A state prediction method and device for long-distance driving of a pure electric vehicle

Abstract

The application provides a prediction device of a state prediction method applied to long-distance driving of a pure electric vehicle, comprising a driving computer (1) and an air temperature measuring module (2), a nominal state of charge measuring module (3), an odometer measuring module (4) and a display screen (5) electrically connected with the driving computer (1). The application also provides a state prediction method of long-distance driving of a pure electric vehicle: the capacity correction coefficient and the discharge power coefficient under different conditions are measured and stored before the vehicle is delivered; the starting place and the destination are determined before the vehicle drives, and the current air temperature, the current position and the current nominal state of charge of the battery pack are obtained and stored during the driving; the state of charge reduction per unit driving distance is calculated; the driving path is equally divided into n intervals, the state of charge correction coefficient prediction sequence is calculated after the temperature prediction sequence is obtained, and the correction state of charge prediction sequence and the discharge power coefficient prediction sequence are formed. The state prediction method of long-distance driving of the pure electric vehicle is scientific, reasonable, accurate and reliable.

Description

Technical Field

[0001] This invention relates to the field of electric vehicles, and in particular to a method and apparatus for predicting the state of a pure electric vehicle during long-distance driving. Background Technology

[0002] Electric vehicles are becoming increasingly popular due to their advantages such as energy saving, environmental protection, and comfort, with the vast majority being pure electric vehicles powered by lithium-ion battery packs. However, because charging the battery pack takes much longer than refueling a traditional gasoline vehicle, and given the limited number and coverage of charging infrastructure, pure electric vehicle owners generally face "range anxiety" during long-distance driving: the worry that the battery pack's available power or power output will be insufficient after traveling a considerable distance.

[0003] In addition to the problem of insufficient energy during long-distance driving, which is the same as that faced by traditional fuel vehicles, the available power and output capacity of the battery pack of pure electric vehicles are also affected by the ambient temperature. This is because the available power and discharge power of chemical energy storage devices, such as lithium-ion batteries, are significantly affected by temperature. Under the same conditions, the lower the temperature, the less available power and the lower the discharge power.

[0004] Current known technical solutions, while considering the impact of distance on the available battery power and discharge power of pure electric vehicles during long-distance driving, fail to adequately account for the influence of environmental temperature changes due to time and geographical location. Therefore, it is necessary to develop new technologies that fully utilize relevant intelligent connected vehicle technologies to predict the status of pure electric vehicles during long-distance driving, allowing drivers to anticipate potential changes in vehicle status and make appropriate preparations. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a scientific, reasonable, logically clear, accurate, reliable, and user-friendly method for predicting the state of a pure electric vehicle during long-distance driving, and also provides a corresponding low-cost, widely applicable prediction device and an electric vehicle equipped with the device.

[0006] According to a first aspect of the present invention, a method for predicting the state of a pure electric vehicle during long-distance driving is provided. The technical solution is a method for predicting the state of a pure electric vehicle during long-distance driving, wherein the pure electric vehicle is powered by a battery pack. Before the pure electric vehicle leaves the factory, the capacity correction coefficient FQ at different temperatures and the discharge power coefficient FP at different temperatures and different nominal state of charge combinations are measured experimentally and stored in a data table. The capacity correction coefficient FQ at a certain temperature is the ratio of the total discharge capacity at that temperature to the rated capacity of the battery pack, and the discharge power coefficient FP at a certain temperature and nominal state of charge combination is the ratio of the pulse discharge power at that combination to the maximum value of the pulse discharge power at all combinations.

[0007] Before a long-distance trip, inform the vehicle's computer of the departure and destination points; during the trip, perform the following steps at fixed time intervals:

[0008] Step S1: Obtain and store the current ground temperature T0, current ground location, and current nominal state of charge SOCB0 of the battery pack;

[0009] Step S2: Calculate the decrease in state of charge q per unit distance traveled from the starting point to the current point. B ;

[0010] Step S3: Divide the path from the current location to the destination into n equal intervals, with each interval having a length of ΔL;

[0011] Step S4: By connecting to navigation and weather forecast software, predict the temperature change with travel distance during the journey from the current location to the destination: Let the predicted temperature at the end of the i-th interval be T. i This yields the predicted temperature sequence [T0, T1, T2, ..., T]. i ,…,T n [, where T0 is the current ground temperature, T1 to T] n The predicted temperature values ​​are from the end point of the first interval to the end point of the nth interval;

[0012] Step S5: Based on the temperature prediction sequence and by referring to the data table, interpolate and calculate the change of the state-of-charge correction coefficient with the travel distance during the journey from the current location to the destination, and form a state-of-charge correction coefficient prediction sequence [FQ0, FQ1, FQ2, ..., FQ]. i ..., FQ n ], where FQ0 is the current ground charge state correction coefficient, and FQ1 to FQ n The predicted values ​​of the state-of-charge correction coefficients from the end point of the first interval to the end point of the nth interval;

[0013] Step S6: Calculate the change in corrected state of charge with distance traveled from the current location to the destination, and form a corrected state of charge prediction sequence [SOCX0, SOCX1, SOCX2, ..., SOCX]. i SOCX n ], where SOCX0 is the current corrected state of charge, SOCX1 to SOCX n The corrected state-of-charge prediction values ​​are from the end point of the first interval to the end point of the nth interval, and the corrected state-of-charge prediction value is SOCX for the i-th interval. i The formula for calculation is:

[0014] SOCX i =FQ i ×(SOCB0-ΔL×q B ×i) (1)

