Battery state-of-energy health assessment method and apparatus, and electronic device and readable storage medium

Abstract

A battery state-of-energy health assessment method and apparatus, and an electronic device and a readable storage medium. The method comprises: acquiring state-of-energy health data, wherein the state-of-energy health data is the ratio of dischargeable energy of a battery under fixed C-rate operating conditions at different states of aging to dischargeable energy of the battery under the same C-rate operating conditions at an initial stage of the battery (S101); acquiring charging energy of the battery of a target vehicle at different temperatures, and acquiring the ratio of the charging energy to theoretical charging energy (S102); and using a preset algorithm to assess the state-of-energy health data and the ratio of the charging energy to the theoretical charging energy, in order to obtain an assessment result (S103). In the method, acquiring battery state-of-energy health data and the ratio of charging energy to theoretical charging energy achieves comprehensive assessment of battery state-of-energy health from two perspectives, so as to more rationally estimate the residual driving range of electric vehicles, thereby achieving more accurate estimation results.

Description

Battery energy state health assessment methods, devices, electronic equipment, and readable storage media

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 2024106731644, filed on May 28, 2024, entitled “Battery Energy State Health Assessment Method, Apparatus, Electronic Device and Readable Storage Medium”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of automotive technology, and in particular to a method, apparatus, electronic device, and readable storage medium for assessing the energy state health of a battery. Background Technology

[0004] Estimating the remaining driving range of an electric vehicle (EV) refers to predicting how far the EV can still travel based on its remaining battery power, allowing drivers to plan their subsequent journeys accordingly. With the increasing prevalence of electric vehicles, higher demands are being placed on the accuracy of these remaining driving range estimates.

[0005] Currently, the remaining driving range of electric vehicles is usually estimated based on the battery's calibration parameters, with little consideration given to the impact of battery aging on the driving range. Even when it is considered, it is usually from the perspective of capacity-based State of Health (SOH), and rarely from the perspective of energy, to assess the impact of battery aging on the driving range.

[0006] It is evident that the relevant technical solutions used to estimate the remaining driving range of electric vehicles have problems such as insufficient consideration of factors and inaccurate estimation results.

[0007] Summary of the Invention

[0008] The main objective of this disclosure is to provide a method, apparatus, electronic device, and readable storage medium for assessing the state of energy of a battery, with the aim of more reasonably estimating the remaining driving range of an electric vehicle and making the estimation results more accurate.

[0009] In a first aspect, this disclosure provides a method for assessing the energy state health of a battery, including:

[0010] Acquire energy state health data, wherein the energy state health data is the ratio of the energy that the battery can release under a fixed rate of operation in different aging states to the energy that the battery can release under the same rate of operation in the initial stage of the battery.

[0011] The charging energy of the target vehicle battery at different temperatures is obtained, and the ratio of the charging energy to the theoretical charging energy is obtained.

[0012] A preset algorithm is used to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and the evaluation results are obtained.

[0013] In an optional implementation, the energy state health data is obtained based on test data from multiple sets of experimental batteries. The test data from the multiple sets of experimental batteries includes: data on the correlation between storage time and battery aging degree under different preset temperatures and preset SOC conditions; and a storage time ratio coefficient obtained based on the ratio of storage time corresponding to preset temperature and preset SOC to storage time corresponding to standard temperature and standard SOC, under the premise of achieving the same battery aging degree.

[0014] The storage time at different temperatures and SOCs when the target vehicle is powered on is multiplied by the corresponding storage time ratio coefficient to obtain the standard storage time. The historical cumulative storage time is then calculated from the standard storage time. Based on the historical cumulative storage time and the correlation data between storage time and battery aging under standard temperature and standard SOC conditions, the energy state health data is calculated.

[0015] In an optional implementation, the energy state health data is obtained based on test data from multiple sets of experimental batteries. The test data from the multiple sets of experimental batteries includes: data on the correlation between the number of charge-discharge cycles and the degree of battery aging under different preset temperatures; and a charge-discharge cycle ratio coefficient obtained based on the ratio of the number of charge-discharge cycles corresponding to the preset temperature to the number of charge-discharge cycles corresponding to the standard temperature, under the premise of reaching the same degree of battery aging.

[0016] The number of charge-discharge cycles at different temperatures when the target vehicle is powered on is multiplied by the corresponding charge-discharge cycle ratio coefficient to obtain the standard charge-discharge cycle. The historical cumulative charge-discharge cycle is then calculated from the standard charge-discharge cycle. Based on the historical cumulative charge-discharge cycle and the correlation data between the number of charge-discharge cycles and the degree of battery aging under standard temperature conditions, the energy state health data is calculated.

[0017] In an optional implementation, the test data of the multiple sets of experimental batteries are collected and calculated by connecting to the battery circuit of the target vehicle.

[0018] In an optional implementation, obtaining the charging energy of the target vehicle battery at different temperatures and obtaining the ratio of the charging energy to the theoretical charging energy includes:

[0019] The actual temperature and initial SOC are detected and obtained for each charge, and the cutoff SOC and the actual amount of electricity charged in this charge are also obtained.

[0020] The actual charging power is compared with the theoretical power required to charge from the initial SOC to the cutoff SOC at the same temperature to obtain the ratio of the charging energy to the theoretical charging energy.

