SOC prediction system

Abstract

Provided is an SOC prediction system (200) that predicts a battery state at a future planned waypoint of a host vehicle using energy consumption prediction parameters, the SOC prediction system being an energy consumption prediction device for enabling accurate travel planning by appropriately correcting the energy consumption prediction parameters. The SOC prediction system (200) comprises: a charge state prediction result calculation unit (300) that calculates energy consumption on the basis of the energy consumption prediction parameters, and predicts a future charge state of the battery of the host vehicle; and an SOC prediction result correction unit (310) that compares the charge state prediction result output from the charge state prediction result calculation unit (300) with the actual measurement result of the charge state of the battery measured while the host vehicle is actually traveling, calculates an error, corrects the charge state prediction result on the basis of the error, and outputs the corrected charge state prediction result as an SOC prediction result.

Description

[Technical Field]

[0001] The present invention relates to a SOC (State of Charge) prediction system for predicting the battery state at a future planned passing point in a motor-driven vehicle. [Background technology]

[0002] 2. Description of the Related Art In recent years, vehicles that are driven by motors rather than engines, such as hybrid vehicles and electric vehicles, have been developed from the standpoint of environmental protection.

[0003] The motor is powered by a battery installed in the vehicle, but in order to make efficient use of the charged energy without waste, a system is being considered that uses information on elevation differences along the route set in advance in the navigation system and the driving conditions of other vehicles obtained via the network to create charging and driving plans and manage the remaining battery power.

[0004] In order to accurately implement the above-described mechanism, it is necessary to improve the accuracy of prediction of the battery state at future passing points, and inventions relating to battery state prediction techniques have been made.

[0005] In Patent Document 1, when predicting electricity consumption for each section of a driving route, the actual electricity consumption values ​​of other vehicles in the section to be predicted are obtained from a database on a network, and the electricity consumption prediction of the vehicle itself is corrected, thereby considering improving the accuracy of the electricity consumption prediction.

[0006] Furthermore, Patent Document 2 invents a technology that, when predicting electricity consumption for each section of a driving route, stores in a database the speeds at which other vehicles are traveling on the route, and corrects the predicted electricity consumption of the vehicle itself based on the averaged speed information. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 2019-086430 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-214294 Summary of the Invention [Problem to be solved by the invention]

[0008] However, with the technology described in Patent Document 1, for example, there is a possibility that the database of actual electricity consumption values ​​may not be sufficient on routes with low traffic volume or in areas with poor communication conditions, and it is expected that it will be difficult to improve the accuracy of electricity consumption predictions on routes with little information in the database.

[0009] Furthermore, in the technology described in Patent Document 2, while the database is filled using data acquired from various vehicles, the averaged information does not necessarily match the current vehicle conditions when the electricity consumption prediction is performed, and there is a possibility that the predicted electricity consumption will differ from the actual consumption.

[0010] Furthermore, although the above-mentioned Patent Documents 1 and 2 are premised on information acquisition from a network, the parameters for predicting electricity consumption are not limited to the network and may be acquired offline.

[0011] When predicting electricity consumption, whether online or offline, deviations in the energy balance can occur to a certain extent due to the vehicle's warm-up state, driver characteristics, fluctuations in road resistance due to tire air pressure, fluctuations in running resistance due to wind while driving, and battery degradation.

[0012] The object of the present invention has been made in consideration of such problems, and is to provide an SOC prediction system that is an energy consumption prediction device that appropriately corrects energy consumption prediction parameters and enables the implementation of an accurate driving plan in an SOC prediction system that predicts the battery state at future planned points through which the vehicle is to pass using energy consumption prediction parameters acquired online or offline. [Means for solving the problem]

[0013] In order to achieve the above object, the present invention is configured as follows.

[0014] An SOC prediction system that predicts energy consumption required for driving and predicts a future state of charge of a battery of the vehicle, comprising: a state of charge prediction result calculation unit that calculates the energy consumption based on a parameter for predicting energy consumption and predicts a future state of charge of the battery of the vehicle; and an SOC prediction result correction unit that compares a state of charge prediction result output from the state of charge prediction result calculation unit with an actual state of charge measurement result of the battery measured when the vehicle actually drives, calculates an error, corrects the state of charge prediction result based on the error, and outputs the corrected SOC prediction result. The SOC prediction result correction unit includes an SOC prediction result storage unit that stores the state of charge prediction result output from the state of charge prediction result calculation unit, and an SOC actual measurement result storage unit that stores the state of charge actual measurement result of the battery measured when the host vehicle actually travels. an SOC prediction result comparison unit that compares the state of charge prediction result stored in the SOC prediction result storage unit with the state of charge actual measurement result stored in the SOC actual measurement result storage unit and calculates the error; and an SOC prediction result correction value calculation unit that calculates a correction value for the state of charge prediction result based on the error calculated by the SOC prediction result comparison unit; and an SOC prediction correction result output unit that corrects the state of charge prediction result based on the correction value calculated by the SOC prediction result correction value calculation unit and outputs the corrected state of charge prediction result, wherein the SOC prediction result comparison unit of the SOC prediction result correction unit compares the state of charge prediction result with the state of charge actual measurement result either after a predetermined time has elapsed since the time when the state of charge prediction result calculation unit predicted the future state of charge of the battery or after traveling a predetermined distance from the point where the state of charge of the future battery was predicted, and the target points where the state of charge prediction result and the state of charge actual measurement result are compared are any one or more points within a range of possible points where the state of charge prediction result and the state of charge actual measurement result can be compared, and the point where the state of charge prediction result and the state of charge actual measurement result are compared is a point after traveling the predetermined distance, and the SOC prediction result comparison unit compares the state of charge prediction result at the point after traveling the predetermined distance with the state of charge actual measurement result at the point after traveling the predetermined distance to calculate an error, The SOC prediction result correction value calculation unit calculates a correction value for the state of charge prediction result by weighting the error calculated by the SOC prediction result comparison unit so that the correction amount increases as the distance from the point after the predetermined distance has passed or as time has passed since the point after the predetermined distance has passed. . [Effects of the Invention]

[0015] According to the present invention, it is possible to provide an SOC prediction system that can improve the accuracy of the energy consumption estimation result by comparing the past vehicle conditions with the energy consumption estimation parameters, reviewing whether the prediction was correct after driving a certain distance, and if there is a discrepancy between the prediction and the actual value, correcting the prediction result with a correction value calculated based on the discrepancy.

