An estimation method and system for the remaining range of an electric vehicle

Abstract

The application discloses a kind of estimation method and system of electric vehicle remaining range, it is related to new energy whole vehicle control technical field, including obtaining necessary data information of electric vehicle, using fusion model to calculate the remaining power of electric vehicle battery;According to driving data, calculate the average power consumption of range per 100 kilometers;Calculate the remaining range of electric vehicle, execute range display strategy.The application provides more accurate range estimation by continuously monitoring the state of vehicle battery, helps driver better plan trip, reduces anxiety caused by inaccurate estimation;Early detection of battery problems helps drivers better maintain the battery, and accurately estimates the available range;Increase the confidence of the driver on electric vehicles, drivers are more likely to plan long trips, reduce unnecessary worry, improve the practicality of electric vehicles;Help to improve the driving experience, reduce the discomfort of the driver caused by the uncertainty of the range.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle control technology, and in particular to a method and system for estimating the remaining driving range of an electric vehicle. Background Technology

[0002] Currently, the calculation of electric vehicle range is primarily based on the remaining charge of the battery and the average energy consumption in the preceding period. This method uses battery capacity and battery state to estimate the vehicle's remaining driving range. Typically, electric vehicles determine the remaining energy of the battery based on its capacity and voltage. Then, by referencing previous driving data, such as energy consumption, driving habits, and environmental conditions, the estimated driving range is calculated.

[0003] While current methods for estimating the driving range of electric vehicles provide useful information to drivers to some extent, they have several shortcomings: These methods rely solely on historical data and static parameters, failing to consider real-time changes in driving conditions, vehicle status, and charging infrastructure. This can lead to inaccurate range estimates, especially under specific driving conditions. Given the high level of concern electric vehicle drivers have about range, inaccurate estimates can increase anxiety, potentially deterring long-distance driving due to uncertainty and impacting the actual usability of the electric vehicle. Furthermore, existing methods often neglect battery health, which varies over time and charging cycles. Therefore, even with constant battery capacity, battery health issues can reduce the actual usable driving range. Summary of the Invention

[0004] In view of the problems existing in the estimation methods of the remaining driving range of electric vehicles, this invention is proposed to improve the accuracy of electric vehicle driving range estimation by taking into account more real-time factors, including vehicle status, driving environment and battery health, to provide more reliable driving range information and reduce driver anxiety.

[0005] Therefore, the problem to be solved by the present invention is to provide a method and system for estimating the remaining driving range of an electric vehicle.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for estimating the remaining driving range of an electric vehicle, comprising: acquiring necessary data information of the electric vehicle; calculating the remaining battery power of the electric vehicle using a fusion model; acquiring average current, voltage, and driving time based on the electric vehicle speed; calculating the average power consumption per 100 kilometers of driving range; calculating and acquiring the remaining driving range of the electric vehicle; executing a driving range display strategy; and completing the estimation of the remaining driving range of the electric vehicle.

[0008] As a preferred embodiment of the method for estimating the remaining driving range of the electric vehicle described in this invention, the necessary data of the electric vehicle includes the initial SOC value of the battery, the rated capacity of the battery, the charging and discharging efficiency of the battery, the battery current and voltage values ​​during vehicle operation, and the displayed driving range.

[0009] As a preferred embodiment of the method for estimating the remaining driving range of an electric vehicle according to the present invention, the fusion model specifically includes: when the vehicle starts, obtaining an initial SOC0 value; and calculating a new SOC1 value using the ampere-hour integration method, the relevant calculation formula being as follows:

[0010]

[0011] In the formula, C N η is the rated capacity of the battery; I is the charge / discharge efficiency of the battery; I is the battery discharge current; the SOC2 correction value is calculated using an optimization algorithm, the correction value SOC2 is compared with the SOC1 value, and the remaining power of the electric vehicle battery is calculated based on the comparison results.