[0021] In an optional implementation, the step of using a preset algorithm to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and obtaining the evaluation result, includes:

[0022] The energy state health data and the weight of the ratio of charging energy to theoretical charging energy on the battery energy state health assessment are obtained by training a preset neural network.

[0023] The preset algorithm is constructed using the influence weights;

[0024] The preset algorithm is used to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained.

[0025] In an optional implementation, the step of using a preset algorithm to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and obtaining the evaluation result, includes:

[0026] If the ratio of the charging energy to the theoretical charging energy meets the preset conditions, the preset algorithm is used to evaluate the energy state health data and obtain the evaluation result.

[0027] Secondly, this disclosure provides a battery energy state health assessment device, comprising:

[0028] The energy release acquisition module is configured to acquire energy state health data, wherein the energy state health data is the ratio of the energy that the battery can release under a fixed rate under different aging states to the energy that the battery can release under the same rate under the initial stage of the battery.

[0029] The energy acquisition module is configured to acquire the charging energy of the target vehicle battery at different temperatures, and to acquire the ratio of the charging energy to the theoretical charging energy.

[0030] The evaluation module is configured to use a preset algorithm to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and obtain the evaluation results.

[0031] Thirdly, this disclosure provides an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform a method as described in any of the foregoing embodiments.

[0032] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the method as described in any of the foregoing embodiments. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments or related technologies of this disclosure, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0034] Figure 1 is a schematic flowchart of a battery energy state health assessment method provided in an embodiment of this disclosure;

[0035] Figure 2 is a schematic flowchart of a battery energy state health assessment method provided in another embodiment of this disclosure;

[0036] Figure 3 is a schematic diagram of the complete process of the battery energy state health assessment method disclosed in this paper;

[0037] Figure 4 is a schematic flowchart of a battery energy state health assessment method provided in another embodiment of this disclosure;

[0038] Figure 5 is a schematic diagram of a battery energy state health assessment device provided in an embodiment of this disclosure;

[0039] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0041] Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely to illustrate selected embodiments of the disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0042] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] The following detailed description of some embodiments of this disclosure is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0044] Current methods for estimating the remaining driving range of electric vehicles typically involve subtracting the current driving range from the range the battery could have achieved in its new state to arrive at an estimate of the remaining driving range. Alternatively, they may collect the current percentage of battery charge remaining and estimate the corresponding remaining driving range based on this percentage and the battery capacity. Both methods are inaccurate because the former does not consider the aging and degradation that occurs during battery use, and the latter only assesses the remaining driving range from a capacity perspective. Therefore, this disclosure aims to propose a battery energy state health assessment method that can more reasonably estimate the remaining driving range of electric vehicles, thereby making the estimation results more accurate.

[0045] Figure 1 is a schematic flowchart of a battery energy state health assessment method provided in an embodiment of this disclosure. The execution subject of this method can be an electronic terminal such as a computer, or other devices configured to estimate the remaining driving range of an electric vehicle. As shown in Figure 1, the method includes:

[0046] S101. Obtain energy state health data, wherein the energy state health data is the ratio of the energy that the battery can release under fixed rate conditions in different aging states to the energy that the battery can release under the same rate conditions in the initial stage of the battery.

[0047] The aforementioned energy state health data, also known as the ratio of the energy that a battery can release under fixed-rate conditions in different aging states to the energy that a battery can release under the same rate conditions in its initial stage (i.e., when it is brand new), can reflect the health status of the battery from an energy perspective. The industry also refers to this energy state health data as energy SOH (full name: State of health).

[0048] For example, a battery fully charged in its brand-new state can release 100 kWh of energy under a fixed operating rate. However, after a period of storage or use, the battery will age and degrade. If the battery is fully charged again under the same fixed operating rate, it can only release 90 kWh of energy. The ratio of the 90 kWh of energy released when the battery is fully charged to the 100 kWh of energy released when it is fully charged in its brand-new state can be used to obtain the current energy state health data of the battery, i.e., the energy SOH is 90%.

[0049] The specific type of magnification and operating condition mentioned above under the fixed magnification condition can be freely selected or set according to the actual situation, and there are no restrictions here.

[0050] S102. Obtain the charging energy of the target vehicle battery at different temperatures, and obtain the ratio of the above charging energy to the theoretical charging energy.

[0051] The aforementioned different temperatures can be, for example, 25 degrees Celsius, 35 degrees Celsius, 45 degrees Celsius, etc., and can be determined based on actual conditions; no restrictions are imposed here. The aforementioned charging energy refers to the energy actually charged into the battery during a single charge, while the aforementioned theoretical charging energy refers to the energy that the battery can theoretically be charged in its brand-new state.

[0052] For example, if a battery is charged with 90 kWh of energy in a single charge, and under the same starting and ending percentage of charge (also known in the industry as SOC, or State of Charge), for example, charging from 30% SOC to 100% SOC (the specific percentage is not limited here), the battery can theoretically be charged with 100 kWh of energy in its new state. The ratio of the 90 kWh charged to the theoretically 100 kWh that the battery can be charged in its new state is used to obtain a kind of energy state health data. This data is obtained from the perspective of the charged energy, meaning the battery's SOH is 90%.

[0053] S103. Using a preset algorithm, evaluate the above energy state health data and the ratio of the above charging energy to the theoretical charging energy, and obtain the evaluation results.