[0016] Furthermore, by improving the accuracy of the energy consumption estimation results, it is possible to provide an SOC prediction system that enables accurate planned operation to be carried out. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 2 is a diagram illustrating the operation of scheduled engine control using the SOC prediction control of the SOC prediction system of the present invention. [Figure 2] 1 is a system configuration diagram showing an embodiment in which an SOC prediction system to which the present invention is applied is applied to a control device mounted on a vehicle. [Figure 3]FIG. 1 is a block diagram illustrating a configuration of an embodiment of the present invention. [Figure 4] 1 is a table showing an example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 5] 1 is a table showing an example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 6] 1 is a table showing an example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 7] 1 is a table showing an example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 8] 1 is a table showing an example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 9] 10 is a table showing another example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 10] 10 is a table showing another example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 11] 10 is a table showing another example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 12] 10 is a table showing another example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 13] 10 is a table showing another example of an SOC prediction result and a correction method according to an embodiment of the present invention. [Figure 14] 10 is a table showing still another example of the SOC prediction results and correction method according to an embodiment of the present invention. [Figure 15] 10 is a table showing still another example of the SOC prediction results and correction method according to an embodiment of the present invention. [Figure 16] 10 is a table showing still another example of the SOC prediction results and correction method according to an embodiment of the present invention. [Figure 17] 10 is a table showing still another example of the SOC prediction results and correction method according to an embodiment of the present invention. [Figure 18] 10 is a table showing still another example of the SOC prediction results and correction method according to an embodiment of the present invention. [Figure 19] 10 is a flowchart illustrating processing by an SOC prediction result correction unit according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of the SOC prediction system according to the present invention will be described in detail with reference to the drawings. The drawings specifically show an example of application to a vehicle, but the drawings are for explaining the basic principles of the present invention, and the scope of the present invention is not limited to the scope shown in the drawings.

[0019] The present invention is directed to vehicles that are driven by a motor using electricity stored in a battery, such as hybrid vehicles or electric vehicles. Hybrid vehicles can be either series or parallel, and there are no particular limitations on the role of the internal combustion engine and the motor.

[0020] In hybrid vehicles, efficient use of the battery extends the driving range and is beneficial for fuel economy and electricity cost.

[0021] Fuel-efficient driving can be achieved by planning engine drive and engine power generation for sections set using navigation systems, and scheduling where the engine will start and where it will regenerate. [Example]

[0022] Example 1 The effect of scheduling the power generation plan of an internal combustion engine will be explained using FIG.

[0023] FIG. 1 is a diagram illustrating the operation of scheduled engine control using SOC prediction control of the SOC prediction system according to the present invention.

[0024] For example, if the vehicle is traveling with a low onboard battery before approaching an uphill slope, the motor will not have enough power to drive the vehicle when it approaches the slope, and the engine will be over-revved in the forced power generation section shown in Figure 1(a) as it travels uphill, resulting in poor fuel economy.

[0025] Furthermore, if the vehicle battery is fully charged while driving downhill, a section where regenerative charging is not possible will occur, as shown in Figure 1(b), and the energy obtained through regeneration will not be stored in the battery and will have to be discarded.

[0026] In this way, in a hybrid vehicle, energy loss occurs when switching between running by the engine or power generation and motor drive is performed unplanned.

[0027] Therefore, SOC (state of charge) predictive control is implemented to estimate the remaining SOC at future planned passing points, and the timing to start charging the engine and the charging period are calculated and controlled.

[0028] When the engine is charging, the engine is controlled to optimize fuel economy. When charging by regeneration is expected, the SOC level is controlled to reduce it in advance, minimizing engine charging and contributing to improved fuel economy.

[0029] However, even if an engine charging schedule is created using SOC predictive control as described above, the schedule may not be followed depending on the actual road conditions and vehicle conditions. For example, there may be cases where the battery runs out earlier than expected, making it necessary to charge the battery using the engine.

[0030] Therefore, accurate prediction of the remaining SOC is necessary to accurately schedule engine start in a hybrid vehicle.

[0031] FIG. 2 shows an embodiment in which an SOC prediction system 200 according to the present invention is applied to a control device mounted on a vehicle.

[0032] In FIG. 2, the SOC prediction system 200 includes a microcomputer 201 (CPU) and a RAM 202 ( R andom A access M emory) and ROM203( Read O nly M It is equipped with a nonvolatile ROM 204 (EEPROM).

[0033] The SOC prediction system 200 receives as input external information obtained based on information from a camera sensor 205 attached to the vehicle, vehicle speed information obtained from a vehicle speed sensor 206, location information obtained from a GPS sensor 207, and information from a network receiver 208 that receives various information from an information center 216, such as time information, traffic congestion information, and weather information from an external network.

[0034] In addition, the SOC prediction system 200 communicates with a battery control unit 210 connected to the battery 209 to obtain the current SOC state.

[0035] Battery 209 stores power for driving motor 211. Motor 211 is connected to motor control unit 212, which calculates the driving force according to the driver's acceleration request via accelerator pedal sensor 213 and controls the motor based on the command.

[0036] Based on the SOC prediction result calculated by the SOC prediction system 200, the SOC prediction system 200 plans the engine charging start timing and charging section schedule, and sends instructions to the engine control module 214 to start and stop charging according to the plan.

[0037] The engine control module 214 controls the engine 215 based on commands to start and stop charging, and starts and stops the engine. The electric power generated by the engine 215 is sent to the battery 209 and charged.

[0038] Although the functional arrangement of the sensors and each control module is defined here for the purpose of easily understanding the configuration of the present invention, the installation of each sensor and the arrangement of the control module with which it communicates may be changed. Also, the SOC prediction system 200 of the present invention may be integrated into one of the functions of the battery control unit 210 or another control module.