[0015] In the formula, FQ i SOCB0 is the predicted value of the state-of-charge correction coefficient at the end point of the i-th interval obtained in step S4, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ;

[0016] Step S7: Calculate the change of discharge power coefficient FP with travel distance during the journey from the current location to the destination, and form a predicted sequence of discharge power coefficient FP [FP0, FP1, FP2, ..., FP]. i , ...,FP n ], where FP0 is the current ground discharge power coefficient, FP1 to FP n The predicted discharge power coefficient is the value from the end of the first interval to the end of the nth interval; specifically, the predicted discharge power coefficient FP is the value from the end of the i-th interval. i The calculation consists of the following sub-steps:

[0017] Sub-step S7.1: Calculate the nominal state of charge SOCB at the end of the i-th interval. i :

[0018] SOCB i =SOCB0-ΔL×q B ×i (2)

[0019] In the formula, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ;

[0020] Sub-step S7.2: The nominal charged state SOCB at the end of the i-th interval point. i and temperature forecast value T i The predicted value FP of the i-th discharge power coefficient is obtained by querying the data table and interpolating. i .

[0021] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving includes a data table obtained through experimental measurements. The values ​​for different temperatures are taken at 5°C intervals, ranging from -20°C to 50°C. The values ​​for different nominal states of charge (NSCs) are taken at 5% intervals, ranging from 0% to 100%. The nominal NSC value is the ratio of the maximum discharge capacity that the electric vehicle in that NSC can release when transferred to a rated temperature environment and driven under rated operating conditions to the rated capacity of the battery pack. The rated capacity of the battery pack is the discharge capacity of the electric vehicle during the entire process of being fully charged at the rated temperature environment and then driven under rated operating conditions to the minimum permissible voltage state of the battery pack; its value is given by the manufacturer. The table of discharge power coefficients (FP) under different combinations of temperatures and nominal NSCs consists of a series of sub-tables, each sub-table corresponding to a temperature value and including the discharge power coefficients (FP) corresponding to different nominal NSCs at that temperature value.

[0022] The above-mentioned method for predicting the state of a pure electric vehicle during long-distance driving includes the following test steps for testing the total discharge capacity at a certain temperature:

[0023] Step A1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour;

[0024] Step A2: Charge the electric vehicle to full capacity using the rated charging regime;

[0025] Step A3: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour;

[0026] Step A4: Run the electric vehicle under rated conditions to the minimum permissible voltage state of the battery pack, and record the discharge capacity of the entire driving process. This is the total discharge capacity at a certain temperature to be measured.

[0027] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving includes the following test steps for testing the pulse discharge power under a specific temperature and nominal state of charge combination:

[0028] Step B1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour;

[0029] Step B2: Charge the electric vehicle to full capacity using the rated charging regime;

[0030] Step B3: Discharge the battery pack of the electric vehicle to the nominal state of charge to be tested;

[0031] Step B4: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour;

[0032] Step B5: Discharge the battery pack of the electric vehicle at a constant current rate of 1C for 10s, and measure and record the discharge power value at the end of the discharge. This value is the pulse discharge power under a certain temperature and nominal state of charge combination to be measured.

[0033] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving, specifically the method for calculating the state-of-charge correction coefficient based on the temperature prediction sequence and data table interpolation in step S5, is as follows: To predict the state-of-charge correction coefficient FQ at the end of the i-th interval... i Take the following steps:

[0034] Step C1: Read the predicted temperature value T at the end of the i-th interval from the temperature prediction sequence in step S4. i ;

[0035] Step C2: From the table of capacity correction factors FQ at different temperatures, find the value corresponding to T. i The two closest temperature values ​​T a and T b And the corresponding capacity correction factor FQ a and FQ b ;

[0036] Step C3: Perform linear interpolation according to the following formula to obtain the charge state correction coefficient FQ at the end of the i-th interval. i :

[0037]

[0038] In the aforementioned method for predicting the state of a pure electric vehicle during long-distance driving, sub-step S7.2 involves querying a data table and interpolating to obtain the predicted value FP of the i-th discharge power coefficient FP. i The specific steps are as follows:

[0039] Step D1: From the table of discharge power coefficients FP under different temperatures and nominal state-of-charge combinations, find the value corresponding to the predicted temperature T. i The sub-table corresponding to the closest temperature;

[0040] Step D2: From the table in Step D1, find the nominal state of charge SOCB at the end time of the i-th interval point. i The two closest nominal charged states SOCB a and SOCB b and the corresponding discharge power coefficients FP a and FP b ;

[0041] Step D3: Perform linear interpolation according to the following formula to obtain the predicted value of the discharge power coefficient FP at the end of the i-th interval. i :

[0042]

[0043] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving also includes visually displaying the latest prediction results of steps S6 and S7 at fixed time intervals during the driving process.

[0044] Draw a straight line segment, marking the current location and destination at its starting and ending points respectively; divide this straight line segment into n equal intervals, where n is the number of intervals in step S3, and set the width of the i-th interval to SOCX. i ×W, let its grayscale value be FP. i ×100%, of which SOCX i and FP i These are the i-th corrected state-of-charge prediction value and the i-th discharge power coefficient prediction value in the prediction result sequence, respectively. W is the baseline width. 0% grayscale value is displayed as pure white, and 100% grayscale value is displayed as pure black.

[0045] In the above-mentioned method for predicting the state of a pure electric vehicle during long-distance driving, the baseline width W is between 1 cm and 10 cm.

[0046] Preferably, the above testing process involves placing the pure electric vehicle in a temperature-controlled laboratory to conduct simulated operating condition tests under the required conditions, rather than conducting relevant driving tests in a real outdoor road environment.