[0054] The function of the aforementioned preset algorithm is mainly to comprehensively evaluate the energy state health data and the ratio of charging energy to theoretical charging energy. In other words, it comprehensively evaluates the current energy state health of the battery by combining the two data: "the current energy state health data of the battery obtained from the perspective of energy released" and "the current energy state health data of the battery obtained from the perspective of energy charged".

[0055] The battery's current state of energy health, as assessed above, can be configured to combine the battery's current SOC and the electric vehicle's driving energy consumption to estimate the electric vehicle's remaining driving range.

[0056] In this embodiment, energy state health data is acquired, wherein the energy state health data is the ratio of the energy that the battery can release under fixed-rate conditions in different aging states to the energy that the battery can release under the same rate conditions in the initial stage. The charging energy of the target vehicle battery at different temperatures is acquired, and the ratio of the charging energy to the theoretical charging energy is obtained. A preset algorithm is used to evaluate the energy state health data and the ratio of charging energy to theoretical charging energy to obtain the evaluation result. In this embodiment, by acquiring battery energy state health data and the ratio of charging energy to theoretical charging energy, a comprehensive evaluation of battery energy state health is achieved from two perspectives, thereby more reasonably estimating the remaining driving range of the electric vehicle and making the estimation result more accurate.

[0057] Optionally, based on the above embodiments, the energy state health data can be obtained from test data of multiple sets of experimental batteries. The test data of the multiple sets of experimental batteries includes: data on the correlation between storage time and battery aging degree under different preset temperatures and preset SOC conditions; and storage time ratio coefficient obtained based on the ratio of storage time corresponding to preset temperature and preset SOC to storage time corresponding to standard temperature and standard SOC under the premise of achieving the same battery aging degree.

[0058] The storage time at different temperatures and SOCs obtained when the target vehicle is powered on is multiplied by the corresponding storage time ratio coefficient to obtain the standard storage time. The historical cumulative storage time is then calculated from the standard storage time. Based on the historical cumulative storage time and the correlation data between storage time and battery aging under standard temperature and standard SOC conditions, the energy state health data is calculated.

[0059] For example, the test data of the above-mentioned multiple sets of experimental batteries can be obtained through testing:

[0060] First, the experimental battery is stored under preset temperature and preset SOC conditions. The preset temperature can be, for example, 25 degrees Celsius, 35 degrees Celsius, or 45 degrees Celsius, and the preset SOC can be, for example, 60%, 70%, or 80% SOC. The specific SOC can be freely chosen based on actual conditions and is not limited here. Every time the preset storage time is reached, such as 50 days, 100 days, or 150 days, the experimental battery is tested. The test method can be to fully charge the experimental battery and release its energy once under a fixed rate condition. The energy that the experimental battery can release under a fixed rate is compared with the energy that it can release under the same rate at its initial stage (i.e., the latest time) to obtain the current energy SOH of the experimental battery. Combined with the storage time corresponding to the current energy SOH of the experimental battery, the correlation data between storage time and battery aging degree under the above-mentioned preset temperature and preset SOC conditions can be obtained. With the accumulation of multiple storage for the preset time and multiple tests, this correlation data between storage time and battery aging degree can be formed into a table or a function, etc., without any restrictions.

[0061] By adjusting the preset temperature and preset SOC and repeating the above steps, the correlation data between storage time and battery aging degree under different preset temperature and preset SOC conditions can be obtained.

[0062] The reason for obtaining the above data on the correlation between storage time and battery aging under different preset temperatures and preset SOC conditions is that the temperature and SOC conditions for battery storage and placement are uncertain and change with each user's use of the vehicle during actual use. Therefore, the degree of battery aging caused by this storage must be calculated separately for each storage and placement temperature and SOC condition.

[0063] However, no matter how many different preset temperatures and preset SOC conditions are tested to obtain data on the relationship between storage time and battery aging, it is difficult to exhaust all the temperature and SOC conditions in actual use. Therefore, finding the storage time conversion relationship between the above-mentioned different preset temperatures and preset SOC conditions, i.e., the above-mentioned storage time ratio coefficient, is the only way to solve this problem.

[0064] For example, the aforementioned storage time ratio can be obtained through calculation:

[0065] First, select a standard temperature and standard SOC condition, such as 25 degrees Celsius and 80% SOC. The specific condition can be chosen based on actual circumstances and is not limited here. Then, following the steps above, test the correlation data between storage time and battery aging under this standard temperature and standard SOC condition. For example, after 500 days, the battery under this standard temperature and standard SOC condition will age to 95% SOH.

[0066] Then, select multiple preset temperatures and preset SOC conditions, and test the correlation data between storage time and battery aging degree under different preset temperature and preset SOC conditions according to the above steps. For example, the battery will age to 95% SOH after 400 days at 45 degrees Celsius and 60% SOC, and the battery will age to 95% SOH after 600 days at 10 degrees Celsius and 50% SOC.