[0039] The above-described vehicle system configuration is well known, and therefore further explanation will be omitted.

[0040] FIG. 3 is a block diagram of the SOC prediction system 200 of the present invention described in FIG.

[0041] Basically, the vehicle's energy consumption is calculated by adding up the gradient resistance, air resistance, rolling resistance, and acceleration resistance. The SOC prediction result calculation unit 300 calculates the SOC by subtracting the energy consumption calculated using the running resistance calculation formula from the remaining SOC.

[0042] Furthermore, if the plan also includes engine charging along the way, the SOC prediction result can be output taking charging into account by adding the charging energy that increases during the engine start section, taking into account the power calculation per unit time and the charging rate that vary depending on the engine speed, but this will not be explained here.

[0043] The various running resistances calculated by the SOC prediction result calculation unit 300 will be described.

[0044] The vertical energy calculation unit 301 receives gradient information at a point to be passed in the future.

[0045] The gradient information is a set of points at regular intervals along the planned route, and includes multiple gradient information between adjacent points. Based on this gradient information between each route, the vertical energy can be calculated by applying the following formula (3.1) for gradient resistance. Grade resistance = total vehicle weight × gravitational acceleration × sinθ (3.1)

[0046] Next, the horizontal energy calculation unit 303 calculates the horizontal energy.

[0047] In order to calculate the horizontal energy, it is necessary to estimate the speed at which the vehicle will travel at the aforementioned future planned passing points or on the route.

[0048] For example, when predicting the speed at a future destination, it is possible to apply the speed limit obtained from navigation information or camera information. However, the speed limit and the predicted speed do not necessarily match depending on the driving style and road conditions.

[0049] Therefore, instead of applying the speed limit directly to the speed forecast, the prediction parameter correction unit 302 uses the speed limit as the reference speed, compares the reference speed with the vehicle speed, and calculates the speed forecast of how fast the vehicle will travel at the point it is scheduled to pass in the future.

[0050] The speed forecast calculated by the prediction parameter correction unit 302 is input to the horizontal energy calculation unit 303, and the speed forecast is substituted into the running speed in the following equation (3.2) for air resistance. The energy for rolling resistance is also calculated using equation (3.3) to find the horizontal energy. Air resistance = (1 / 2) × Cd value × air density × frontal area × driving speed (3.2) Rolling resistance = Coefficient of rolling resistance × Total vehicle weight × Gravitational acceleration (3.3)

[0051] Vertical and horizontal energy have a dominant effect on energy consumption, but other energy consumption such as that due to electrical loads and acceleration / deceleration causes errors between the SOC prediction results and actual results.

[0052] Therefore, the electric load energy calculation unit 304 calculates the energy consumption other than the energy related to driving. The electric load energy calculation unit 304 inputs the status of each electric load, such as the air conditioner usage status, A / C switch status, headlight switch status, etc., and calculates how many joules of energy will be consumed per unit time depending on the electric load status. The electric load is multiplied by the time calculated by the travel time calculation unit 305, which is calculated from the SOC prediction target point distance and the expected arrival time, to calculate the electric load energy consumption.

[0053] The gradient information, the reference vehicle speed, the vehicle speed, the A / C switch state, the headlight switch state, the distance to the SOC prediction target point, and the predicted arrival time are parameters for predicting energy consumption.

[0054] The total energy consumption calculation unit 306 calculates the total energy consumption by adding up the vertical energy, horizontal energy, and electrical load energy. The SOC prediction result output unit 307 subtracts the SOC percentage corresponding to the energy consumption for each route between the future planned passing points calculated as described above from the current SOC, and outputs the SOC prediction result.

[0055] Generally, the calculations up to this point can predict the SOC to a certain extent, but parameters such as road resistance used to calculate horizontal energy vary from point to point.

[0056] In addition, the slope of the SOC increase / decrease varies depending on the acceleration / deceleration energy and the state of battery degradation, which causes errors between the predicted SOC result and the actual result, making it difficult to accurately implement scheduled engine control for hybrid vehicles.

[0057] The present invention is configured to further store the SOC prediction result, compare it with the actual SOC, calculate a correction value from the discrepancy between the prediction and the actual SOC, and further correct the SOC prediction result output by the SOC prediction result output unit 307, thereby enabling improvement in accuracy.

[0058] The SOC prediction result corrector 310 corrects the prediction result of the SOC prediction result output unit 307 .

[0059] The SOC prediction result storage unit 311 stores the SOC prediction results between future planned passing points output by the SOC prediction result output unit 307. The actual SOC measurement value is also stored in the SOC actual measurement result storage unit 312. The results stored in the SOC prediction result storage unit 311 and the SOC actual measurement result storage unit 312 are compared by the SOC prediction result comparison unit 313 after a given time has elapsed or after traveling a predetermined distance. The SOC prediction result comparison unit 313 compares the actual SOC measurement value at the current point or a past actual SOC measurement value with the SOC prediction result to be compared, and calculates an error.

[0060] The SOC prediction result correction value calculation unit 314 calculates a SOC prediction result correction value from the error calculated by the SOC prediction result comparison unit 313 .

[0061] The SOC prediction correction result output unit 315 corrects the SOC prediction result calculated from the outlook for energy consumption calculated by the SOC prediction result calculation unit 300 using the SOC prediction result correction value calculated by comparing the SOC prediction result with the actual SOC measurement result.

[0062] In this way, when the SOC prediction result serving as the base for correction, calculated by solving for the energy consumption required to reach a future planned passing point, is corrected using the correction value calculated from the stored past prediction results and the actual SOC, errors due to the vehicle state and driver characteristics that are not included in the output of SOC prediction result calculation unit 300 are corrected by SOC prediction result correction unit 310.

[0063] For example, the rolling resistance in the above formula (3.3) varies depending on the road surface condition and also changes depending on the condition of the vehicle's tires.

[0064] In addition, the formulas for calculating horizontal and vertical energy include vehicle weight, but the vehicle weight differs when there is one occupant and when there are five occupants.