[0047] According to a second aspect of the present invention, a prediction device is provided for the state prediction method applied to the above-mentioned long-distance driving process of a pure electric vehicle, comprising a vehicle computer and electrically connected thereto a temperature measurement module, a nominal state-of-charge measurement module, a mileage measurement module, and a display screen, wherein:

[0048] The temperature measurement module is used to measure the ambient temperature of the pure electric vehicle in real time.

[0049] The nominal state-of-charge metering module measures the amount of charge transferred during the charging and discharging process of the battery pack of a pure electric vehicle in real time using the ampere-hour integration method.

[0050] The mileage measurement module is used to measure the driving mileage of pure electric vehicles in real time.

[0051] The display screen is used for the visualization of the state prediction results during the long-distance driving process of pure electric vehicles;

[0052] The vehicle computer includes a storage module and a computing module and a networking module electrically connected thereto. The storage module is used to store various measurement, metering, networking and calculation data. The computing module is used for calculation. The networking module is used to connect to navigation software and weather forecast software and obtain navigation and weather forecast information in real time.

[0053] According to a third aspect of the present invention, a pure electric vehicle is provided, the pure electric vehicle being equipped with the aforementioned state prediction device for long-distance driving of the pure electric vehicle.

[0054] This invention defines two parameters: nominal state of charge (SOCB) and corrected state of charge (SOCX). The former depends only on the charge on the positive and negative plates of the battery pack and is independent of external environmental factors. Regardless of the current environment of the electric vehicle, assuming it is transferred to a rated temperature environment and driven under rated operating conditions, the ratio of the maximum discharge capacity that can be released during the entire driving process to the rated capacity of the battery pack itself is the nominal SOCB. The latter considers the influence of ambient temperature on the actual discharge capacity of the battery pack and introduces a corrected capacity correction factor FQ. The corrected SOCX of the battery pack at a certain ambient temperature is the product of its nominal SOCB and the corrected capacity correction factor FQ at that temperature. It represents the ratio of the maximum actual discharge capacity that the electric vehicle can release during the entire driving process under rated operating conditions at a certain ambient temperature to the rated capacity of the battery pack itself. Typically, the end of battery pack discharge is marked by the battery pack's terminal voltage reaching its minimum permissible voltage value.

[0055] Based on the above definition, the nominal state of charge (SOCB) is used as the benchmark for intermediate calculations during the prediction process. The prediction results are then converted into the corrected state of charge (SOCX) under the specific ambient temperature and output. This makes the prediction calculation process scientific, reasonable and logically clear.

[0056] For the nominal state of charge (SOCB) at any moment during the operation of a pure electric vehicle, based on the initial nominal SOCB, the charging and discharging current of the battery pack can be monitored online. The change in charge can be statistically obtained by integrating the current over time (ampere-hour integration method) and converted into the change in nominal SOCB. Finally, the nominal SOCB at any moment can be obtained by subtracting the change in nominal SOCB from the initial nominal SOCB.

[0057] The initial nominal state of charge (SOCB) during operation can be obtained from the vehicle's computer and calibrated and corrected periodically. For example, when fully charged at the rated temperature and under the rated charging regime, its nominal SOCB can be calibrated to 100%.

[0058] Based on the above description, other advantages of the present invention are readily apparent:

[0059] 1. This invention is based on the nominal state of charge (SOCB) during driving. By connecting to navigation and weather forecast software and utilizing pre-stored data tables, it focuses on the impact of temperature changes caused by significant variations in time and geographical location during long-distance driving of pure electric vehicles on the release capacity and power of the battery pack. The prediction calculation process involves real-time updates of data input and output, ensuring accuracy and reliability. The prediction results are visualized through line segment color depth and width, providing a better user experience.

[0060] 2. The state prediction device for long-distance driving of pure electric vehicles of the present invention makes full use of the on-board computer, ambient temperature sensor and software operation and networking equipment commonly equipped in existing electric vehicles. It basically does not add new hardware, so the cost is low and it can be applied to various electric vehicles. Attached Figure Description

[0061] Figure 1 This is a visualization of the status of a pure electric vehicle at a certain location during a long-distance journey, as shown in this embodiment of the invention.

[0062] Figure 2 This is a schematic diagram of the configuration of the state prediction device for a pure electric vehicle during long-distance driving in an embodiment of the present invention. In the figure, 1 is the vehicle computer, 2 is the temperature measurement module, 3 is the nominal state of charge metering module, 4 is the mileage metering module, 5 is the display screen, 6 is the navigation software, 7 is the weather forecast software, 11 is the storage module, 12 is the calculation module, and 13 is the networking module. Detailed Implementation

[0063] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0064] A method for predicting the state of a pure electric vehicle during long-distance driving, wherein the pure electric vehicle is powered by a battery pack. Before leaving the factory, the pure electric vehicle is experimentally measured and the data is stored in a table as a capacity correction coefficient FQ at different temperatures and a discharge power coefficient FP at different temperatures and different nominal state of charge combinations. The capacity correction coefficient FQ at a certain temperature is the ratio of the total discharge capacity at that temperature to the rated capacity of the battery pack, and the discharge power coefficient FP at a certain temperature and nominal state of charge combination is the ratio of the pulse discharge power at that combination to the maximum value of the pulse discharge power at all combinations.