[0012] As a preferred embodiment of the method for estimating the remaining driving range of an electric vehicle according to the present invention, the optimization algorithm specifically includes initializing the state variable x0, P0;

[0013]

[0014] In the formula, x0 is a variable. P is the mean; P0 is the covariance; construct the s-point and weights;

[0015]

[0016] In the formula, Let be an N-dimensional lower triangular square root matrix, where i is the i-th column;

[0017]

[0018] In the formula, The weights are the mean. The covariance weights are α; the distribution of the predicted model values ​​is α; the system distribution is β; the mean of the system state is calculated. and variance

[0019]

[0020]

[0021] In the formula, P k / k-1 Q is the predicted value of the system state covariance. kThe covariance is used; a transformation is performed to obtain a new s point, and the final model prediction value, covariance matrix, and K value are obtained.

[0022]

[0023]

[0024]

[0025] K = P (xy)k / k-1 P (y)k / k-1

[0026] In the formula, These are the predicted observations; P is the mean of the predicted observations. (xy)k / k-1 P represents the predicted observation covariance. (y)k / k-1 The predicted covariance is calculated; new measurement data is acquired, and the predicted state estimate is fused with the new measurement data to generate a new state estimate; the state covariance matrix is ​​updated to reflect the error after measurement. When the measurement data is reliable, the measurement data is used as the final data; when the measurement data is unreliable, the system model data is used as the final data.

[0027] In a preferred embodiment of the method for estimating the remaining driving range of an electric vehicle according to the present invention, the comparison results include: when the difference between the SOC2 correction value calculated using the optimization algorithm and the SOC1 value is less than 5% of the SOC1 value, the SOC2 correction value calculated using the optimization algorithm is used as the final SOC value, and the remaining battery capacity of the electric vehicle is calculated based on the correction value; when the difference between the SOC2 correction value calculated using the optimization algorithm and the SOC1 value is greater than 10% of the SOC1 value, the calculation result is discarded, the calculation process is repeated, the calculation is performed again, and an alarm signal is issued to warn that the calculation result is abnormal; the remaining battery capacity E is calculated and obtained. r The relevant calculation formulas are as follows:

[0028] E = EE

[0029] E r =EE c

[0030]

[0031] In the formula, E represents the total battery capacity; E c U represents the total energy consumption of a car. b I is the battery pack terminal voltage. b Let t be the discharge current and t be the travel time.

[0032] As a preferred embodiment of the method for estimating the remaining driving range of an electric vehicle according to the present invention, the relevant calculation formula for the remaining driving range of the electric vehicle is as follows:

[0033]

[0034]

[0035] In the formula, P is the average energy consumption for traveling a distance of x; ΔE is the energy consumed for traveling this distance; and Range is the remaining driving range of the electric vehicle.

[0036] As a preferred embodiment of the method for estimating the remaining driving range of an electric vehicle according to the present invention, the driving range display strategy specifically includes calculating and obtaining the values ​​of △Range1 and △Range2, and the calculation formula is as follows:

[0037] △Range1 = Actual Range - Displayed Range

[0038] △Range2 = Range displayed - Range actual

[0039] Based on the relationship between the actual calculated range and the displayed range, and in conjunction with the vehicle's speed, a pre-set range display strategy is executed. The numerical relationship includes when the actual calculated range is higher than the displayed range, when the actual calculated range is lower than the displayed range, when the vehicle is idling, when the calculated range is less than 50 km, or when the remaining battery power is ≤10 kWh.

[0040] Secondly, embodiments of the present invention provide a system for estimating the remaining driving range of an electric vehicle, comprising: an acquisition module for acquiring necessary data information of the electric vehicle and calculating the remaining battery power of the electric vehicle using a fusion model; a calculation module for calculating the average energy consumption per 100 kilometers of driving range based on the electric vehicle speed, acquired current, voltage, and driving time data; and an estimation module for calculating and acquiring the remaining driving range of the electric vehicle, executing a driving range display strategy, and completing the estimation of the remaining driving range of the electric vehicle.

[0041] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the above-described method.