[0065] In addition, the result of the above air resistance equation (3.2) will differ depending on wind direction, wind speed, and air pressure.

[0066] Furthermore, a new battery and a deteriorated battery will not have the same remaining SOC even if they consume the same amount of energy.

[0067] Furthermore, the error in acceleration / deceleration energy caused by differences in acceleration during acceleration and differences in braking methods also differs between drivers.

[0068] As explained in the above example, errors due to road resistance, differences in the number of passengers, battery degradation, driver characteristics, etc. are included in the error components calculated by the SOC prediction result comparison unit 313, and correcting these errors improves the accuracy of the SOC prediction result.

[0069] According to the method for correcting the SOC prediction result in the first embodiment of the present invention, the accuracy of the SOC prediction is improved, and accurate control can be performed based on the scheduled engine charging start timing and charging time of the hybrid vehicle.

[0070] 4 to 8 show changes in the prediction results when corrections are made to the SOC prediction results according to the present invention.

[0071] The SOC prediction result comparison unit 313 of the SOC prediction result correction unit 310 compares the predicted result of the state of charge with the actual measurement result of the state of charge either after a predetermined time (e.g., 30 seconds or 1 minute) has elapsed since the time when the state of charge prediction result calculation unit 300 predicted the future state of charge of the battery 209, or after traveling a predetermined distance from the point where the future state of charge of the battery 209 was predicted.

[0072] In the examples shown in FIGS. 4 to 8, the predicted result of the state of charge is compared with the actual measurement result of the state of charge at a timing when a predetermined time has elapsed from the predicted time.

[0073] In Figure 4, in the situation of traveling a distance of 4000 m, SOC prediction is carried out every 200 m up to 1000 m ahead every 1000 m. In Figures 5 to 8, the SOC prediction result correction value is calculated by comparing the SOC prediction result 1000 m ahead with the actual SOC, and the correction of the SOC prediction result is shown.

[0074] <Step at X00 point> First, calculate the SOC prediction results from X01 to X05 at X00.

[0075] This prediction result is stored in the SOC prediction result storage unit 311 in Figure 3.

[0076] <Step at X05 point> As shown in Figure 5, when arriving at the X05 point, compare the SOC prediction result at the X05 point stored in the SOC prediction result storage unit 311 at the X00 point with the SOC actual measurement result at the X05 point.

[0077] At the X05 point, the SOC prediction result stored in the SOC prediction result storage unit 311 is 63%, and the SOC actual measurement result stored in the SOC actual measurement result storage unit 312 is 62%. Therefore, the error calculated by the SOC prediction result comparison unit 313 is minus 1%.

[0078] Next, as shown in Figure 6, at the X05 point, perform SOC prediction up to 1000 m ahead again.

[0079] At the X05 point, the SOCs of the points from X06 to X10 are predicted to be 60%, 57%, 58%, 56%, and 55% respectively. Store this value in the SOC prediction result storage unit 311.

[0080] Obtain the correction value in the SOC prediction result correction value calculation unit 314 from the error calculated by the SOC prediction result comparison unit 313.

[0081] In the examples shown in FIGS. 6 to 8, correction is performed by offsetting the error, and the error calculated by the SOC prediction result comparison unit 313 is added directly to the SOC prediction result to perform correction.

[0082] The corrected SOC prediction results for X06 to X10 are 59%, 56%, 57%, 55%, and 54%, respectively, reflecting the minus 1% error calculated by the SOC prediction result comparison unit 313. These corrected SOC prediction results are used for scheduling engine control of the hybrid vehicle.

[0083] <Step at point X10 in Figure 7> As in the step at point X05, when point X10 is reached, the SOC prediction result at point X10 stored in SOC prediction result storage unit 311 at point X05 is compared with the SOC actual measurement result at point X10.

[0084] At point X10, the SOC prediction result is 55% and the SOC actual measurement result is 54%, so the error calculated by SOC prediction result comparison unit 313 is minus 1%.

[0085] Hereafter, the prediction step and correction step are omitted because they are performed in the same manner as in X05.

[0086] At point X10, SOC prediction is again performed up to 1000 m ahead, and as a result, the SOC at point X15 is predicted to be 49%. This value is stored in SOC prediction result storage unit 311.

[0087] Furthermore, the corrected SOC prediction results for X11 to X15 reflect the above-mentioned error of minus 1% and are 51%, 53%, 53%, 51%, and 48%, respectively.

[0088] The corrected SOC prediction result is used for scheduling engine control of the hybrid vehicle.

[0089] <Step at point X15 in Figure 8> As in the step at point X10, when point X15 is reached, the SOC prediction result at point X15 stored in SOC prediction result storage unit 311 at point X10 is compared with the SOC actual measurement result at point X15.

[0090] At point X15, the SOC prediction result is 49% and the SOC actual measurement result is 46%, so the error calculated by SOC prediction result comparison unit 313 is minus 3%.

[0091] Hereafter, the prediction step and correction step are omitted because they are performed in the same manner as in X10.

[0092] At point X15, SOC prediction is again performed up to 1000 m ahead, and as a result, the SOC at point X20 is predicted to be 40%. This value is stored in SOC prediction result storage unit 311.

[0093] Furthermore, the corrected SOC prediction results for X16 to X20 reflect the above-mentioned error of minus 3% and are 45%, 43%, 40%, 37%, and 37%, respectively.

[0094] The corrected SOC prediction result is used for scheduling engine control of the hybrid vehicle.

[0095] By repeating the above steps in order, the SOC prediction result calculated by the SOC prediction result calculation unit 300 in FIG. 3 is corrected by the SOC prediction result correction unit 310, thereby improving the accuracy of the SOC prediction.

[0096] In the present invention, the timing of comparing the SOC prediction result with the actual SOC can be any timing after the SOC prediction is performed, and can be changed depending on the situation.

[0097] In the above embodiment, the SOC prediction result and the SOC prediction result correction value are recalculated and implemented every 1000 m, but they may also be recalculated every 200 m.

[0098] It may also be executed at periodic time intervals, such as every 1 second.