[0065] Before a long-distance trip, inform the vehicle's computer of the departure and destination points; during the trip, perform the following steps at fixed time intervals:

[0066] Step S1: Obtain and store the current ground temperature T0, current ground location, and current nominal state of charge SOCB0 of the battery pack;

[0067] Step S2: Calculate the decrease in state of charge q per unit distance traveled from the starting point to the current point. B ;

[0068] Step S3: Divide the path from the current location to the destination into n equal intervals, with each interval having a length of ΔL;

[0069] Step S4: By connecting to navigation and weather forecast software, predict the temperature change with travel distance during the journey from the current location to the destination: Let the predicted temperature at the end of the i-th interval be T. i This yields the predicted temperature sequence [T0, T1, T2, ..., T]. i ,…,T n [, where T0 is the current ground temperature, T1 to T] n The predicted temperature values ​​are from the end point of the first interval to the end point of the nth interval;

[0070] Step S5: Based on the temperature prediction sequence and by referring to the data table, interpolate and calculate the change of the state-of-charge correction coefficient with the travel distance during the journey from the current location to the destination, and form a state-of-charge correction coefficient prediction sequence [FQ0, FQ1, FQ2, ..., FQ]. i ..., FQ n ], where FQ0 is the current ground charge state correction coefficient, and FQ1 to FQ n The predicted values ​​of the state-of-charge correction coefficients from the end point of the first interval to the end point of the nth interval;

[0071] Step S6: Calculate the change in corrected state of charge with distance traveled from the current location to the destination, and form a corrected state of charge prediction sequence [SOCX0, SOCX1, SOCX2, ..., SOCX]. i SOCX n ], where SOCX0 is the current corrected state of charge, SOCX1 to SOCX n The corrected state-of-charge prediction values ​​are from the end point of the first interval to the end point of the nth interval, and the corrected state-of-charge prediction value is SOCX for the i-th interval. i The formula for calculation is:

[0072] SOCX i =FQ i ×(SOCB0-ΔL×q B ×i) (1)

[0073] In the formula, FQ i SOCB0 is the predicted value of the state-of-charge correction coefficient at the end point of the i-th interval obtained in step S4, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q BThe decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ;

[0074] Step S7: Calculate the change of discharge power coefficient FP with travel distance during the journey from the current location to the destination, and form a predicted sequence of discharge power coefficient FP [FP0, FP1, FP2, ..., FP]. i , ...,FP n ], where FP0 is the current ground discharge power coefficient, FP1 to FP n The predicted discharge power coefficient is the value from the end of the first interval to the end of the nth interval; specifically, the predicted discharge power coefficient FP is the value from the end of the i-th interval. i The calculation consists of the following sub-steps:

[0075] Sub-step S7.1: Calculate the nominal state of charge SOCB at the end of the i-th interval. i :

[0076] SOCB i =SOCB0-ΔL×q B ×i (2)

[0077] In the formula, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ;

[0078] Sub-step S7.2: The nominal charged state SOCB at the end of the i-th interval point. i and temperature forecast value T i The predicted value FP of the i-th discharge power coefficient is obtained by querying the data table and interpolating. i .

[0079] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving includes a data table obtained through experimental measurements. The values ​​for different temperatures are taken at 5°C intervals, ranging from -20°C to 50°C. The values ​​for different nominal states of charge (NSCs) are taken at 5% intervals, ranging from 0% to 100%. The nominal NSC value is the ratio of the maximum discharge capacity that the electric vehicle in that NSC can release when transferred to a rated temperature environment and driven under rated operating conditions to the rated capacity of the battery pack. The rated capacity of the battery pack is the discharge capacity of the electric vehicle during the entire process of being fully charged at the rated temperature environment and then driven under rated operating conditions to the minimum permissible voltage state of the battery pack; its value is given by the manufacturer. The table of discharge power coefficients (FP) under different combinations of temperatures and nominal NSCs consists of a series of sub-tables, each sub-table corresponding to a temperature value and including the discharge power coefficients (FP) corresponding to different nominal NSCs at that temperature value.

[0080] The above-mentioned method for predicting the state of a pure electric vehicle during long-distance driving includes the following test steps for testing the total discharge capacity at a certain temperature:

[0081] Step A1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour;

[0082] Step A2: Charge the electric vehicle to full capacity using the rated charging regime;

[0083] Step A3: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour;

[0084] Step A4: Run the electric vehicle under rated conditions to the minimum permissible voltage state of the battery pack, and record the discharge capacity of the entire driving process. This is the total discharge capacity at a certain temperature to be measured.

[0085] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving includes the following test steps for testing the pulse discharge power under a specific temperature and nominal state of charge combination:

[0086] Step B1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour;

[0087] Step B2: Charge the electric vehicle to full capacity using the rated charging regime;

[0088] Step B3: Discharge the battery pack of the electric vehicle to the nominal state of charge to be tested;

[0089] Step B4: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour;

[0090] Step B5: Discharge the battery pack of the electric vehicle at a constant current rate of 1C for 10s, and measure and record the discharge power value at the end of the discharge. This value is the pulse discharge power under a certain temperature and nominal state of charge combination to be measured.

