processing The number of data in the specified period (the number of records added to the database 25) is totaled for each setting range of the motor command torque and the motor rotational speed, and “consumption travel” and “ It is stored in a frequency map created for “regenerative running”.

  Note that the fuel efficiency map and frequency map are obtained by mapping data on the motor torque map with the motor rotation speed and the motor command torque as the grid axes. The motor torque is actually a vehicle vibration or a sudden torque. Due to fluctuations, it takes very peaky characteristics. Therefore, in creating the fuel consumption map and the frequency map, the motor command torque and the motor rotation speed are respectively moving averaged, and the map is smoothed by mapping based on the moving average value, so that the tendency is easily seen.

(4) Frequency data totaling process The total frequency during the specified period, the frequency for each travel type, the power consumption and the travel distance are totaled, and the travel tendency of each EV is grasped.

(5) Average fuel consumption calculation process The weighting by the frequency map is reflected on the fuel consumption stored in the fuel consumption map, and this is calculated as the average fuel consumption. This average fuel consumption is calculated for consumption travel and regenerative travel, respectively.

(6) Traveling distance predicted value calculation process For the remaining electric energy of each EV, a travelable distance is predicted based on a power history based on the fuel consumption map and the frequency map and a travel tendency grasped from the travel history. Specifically, as will be described later, the predicted travel distance is calculated based on the average fuel consumption and the predicted value of power consumption based on the frequency data.

  The processes (1) to (6) are executed as a trigger function of the DBMS, a periodic task of the OS, and a process by a request from another AP. The instantaneous fuel consumption calculation process is a trigger process registered in advance in the DBMS, and the fuel consumption map totaling process, the frequency map totaling process, the frequency data totaling process, and the average fuel consumption calculating process are started at a time designated as an OS task. Is done. On the other hand, the travel distance predicted value calculation process is executed in response to a request from the display AP, and returns a display view such as the predicted travel distance, predicted remaining power, and fuel consumption to the display AP.

  Specifically, when the ECU data from the in-vehicle terminal 5 is received by the communication server 15 and the ECU data is registered in the database 25 and a record is added, an instantaneous fuel consumption calculation process is performed triggered by the addition of this record. The Further, the fuel consumption map totaling process is performed on the premise of operation in a long cycle such as once every day, for example, and the frequency map totaling process is, for example, every minute compared to the fuel consumption map totaling process. Implemented on the premise of short-term operation. The frequency map aggregation process and the frequency data aggregation process are simultaneously performed at the same interval.

  Next, details of the processes (1) to (6) will be described with reference to the flowcharts of FIGS.

  FIG. 2 is a flowchart showing the instantaneous fuel consumption calculation process, which is executed when the communication server 15 receives ECU data from the in-vehicle terminal 5, registers it in the database 25, and adds a record.

  When the instantaneous fuel consumption calculation process is activated, first, the contents of the record added to the database 25 are checked in step S1. When the accumulated travel distance, the expected remaining power, the motor command torque moving average value, and the motor rotation speed moving average value stored in each field of the record are normal values (values not NULL), the process proceeds from step S1 to step S2. If it is an abnormal value (such as a NULL value), the process is terminated.

  In step S2, previously registered ECU data is acquired. However, ECU data registered before the specified period is not considered. In addition, it is assumed that the value of the designated period has been acquired separately from the table storing the setting information of the travel distance prediction AP.

  Next, the process proceeds to step S3, where the cumulative travel distance at the previous collection date and time (t−1) obtained from the registered ECU data is subtracted from the cumulative travel distance at the current collection date and time t to calculate the distance difference value Δd. . Further, in step S4, the predicted residual power at the previous collection date (t-1) obtained from the registered ECU data is subtracted from the predicted residual power at the current collection date t to calculate a power difference value Δp.

Next, the process proceeds to step S5, and the traveling type is classified based on the distance difference value Δd and the power difference value Δp. The travel types are classified into travel types = 1 to 4 under the following conditions, and are four types of “consumption travel”, “regenerative travel”, “inertia travel”, and “idling”.
・ Running type = 1
When Δd> 0 and Δp <0, it is defined as “consumption travel” that travels while consuming battery power.
・ Running type = 2
When Δd> 0 and Δp> 0, “regenerative travel” is performed in which the battery travels while regenerating power.
・ Travel type = 3
When Δd> 0 and Δp = 0, it is assumed that “inertia travel” is performed in which the battery power consumption and power regeneration are offset.
・ Driving type = 4
When Δd = 0, the vehicle is in a stopped (motor stopped) state and corresponds to “idling operation” of an automobile equipped with an internal combustion engine, and therefore the traveling type is set to “idling”.