[0099] For example, if the number of passengers changes, i.e., if the total vehicle weight changes significantly, or if the driver's characteristics change due to a change in driver, the SOC prediction error increases significantly. If the error is large, the frequency of correcting the predicted SOC can be increased to converge the error quickly, and if it is determined that the error has converged, the frequency of correcting the SOC can be decreased to prevent the SOC prediction result from being updated more than necessary.

[0100] In the present invention, the point at which the past SOC prediction results and the actual SOC are compared may be one point where past predictions were made, or any number of points may be selected.

[0101] For example, if any one point is selected, the system compares the predicted SOC for the current location at the point where the previous prediction was made with the actual SOC for the current location. Corrections are made based on the error between the most recent prediction and actual results, so corrections to SOC prediction results always reflect the latest information, making it possible to absorb SOC errors that occur due to ever-changing conditions such as road conditions and driving conditions, thereby improving SOC prediction accuracy.

[0102] Furthermore, when multiple arbitrary points are selected, the SOC prediction results at the multiple points stored in the SOC prediction result storage unit 311 at the time of the previous SOC prediction result calculation are compared with the SOC actual measurement results at the multiple points stored in the SOC actual measurement result storage unit 312 to calculate a corrected value for the SOC prediction results.

[0103] In the examples shown in Figures 9 to 13, when calculating the correction value at point X05 to correct the SOC prediction results for the following points X06 to X10, the SOC prediction results for points X03 to X05 are compared with the SOC measurement results.

[0104] In the examples shown in Figures 9 to 13, the points at which the state of charge prediction results and actual state of charge measurement results are compared are a point after traveling a predetermined distance and multiple points before traveling the predetermined distance, and SOC prediction result comparison unit 313 compares the state of charge prediction results at the point after traveling the predetermined distance and multiple points before traveling the predetermined distance with the state of charge measurement results at the point after traveling the predetermined distance and multiple points before traveling the predetermined distance to calculate multiple errors, and SOC prediction result correction value calculation unit 314 calculates the average value of the multiple errors calculated by SOC prediction result comparison unit 313 and calculates a correction value for the state of charge prediction result based on the calculated average value.

[0105] <Step at point X05 in Figure 10> When the vehicle arrives at point X05, the SOC prediction results for points X03 to X05 stored in SOC prediction result storage unit 311 at point X00 are compared with the SOC actual measurement results for points X03 to X05.

[0106] At point X03 in Figure 10, the predicted SOC result is 69%, the actual SOC measurement result is 66%, and the error is minus 3%.

[0107] At point X04 in Figure 10, the predicted SOC result is 66%, the actual SOC measurement result is 63%, and the error is minus 3%.

[0108] At point X05 in Figure 10, the predicted SOC result is 63%, the actual SOC measurement result is 62%, and the error is minus 1%.

[0109] In the SOC prediction result correction value calculation unit 314, the average error for points X03 to X05 is calculated to be minus 2.3% ((-3-3-1) / 3 ≒ -2.3), and this value is used as the correction value to correct the SOC prediction result for the next section from X06 to X10.

[0110] 11, at point X05, the SOCs at points X06 to X10 were predicted to be 60%, 57%, 58%, 56%, and 55%, respectively. These values ​​are stored in the SOC prediction result storage unit 311.

[0111] On the other hand, when the SOC prediction result correction value of minus 2.3% obtained above is added to the SOC prediction result, the corrected SOC prediction results for points X06 to X10 are 57.7%, 54.7%, 55.7%, 53.7%, and 52.7%, respectively, as shown in Figure 11.

[0112] The corrected SOC prediction result is used for scheduling engine control of the hybrid vehicle.

[0113] The predictions for points X11 to X15 when arriving at point X10 are shown in FIG. 12, and the predictions for points X16 to X20 when arriving at point X15 are shown in FIG.

[0114] Comparing the SOC prediction results from multiple points with the actual SOC averages out errors and prevents excessive correction of the prediction results. For example, it is possible to absorb SOC errors caused by energy consumption from electrical loads other than the energy consumed by the motor drive, thereby improving the accuracy of the SOC prediction.

[0115] Furthermore, the present invention is characterized in that the weight of the correction value calculated from the error between the SOC prediction result and the actual SOC is changed depending on the distance to the future planned passing point that is the target of SOC prediction.

[0116] 14 to 18, an example will be described in which the SOC prediction result correction value is increased as the prediction target point becomes farther away when calculating the correction value at point X05 to correct the SOC prediction result for the next points X06 to X10, when calculating the correction value at point X10 to correct the SOC prediction result for the next points X11 to X15, and when calculating the correction value at point X15 to correct the SOC prediction result for the next points X16 to X20.

[0117] 14 to 18, SOC prediction result comparison unit 313 compares the state of charge prediction result at the point after traveling a predetermined distance with the state of charge actual measurement result at the point after traveling a predetermined distance to calculate an error, and SOC prediction result correction value calculation unit 314 calculates a correction value for the state of charge prediction result by assigning a weight to the error calculated by SOC prediction result comparison unit 313 so that the correction amount increases as the distance from the point after traveling a predetermined distance increases. However, it is also possible to configure the correction value for the state of charge prediction result to be calculated by assigning a weight so that the correction amount increases as time passes from the point after traveling a predetermined distance.

[0118] <Step at point X05 in Figure 15> When the vehicle arrives at point X05, the SOC prediction result for point X05 stored in SOC prediction result storage unit 311 at point X00 is compared with the SOC actual measurement result at point X05.

[0119] At point X05, the SOC prediction result is 63% and the SOC actual measurement result is 61%, so the error calculated by SOC prediction result comparison unit 313 is minus 2%.

[0120] Next, at point X05, SOC prediction is again performed up to 1000 m ahead.

[0121] 16, at point X05, the SOCs at points X06 to X10 are predicted to be 60%, 57%, 58%, 56%, and 55%, respectively. These values ​​are stored in the SOC prediction result storage unit 311. A correction value is calculated in a SOC prediction result correction value calculation unit 314 from the error calculated in the SOC prediction result comparison unit 313 .