[0091] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving, specifically the method for calculating the state-of-charge correction coefficient based on the temperature prediction sequence and data table interpolation in step S5, is as follows: To predict the state-of-charge correction coefficient FQ at the end of the i-th interval... i Take the following steps:

[0092] Step C1: Read the predicted temperature value T at the end of the i-th interval from the temperature prediction sequence in step S4. i ;

[0093] Step C2: From the table of capacity correction factors FQ at different temperatures, find the value corresponding to T. i The two closest temperature values ​​T a and T b And the corresponding capacity correction factor FQ a and FQ b ;

[0094] Step C3: Perform linear interpolation according to the following formula to obtain the charge state correction coefficient FQ at the end of the i-th interval. i :

[0095]

[0096] In the aforementioned method for predicting the state of a pure electric vehicle during long-distance driving, sub-step S7.2 involves querying a data table and interpolating to obtain the predicted value FP of the i-th discharge power coefficient FP. i The specific steps are as follows:

[0097] Step D1: From the table of discharge power coefficients FP under different temperatures and nominal state-of-charge combinations, find the value corresponding to the predicted temperature T. i The sub-table corresponding to the closest temperature;

[0098] Step D2: From the table in Step D1, find the nominal state of charge SOCB at the end time of the i-th interval point. i The two closest nominal charged states SOCB a and SOCB b and the corresponding discharge power coefficients FP a and FP b ;

[0099] Step D3: Perform linear interpolation according to the following formula to obtain the predicted value of the discharge power coefficient FP at the end of the i-th interval. i :

[0100]

[0101] The aforementioned method for predicting the state of a pure electric vehicle during long-distance driving also includes visually displaying the latest prediction results of steps S6 and S7 at fixed time intervals during the driving process.

[0102] Draw a straight line segment, marking the current location and destination at its starting and ending points respectively; divide this straight line segment into n equal intervals, where n is the number of intervals in step S3, and set the width of the i-th interval to SOCX. i ×W, let its grayscale value be FP. i ×100%, of which SOCX i and FP i These are the i-th corrected state-of-charge prediction value and the i-th discharge power coefficient prediction value in the prediction result sequence, respectively, and W is the baseline width.

[0103] In the above-mentioned method for predicting the state of a pure electric vehicle during long-distance driving, the baseline width W is between 1 cm and 10 cm.

[0104] See above for the display effect. Figure 1 .

[0105] Furthermore, the above testing process involves placing the pure electric vehicle in a temperature-controlled laboratory to simulate the required operating conditions, rather than conducting relevant driving tests in a real outdoor road environment.

[0106] like Figure 2 As shown, a prediction device for the state prediction method applied to the above-mentioned pure electric vehicle during long-distance driving includes a vehicle computer 1 and electrically connected to it a temperature measurement module 2, a nominal state of charge measurement module 3, a mileage measurement module 4, and a display screen 5, wherein:

[0107] The temperature measurement module 2 is used to measure the ambient temperature of the pure electric vehicle in real time.

[0108] The nominal state-of-charge metering module 3 measures the amount of charge transfer during the charging and discharging process of the battery pack of the pure electric vehicle in real time using the ampere-hour integration method.

[0109] The mileage measurement module 4 is used to measure the driving mileage of the pure electric vehicle in real time.

[0110] The display screen 5 is used for the visualization of the state prediction results of the long-distance driving process of the pure electric vehicle;

[0111] The vehicle computer 1 includes a storage module 11 and a computing module 12 and a networking module 13 electrically connected thereto. The storage module 11 is used to store various measurement, metering, networking and calculation data. The computing module 12 is used for calculation. The networking module 13 is used to connect to navigation software and weather forecast software and obtain navigation and weather forecast information in real time.

[0112] A pure electric vehicle, wherein the pure electric vehicle is equipped with the aforementioned state prediction device for long-distance driving of the pure electric vehicle.

[0113] For the nominal state of charge (SOCB) at any moment during the operation of a pure electric vehicle, based on the initial nominal SOCB, the charging and discharging current of the battery pack can be monitored online. The change in charge can be statistically obtained by integrating the current over time (ampere-hour integration method) and converted into the change in nominal SOCB. Finally, the nominal SOCB at any moment can be obtained by subtracting the change in nominal SOCB from the initial nominal SOCB.

[0114] The initial nominal state of charge (SOCB) during operation can be obtained from the vehicle's computer and calibrated and corrected periodically. For example, when fully charged at the rated temperature and under the rated charging regime, its nominal SOCB can be calibrated to 100%.

[0115] Example

[0116] Please refer to Figures 1 to 2 Understand this embodiment.

[0117] The battery pack of a certain pure electric vehicle consists of 200 lithium iron phosphate batteries connected in series, with a rated capacity of 100Ah, a rated voltage of 640V, and a maximum and minimum permissible voltage of 730V and 500V during charging and discharging, respectively. The rated ambient temperature is 25℃, the rated operating conditions are the vehicle operating conditions specified by the manufacturer, and the rated charging regime is to charge at a constant current rate of 0.1C (i.e., a current value of 10A) to the maximum permissible voltage.

[0118] Before leaving the factory, the electric vehicle underwent simulated operating condition testing in a laboratory with controlled ambient temperature.

[0119] The total discharge capacity and capacity correction factor FQ obtained at different temperatures are shown in Table 1. In Table 1, the capacity correction factor FQ at a certain temperature is the ratio of the total discharge capacity measured at that temperature to the rated capacity of 100Ah.

[0120] Table 1. Test results of capacity correction factor FQ at different temperatures.

[0121] Temperature / ℃ Total discharge capacity / Ah Capacity correction factor FQ -20 25 0.25 -15 43 0.43 -10 57 0.57 -5 67 0.67 0 76 0.76 5 83 0.83 10 89 0.89 15 94 0.94 20 98 0.98 25 100 1.00 30 102 1.02 35 103 1.03 40 104 1.04 45 106 1.06 50 106 1.06

[0122] For the series of tables on the discharge power coefficient FP under different temperatures and nominal state of charge combinations obtained from testing and calculation, due to space limitations, only a sub-table of the discharge power coefficient FP under different nominal state of charge at 15℃ is given, as shown in Table 2. In this embodiment, the maximum pulse power obtained by testing is 67kW, corresponding to a temperature of 50℃ and 100% nominal state of charge. Therefore, the discharge power coefficient FP at a certain temperature in Table 2 is the ratio of the pulse discharge power measured at that temperature to the maximum pulse discharge power of 67kW.