After classifying the travel type in step S5, the process proceeds to step S6, and the distance difference value Δd is expressed as shown in the following equation (1) for consumption travel and regenerative travel of travel type = 1, 2. The instantaneous fuel consumption ms is calculated by dividing by the power difference value Δp. In step S7, information such as instantaneous fuel consumption ms, travel type, data collection date / time, distance difference value Δd, power difference value Δp, motor command torque moving average value, motor rotation speed moving average value, etc. is registered in the database 25. End the process.
ms = Δd / Δp (1)

  The instantaneous fuel consumption ms calculated by the above instantaneous fuel consumption calculation processing is totaled by the fuel consumption map totaling processing of FIG. 3 that is started as a task for each specified time (for example, every day), and stored in the fuel consumption map.

  In the fuel consumption map totaling process, the vehicle ID to be processed and the time at which the totaling process is executed are acquired from the database 25 in the first step S10. Next, in step S11, motor command torque and motor rotation speed range information for consumption / regenerative travel are acquired from the database 25, and in step S12, the fuel consumption default for each region (i, j) of the motor command torque / motor rotation speed is obtained. Get the value M0ij. The fuel efficiency default value M0ij stores the fuel efficiency in standard driving as an initial value until the fuel efficiency map is mapped with actual driving data.

  Next, the process proceeds to step S13, where the average fuel consumption value Mreal [±] ij (in the sign [±]) is obtained by averaging the instantaneous fuel consumption ms in the designated period within the motor command torque / motor rotation speed range (i, j). + Represents regenerative travel, and-represents consumption travel). The specified period starts from the day before the fuel consumption map aggregation period from the day before the aggregation execution time, and ends as the day before the aggregation execution date.

In the subsequent step S14, the fuel efficiency value M [±] ij to be registered in the fuel efficiency map is calculated by applying the following equation (2) to the fuel efficiency average value Mreal [±] ij. However, n [±] is the total frequency (total number of records) for each travel type, n_startup is the valid period (default valid period) to which the default value M0ij is applied, and if the number of records exceeds the default valid period, The default value M0ij is not applied.
M [±] ij = ((Mreal [±] ij−M0ij) / n_startup) × n [±] + M0ij (2)

  In step S15, it is checked whether or not the above processing has been executed for each region of the motor command torque and the motor rotation speed. If there is an area that has not yet been processed, the process returns from step S15 to step S13 to repeat the above process, and when the processes for all areas have been completed, the process proceeds from step S15 to step S16 to calculate. The fuel consumption value M [±] ij is registered in the fuel consumption map, and the task is completed.

  Next, the frequency map totaling process of FIG. 4 will be described. In this frequency map tabulation process, in a first step S20, the vehicle ID to be processed and the time when the tabulation process is executed are acquired from the database 25. Next, it progresses to step S21, the motor command torque of consumption / regenerative driving | running | working and motor rotation speed range information are acquired from the database 25, and the frequency and totaling date / time which were registered previously are acquired from a frequency map at step S22. In this case, since the aggregation period varies depending on the day, the last acquisition value is subject to the same day of the aggregation execution date and time.

  Next, the process proceeds to step S23, and for each area of motor command torque and motor rotation speed, the count (number of records) until the count is performed is counted from the previously registered count date and time. If the frequency information registered last time does not exist, aggregation is performed starting from 0 o'clock before the frequency map aggregation period from the date of aggregation.

  In step S24, it is checked whether or not the frequency count has been completed for the entire range of the motor command torque and the motor rotation speed. If there is an area that has not yet been aggregated, the process returns from step S24 to step S23 to continue the aggregation process, and when frequency aggregation for all areas has been completed, the process proceeds from step S24 to step S25. In step S25, the total frequency within the period is calculated for each travel type, registered in the frequency map in step S26, and the task ends.

  At the same time as the frequency map totaling process, a coefficient map totaling process for storing a coefficient for weighting the frequency data is performed in consideration of fluctuation factors due to driving conditions such as road conditions and weather conditions. The coefficient maps are created in correspondence with the consumption travel frequency map and the regenerative travel frequency map, respectively, and the distribution ratio coefficient W [±] ij used when calculating the average fuel consumption M [±] described later is the motor command torque. • Stored for each motor speed range.