[0067] Based on the correlation data between storage time and battery aging degree under the aforementioned standard temperature and standard SOC conditions, as well as the correlation data between storage time and battery aging degree under multiple different preset temperature and preset SOC conditions, under the premise of achieving the same battery aging degree, the storage time ratio coefficient is obtained by comparing the storage time corresponding to different preset temperatures and preset SOCs with the storage time corresponding to preset standard temperatures and preset standard SOCs. For example, aging a battery to 95% SOH in 400 days under 45 degrees Celsius and 60% SOC conditions with aging to 95% SOH in 500 days under 25 degrees Celsius and 80% SOC conditions (used as the standard temperature and standard SOC conditions in this example), by comparing 500 days with 400 days, the storage time ratio coefficient under 45 degrees Celsius and 60% SOC conditions is 1.25. This storage time ratio coefficient means that storing a battery for 1 day under 45 degrees Celsius and 60% SOC conditions is equivalent to storing it for 1.25 days under standard temperature and standard SOC conditions.

[0068] Similarly, storage time ratio coefficients can be obtained under other preset temperature and preset SOC conditions. These storage time ratio coefficients can eventually form a function to represent the storage time conversion relationship between any temperature and any SOC condition and standard temperature and standard SOC condition. Obviously, the more different preset temperature and preset SOC conditions there are in the above steps, the more accurate the function formed by the storage time ratio coefficients will be. No specific restrictions are made here.

[0069] Based on this, in the actual estimation of the remaining driving range of electric vehicles, the storage time at different temperatures and SOCs obtained when the target vehicle is powered on is multiplied by the corresponding storage time ratio coefficient mentioned above to obtain the converted standard storage time. The standard storage time is accumulated each time to obtain the historical cumulative storage time. The historical cumulative storage time refers to the time that the battery of the target vehicle is stored and placed under standard temperature and standard SOC conditions. Combined with the above-mentioned correlation data between storage time and battery aging degree under standard temperature and standard SOC conditions, the battery energy state health data of the target vehicle can be obtained. This energy state health data can show the changes in the energy state health of the target vehicle's battery due to storage and placement.

[0070] In addition, based on the embodiment in Figure 1, the above-mentioned energy state health data can also be obtained from the test data of multiple sets of experimental batteries. The test data of the multiple sets of experimental batteries includes: the correlation data between the number of charge-discharge cycles and the degree of battery aging under different preset temperature conditions; and the charge-discharge cycle ratio coefficient obtained based on the ratio of the number of charge-discharge cycles corresponding to the preset temperature to the number of charge-discharge cycles corresponding to the standard temperature, under the premise of reaching the same degree of battery aging.

[0071] The number of charge-discharge cycles at different temperatures when the target vehicle is powered on is multiplied by the corresponding charge-discharge cycle ratio coefficient to obtain the standard charge-discharge cycle. The historical cumulative charge-discharge cycle is then calculated from the standard charge-discharge cycle. Based on the historical cumulative charge-discharge cycle and the correlation data between the number of charge-discharge cycles and the degree of battery aging under standard temperature conditions, the energy state health data is calculated.

[0072] For example, the test data of the above-mentioned multiple sets of experimental batteries can be obtained through testing:

[0073] First, the experimental battery is charged and discharged under a preset temperature condition. The preset temperature can be, for example, 25 degrees Celsius, 35 degrees Celsius, 45 degrees Celsius, etc., and can be freely chosen according to the actual situation. There are no restrictions here. After each preset number of charge and discharge cycles, such as 50, 100, or 150 cycles, the experimental battery is tested. The test method can be to fully charge the experimental battery and release energy once at a fixed rate. The energy that the experimental battery can release under the fixed rate condition is compared with the energy that can be released under the same rate condition in the initial stage (i.e., the latest time). This gives the current energy SOH of the experimental battery. Combined with the number of charge and discharge cycles corresponding to the current energy SOH, the correlation data between the number of charge and discharge cycles and the degree of battery aging under the preset temperature condition can be obtained. As the preset number of charge and discharge cycles and multiple tests are accumulated, this correlation data between the number of charge and discharge cycles and the degree of battery aging can be formed into a table or a function, etc. There are no restrictions here.

[0074] By adjusting the preset temperature and repeating the above steps according to the above process, the correlation data between the number of charge / discharge cycles and the degree of battery aging under different preset temperature conditions can be obtained.

[0075] The reason for obtaining the correlation data between the number of charge-discharge cycles and the degree of battery aging under different preset temperature conditions is that the temperature conditions for battery charging and discharging in electric vehicles are uncertain and vary with each user's use of the vehicle. Therefore, the degree of battery aging caused by each charge-discharge cycle must be calculated separately for each charge-discharge cycle.

[0076] However, no matter how many different preset temperature conditions are tested, it is difficult to exhaust all the temperature conditions in actual use. Therefore, only by finding the conversion relationship of charge and discharge cycles between the above-mentioned different preset temperature conditions, that is, the above-mentioned charge and discharge cycle ratio coefficient, can this problem be solved.

[0077] For example, the aforementioned charge-discharge cycle ratio can be obtained through calculation:

[0078] First, select a standard temperature condition, such as 25 degrees Celsius. The specific temperature can be chosen based on the actual situation and is not limited here. Then, following the steps above, test the correlation data between the number of charge-discharge cycles and the degree of battery aging under this standard temperature condition. For example, after 500 charge-discharge cycles, the battery under this standard temperature condition will age to 95% of its energy SOH.

