A vehicle remaining driving range prediction method, system, device and storage medium

Abstract

This invention belongs to the field of electric vehicle technology, and particularly relates to a method, system, device, and storage medium for predicting the remaining driving range of a vehicle. It aims to solve the technical problem in existing technologies that fail to consider the impact of battery aging on the battery's usable capacity, potentially leading to decreased calculation accuracy and affecting user experience. The method includes: acquiring operating parameter data of the electric vehicle; preprocessing the operating parameter data to obtain preprocessed data; calculating the energy consumption of the electric vehicle during discharge based on the preprocessed data to obtain a discharge energy consumption value; calculating the charging capacity during charging based on the preprocessed data, and calculating the current state of equilibrium (SOH) of the battery based on the capacity value; and calculating a predicted value for the remaining driving range of the vehicle based on the discharge energy consumption value and the SOH value to obtain a prediction result.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle technology, and in particular relates to a method, system, device and storage medium for predicting the remaining driving range of a vehicle. Background Technology

[0002] As the global automotive industry accelerates its transformation towards new energy sources, lithium-ion batteries, with their significant advantages such as high energy density, long cycle life, and low self-discharge rate, have become the core power source for electric vehicles and various electric transportation tools. In practical use, the remaining driving range is one of the key indicators that users are most concerned about. Accurately calculating the remaining driving range not only provides users with reliable travel references, effectively alleviating "range anxiety" and thus increasing user acceptance and experience of electric vehicles; for the vehicle's battery management system (BMS), accurate calculation of the remaining driving range is also a crucial foundation for achieving rational battery management, optimizing charging and discharging strategies, extending battery life, and ensuring safe vehicle operation.

[0003] Vehicle energy consumption is influenced by a variety of complex factors, including road conditions (such as urban congestion, highway driving, and mountain roads), driving habits (such as rapid acceleration, sudden braking, and smooth driving), vehicle load, ambient temperature, and dynamic changes in battery performance. The State of Health (SOH) of a power battery reflects the degree of performance degradation caused by complex internal physicochemical changes during long-term use, such as capacity loss and increased internal resistance, which also has a crucial impact on remaining driving range.

[0004] In the existing technology, the real-time power consumption of the vehicle is calculated, and the remaining available power of the vehicle is calculated based on the SOC. Finally, the current remaining driving range is calculated based on the real-time power consumption and the available power.

[0005] The existing technology has the following technical problems: 1. Existing technology does not take into account the impact of battery aging on the available battery capacity, which may lead to a decrease in calculation accuracy and affect user experience.

[0006] 2. Existing technologies are still not adaptable to complex working conditions, and the calculation process is complex, making it difficult to meet real-time requirements. Summary of the Invention

[0007] This invention provides a method, system, device, and storage medium for predicting the remaining driving range of a vehicle, aiming to solve the technical problems in the prior art, such as not considering the impact of battery aging on the available battery power, which may lead to a decrease in calculation accuracy and affect user experience; and the complex calculation process, which makes it difficult to meet real-time requirements.

[0008] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for predicting the remaining driving range of a vehicle, comprising: Obtain operating parameter data for electric vehicles; The operating parameter data is preprocessed to obtain preprocessed data; Based on the preprocessed data, the energy consumption of the electric vehicle during the discharge process is calculated to obtain the discharge energy consumption value; Based on the preprocessed data, the capacity value charged during the charging process is calculated, and the SOH value of the battery in the current state is calculated based on the capacity value. Based on the discharge energy consumption value and the SOH value, the predicted value of the vehicle's remaining driving range is calculated, and the prediction result is obtained.

[0009] Furthermore, the aforementioned operating parameter data includes: acquisition time, current data, voltage data, driving mileage, and battery pack temperature.

[0010] Furthermore, the above-mentioned preprocessing of the operating parameter data yields preprocessed data as follows: The operating parameter data is cleaned to obtain cleaned data; Outlier handling was performed on the cleaned data using statistical methods to obtain preprocessed data.