[0043] The beneficial effects of this invention are that it takes into account changes in the real-time driving environment, vehicle status, and charging infrastructure. By continuously monitoring the vehicle's surrounding conditions and driving behavior, such as road conditions, temperature, and speed, as well as battery status, it can provide more accurate range estimates, helping drivers better plan their trips and reducing anxiety caused by inaccurate estimates. It also monitors battery health, including changes in battery capacity over time and charging cycles. Battery health monitoring can detect battery problems early, such as capacity degradation or reduced charging efficiency, thus helping drivers better maintain the battery and accurately estimate the available range. By providing more accurate range estimates, this method can increase driver confidence in electric vehicles, making it easier for drivers to plan long-distance trips and reducing unnecessary worries, thereby improving the actual usability of electric vehicles. Accurate range estimates can help drivers plan charging time and locations more effectively, avoiding overcharging or undercharging and improving charging efficiency. It also helps improve the driving experience, reducing driver discomfort caused by range uncertainty, allowing drivers to choose driving modes with more confidence and better adapt to the characteristics of electric vehicles. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0045] Figure 1 A flowchart illustrating a method for estimating the remaining driving range of an electric vehicle.

[0046] Figure 2 An estimation strategy diagram for estimating the remaining driving range of electric vehicles. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Example 1

[0051] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for estimating the remaining driving range of an electric vehicle, including:

[0052] S1: Obtain the necessary data information of the electric vehicle and use the fusion model to calculate the remaining battery power of the electric vehicle.

[0053] Specifically, essential data for electric vehicles includes the battery's initial SOC value, rated battery capacity, battery charge and discharge efficiency, battery current and voltage values ​​during vehicle operation, and displayed driving range.

[0054] The fusion model specifically includes obtaining the initial SOC0 value when the car starts.

[0055] The new SOC1 value is calculated using the ampere-hour integration method, and the relevant calculation formula is as follows:

[0056]

[0057] In the formula, C N η is the rated capacity of the battery; η is the charge / discharge efficiency of the battery; I is the discharge current of the battery.

[0058] The SOC2 correction value is calculated using an optimization algorithm. The correction value SOC2 is then compared with the SOC1 value, and the remaining battery capacity of the electric vehicle is determined based on the comparison results.

[0059] The optimization algorithm specifically includes initializing the state variable x0, P0.

[0060]

[0061] In the formula, x0 is a variable. Let P be the mean and P0 be the covariance. Construct the s-point and weights.

[0062]

[0063] In the formula, Let be an N-dimensional lower triangular square root matrix, where i is the i-th column.

[0064]

[0065] In the formula, The weights are the mean. The covariance weights are α; the distribution of the predicted model values ​​is α; the system distribution is β; the mean of the system state is calculated. and variance

[0066]

[0067]

[0068] In the formula, P k / k-1 Q is the predicted value of the system state covariance. k The covariance is used to transform the model to obtain a new s-point, which leads to the final model prediction, covariance matrix, and K value.

[0069]

[0070]

[0071]

[0072] K = P (xy)k / k-1 P (y)k / k-1

[0073] In the formula, These are the predicted observations; P is the mean of the predicted observations. (xy)k / k-1 P represents the predicted observation covariance. (y)k / k-1 This represents the predicted covariance.

[0074] New measurement data is acquired, and the predicted state estimate is fused with the new measurement data to generate a new state estimate. The state covariance matrix is ​​updated to reflect the error after measurement. When the measurement data is reliable, it is used as the final data; when the measurement data is unreliable, the system model data is used as the final data.

[0075] The comparison results include that when the difference between the SOC2 correction value calculated using the optimization algorithm and the SOC1 value is less than 5% of the SOC1 value, the SOC2 correction value calculated using the optimization algorithm is used as the final SOC value, and the remaining battery capacity of the electric vehicle is calculated based on the correction value.

[0076] When the difference between the SOC2 correction value and the SOC1 value calculated using the optimization algorithm is more than 10% higher than the SOC1 value, the calculation result is discarded, the calculation process is repeated, the calculation is performed again, and an alarm signal is issued to warn that the calculation result is abnormal.

[0077] Calculate and obtain the remaining battery power Er The relevant calculation formulas are as follows:

[0078] E = EE

[0079] E r =EE c

[0080]

[0081] In the formula, E represents the total battery capacity; E c U represents the total energy consumption of a car. b I is the battery pack terminal voltage. b Let t be the discharge current and t be the travel time.

[0082] S2: Based on the electric vehicle's speed, obtain the average current, voltage, and driving time to calculate the average energy consumption per 100 kilometers of driving range.