[0122] In the example of Figure 16, correction is performed by offsetting the error, and the weighting is changed so that the ratio of the error calculated by the SOC prediction result comparison unit 313 increases as the distance from the current location X05 increases.

[0123] The corrected values ​​for the SOC prediction results at points X06 to X10 are calculated to be minus 2.2% (-2 + (-2 / 10) = -2.2), minus 2.4% (-2.2 + (-2 / 10) = -2.4), minus 2.6% (-2.4 + (-2 / 10) = -2.6), minus 2.8% (-2.6 + (-2 / 10) = -2.8), and minus 3% (-2.8 + (-2 / 10) = -3.0), respectively.

[0124] When the SOC prediction results are corrected using this correction value, the results are 57.8%, 54.6%, 55.4%, 53.2%, and 52%, respectively.

[0125] The corrected SOC prediction result is used for scheduling engine control of the hybrid vehicle.

[0126] This correction is effective when predicting the SOC in a vehicle with a deteriorated battery, for example.

[0127] When a battery is deteriorated, the SOC is expected to decrease more significantly compared to when it was new, even with the same amount of energy consumed. The longer the distance from the SOC prediction target point, the greater the amount of error correction. Therefore, the correction amount is set small for SOC prediction target points close to the current location, and large for SOC prediction target points far from the current location.

[0128] In this way, it is possible to improve the accuracy of SOC prediction by calculating and correcting the error between the SOC prediction result and the actual value due to battery degradation by calculating an energy consumption prediction correction value that matches the actual situation.

[0129] The predictions for points X11 to X15 when arriving at point X10 are shown in FIG. 17, and the predictions for points X16 to X20 when arriving at point X15 are shown in FIG.

[0130] FIG. 19 is a flowchart showing the flow of control steps related to the SOC prediction result corrector 310 in the SOC prediction system 200 shown in FIG.

[0131] The program for executing this flowchart is processed by the CPU 201 of the SOC prediction system 200, and is executed periodically at a predetermined start timing (for example, every 1 second).

[0132] The SOC prediction system 200 also includes a function for updating the vehicle's position information by comparing the coordinates of the vehicle's position received by the GPS sensor 207 with map information previously written in the ROM 203 .

[0133] This flowchart also shows an example of a correction method in which multiple SOC prediction results and actual SOC measurement results shown in Figures 9 to 13 are compared to calculate the average SOC prediction error, and the SOC prediction result at the future prediction target point is offset by the average prediction error.

[0134] Hereinafter, each step of this flowchart will be described in detail.

[0135] <<Step S701>> Check whether the conditions for SOC prediction are met.

[0136] The conditions for SOC prediction are met when the vehicle position reaches a predetermined point or when a predetermined time has passed since the previous SOC prediction.

[0137] If the condition is met, the process proceeds to step S702, where the SOC prediction result calculation unit 300 executes calculation.

[0138] If the condition is not met, the calculation by the SOC prediction result calculation unit 300 is skipped and the process proceeds to step S708.

[0139] <<Step S702>> The variable a in step S702 means the array number for storing the prediction result.

[0140] In step S702, the number of the array for storing the prediction result at the point to be predicted for the future passing point is initialized. In step S702, the array number 1 is set to the variable a. The process proceeds from step S702 to step S703.

[0141] <<Step S703>> X in step S703 is location information. X is in the form of a multiple array, and stores the distance from the current location to the prediction target point.

[0142] Information for setting a predicted target point of a future passing point is initialized. In step S703, X[0] is set to the current location. The process proceeds from step S703 to step S704.

[0143] <<Step S704>> The distance from the current location is stored in X[a] (X[a]=X[0]+K). The variable K is the distance between the prediction target points. Then, the process proceeds to step S705.

[0144] <<Step S705>> The SOC prediction result at point X[a] is stored. This SOC prediction result is the SOC prediction result at point X[a] calculated by the SOC prediction result calculation unit 300 shown in FIG.

[0145] The SOC prediction result at point X[a] is stored in variable Y[a].

[0146] In step S705, the SOC prediction result Y[a] is stored in the SOC prediction result storage unit 311. Then, the process proceeds to step S705.

[0147] <<Step S706>> The array number (variable a) for storing the prediction result is incremented, and the process proceeds to step S707.

[0148] <<Step S707>> Determine whether variable a is greater than n, where n is the total number of target points for prediction that are planned to be passed through in the future. By determining whether variable a is greater than n, variable a is incremented and it is confirmed whether calculation of the SOC prediction results for all target points for prediction has been completed.

[0149] If the calculation of the SOC prediction results for all the prediction target points has not been completed, the process returns to step S704, and the processes from step S704 to step S706 are repeated.

[0150] If it is determined in step S707 that all calculations of the SOC prediction results have been completed, the process proceeds to step S708.

[0151] <<Step S708>> It is determined whether the variable b is greater than 0. The variable b in step S708 is an array number for storing the SOC measurement result in the SOC measurement result storage unit 312 for comparison with the SOC prediction result stored in the SOC prediction result storage unit 311 shown in FIG.

[0152] If the initialization of the variable b is not completed in step S708, the process proceeds to step S709, where the initialization is performed, and then the process proceeds to step S710.

[0153] If it is determined in step S708 that the initialization is complete, the process also proceeds to step S710.

[0154] <<Step S709>> In step S709, the array number b for storing the SOC measurement results is initialized (b=1).

[0155] <<Step S710>> In step S710, it is determined whether the current location is point X[b]. X is the same value as in step S703, and stores the distance between the current location and the prediction target point.

[0156] As the flowchart is executed, the vehicle moves to the target prediction point. At this time, in order to store the SOC measurement results when passing the target prediction point, it is confirmed in step S710 whether the current location has reached the target prediction point.

[0157] If the location is not a storage location for the SOC measurement results, step S711 is skipped and the process proceeds to step S713. If the location is a storage location for the SOC measurement results, the process proceeds to step S711.

[0158] <<Step S711>> If it is determined in step S710 that the actual SOC measurement result at point X[b] is to be stored, the actual SOC measurement result at point X[b] is stored in variable Z[b], and the process proceeds to step S712.