[0079] Then, select multiple preset temperature conditions and test the correlation data between the number of charge-discharge cycles and the degree of battery aging under different preset temperature conditions according to the above steps. For example, after 400 charge-discharge cycles at 45 degrees Celsius, the battery ages to 95% SOH, and after 600 charge-discharge cycles at 10 degrees Celsius, the battery ages to 95% SOH.

[0080] Based on the correlation data between the number of charge / discharge cycles and the degree of battery aging under the aforementioned standard temperature conditions, as well as the correlation data between the number of charge / discharge cycles and the degree of battery aging under multiple different preset temperature conditions, and under the premise of achieving the same degree of battery aging, the ratio of the number of charge / discharge cycles corresponding to different preset temperatures to the number of charge / discharge cycles corresponding to the preset standard temperature is used to obtain the charge / discharge cycle ratio coefficient. For example, 400 charge / discharge cycles at 45 degrees Celsius, aging the battery to 95% SOH, and 500 charge / discharge cycles at 25 degrees Celsius (using this as the standard temperature condition in this example), aging the battery to 95% SOH, can be compared with 400 charge / discharge cycles to obtain a charge / discharge cycle ratio coefficient of 1.25 at 45 degrees Celsius. This charge / discharge cycle ratio coefficient means that one charge / discharge cycle at 45 degrees Celsius is equivalent to 1.25 charge / discharge cycles at the standard temperature.

[0081] Similarly, the charge-discharge ratio coefficients under other preset temperature conditions can be obtained. These charge-discharge ratio coefficients can eventually form a function to represent the conversion relationship between the number of charge-discharge cycles under any temperature condition and the standard temperature condition. Obviously, the more different preset temperature conditions there are in the above steps, the more accurate the function formed by the charge-discharge ratio coefficients will be. No specific restrictions are made here.

[0082] Based on this, in the actual estimation of the remaining driving range of electric vehicles, the number of charge-discharge cycles at different temperatures obtained when the target vehicle is powered on is multiplied by the corresponding charge-discharge cycle ratio coefficient mentioned above to obtain the converted standard charge-discharge cycle. The cumulative number of standard charge-discharge cycles is then accumulated to obtain the historical cumulative charge-discharge cycle. This historical cumulative charge-discharge cycle refers to the number of times the target vehicle's battery has been charged and discharged under standard temperature conditions. Combined with the aforementioned correlation data between the number of charge-discharge cycles and the degree of battery aging under standard temperature conditions, the energy state health data of the target vehicle's battery can be obtained. This energy state health data can show the changes in the energy state health of the target vehicle's battery caused by charge-discharge.

[0083] Subtracting the aforementioned "changes in the state of energy health of the target vehicle's battery due to storage and placement" and "changes in the state of energy health of the target vehicle's battery due to charging and discharging" from the target vehicle's battery's initial energy state (i.e., when brand new) of 100% SOH, we can obtain the aforementioned "current state of energy health data of the battery from the perspective of released energy".

[0084] Optionally, based on the above embodiments, the test data of the above multiple sets of experimental batteries can be collected and calculated by connecting to the battery circuit of the target vehicle.

[0085] Specifically, the battery circuit of the target vehicle can be connected to a current sensor and a voltage sensor, etc. When testing the energy that the battery of the target vehicle can release under a fixed rate condition, the power of the battery circuit of the target vehicle is calculated by collecting the current and voltage of the battery circuit of the target vehicle, and then the energy that the battery of the target vehicle can release under a fixed rate condition is calculated by combining the energy release time of the battery circuit of the target vehicle, and the energy that the battery of the target vehicle can release under a fixed rate condition is used in the calculation in the aforementioned embodiment.

[0086] Figure 2 is a schematic flowchart of a battery energy state health assessment method provided in another embodiment of this disclosure. As shown in Figure 2, based on the embodiment in Figure 1, a preset analysis algorithm is used to obtain the charging energy of the target vehicle battery at different temperatures, and to obtain the ratio of the above charging energy to the theoretical charging energy, including:

[0087] S201. Detect and obtain the actual temperature and initial SOC during each charge, and obtain the cutoff SOC and the actual charge amount for this charge.

[0088] For example, when a target vehicle starts charging, the actual temperature is detected to be 30 degrees Celsius, the initial SOC is 37% SOC, and the ending SOC is 100% SOC. The actual energy charged is 55 kWh. That is, in an environment of 30 degrees Celsius, the vehicle charges from 37% SOC to 100% SOC, and a total of 55 kWh of energy is charged.

[0089] The actual temperature mentioned above can be obtained through a temperature sensor, which can be placed on the target vehicle's battery or any other suitable location; there are no restrictions. The actual charge amount can also be obtained by collecting and calculating the current and voltage of the target vehicle's battery circuit using the aforementioned current and voltage sensors.

[0090] S202. Compare the actual charge amount with the theoretical charge amount required to charge from the initial SOC to the cutoff SOC at the same temperature, and obtain the ratio of the charging energy to the theoretical charging energy.

[0091] For example, in the initial stage (i.e., when brand new), the target vehicle battery can be charged from 37% SOC to 100% SOC at an environment of 30 degrees Celsius, yielding 63 kWh of energy. Using the example above, the actual energy charged (55 kWh) is compared to this 63 kWh, which is approximately 87.3%. This is the aforementioned "current energy state health data of the battery from the perspective of charged energy."