[0083] Specifically, the formula for calculating the remaining driving range of an electric vehicle is as follows:

[0084]

[0085]

[0086] In the formula, P is the average energy consumption for traveling a distance of x; ΔE is the energy consumed for traveling this distance; and Range is the remaining driving range of the electric vehicle.

[0087] S3: Calculate and obtain the remaining driving range of the electric vehicle, execute the driving range display strategy, and complete the estimation of the remaining driving range of the electric vehicle.

[0088] Specifically, the range display strategy includes calculating the values ​​of △Range1 and △Range2, using the following formula:

[0089] △Range1 = Actual Range - Displayed Range

[0090] △Range2 = Range displayed - Range actual

[0091] Based on the relationship between the actual calculated range and the displayed range, and in conjunction with the vehicle's speed, a pre-set range display strategy is executed. The numerical relationship includes when the actual calculated range is higher than the displayed range, when the actual calculated range is lower than the displayed range, when the vehicle is idling, when the calculated range is less than 50 km, or when the remaining battery power is ≤10 kWh.

[0092] When the actual calculated range is greater than the displayed range, if 0km < △Range1 < 5km and V > 1km / h, the range display strategy is to not update the range and not make any changes; if △Range1 > 5km and V > 1km / h, the range display strategy is to increase the range display by 1km for every 2km increase in driving distance.

[0093] When the actual calculated driving range is less than the displayed value, when V > 1 km / h, if 1 ≤ △Range2 ≤ 3, the driving range display strategy is to decrease the displayed driving range by 1 km for every 1 km driven; if 3 < △Range2 ≤ 7, the driving range display strategy is to decrease the displayed driving range by 1 km for every 0.8 km driven; if 7 < △Range2 ≤ 12, the driving range display strategy is to decrease the displayed driving range by 1 km for every 0.6 km driven; if 12 < △Range2 ≤ 12, the driving range display strategy is to decrease the displayed driving range by 1 km for every 0.6 km driven; if 12 < △Range2 ≤ 12, the driving range display strategy is to decrease the displayed driving range by 1 km for every 0.6 km driven. When e2≤18, the range display strategy is to decrease the displayed range by 1KM for every 0.5km driven; if 18<△Range2≤25, the range display strategy is to decrease the displayed range by 1KM for every 0.4km driven; if 25<△Range2≤45, the range display strategy is to decrease the displayed range by 1KM for every 0.2km driven; if △Range2>45, the range display strategy is to synchronize the displayed range with the actual range.

[0094] When the vehicle is idling, if △Range≠0 and V=0km / h, the range display strategy is to calculate the actual range based on the remaining battery power in real time, and the range display changes by 1km every 40s to get closer to the actual range; if the calculated range is less than 50km or the remaining battery power is ≤10kw.h, the range display strategy is to immediately display a warning value.

[0095] When the vehicle is idling, if △Range≠0 and V=0km / h, the range display strategy is to calculate the actual range based on the remaining battery power in real time, and the range display changes by 1km every 40s to get closer to the actual range; if the calculated range is less than 50km or the remaining battery power is ≤10kw.h, the range display strategy is to immediately display a warning value.

[0096] Furthermore, this embodiment also provides a system for estimating the remaining driving range of an electric vehicle, including: an acquisition module for acquiring necessary data information of the electric vehicle and calculating the remaining battery power of the electric vehicle using a fusion model; a calculation module for acquiring average current, voltage, and driving time based on the electric vehicle speed and calculating the average power consumption per 100 kilometers of driving range; and an estimation module for calculating and acquiring the remaining driving range of the electric vehicle, executing a driving range display strategy, and completing the estimation of the remaining driving range of the electric vehicle.

[0097] This embodiment also provides a computer device applicable to the method for estimating the remaining driving range of an electric vehicle, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement all or part of the steps of the method described in the above embodiments of the present invention.