[0159] <<Step S712>> When the actual SOC measurement results have been stored in variable Z in step S711, the value of variable b is incremented in step S712 so that the actual SOC measurement results for the next prediction target point are stored. Then, the process proceeds to step S713.

[0160] <<Step S713>> In step S713, it is determined whether the current location is a SOC correction value calculation point.

[0161] If the SOC correction value calculation point is not reached, the subsequent steps are skipped and the process ends.

[0162] If it is determined in step S713 that the point is a SOC correction value calculation point, the process proceeds to step S714, where a step of calculating a correction value for the SOC prediction result is carried out.

[0163] <<Step S714>> In step S714, the SOC prediction result comparison unit 313 sets the number d to be compared between the SOC prediction results stored in the SOC prediction result storage unit S311 and the SOC actual measurement results stored in the SOC actual measurement result storage unit S312. The number d is a variable, and is used to compare the SOC prediction results and the SOC actual measurement results. The variable c is a counter value when comparing the SOC prediction results and actual SOC measurement results for the variable d.

[0164] Here, the value of the variable d is set to 1 or more, and the maximum value is set to be less than the variable b.

[0165] When correction is performed based on the latest SOC measurement results, the value of variable d is substituted with the value of variable b minus 1. The process proceeds from step S714 to step S715.

[0166] <<Step S715>> Step S715 is a step in which the SOC prediction result comparison unit 313 compares the SOC prediction result with the SOC actual measurement result.

[0167] The SOC prediction result stored in variable Y[c] in step S705 is subtracted from the SOC actual measurement result stored in Z[c] in step S711, and the result is stored in variable G[c] indicating the SOC prediction error. Then, the process proceeds to step S716.

[0168] <<Step S716>> In step S716, the value of variable c is decremented by 1 to change the reference destination of the array storing the SOC prediction results and SOC actual measurement results to be compared, and then the process proceeds to step S717.

[0169] <<Step S717>> In step S717, it is determined whether the variable C is 0, and whether the processing of the SOC prediction result comparison unit 313 is completed.

[0170] If the processing by the SOC prediction result comparison unit 313 is not yet complete, the process proceeds to step S715, and the comparison processing is repeated.

[0171] If the processing by the SOC prediction result comparison unit 313 is completed, the process proceeds to step S718.

[0172] <<Step S718>> In step S718, the SOC prediction result correction value calculation unit 314 calculates the SOC prediction result correction value.

[0173] As a preprocessing step for calculating the corrected SOC prediction result, the average value G_Ave of each prediction error at the target prediction point calculated from the multiple SOC prediction results and SOC measurement results shown in Figures 9 to 13 is calculated using the following equation (3.4). G_Ave=(G[d]+G[d-1]···+G[1]) / d···(3.4)

[0174] Then, the process proceeds to step S719.

[0175] <<Step S719>> In step S719, a correction value for the SOC prediction result is determined.

[0176] Here, the average value G_Ave of the prediction errors calculated in step S718 is used as the correction value for the SOC prediction result. The variable H is a correction value that is commonly used for the SOC prediction results of the prediction target points among the future planned passing points.

[0177] If a corrected SOC prediction result value is to be assigned to each prediction target location, the corrected SOC prediction result value corresponding to each prediction target location is stored as an array in variable H. Then, the process proceeds to step S720.

[0178] <<Step S720>> In step S720, the SOC prediction result is corrected using the SOC prediction result correction value, and the position of the array of SOC prediction results stored in variable Y that is the target of correction is set to obtain the corrected SOC prediction result. Variable e is the first array element number for which the corrected SOC prediction result is to be obtained. Since the current location is the point where variable b is minus 1, variable b is substituted for variable e. Then, the process proceeds to step S721.

[0179] <<Step S721>> In step S721, the SOC prediction results calculated at the current location are corrected for each of the points that have not yet been passed by using the SOC prediction result correction value calculated in step S719, and the corrected SOC prediction results are output.

[0180] The correction method here is to simply add the correction value calculated in step S719.

[0181] The corrected SOC prediction result Y_hos[e] is obtained by adding the SOC prediction result correction value H calculated in step S719 to the SOC prediction result Y[e] calculated in step S705.

[0182] This corrected SOC prediction result Y_hos is output from the SOC prediction result correction unit 310 in Fig. 3 and is used in control. Then, the process proceeds to step S722.

[0183] <<Step S722>> In step S722, the value of the variable e is incremented by 1 to change the reference position for calculating the corrected SOC prediction result, and then the process proceeds to step S723.

[0184] <<Step S723>> In step S723, it is determined whether the calculation of the corrected SOC prediction result has been completed up to the last array.

[0185] That is, it is determined whether the variable e is greater than n, and if it is determined that the calculation of the corrected SOC prediction result has been completed up to the last array, the process proceeds to step S724.

[0186] In step S723, if the variable e is equal to or less than the above and the calculation of the corrected SOC prediction result is not yet completed, the process returns to step S721, and the calculation process of the corrected SOC prediction result is repeatedly executed.

[0187] <<Step S724>> Step S724 is post-processing after the series of SOC prediction results have been corrected.

[0188] When calculating the next SOC prediction result, the value of variable b is set to 1 so that the array storing the SOC prediction result and the array storing the SOC actual measurement result are compared uniformly. Then, the process ends.

[0189] As described above, according to the first embodiment of the present invention, the SOC prediction system 200 can be provided by comparing the predicted energy consumption value obtained by comparing the past charging status of the vehicle with the energy consumption estimation parameters, with the actually measured energy consumption value after traveling a certain distance, and if there is a discrepancy between the predicted energy consumption value and the actual energy consumption value, correcting the prediction result with a correction value calculated based on the discrepancy.Furthermore, it is possible to provide the SOC prediction system 200 that can improve the accuracy of the energy consumption estimation result by improving the accuracy of the energy consumption estimation result.

[0190] Example 2 In the first embodiment described above, the reference vehicle speed for solving for horizontal energy is calculated based on the past driving history of the vehicle, the speed limit acquired from external information or navigation information, and the like.