[0011] Furthermore, based on the aforementioned preprocessed data, the energy consumption of the electric vehicle during the discharge process is calculated, and the specific discharge energy consumption value is as follows: Based on the ampere-hour integral method, the preprocessed data is divided into multiple different driving segments using a piecewise integration method; Based on the acquisition time, current data, and voltage data of each driving segment, the energy change of that driving segment is calculated; Based on the energy change of each driving segment and the corresponding driving mileage, the driving energy consumption is calculated to obtain the discharge energy consumption value.

[0012] Furthermore, before dividing the preprocessed data into multiple different driving segments using the piecewise integration method based on the ampere-hour integration method, the method further includes: The current data is filtered using a moving average filter.

[0013] Furthermore, the calculation of the charging capacity value during the charging process based on the preprocessed data, and the calculation of the SOH value of the battery in the current state based on the capacity value, are specifically as follows: The capacity to be charged within a preset time is calculated using the ampere-hour integration method based on the current data and the acquisition time. Based on the SOC values ​​before and after charging, calculate the SOC change value, and combine this with the battery's rated capacity to calculate the battery's SOH value under the current condition.

[0014] Furthermore, based on the discharge energy consumption value and the SOH value, the predicted value of the vehicle's remaining driving range is calculated, and the specific prediction result is as follows: Calculate the currently available battery energy based on the battery's charge, SOH value, and factory-specified energy. Based on the currently available battery energy and the discharge energy consumption value, the predicted value of the vehicle's remaining driving range is calculated, and the prediction result is obtained.

[0015] Secondly, to solve the above-mentioned technical problems, the present invention also provides a vehicle remaining driving range prediction system, comprising: The data acquisition module is used to acquire the operating parameter data of the electric vehicle; The data preprocessing module is used to preprocess the operating parameter data to obtain preprocessed data; The energy consumption calculation module is used to calculate the energy consumption of the electric vehicle during the discharge process based on the preprocessed data, and obtain the discharge energy consumption value. The SOH calculation module is used to calculate the capacity value charged during the charging process based on the preprocessed data, and to calculate the SOH value of the battery in the current state based on the capacity value. The remaining driving range calculation module is used to calculate the predicted value of the vehicle's remaining driving range based on the discharge energy consumption value and the SOH value, and obtain the prediction result.

[0016] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle remaining driving range prediction method of the present application.

[0017] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the vehicle remaining driving range prediction method of the present application.

[0018] Compared with the prior art, the present invention has the following advantages: This invention takes into account the impact of battery aging on the lifespan of power batteries, improving the accuracy of range calculation results. On the other hand, it avoids the high computational requirements of complex algorithms, reducing costs. At the same time, it can provide electric vehicle users with a more accurate and reliable range reference, effectively alleviating users' "range anxiety" and providing users with a more realistic range reference.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for predicting remaining driving range of a vehicle according to an embodiment of the present invention is shown. Figure 2 A flowchart illustrating the calculation of SOH and real-time energy consumption according to an embodiment of the present invention is shown. Figure 3 A schematic diagram of a vehicle remaining driving range prediction system according to an embodiment of the present invention is shown; Figure 4 A schematic diagram of an electronic device structure according to an embodiment of the present invention is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Figure 1 A flowchart illustrating a method for predicting remaining driving range of a vehicle according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, an embodiment of the present invention provides a method for predicting the remaining driving range of a vehicle, comprising: Obtain operating parameter data for electric vehicles; The operating parameter data is preprocessed to obtain preprocessed data; Based on the preprocessed data, the energy consumption of the electric vehicle during the discharge process is calculated to obtain the discharge energy consumption value; Based on the preprocessed data, the capacity value charged during the charging process is calculated, and the SOH value of the battery in the current state is calculated based on the capacity value. Based on the discharge energy consumption value and the SOH value, the predicted value of the vehicle's remaining driving range is calculated, and the prediction result is obtained.