[0098] This embodiment also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0099] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0100] This invention considers changes in the real-time driving environment, vehicle status, and charging infrastructure. By continuously monitoring the vehicle's surrounding conditions and driving behavior, such as road conditions, temperature, and speed, as well as battery status, it can provide more accurate range estimates, helping drivers better plan their trips and reducing anxiety caused by inaccurate estimates. It also monitors battery health, including changes in battery capacity over time and charging cycles. Battery health monitoring can detect battery problems early, such as capacity degradation or reduced charging efficiency, thus helping drivers better maintain the battery and accurately estimate available range. By providing more accurate range estimates, this method increases driver confidence in electric vehicles, making it easier for drivers to plan long-distance trips and reducing unnecessary worries, thereby improving the actual usability of electric vehicles. Accurate range estimates help drivers plan charging time and locations more effectively, avoiding overcharging or undercharging and improving charging efficiency. It also helps improve the driving experience, reducing driver discomfort caused by range uncertainty, allowing drivers to choose driving modes with more confidence and better adapt to the characteristics of electric vehicles.

[0101] Example 2

[0102] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a method for estimating the remaining driving range of an electric vehicle. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0103]

[0104] This invention considers changes in the real-time driving environment, vehicle status, and charging infrastructure. By continuously monitoring the vehicle's surrounding conditions, driving behavior, and battery status, it can provide more accurate range estimates, helping drivers better plan their trips and reducing anxiety caused by inaccurate estimates. It also monitors battery health, including changes in battery capacity over time and charging cycles. Battery health monitoring can detect battery problems early, such as capacity degradation or reduced charging efficiency, helping drivers better maintain the battery and accurately estimate available range. By providing more accurate range estimates, this method increases driver confidence in electric vehicles, making it easier for drivers to plan long trips and reducing unnecessary worries, thereby improving the actual usability of electric vehicles. Accurate range estimates help drivers plan charging time and locations more effectively, avoiding overcharging or undercharging and improving charging efficiency. It also helps improve the driving experience, reducing driver discomfort caused by range uncertainty, allowing drivers to choose driving modes with more confidence and better adapt to the characteristics of electric vehicles.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating the remaining range of an electric vehicle, characterized in that: The application relates to an electric vehicle necessary data acquisition and display method and device. The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The fusion model comprises the following steps: When the vehicle starts, an initial SOC0 value is acquired; A new SOC1 value is calculated by using the ampere-hour integral method, and the related calculation formula is as follows: In the formula, C N is the rated capacity of the battery; η is the charge-discharge efficiency of the battery; I is the discharge current of the battery; The remaining battery capacity of the electric vehicle is calculated by comparing the corrected SOC2 value with the SOC1 value. The optimization algorithm comprises the following steps: Initialize state variable x0, P0; where x0 is a variable, is the mean; P0 is the covariance; New s points are obtained by transformation, and the final model prediction value, the covariance matrix and the K value are obtained. wherein is an N-dimensional lower triangular square root matrix, i is the ith column; wherein is the mean weight, is the covariance weight; a is the distribution of the prediction model values; β is the system distribution. Computing a mean value of a system state and a variance In the formula, P k / k-1 is a system state covariance prediction value; Q k is a covariance; New measurement data are acquired, the predicted state estimation is fused with the new measurement data to generate new state estimation, and the state covariance matrix is updated to reflect the measurement error; when the measurement data is reliable, the measurement data is taken as the final data; when the measurement data is unreliable, the system model data is taken as the final data. K = P (xy)k / k-1 P (y)k / k-1 wherein is the predicted observation; is the mean of the predicted observation; P (xy)k / k-1 is the predicted observation covariance; P (y)k / k-1 is the predicted covariance; The electric vehicle necessary data comprises an initial SOC0 value of the battery, a rated capacity of the battery, a battery charging and discharging efficiency, a battery current value and a voltage value during vehicle operation, and displayed range.