  Next, the frequency data totaling process of FIG. 5 will be described. In the frequency data totaling process, the vehicle ID to be processed and the time at which the totaling process is executed are acquired from the database 25 in the first step S30. Next, the process proceeds to step S31, and frequency data registered last time is acquired from the database 25. The target of the previous acquisition value is on the same day as the totalization date.

  In the subsequent step S32, the frequency data from the previously registered aggregation date and time to the aggregation execution date and time are acquired and aggregated. When the frequency information registered last time does not exist, aggregation is performed starting from 0 o'clock before the frequency aggregation period from the date of aggregation.

Thereafter, the process proceeds to step S33, and the following equations (3) to (5) are applied, and the ratio of inertial travel to all frequencies (inertia travel rate) Re and the ratio of regenerative travel to all frequencies (regenerative travel rate) R [+ ], The idling ratio (idling rate) Ridle for all frequencies is calculated.
Re = (De / Ne) × We (3)
R [+] = P [+] / N [+] (4)
Ridle = (Pidle / Nidle) × Widle (5)
However, Ne: Number of records of inertial running (frequency)
De: Cumulative mileage of inertial running
We: Inertia frequency coefficient (coefficient for weighting)
N [+]: Number of regenerative driving records (frequency)
P [+]: Total power consumption for regenerative driving
Pidle: Total power consumption when idling
Widle: idling frequency coefficient (coefficient for weighting)

Further, the process proceeds to step S34, and the total power consumption P [+] for regenerative travel, the total power consumption P [-] for consumed travel, and the total idle power consumption Pidle are added as shown in the following equation (6). The total power consumption total Pall is calculated. And it progresses to step S35, the total frequency (total frequency) during the total period, the total frequency for each traveling type, the traveling rate Re, R [+], Ridle, and the total power consumption P [+] for each traveling type, P [-], Pidle, total power consumption total Pall, etc. are registered in the database 25, and the task is terminated.
Pall = P [+] + P [−] + Pidle (6)

  Next, the average fuel consumption calculation process of FIG. 6 will be described. This average fuel consumption calculation process is executed after the frequency map aggregation process and the frequency data aggregation process are completed. First, in step S40, the vehicle ID and execution time are acquired. In step S41, motor command torque and motor rotation speed range information for consumption / regenerative travel are acquired from the database 25.

Next, the process proceeds to step S42, and when the fuel consumption value M [±] ij for each region of the motor command torque / motor rotation speed is acquired from the fuel consumption map, in step S43, the frequency closest to the processing execution time for each travel type from the frequency map. N [±] ij and total frequency N [±] are acquired. And it progresses to step S44, the following (7) Formula is applied to the acquired data, and average fuel consumption M [+/-] is calculated.
M [±] = ΣM [±] ij × N [±] ij × W [±] ij / N [±] (7)
N [±] ij: Frequency for each area of motor command torque and motor rotation speed
N [±]: Frequency for each driving type during the frequency counting period
W [±] ij: Motor command torque / motor rotational speed distribution ratio coefficient for each region In the equation (7), as described above, the subscript + represents regenerative travel, and the subscript-represents consumption travel. The average fuel consumption M [+] and the average fuel consumption M [-] of consumption travel are calculated individually. Further, the distribution ratio coefficient W [±] ij is calculated at the time of the frequency map aggregation process described above.

  Then, it progresses to step S45 and it is investigated whether the above process was performed about each area | region of motor command torque and motor rotation speed. As a result, when there is an area that has not been processed yet, the process returns from step S45 to step S42 and the above process is repeated. When the processes for all the areas have been completed, the process proceeds from step S45 to step S46 to calculate. The average fuel consumption M [±] for each travel type (consumed travel / regenerative travel) is registered in the database 25 and the task is terminated.

  Next, the predicted travel distance calculation process in FIG. 7 will be described. As described above, the travel distance predicted value calculation process is a process performed in response to a request from the display AP, and a list of ECU data to be displayed is acquired from the database 25 in the first step S50. Subsequently, it progresses to step S51 and the presence or absence of the prediction residual electric power in ECU data is determined. If there is no expected remaining power, the process jumps to step S59 and returns a display view including information indicating that there is no remaining power to the display AP. Predict the distance that can be driven by the remaining power of the vehicle.