[0123] Table 2. Test results of discharge power factor (FP) under different nominal states of charge at 15℃.

[0124]

[0125]

[0126] At 9:00 AM on a winter day, I started driving this pure electric vehicle from point A to point B, 500 km away. Before driving, the electric vehicle had been fully charged under the rated charging conditions and rated charging regime at the rated temperature, so its nominal state of charge (SOCB) was 100%.

[0127] The pure electric vehicle performs prediction and visualization operations every 1 minute during its driving process. At 9:30, the car reaches point C, which is 50km away from the starting point A. Taking this moment as an example, the relevant prediction process is introduced.

[0128] Step S1: Obtain and store the current ground temperature (i.e., C location) T0 = 16℃, the current location (C location on the navigation software map) and the current nominal state of charge of the battery pack SOCB0 = 94%.

[0129] Step S2: Calculate the decrease in state of charge q per unit distance traveled from the starting point to the current point. B = 0.12% / km.

[0130] When location C is 50km from location A, and the nominal state of charge (SOCB) of the battery pack is 100% and 94% at locations A and C respectively, the decrease in SOCB per unit distance is q. B =(100%-94%) / 50km=0.12% / km.

[0131] Step S3: Divide the path from the current location (i.e., location C) to the destination (i.e., location B) into n = 9 intervals with an equal distance. The length of each interval is ΔL = 50km, and the ending points of each interval are location C1, location C2, ..., location C8, and location B, respectively.

[0132] Step S4: By connecting to navigation and weather forecast software, predict the temperature change with travel distance during the journey from the current location to the destination: Let the predicted temperature at the end of the i-th interval be T. i This yields the predicted temperature sequence [T0, T1, T2, ..., T]. i ,…,T n ] = [16℃, 15℃, 13℃, 10℃, 11℃, 9℃, 8℃, 6℃, 5℃, 2℃], where T0 is the current local temperature, and T1 to T2 are the temperatures between T0 and T1. n The predicted temperature values ​​are from the end point of the first interval to the end point of the nth interval.

[0133] With the help of network connectivity, the arrival times at locations C1, C2, ..., C8, and B are predicted by navigation software. Then, based on the locations and their corresponding arrival times, the above temperature prediction sequence is obtained by weather forecast software.

[0134] Step S5: Based on the temperature prediction sequence and by referring to the data table, interpolate and calculate the change of the state-of-charge correction coefficient with the travel distance during the journey from the current location to the destination, and form a state-of-charge correction coefficient prediction sequence [FQ0, FQ1, FQ2, ..., FQ]. i ..., FQ n ] = [0.948, 0.94, 0.92, 0.89, 0.9, 0.878, 0.866, 0.842, 0.83, 0.788], where FQ0 is the current ground charge state correction coefficient, and FQ1 to FQ n The predicted values ​​of the state-of-charge correction coefficients from the end point of the first interval to the end point of the nth interval.

[0135] Taking the calculation of FQ0 at location C as an example, the temperature T0 = 16℃. Looking up the table in Table 1, the two temperatures closest to this are 15℃ and 20℃. The corresponding capacity correction factors FQ for these two temperatures are 0.94 and 0.98 respectively. Therefore, the calculation is as follows:

[0136]

[0137] Step S6: Calculate the change in corrected state of charge with distance traveled from the current location to the destination, and form a corrected state of charge prediction sequence [SOCX0, SOCX1, SOCX2, ..., SOCX]. i SOCX n ] = [89.11%, 82.72%, 75.44%, 67.64%, 63%, 56.19%, 50.23%, 43.78%, 38.18%, 31.52%], where SOCX0 is the current corrected state of charge, SOCX1 to SOCX nThe corrected state-of-charge prediction values ​​are from the end point of the first interval to the end point of the nth interval, and the corrected state-of-charge prediction value is SOCX for the i-th interval. i The formula for calculation is:

[0138] SOCX i =FQ i ×(SOCB0-ΔL×q B ×i) (1)

[0139] In the formula, FQ i SOCB0 is the predicted value of the state-of-charge correction coefficient at the end point of the i-th interval obtained in step S4, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B .

[0140] Taking the corrected state-of-charge prediction value SOCX1 at the end point of the first interval (C1) as an example, its calculation formula is as follows:

[0141] SOCX1=FQ1×(SOCB0-ΔL×q B ×1)=0.94×(94%-50km×0.12% / km×1)=82.72%

[0142] Step S7: Calculate the change of discharge power coefficient FP with travel distance during the journey from the current location to the destination, and form a predicted sequence of discharge power coefficient FP [FP0, FP1, FP2, ..., FP]. i , ...,FP n ] = [98.20%, 96.40%, 94.60%, 92.80%, 92.50%, 90.72%, 89.00%, 87.20%, 85.40%, 83.60%], where FP0 is the current ground discharge power coefficient, FP1 to FP n The predicted discharge power coefficient is the value from the end of the first interval to the end of the nth interval; specifically, the predicted discharge power coefficient FP is the value from the end of the i-th interval. i The calculation consists of the following sub-steps:

[0143] Sub-step S7.1: Calculate the nominal state of charge SOCB at the end of the i-th interval. i :

[0144] SOCB i =SOCB0-ΔL×q B ×i (2)

[0145] In the formula, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q BThe decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ;

[0146] Sub-step S7.2: The nominal charged state SOCB at the end of the i-th interval point. i and temperature forecast value T i The predicted value FP of the i-th discharge power coefficient is obtained by querying the data table and interpolating. i .