2. The method of estimating the remaining cruising range of an electric vehicle according to claim 1, characterized by: The comparison result comprises the following steps:

3. The method of estimating the remaining cruising range of an electric vehicle according to claim 2, characterized by: When the difference between the corrected SOC2 value calculated by using the optimization algorithm and the SOC1 value is less than 5% of the SOC1 value, the corrected SOC2 value calculated by using the optimization algorithm is taken as the final SOC value, and the remaining battery capacity of the electric vehicle is calculated according to the corrected value; When the difference between the corrected SOC2 value calculated by using the optimization algorithm and the SOC1 value is more than 10% of the SOC1 value, the calculation result is abandoned, the calculation process is repeated, the calculation is re-performed, and an alarm signal is sent to warn that the calculation result is abnormal. The related calculation formula of the remaining range of the electric vehicle is as follows: The battery remaining power E is calculated r The relevant calculation formula is as follows: E r = E - E c where E is the total battery capacity; E c is the total energy consumption of the vehicle; U b is the terminal voltage of the battery pack; I b is the discharge current, and t is the driving time.

4. The method of estimating the remaining cruising range of an electric vehicle according to claim 3, characterized by: In the formula, P is the average energy consumption of driving x distance, AE is the energy consumed for driving the distance, and Range is the remaining range of the electric vehicle. The range display strategy comprises the following steps:

5. The method of estimating the remaining range of an electric vehicle according to claim 4, characterized in that: The calculation formula is as follows: According to the value relationship between the actual calculation value and the display value of the range, the pre-set range display strategy is executed in combination with the vehicle driving speed; the value relationship comprises that the actual calculation value of the range is greater than the display value, the actual calculation value of the range is less than the display value, the vehicle is in an idle state, the calculation value of the range is less than 50Km or the remaining battery capacity is less than or equal to 10kw.h. The application relates to an electric vehicle necessary data acquisition and display method and device. The method comprises the following steps: The method comprises the following steps:

6. A system for estimating the remaining cruising range of an electric vehicle, based on the method for estimating the remaining cruising range of an electric vehicle according to any one of claims 1 to 5, characterized by: The method comprises the following steps: The fusion model comprises the following steps: When the vehicle starts, an initial SOC0 value is acquired; A new SOC1 value is calculated by using the ampere-hour integral method, and the related calculation formula is as follows: The remaining battery capacity of the electric vehicle is calculated by comparing the corrected SOC2 value with the SOC1 value. The optimization algorithm comprises the following steps: New s points are obtained by transformation, and the final model prediction value, the covariance matrix and the K value are obtained. New measurement data are acquired, the predicted state estimation is fused with the new measurement data to generate new state estimation, and the state covariance matrix is updated to reflect the measurement error; when the measurement data is reliable, the measurement data is taken as the final data; when the measurement data is unreliable, the system model data is taken as the final data. The electric vehicle necessary data comprises an initial SOC0 value of the battery, a rated capacity of the battery, a battery charging and discharging efficiency, a battery current value and a voltage value during vehicle operation, and displayed range. The comparison result comprises the following steps: When the difference between the corrected SOC2 value calculated by using the optimization algorithm and the SOC1 value is less than 5% of the SOC1 value, the corrected SOC2 value calculated by using the optimization algorithm is taken as the final SOC value, and the remaining battery capacity of the electric vehicle is calculated according to the corrected value; When the difference between the corrected SOC2 value calculated by using the optimization algorithm and the SOC1 value is more than 10% of the SOC1 value, the calculation result is abandoned, the calculation process is repeated, the calculation is re-performed, and an alarm signal is sent to warn that the calculation result is abnormal. The related calculation formula of the remaining range of the electric vehicle is as follows: In the formula, P is the average energy consumption of driving x distance, AE is the energy consumed for driving the distance, and Range is the remaining range of the electric vehicle. The range display strategy comprises the following steps: The calculation formula is as follows: According to the value relationship between the actual calculation value and the display value of the range, the pre-set range display strategy is executed in combination with the vehicle driving speed; the value relationship comprises that the actual calculation value of the range is greater than the display value, the actual calculation value of the range is less than the display value, the vehicle is in an idle state, the calculation value of the range is less than 50Km or the remaining battery capacity is less than or equal to 10kw.h. The estimation module is used to calculate and obtain the remaining driving range of the electric vehicle, execute the driving range display strategy, and complete the estimation of the remaining driving range of the electric vehicle. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: When the processor executes the computer program, it implements the steps of the method for estimating the remaining driving range of the electric vehicle according to any one of claims 1 to 5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for estimating the remaining driving range of the electric vehicle according to any one of claims 1 to 5.

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