[0024] like Figure 2 As shown, this invention calculates the vehicle's real-time energy consumption and the power battery's state of health (SOH) based on real-time vehicle operating data. It comprehensively considers the impact of real-time energy consumption and battery health on the driving range, achieving dynamic and accurate calculation of the remaining driving range. Through algorithm optimization, the remaining driving range is updated in real time, providing users with a more realistic driving range reference.

[0025] Optionally, the operating parameter data includes: acquisition time, current data, voltage data, driving mileage, and battery pack temperature.

[0026] Optionally, the operating parameter data is preprocessed to obtain preprocessed data, specifically as follows: The operating parameter data is cleaned to obtain cleaned data; Outlier handling was performed on the cleaned data using statistical methods to obtain preprocessed data.

[0027] In this embodiment, during data cleaning, the consistency and rationality of the data are first checked. For example, data cleaning is performed based on the physical reasonable range of current and voltage. If the value range of a single-unit voltage is less than 0V or greater than 5V, the data is considered invalid and discarded. Similarly, if the current data contains typical invalid values ​​such as 65535, it also needs to be discarded.

[0028] In this embodiment, when handling outliers, the statistical methods include, but are not limited to, the Standard Deviation Method and the Interquartile Range (IQR) method, to avoid errors in the energy consumption calculation process.

[0029] In this embodiment, the standard deviation method removes outliers from the normal distribution using a dynamic threshold μ±ασ, as shown in the following formula:

[0030]

[0031] in, Let n represent the mean and n represent the total number of samples in the cleaned data. This represents the i-th cleaned data, and α represents the threshold coefficient. Indicates standard deviation; Specifically, data cleaning Satisfy | - |>ασ is considered an anomaly (k can be 2 or 3). The advantage of the standard deviation method is that it is computationally efficient and avoids subjective thresholds. In this embodiment, the interquartile range (IQR) method resists skewness interference through quantiles, as shown in the following formula:

[0032]

[0033]

[0034]

[0035]

[0036] in, The first quartile of the array, It is the third quartile of the array. Indicates the interquartile range. This is the lower limit value. This is the upper limit. The advantage of the algorithm is that it does not rely on the mean, is less affected by extreme values, can automatically determine the threshold, and reduce manual intervention.

[0037] Optionally, based on the preprocessed data, the energy consumption of the electric vehicle during the discharge process is calculated, and the discharge energy consumption value is obtained as follows: Based on the ampere-hour integral method, the preprocessed data is divided into multiple different driving segments using a piecewise integration method; Based on the acquisition time, current data, and voltage data for each driving segment, the energy change for that driving segment is calculated; the calculation formula is as follows:

[0038] in, This represents the energy change during the current driving segment, where k represents the k-th sampling point, and K represents the total number of sampling points. This represents the sampling time of the k-th sampling point. This represents the sampling time of the (k-1)th sampling point. This represents the instantaneous current value at time t. k This indicates the segmentation weight, which is based on time or capacity changes. If segmentation is based on time, it can be forced to segment every T hours, such as T=1, to prevent the accumulation of small errors over a long period. If segmentation is based on capacity, it will automatically segment when |ΔQ|> the threshold (e.g., 2% of rated capacity).

[0039] In this embodiment, the calculation accuracy of the pulse charging and discharging process is improved by using a piecewise integration method.

[0040] Based on the energy change of each driving segment and the corresponding driving mileage, the driving energy consumption is calculated to obtain the discharge energy consumption value.

[0041] In this embodiment, the vehicle energy consumption for each driving segment is calculated using the ampere-hour integral method, as shown in the following formula:

[0042] Where ΔE represents the change in battery energy output during this driving segment, measured in joules (J). This indicates the start time of the driving segment. The time t represents the end time of the current driving segment, in seconds (s). I(t) represents the battery current at time t, in amperes (A). U(t) represents the battery voltage at time t, in volts (V). The value of t ranges from the start time of the current driving segment. Until the end time By integrating the currents I(t) and U(t) over a period of time, the energy change ΔE during this period is obtained.