[0092] In this embodiment, the actual temperature and initial SOC are detected and acquired for each charge, along with the cutoff SOC and the actual charge input. The actual charge input is compared with the theoretical charge required to charge from the initial SOC to the cutoff SOC at the same temperature, yielding the ratio of the charging energy to the theoretical charging energy. By detecting and acquiring the actual temperature, initial SOC, cutoff SOC, and actual charge input for each charge, "the current energy state health data of the battery from the perspective of charging energy" is obtained. This provides more perspectives for estimating the remaining driving range of electric vehicles, making the estimation of the remaining driving range of electric vehicles more comprehensive and accurate.

[0093] To more clearly illustrate the steps described in the above embodiments, Figure 3 is a complete flowchart of the battery energy state health assessment method. This flowchart not only illustrates the complete process of the above battery energy state health assessment method, but also shows the logical relationship between the various embodiments. As shown in the figure, the complete process of the battery energy state health assessment method includes:

[0094] S301. Obtain data on the correlation between storage time and battery aging under different preset temperatures and preset SOC conditions.

[0095] S302, Obtain the storage time ratio coefficient.

[0096] S303. Multiply the storage time at different temperatures and SOCs obtained when the target vehicle is powered on by the corresponding storage time scaling factor to obtain the standard storage time.

[0097] S304. The historical cumulative storage time is obtained by summing the standard storage time.

[0098] S305. Based on historical cumulative storage time and the correlation data between storage time and battery aging under standard temperature and standard SOC conditions, the changes in the energy state health of the battery due to storage and placement are calculated.

[0099] The above S301-S305 obtains the correlation data between storage time and battery aging degree under different preset temperature and preset SOC conditions through testing; and obtains the storage time ratio coefficient based on the ratio of the storage time corresponding to the preset temperature and preset SOC to the storage time corresponding to the standard temperature and standard SOC under the premise of achieving the same battery aging degree; and obtains "the change in the energy state health of the target vehicle's battery due to storage and placement" in the actual remaining driving range estimation of electric vehicles.

[0100] S311. Obtain data on the correlation between the number of charge / discharge cycles and the degree of battery aging under different preset temperature conditions.

[0101] S312, Obtain the charge / discharge cycle ratio coefficient.

[0102] S313. Multiply the number of charge and discharge cycles at different temperatures obtained when the target vehicle is powered on by the corresponding charge and discharge ratio coefficient to obtain the standard number of charge and discharge cycles.

[0103] S314. The historical cumulative number of charge and discharge cycles is obtained by summing the standard charge and discharge cycles.

[0104] S315. Based on the historical cumulative number of charge and discharge cycles and the correlation data between the number of charge and discharge cycles and the degree of battery aging under standard temperature conditions, the change in the energy state health of the battery due to charge and discharge is calculated.

[0105] The above S311-S315 obtains the correlation data between the number of charge-discharge cycles and the degree of battery aging under different preset temperature conditions through testing; and obtains the charge-discharge cycle ratio coefficient based on the ratio of the number of charge-discharge cycles corresponding to the preset temperature to the number of charge-discharge cycles corresponding to the standard temperature, under the premise of reaching the same degree of battery aging; and obtains the "change in the energy state health of the target vehicle's battery due to charge-discharge" in the estimation of the remaining driving range of the actual electric vehicle.

[0106] S316. Obtain energy state health data (current energy state health data of the battery obtained from the perspective of energy released).

[0107] Based on the aforementioned "changes in the energy state health of the target vehicle's battery due to storage and placement" and "changes in the energy state health of the target vehicle's battery due to charging and discharging," we obtain "the current energy state health data of the battery from the perspective of energy release." In other words, we acquire energy state health data, where the aforementioned energy state health data is the ratio of the energy that the battery can release under fixed-rate conditions in different aging states to the energy that the battery can release under the same rate conditions in the initial stage (i.e., when it is brand new).

[0108] S321. Detect and obtain the actual temperature and initial SOC during each charge, and obtain the cutoff SOC and the actual charge amount for this charge.

[0109] S322. Compare the actual charge amount corresponding to the current actual charge SOC with the theoretical charge amount required to charge from the starting SOC to the ending SOC at the same temperature (obtained in advance by measurement), and obtain the ratio of the charging energy to the theoretical charging energy (the current energy state health data of the battery obtained from the perspective of charging energy).

[0110] The above steps S321-S322 involve detecting and obtaining the actual temperature and initial SOC during each charge, as well as obtaining the cutoff SOC and the actual charge input. The actual charge input is then compared with the theoretical charge required to charge from the initial SOC to the cutoff SOC at the same temperature. The ratio of the charging energy to the theoretical charging energy is obtained, resulting in "the current energy state health data of the battery from the perspective of charging energy." In other words, the charging energy of the target vehicle battery at different temperatures is obtained, and the ratio of the charging energy to the theoretical charging energy is also obtained.

[0111] S323. Using a preset algorithm, evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and obtain the evaluation results.

[0112] Finally, a preset algorithm is used to comprehensively evaluate the battery's current energy state health data obtained from the perspective of energy release and the battery's current energy state health data obtained from the perspective of energy input, and the remaining driving range of the target vehicle is evaluated. That is, the preset algorithm is used to evaluate the above energy state health data and the ratio of the above charging energy to the theoretical charging energy to obtain the evaluation result.