  In order to predict the distance that can be traveled with the current remaining power, the travel distances of consumption travel, regenerative travel, and inertial travel can be predicted, and these predicted values can be summed. The predicted value of the distance traveled during consumption travel predicts the amount of power consumed during consumption travel and idling travel, and the average consumption consumption as the expected fuel consumption by adding the current travel trend to the predicted value of power consumption. It can be calculated by multiplying the fuel consumption. Similarly, the predicted value of the travel distance in regenerative travel predicts the amount of electric power collected in regenerative travel, and the average of the regenerative travel as the predicted fuel consumption that takes into account the current driving tendency to the predicted power amount. It can be calculated by multiplying the fuel consumption. Furthermore, the predicted value of the traveling distance in the inertial traveling can be calculated by predicting the frequency of inertial traveling from the current traveling tendency and multiplying the predicted value of the frequency of inertial traveling by the inertial traveling rate. .

  In order to perform the above process, in step S52 following step S51, first, the collection date and time and the latest frequency information of the target vehicle are acquired from the database 25. Then, in step S53, the average fuel consumption M [-] during consumption travel and the average fuel consumption M [+] during regenerative travel are acquired, and after step S54, the vehicle side limits the expected remaining power in the ECU data. The value obtained by subtracting the minimum electric power limit value, which is the remaining power amount, is assumed as the expected remaining power Premain, and the total frequency Nall, the consumed driving frequency N [-], the regenerative driving frequency N [+], the inertia driving frequency Ne, The prediction is performed for the frequency Nidle.

Specifically, in step S54, a predicted value (predicted total frequency) Nremain of the total frequency is calculated using the following equation (8). Furthermore, in step S55, using the following formulas (9) to (12), the predicted value of the frequency for each travel type (expected consumption travel frequency Nx [-], expected regenerative travel frequency Nx [+], predicted inertial travel) Frequency Nex, expected idling frequency Nidlex).
Nremain = (Premain × Nall) / Pall│ (8)
Nx [−] = (Nremain × N [−]) / Nall (9)
Nx [+] = (Nremain × N [+]) / Nall (10)
Nex = (Nremain × Ne) / Nall (11)
Nidlex = (Nremain × Nidle) / Nall (12)

In step S56, the predicted idling power Pidlex, which is a predicted value of the power consumed during idling, is calculated using the predicted value of the frequency during idling and regenerative travel, using the following formulas (13) and (14). An expected regenerative running power Px [+] that is a predicted value of the obtained power is calculated. Furthermore, as shown in the following equation (15), the predicted consumed running power Px [−, which is a predicted value of the power that decreases from the remaining power during the consumed running, from the expected idling power Pidlex and the expected regenerative running power Px [+]. ] Is calculated.
Pidlex = Ridle × Nidlex (13)
Px [+] = R [+] × Nx [+] (14)
Px [-] = Premain-Pidlex + Px [+] (15)

In step S57 subsequent to step S56, as shown in the following equations (16) and (17), the estimated travel distance Dx [−] of the consumption travel and the predicted travel distance Dx [+] of the regenerative travel are average fuel consumption, respectively. It is calculated from M [−], predicted consumption travel power Px [−], average fuel consumption M [+], and expected regenerative travel power Px [+]. Further, as shown in the following equation (18), the estimated travel distance Dex of inertial travel is calculated from the travel rate Re and the predicted inertial travel frequency Nex.
Dx [−] = M [−] × Px [−] (16)
Dx [+] = M [+] × Px [+] (17)
Dex = Re × Nex (18)

In step S58, the estimated travel distance Dx [-] for consumption travel, the predicted travel distance Dx [+] for regenerative travel, and the predicted travel distance Dex for inertia travel are summed as shown in the following equation (19). The predicted travel distance Dx is calculated, and the display view including the predicted travel distance Dx is returned to the display AP in step S59.
Dx = Dx [-] + Dx [+] + Dex (19)

  Thereby, the distance that can be traveled with the current remaining power can be accurately displayed on the navigation device or the like of each EV. Furthermore, using the fuel consumption map and the frequency map, for example, diagnostic information as shown in (a) below and driving support information such as driving management information as shown in (b) are generated, and various services are provided to the driver. Can be provided.