[0043] The formula for calculating driving energy consumption is shown below:

[0044]

[0045] in, This indicates the actual mileage traveled by the vehicle within that driving segment. Indicates the time of electric vehicles The distance traveled at any given time is expressed in kilometers (km). Indicates the time of electric vehicles Mileage at any given time This indicates the energy consumption of the vehicle per 100 kilometers for this driving segment, expressed in kilowatt-hours (kWh / 100 km).

[0046] Optionally, before dividing the preprocessed data into multiple different driving segments using a piecewise integration method based on the ampere-hour integration method, the method further includes: The current data is filtered using a moving average filter, as shown in the following formula:

[0047] in, This represents the filtered current value, 1 / N represents the normalization coefficient, and t represents the current time. M represents the sampling time in the filtering process, and M represents the sliding window. This represents the current value at time l.

[0048] In this embodiment, when using ampere-hour integration for calculation, the current signal is first preprocessed using a moving average filter. Current sampling signals often contain high-frequency noise, such as sensor zero drift and PWM interference. Direct integration would cause the SOC estimation error to accumulate over time. Calculating the mean within a moving window suppresses high-frequency noise and requires less computational power.

[0049] Optionally, based on the preprocessed data, the capacity value charged during the charging process is calculated, and the SOH value of the battery in the current state is calculated based on the capacity value, specifically as follows: The capacity to be charged within a preset time is calculated using the ampere-hour integration method, based on the current data and the acquisition time; as shown in the following formula:

[0050] Where I(t) represents the battery current at time t, in amperes (A), and t represents the charging time, in seconds (h). By integrating the current I(t) over a period of time, the corresponding charging capacity Δ is obtained. The unit is ampere-hour (Ah).

[0051] Based on the SOC values ​​before and after charging, calculate the SOC change. Then, combining this with the battery's rated capacity, calculate the SOH value of the battery in its current state. The formula is as follows:

[0052] in, This indicates the SOC value after charging. C0 represents the SOC value before charging and C0 represents the rated capacity of the power battery. In this embodiment, the SOC values ​​before and after charging are obtained, the change in SOC is calculated, and the estimated SOH value under the current state is obtained through a formula.

[0053] Optionally, based on the discharge energy consumption value and the SOH value, a predicted value for the vehicle's remaining driving range is calculated, and the prediction result is as follows: Calculate the currently available battery energy based on the battery's capacity, its state of equilibrium (SOH) value, and its factory-specified energy rating. As shown in the following formula:

[0054] in, The SOC represents the battery pack's nominal energy (kWh) at the time of manufacture, the SOC represents the current percentage of battery charge, and the SOH represents the SOH value. Based on the currently available battery energy and the discharge energy consumption value, calculate the predicted remaining driving range of the vehicle. The prediction result is obtained as shown in the following formula:

[0055] in, This represents the calculated real-time energy consumption, where 'a' represents the correction factor affected by ambient temperature and driving habits, and is set to 1 by default.

[0056] Based on and Figure 1 Using the same principle as the method shown, this embodiment of the invention also provides a vehicle remaining driving range prediction system, such as... Figure 3 As shown, it includes: The data acquisition module is used to acquire the operating parameter data of the electric vehicle; The data preprocessing module is used to preprocess the operating parameter data to obtain preprocessed data; The energy consumption calculation module is used to calculate the energy consumption of the electric vehicle during the discharge process based on the preprocessed data, and obtain the discharge energy consumption value. The SOH calculation module is used to calculate the capacity value charged during the charging process based on the preprocessed data, and to calculate the SOH value of the battery in the current state based on the capacity value. The remaining driving range calculation module is used to calculate the predicted value of the vehicle's remaining driving range based on the discharge energy consumption value and the SOH value, and obtain the prediction result.