[0113] Figure 4 is a schematic flowchart of a battery energy state health assessment method according to another embodiment of this disclosure. As shown in Figure 4, the above-mentioned energy state health data and the ratio of charging energy to theoretical charging energy are evaluated using a preset algorithm to obtain the evaluation results, including:

[0114] S401. Use a preset neural network to train and obtain the above-mentioned energy state health data and the influence weight of the ratio of the above-mentioned charging energy to the theoretical charging energy on the battery energy state health assessment.

[0115] Among them, a large amount of known energy state health data and the ratio of the above-mentioned charging energy to the theoretical charging energy, along with the corresponding actual measured target vehicle range data, can be used as training data. After manual identification of whether the above-mentioned actual measured target vehicle range data is closer to the energy state health data or to the above-mentioned ratio of charging energy to the theoretical charging energy, the data is labeled and used for training the preset neural network.

[0116] In addition, for example, since the above data acquisition cycle is long and the cost is high, in addition to using the above method to train the preset neural network, existing research data related to the influence weight of the energy state health data and the ratio of the charging energy to the theoretical charging energy on the battery energy state health assessment can also be used as training data to enable the preset neural network to obtain more accurate and reliable influence weight results.

[0117] The specific methods and data used to train the preset neural network can be freely chosen based on the actual situation, and no restrictions are imposed here.

[0118] S402. Construct the above-mentioned preset algorithm using the aforementioned influence weights.

[0119] The preset algorithm can be a weighted average calculation formula directly obtained based on the above energy state health data and the influence weight of the ratio of charging energy to theoretical charging energy on the battery energy state health assessment, or it can be an algorithm model obtained through more complex training on this basis. No restrictions are imposed here.

[0120] S403. Using the aforementioned preset algorithm, evaluate the aforementioned energy state health data and the ratio of the aforementioned charging energy to the theoretical charging energy, and obtain the aforementioned evaluation results.

[0121] In other words, the battery's current state of energy health is comprehensively assessed by combining the two data points: "the current state of energy health data of the battery obtained from the perspective of energy release" and "the current state of energy health data of the battery obtained from the perspective of energy input." Then, by combining the battery's current SOC and the electric vehicle's driving energy consumption, the remaining driving range of the electric vehicle is estimated.

[0122] In this embodiment, a preset neural network is used to train and obtain the influence weights of the aforementioned energy state health data and the ratio of charging energy to theoretical charging energy on the battery energy state health assessment. A preset algorithm is then constructed based on these influence weights. This preset algorithm is used to evaluate the aforementioned energy state health data and the ratio of charging energy to theoretical charging energy, obtaining the evaluation results. By training a preset neural network to obtain the influence weights of data from two perspectives on the battery energy state health assessment, the preset algorithm is constructed based on these influence weights, making the construction of the preset algorithm more scientific and reasonable.

[0123] In addition, the method may also include:

[0124] If the ratio of the above-mentioned charging energy to the theoretical charging energy meets the preset conditions, the above-mentioned preset algorithm is used to evaluate the above-mentioned energy state health data and obtain the above-mentioned evaluation results.

[0125] Since the above energy status health data is the result of long-term historical accumulation, the result is relatively stable, highly accurate, and has small short-term fluctuations; while the ratio of charging energy to theoretical charging energy is data obtained from a single charge, the data is relatively unstable and may differ significantly from the data obtained from the previous charge. Therefore, it is necessary to preset conditions to limit the use of the ratio of charging energy to theoretical charging energy.

[0126] For example, the aforementioned preset conditions could be such as the current charging time being less than a preset duration, for example, less than 10 minutes; or the current charging SOC change being less than a preset value, for example, charging only from 37% SOC to 45% SOC, with an SOC change of less than 10%; or the ratio of the current charging energy to the theoretical charging energy differing from the previous ratio by a preset difference, for example, the current ratio is 87%, the previous ratio was 95%, and the difference is greater than 1%. In these cases, the ratio of the current charging energy to the theoretical charging energy should not be used in the current battery energy state health assessment. The current energy state health data can be used alone to complete the battery energy state health assessment. For example, if the current energy state health data is 90%, then 90% of the energy that the battery can release in its new state is taken as the theoretical energy that can be released when the battery reaches 100% SOC. Combined with the current actual SOC and the driving energy consumption of the electric vehicle, the remaining driving range of the electric vehicle can be estimated. In this case, the weight of the aforementioned energy state health data and the aforementioned ratio of charging energy to theoretical charging energy on the battery energy state health assessment is not effective.

[0127] As for the specific preset conditions, they can be set freely according to the situation, and no restrictions are imposed here.

[0128] Figure 5 is a schematic diagram of a battery energy state health assessment device provided in an embodiment of this disclosure. This device can perform the above-described battery energy state health assessment method. As shown in Figure 5, the device includes:

[0129] The energy acquisition module 510 is configured to acquire energy state health data, wherein the energy state health data is the ratio of the energy that the battery can release under fixed rate conditions in different aging states to the energy that the battery can release under the same rate conditions in the initial stage of the battery.

[0130] The energy acquisition module 520 is configured to acquire the charging energy of the target vehicle battery at different temperatures and to acquire the ratio of the above charging energy to the theoretical charging energy.