(A) Diagnosis information on vehicle characteristics and driving characteristics The fuel consumption maps are compared between the vehicles to diagnose the presence or absence of aging or failure of the vehicles, and notify the driver of the diagnosis results.
Compare frequency maps between drivers to diagnose driving skills. For example, an “eco driving model pattern” or the like is set in advance as a recommended pattern, and the proximity to the pattern is evaluated. Specifically, a pattern that does not use the high torque region so much and uses a lot of negative (regenerative) torque is set as a recommended pattern.

(B) Driving management information By comparing the frequency maps between routes on the map, the route with the best fuel efficiency is detected, and the “driving plan” is automatically created. For example, since the product of the fuel consumption and the route distance is the total power consumption, the route that minimizes the total power consumption is selected as the “recommended route” and presented to the driver. The difference between the “driving plan” and the actual road condition is corrected based on VICS and the like.

  In addition, according to the automatically created “operation plan”, the route information refers to the use area on the torque map at each point in the route, and the recommended accelerator opening and regeneration points are visualized and displayed on the navigation device. And eco-driving is realized by making the driver aware of such driving. Alternatively, driving assistance is performed by outputting a voice guidance of the driving policy according to the created “driving plan” (voice guidance such as “regeneration here” and “depressing the accelerator too much”).

  The road condition is visualized by displaying past driving type composition ratio information on a map or navigation device. The driving type composition ratio information is the percentage of consumption traveling, the percentage of regenerative traveling, the percentage of inertial traveling, and the percentage of idling during the previous traveling. In particular, it is possible to know the frequency of occurrence of traffic jams at the idling rate.

  In addition, driving skills can be judged by comparing energy consumption and required time between drivers in the same route, similar time zone and road conditions, and display of the judgment results improves driving skills. Can be promoted.

  In the above embodiment, an example of a system that collects and analyzes data transmitted from the in-vehicle terminal 5 of each EV in the data center 10 has been described. However, each EV includes a database server and each EV. It can also be configured as a single system.

Configuration diagram of energy management system Flow chart of instantaneous fuel consumption calculation process Flow chart of fuel consumption map aggregation process Flow chart of frequency map aggregation process Frequency data aggregation processing flowchart Flow chart of average fuel consumption calculation process Flow chart of mileage prediction value calculation processing

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Energy management system 5 In-vehicle terminal 10 Data center 15 Communication server 20 Database server 25 Database 30 Analysis part 50 Display apparatus

Claims (6)

In an energy management system for electric vehicles that collects and analyzes vehicle data stored in a database and manages energy,
A fuel consumption map registration means for calculating a travel distance per unit electric energy as a fuel consumption for each region of the map with the motor command torque and the motor rotation speed as axes, and registering the fuel consumption map in the database.
For each area of the map with the motor command torque and the motor rotation speed as axes, the number of additional data for each registration in the database is calculated as a frequency representing the running state and is registered in the frequency map of the database. Means,
Based on the fuel consumption map and the power history and the travel history based on the frequency map, a travelable distance prediction unit that predicts a distance that can be traveled with the remaining power of the current vehicle and notifies the driver of the predicted travelable distance. An energy management system for an electric vehicle, comprising: Each time data is additionally registered in the database, the vehicle driving state is classified into a plurality of driving types based on the change in the driving distance at the time of sampling and the change in the remaining electric energy,
2. The energy management of an electric vehicle according to claim 1, wherein the fuel consumption map and the frequency map are created in correspondence with two types of travel, namely, consumption travel that consumes electric power and regenerative travel that regenerates electric power. system.   Predicting the travelable distance based on a predicted power value calculated by aggregating the frequency map, and an average fuel efficiency reflecting the weight of the frequency map by aggregating data for each area of the fuel efficiency map The energy management system for an electric vehicle according to claim 1 or 2.   4. The energy management system for an electric vehicle according to claim 3, wherein the average fuel consumption is calculated by weighting the current frequency map by applying a coefficient corresponding to the actual change in driving conditions.   The driving support information generating means for generating driving support information using at least one of the fuel efficiency map and the frequency map and notifying the driver is further provided. The electric vehicle energy management system described. The above database is provided in a data center that receives, aggregates and analyzes data transmitted from individual electric vehicles,
6. The driving support information of the vehicle including the travelable distance is transmitted from the data center to an in-vehicle terminal of each vehicle or a terminal of each user. 6. The electric vehicle according to claim 1, Energy management system.

Images