[0191] Although Example 1 describes the configuration of a vehicle system that does not acquire vehicle speed information offline from an external network, a method of acquiring the reference vehicle speed shown in Figure 3 by connecting the vehicle to an external network is also conceivable.

[0192] In the second embodiment, for example, a plurality of vehicles connected to the wireless network 208 are connected to the information center 216 shown in Fig. 2, and speed information of a plurality of other vehicles is stored on the server side. The average speed of the other vehicles when they have traveled the target prediction section in the past is used as an input of the reference vehicle speed for the SOC prediction system 200 in the second embodiment of the present invention to solve for the horizontal energy.

[0193] If additional parameter information for predicting energy consumption, such as weather conditions and road resistance, can be obtained from the network in addition to the average vehicle speed of other vehicles, the parameters used to solve for horizontal energy, such as air resistance that changes depending on air density, wind speed and wind direction, and running resistance caused by road surface roughness, will be closer to reality, improving the accuracy of the SOC prediction results.

[0194] According to the second embodiment, it is possible to obtain the same effect as in the first embodiment. In addition, by acquiring information such as the average speed of other vehicles from the network, the number of parameters for predicting energy consumption increases, thereby improving the accuracy of the SOC prediction result.

[0195] As described above, the present invention is configured such that an SOC prediction system that predicts the SOC at a point where the vehicle is scheduled to pass in the future corrects the SOC prediction result calculated by solving for horizontal energy, vertical energy, etc., using a correction value calculated based on the error between the past SOC prediction result and the actual SOC prediction result.

[0196] This makes it possible to incorporate SOC prediction errors resulting from factors such as vehicle condition, road condition, driver condition, and battery degradation, which are not included in the SOC prediction results predicted from each energy consumption, into the correction value, thereby improving the accuracy of SOC prediction.

[0197] By improving the accuracy of SOC prediction, it becomes possible to accurately execute scheduled engine control in hybrid vehicles, leading to reduced fuel consumption.

[0198] It should be noted that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to an embodiment having all of the described configurations.

[0199] Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

[0200] Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0201] In addition, block diagrams and flowcharts are used to explain the present invention, but each function and each step in these diagrams used for explanation may be realized by software, by hardware, or in part by either.

[0202] Furthermore, each block in the block diagram may be contained in the same control device, or each block may be located in a different control device and configured through communication between the control devices. [Explanation of symbols]

[0203] 200···SOC prediction system, 201···Microcomputer, 202···Random Access Memory, 203···Read Only Memory, 204···Non-volatile ROM (EEPROM), 205···Camera sensor, 206···Vehicle speed sensor, 207···GPS sensor, 208···Network receiver, 209···Battery, 210···Battery control unit, 211···Motor, 212···Motor control unit, 213···Accelerator pedal sensor, 214···Engine control module, 215··Engine, 216···Information center, 300···SOC prediction result calculation unit, 3 01...Vertical energy calculation unit, 302...Prediction parameter correction unit, 303...Horizontal energy calculation unit, 304...Electric load energy calculation unit, 305...Travel time calculation unit, 306...Total energy consumption calculation unit, 307...SOC prediction result output unit, 310...SOC prediction result correction unit, 311...SOC prediction result storage unit, 312...SOC actual measurement result storage unit, 313...SOC prediction result comparison unit, 314...SOC prediction result correction value calculation unit, 315...SOC prediction correction result output unit

Claims

1. An SOC prediction system that predicts energy consumption required for driving and predicts the future state of charge of a battery of a vehicle, a state-of-charge prediction result calculation unit that calculates the consumed energy based on a parameter for predicting consumed energy and predicts a future state of charge of the battery of the host vehicle; an SOC prediction result correction unit that compares the state of charge prediction result output from the state of charge prediction result calculation unit with an actual state of charge measurement result of the battery measured when the host vehicle actually travels, calculates an error, corrects the state of charge prediction result based on the error, and outputs the corrected SOC prediction result; Equipped with The SOC prediction result correction unit an SOC prediction result storage unit that stores the state of charge prediction result output from the state of charge prediction result calculation unit; an SOC measurement result storage unit that stores the state of charge measurement result of the battery measured while the vehicle is actually running; an SOC prediction result comparison unit that compares the state of charge prediction result stored in the SOC prediction result storage unit with the state of charge actual measurement result stored in the SOC actual measurement result storage unit to calculate the error; an SOC prediction result correction value calculation unit that calculates a correction value for the state of charge prediction result based on the error calculated by the SOC prediction result comparison unit; an SOC prediction correction result output unit that corrects the state of charge prediction result based on the correction value calculated by the SOC prediction result correction value calculation unit and outputs the corrected result; Equipped with The SOC prediction result comparison unit of the SOC prediction result correction unit compares the state of charge prediction result with the actual state of charge measurement result at either a timing after a predetermined time has elapsed from a time when the state of charge prediction result calculation unit predicted the future state of charge of the battery, or a timing after a predetermined distance has been traveled from a point at which the state of charge of the future battery was predicted, a target point at which the predicted result of the state of charge and the actual measurement result of the state of charge are compared is any one or more points within a range of possible points at which the predicted result of the state of charge and the actual measurement result of the state of charge are compared; a point where the predicted result of the state of charge and the actual measurement result of the state of charge are compared is a point after the predetermined distance has been traveled; the SOC prediction result comparison unit compares the state of charge prediction result at the point after the predetermined distance has been traveled with the state of charge actual measurement result at the point after the predetermined distance has been traveled to calculate an error; the SOC prediction result correction value calculation unit calculates a correction value for the state of charge prediction result by weighting the error calculated by the SOC prediction result comparison unit so that the correction amount increases as the vehicle moves farther away from the point after the predetermined distance has been traveled or as time passes since the point after the predetermined distance has been traveled.

2. 2. The SOC prediction system according to claim 1, The SOC prediction system is characterized in that the state-of-charge prediction result calculation unit is connected to an information center through a wireless network, and the parameters for predicting energy consumption are received from the information center.

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