[0057] The vehicle remaining driving range prediction system of this invention can execute the vehicle remaining driving range prediction method provided in this invention. The implementation principle is similar. The actions performed by each module and unit in the vehicle remaining driving range prediction system in each embodiment of this invention correspond to the steps in the vehicle remaining driving range prediction method in each embodiment of this invention. For detailed functional descriptions of each module of the vehicle remaining driving range prediction system, please refer to the descriptions in the corresponding vehicle remaining driving range prediction methods shown above, which will not be repeated here.

[0058] The aforementioned vehicle remaining mileage prediction system can be a computer program (including program code) running on a computer device, such as an application software; the application software can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0059] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0060] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0061] In one alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the electronic device may also include a transceiver, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this electronic device does not constitute a limitation on the embodiments of the present invention.

[0062] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0063] Among these, electronic devices can also be terminal devices. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0064] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0065] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0066] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0067] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A vehicle remaining travel distance prediction method characterized by, The method comprises: acquiring operation parameter data of an electric vehicle; preprocessing the operation parameter data to obtain preprocessing data; based on the preprocessing data, calculating the energy consumption of the electric vehicle during discharging to obtain a discharging energy consumption value; based on the preprocessing data, calculating the capacity value charged during charging, and calculating the SOH value of the battery under the current state according to the capacity value; based on the discharging energy consumption value and the SOH value, calculating the prediction value of the remaining driving range of the vehicle to obtain a prediction result.

2. The method of claim 1, wherein The operation parameter data includes: acquisition time, current data, voltage data, driving range, and battery pack temperature.

3. The method of claim 1, wherein The preprocessing of the operation parameter data to obtain preprocessing data is specifically: performing data cleaning on the operation parameter data to obtain cleaned data; performing abnormal value processing on the cleaned data using a statistical method to obtain preprocessing data.

4. The method of claim 2, wherein Based on the preprocessing data, the energy consumption of the electric vehicle during discharging is calculated to obtain a discharging energy consumption value, which is specifically: based on the ampere-hour integration method, the preprocessing data is divided into multiple different driving segments using piecewise integration; according to the acquisition time, current data and voltage data of each driving segment, the energy change of the driving segment is calculated; according to the energy change of each driving segment and the corresponding driving range, the driving energy consumption is calculated to obtain the discharging energy consumption value.

5. The method of claim 4, wherein Before dividing the preprocessing data into multiple different driving segments using piecewise integration based on the ampere-hour integration method, it further comprises: filtering the current data through sliding average filtering.

6. The method of claim 2, wherein Based on the preprocessing data, the capacity value charged during charging is calculated, and the SOH value of the battery under the current state is calculated according to the capacity value, which is specifically: by the ampere-hour integration method, the capacity value charged within a preset time is calculated according to the current data and the acquisition time; according to the SOC values before and after charging, the SOC change value is calculated, and the SOH value of the battery under the current state is calculated in combination with the rated capacity of the battery.

7. The method of claim 1, wherein Based on the discharging energy consumption value and the SOH value, the prediction value of the remaining driving range of the vehicle is calculated to obtain a prediction result, which is specifically: according to the battery capacity, the SOH value of the battery, and the factory nominal energy of the battery, the current available battery energy is calculated; according to the current available battery energy and the discharging energy consumption value, the prediction value of the remaining driving range of the vehicle is calculated to obtain a prediction result.

8. A vehicle remaining travel distance prediction system characterized by comprising: It comprises: a data acquisition module for acquiring operation parameter data of an electric vehicle; a data preprocessing module for preprocessing the operation parameter data to obtain preprocessing data; an energy consumption calculation module for calculating the energy consumption of the electric vehicle during discharging based on the preprocessing data to obtain a discharging energy consumption value; an SOH calculation module for calculating the capacity value charged during charging based on the preprocessing data, and calculating the SOH value of the battery under the current state according to the capacity value; a remaining driving range calculation module for calculating the prediction value of the remaining driving range of the vehicle based on the discharging energy consumption value and the SOH value to obtain a prediction result.

9. An electronic device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

10. A computer storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the method of any one of claims 1-7.

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