[0147] Taking the predicted discharge power coefficient FP1 at the end of the first interval (location C1) as an example, firstly, calculate the nominal state of charge SOCB1 at the end of the first interval: 94% - 50km × 0.12% / km × 1 = 88%. Then, read the corresponding predicted temperature T1 = 15℃ from the result of step S4. Next, find the two closest nominal state of charge SOCB values ​​in Table 2: 85% and 90%, with corresponding discharge power coefficients FP of 0.955 and 0.970, respectively. Then calculate:

[0148]

[0149] The above prediction results are visualized as follows: Figure 2 In this embodiment, the baseline width W on the display screen 5 is 5cm. The driver observes... Figure 2 This allows for a clear and convenient understanding of the changes in the available battery power and discharge power of a pure electric vehicle during future driving, enabling early preparation. For example, if the available battery power is low in the middle or later stages of driving, one should plan ahead to find a charging station and charge the vehicle promptly, or minimize the power consumption of the passenger cabin's air conditioning and entertainment electronic devices, prioritizing driving needs as much as possible; if the discharge power is low in the middle or later stages of driving, the vehicle's power may be limited, and aggressive driving should be avoided as much as possible.

[0150] This invention, based on the nominal state of charge (SOCB) during driving, connects to navigation and weather forecast software and utilizes pre-stored data tables to focus on the impact of temperature changes caused by significant variations in time and geographical location during long-distance driving of pure electric vehicles on the release capacity and power of the battery pack. The prediction calculation process involves real-time updates of both input and output data, ensuring accuracy and reliability. The prediction results are visualized using line segment color depth and width, providing a better user experience.

[0151] The state prediction device for long-distance driving of pure electric vehicles in this embodiment of the invention makes full use of the on-board computer, ambient temperature sensor and software operation and networking equipment commonly equipped in existing electric vehicles. It basically does not add any new hardware, so the cost is low and it can be applied to various electric vehicles.

Claims

1. A method for predicting the state of a pure electric vehicle during long-distance driving, wherein the pure electric vehicle is powered by a battery pack, characterized in that, Before pure electric vehicles leave the factory, the capacity correction coefficient FQ at different temperatures and the discharge power coefficient FP at different temperatures and nominal state of charge combinations are measured by experiments and stored in a data table: where the capacity correction coefficient FQ at a certain temperature is the ratio of the total discharge capacity at that temperature to the rated capacity of the battery pack, and the discharge power coefficient FP at a certain temperature and nominal state of charge combination is the ratio of the pulse discharge power at that combination to the maximum value of the pulse discharge power at all combinations. Before a long-distance trip, inform the vehicle's computer of the departure and destination points; during the trip, perform the following steps at fixed time intervals: Step S1: Obtain and store the current ground temperature T0, current ground location, and current nominal state of charge SOCB0 of the battery pack; Step S2: Calculate the decrease in state of charge q per unit distance traveled from the starting point to the current point. B ; Step S3: Divide the path from the current location to the destination into n equal intervals, with each interval having a length of ΔL, where n is between 5 and 100. Step S4: By connecting to navigation and weather forecast software, predict the temperature change with travel distance during the journey from the current location to the destination: Let the predicted temperature at the end of the i-th interval be T. i This yields the predicted temperature sequence [T0, T1, T2, ..., T]. i ,…,T n [, where T0 is the current ground temperature, T1 to T] n The predicted temperature values ​​are from the end point of the first interval to the end point of the nth interval; Step S5: Based on the temperature prediction sequence and by referring to the data table, interpolate and calculate the change of the state-of-charge correction coefficient with the travel distance during the journey from the current location to the destination, and form a state-of-charge correction coefficient prediction sequence [FQ0, FQ1, FQ2, ..., FQ]. i ..., FQ n ], where FQ0 is the current ground charge state correction coefficient, and FQ1 to FQ n The predicted values ​​of the state-of-charge correction coefficients from the end point of the first interval to the end point of the nth interval; Step S6: Calculate the change in corrected state of charge with distance traveled from the current location to the destination, and form a corrected state of charge prediction sequence [SOCX0, SOCX1, SOCX2, ..., SOCX]. i SOCX n ], where SOCX0 is the current corrected state of charge, SOCX1 to SOCX n The corrected state-of-charge prediction values ​​are from the end point of the first interval to the end point of the nth interval, and the corrected state-of-charge prediction value is SOCX for the i-th interval. i The formula for calculation is: SOCX i =FQ i ×(SOCB0-ΔL×q B ×i) (1) In the formula, FQ i SOCB0 is the predicted value of the state-of-charge correction coefficient at the end point of the i-th interval obtained in step S4, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ; Step S7: Calculate the change of discharge power coefficient FP with travel distance during the journey from the current location to the destination, and form a predicted sequence of discharge power coefficient FP [FP0, FP1, FP2, ..., FP]. i , ...,FP n ], where FP0 is the current ground discharge power coefficient, FP1 to FP n The predicted discharge power coefficient is the value from the end of the first interval to the end of the nth interval; specifically, the predicted discharge power coefficient FP is the value from the end of the i-th interval. i The calculation consists of the following sub-steps: Sub-step S7.1: Calculate the nominal state of charge SOCB at the end of the i-th interval. i : SOCB i =SOCB0-ΔL×q B ×i (2) In the formula, SOCB0 is the current nominal state of charge obtained in step S1, ΔL is the interval length in step S3, and q B The decrease in state of charge per unit distance traveled, q, is obtained in step S2. B ; Sub-step S7.2: The nominal charged state SOCB at the end of the i-th interval point. i and temperature forecast value T i The predicted value FP of the i-th discharge power coefficient is obtained by querying the data table and interpolating. i ; In sub-step S7.2, the predicted value FP of the i-th discharge power coefficient FP is obtained by querying the data table and interpolating. i The specific steps are as follows: Step D1: From the table of discharge power coefficients FP under different temperatures and nominal state-of-charge combinations, find the value corresponding to the predicted temperature T. i The sub-table corresponding to the closest temperature; Step D2: From the table in Step D1, find the nominal state of charge SOCB at the end time of the i-th interval point. i The two closest nominal charged states SOCB a and SOCB b and the corresponding discharge power coefficients FP a and FP b ; Step D3: Perform linear interpolation according to the following formula to obtain the predicted value of the discharge power coefficient FP at the end of the i-th interval. i :

2. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 1, characterized in that, In the data table obtained through experimental measurements, the values ​​for different temperatures are taken at 5°C intervals, ranging from -20°C to 50°C. The values ​​for different nominal states of charge are taken at 5% intervals, ranging from 0% to 100%. The nominal state of charge is the ratio of the maximum discharge capacity that the electric vehicle in that state of charge can release when transferred to the rated temperature environment and driven under rated operating conditions to the rated capacity of the battery pack. The rated capacity of the battery pack is the discharge capacity of the electric vehicle during the entire process of being fully charged in the rated temperature environment under the rated charging regime and then driven under rated operating conditions to the minimum permissible voltage state of the battery pack, and its value is given by the manufacturer. The table of discharge power coefficient FP under different temperature and different nominal state of charge combinations consists of a series of sub-tables. Each sub-table corresponds to a temperature value and includes the discharge power coefficient FP corresponding to different nominal states of charge under that temperature value.

3. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 1, characterized in that, The test steps for measuring the total discharge capacity at a certain temperature are as follows: Step A1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour; Step A2: Charge the electric vehicle to full capacity using the rated charging regime; Step A3: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour; Step A4: Run the electric vehicle under rated conditions to the minimum permissible voltage state of the battery pack, and record the discharge capacity of the entire driving process. This is the total discharge capacity at a certain temperature to be measured.

4. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 1, characterized in that, The test procedure for the pulse discharge power under a certain temperature and nominal state of charge combination is as follows: Step B1: Transfer the electric vehicle to an environment with rated temperature and leave it there for more than 1 hour; Step B2: Charge the electric vehicle to full capacity using the rated charging regime; Step B3: Discharge the battery pack of the electric vehicle to the nominal state of charge to be tested; Step B4: Transfer the electric vehicle to the environment where the temperature is to be measured and leave it there for more than 1 hour; Step B5: Discharge the battery pack of the electric vehicle at a constant current rate of 1C for 10s, and measure and record the discharge power value at the end of the discharge. This value is the pulse discharge power under the nominal state of charge combination at a certain temperature.

5. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 1, characterized in that, The specific method for calculating the state-of-charge correction coefficient based on the temperature prediction sequence and data table interpolation in step S5 is as follows: To predict the state-of-charge correction coefficient FQ at the end of the i-th interval... i Take the following steps: Step C1: Read the predicted temperature value T at the end of the i-th interval from the temperature prediction sequence in step S4. i ; Step C2: From the table of capacity correction factors FQ at different temperatures, find the value corresponding to T. i The two closest temperature values ​​T a and T b And the corresponding capacity correction factor FQ a and FQ b ; Step C3: Perform linear interpolation according to the following formula to obtain the charge state correction coefficient FQ at the end of the i-th interval. i :

6. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 1, characterized in that, It also includes visualizing the latest prediction results of steps S6 and S7 at fixed time intervals during the driving process: Draw a straight line segment, marking the current location and destination at its starting and ending points respectively; divide this straight line segment into n equal intervals, where n is the number of intervals in step S3, and set the width of the i-th interval to SOCX. i ×W, let its grayscale value be FP. i ×100%, of which SOCX i and FP i These are the i-th corrected state-of-charge prediction value and the i-th discharge power coefficient prediction value in the prediction result sequence, respectively, and W is the baseline width.

7. The state prediction method for a pure electric vehicle during long-distance driving as described in claim 6, characterized in that, The width W of the baseline is between 1 cm and 10 cm.

8. A prediction device applied to the state prediction method for a pure electric vehicle during long-distance driving as described in any one of claims 1-7, characterized in that, It includes a vehicle computer (1) and an electrically connected temperature measurement module (2), a nominal state-of-charge metering module (3), a mileage metering module (4), and a display screen (5), wherein: The temperature measurement module (2) is used to measure the ambient temperature of the pure electric vehicle in real time; The nominal state-of-charge metering module (3) measures the amount of charge transfer during the charging and discharging process of the battery pack of the pure electric vehicle in real time using the ampere-hour integration method; The mileage metering module (4) is used to measure the driving mileage of the pure electric vehicle in real time. The display screen (5) is used for the visualization of the state prediction results of the long-distance driving process of the pure electric vehicle; The vehicle computer (1) includes a storage module (11) and a computing module (12) and a networking module (13) electrically connected thereto. The storage module (11) is used for storing various measurement, metering, networking and calculation data. The computing module (12) is used for calculation. The networking module (13) is used to connect to the navigation software and weather forecast software and obtain navigation and weather forecast information in real time.

9. A pure electric vehicle, characterized in that, The device for predicting the state of a pure electric vehicle during long-distance driving, as described in claim 8, is provided.

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