[0131] The evaluation module 530 is configured to use a preset algorithm to evaluate the above-mentioned energy state health data and the ratio of the above-mentioned charging energy to the theoretical charging energy, and obtain the evaluation results.

[0132] In this embodiment, energy state health data is acquired, wherein the energy state health data is the ratio of the energy that the battery can release under fixed-rate conditions in different aging states to the energy that the battery can release under the same rate conditions in the initial stage. The charging energy of the target vehicle battery at different temperatures is acquired, and the ratio of the charging energy to the theoretical charging energy is obtained. A preset algorithm is used to evaluate the energy state health data and the ratio of charging energy to theoretical charging energy to obtain the evaluation result. In this embodiment, by acquiring battery energy state health data and the ratio of charging energy to theoretical charging energy, a comprehensive evaluation of battery energy state health is achieved from two perspectives, thereby more reasonably estimating the remaining driving range of the electric vehicle and making the estimation result more accurate.

[0133] Optionally, the aforementioned energy state health data is obtained based on test data from multiple sets of experimental batteries. This test data includes: data on the correlation between storage time and battery aging under different preset temperatures and preset SOC conditions; and a storage time ratio coefficient obtained by comparing the storage time at the preset temperature and preset SOC with the storage time at the standard temperature and standard SOC, assuming the same battery aging level. The standard storage time is obtained by multiplying the storage time at different temperatures and SOCs obtained when the target vehicle is powered on by the corresponding storage time ratio coefficient. The historical cumulative storage time is then calculated from this standard storage time. Based on this historical cumulative storage time and the correlation data between storage time and battery aging under standard temperature and standard SOC conditions, the aforementioned energy state health data is calculated.

[0134] Optionally, the aforementioned energy state health data is obtained based on test data from multiple sets of experimental batteries. This test data includes: correlation data between charge / discharge cycles and battery aging under different preset temperatures; and a charge / discharge cycle ratio coefficient, obtained by comparing the charge / discharge cycles at a preset temperature to those at a standard temperature, assuming the same battery aging level. The standard charge / discharge cycles are obtained by multiplying the charge / discharge cycles at different temperatures obtained when the target vehicle is powered on by the corresponding charge / discharge cycle ratio coefficient. The historical cumulative charge / discharge cycles are then calculated from these standard cycles. Finally, the energy state health data is calculated based on the historical cumulative charge / discharge cycles and the correlation data between charge / discharge cycles and battery aging under standard temperature conditions.

[0135] Optionally, the test data of the above-mentioned multiple sets of experimental batteries are collected and calculated by connecting to the battery circuit of the target vehicle.

[0136] Optionally, the above-mentioned energy acquisition module 520 is specifically configured to detect and acquire the actual temperature and initial SOC during each charge, and acquire the cutoff SOC and the actual charge amount in this charge; compare the actual charge amount in this charge with the theoretical charge amount required to charge from the initial SOC to the cutoff SOC at the same temperature, and acquire the ratio of the charging energy to the theoretical charging energy.

[0137] Optionally, the evaluation module 530 is specifically configured to use a preset neural network to train and obtain the influence weights of the energy state health data and the ratio of charging energy to theoretical charging energy on the battery energy state health evaluation; construct the preset algorithm through the influence weights; and use the preset algorithm to evaluate the energy state health data and the ratio of charging energy to theoretical charging energy to obtain the evaluation result.

[0138] Optionally, the evaluation module 530 can also be configured to evaluate the energy state health data and obtain the evaluation result if the ratio of the charging energy to the theoretical charging energy meets the preset conditions.

[0139] The above-described apparatus is configured to perform the method provided in the foregoing embodiments, and its implementation principle and technical effects are similar, so they will not be described again here.

[0140] Figure 6 is a schematic diagram of an electronic device provided in an embodiment of this disclosure. The electronic device can be an electronic terminal such as a computer, or other devices configured to estimate the remaining driving range of an electric vehicle. As shown in Figure 6, the device 600 includes:

[0141] The processor 610, storage medium 620, and bus 630 are connected in communication via bus 630.

[0142] The storage medium 620 stores machine-readable instructions that can be executed by the processor 610. When the electronic device is running, the processor 610 executes the machine-readable instructions to perform the battery energy state health assessment method.

[0143] It should be understood that the structure shown in Figure 6 is only a schematic diagram of the electronic device. The electronic device may include more or fewer components than shown in Figure 6, or have a different configuration than shown in Figure 6. The components shown in Figure 6 can be implemented using hardware, software, or a combination thereof.

[0144] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the battery energy state health assessment method described in the above method embodiments.

[0145] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media include non-transitory computer-readable storage media. The computer-readable storage medium has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0146] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions configured to perform a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0147] In addition, the functional modules in the various embodiments of this disclosure can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0148] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, or the part that contributes to related technologies, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] The above description is merely a preferred embodiment of this disclosure and does not limit the patent scope of this disclosure. Any equivalent structural transformations made based on the inventive concept of this disclosure and the contents of the specification and drawings of this disclosure, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this disclosure. Industrial applicability

[0150] The battery energy state health assessment method provided in this application acquires battery energy state health data, the charging energy of the target vehicle battery at different temperatures, and the ratio of the charging energy to the theoretical charging energy. It then uses a preset algorithm to comprehensively assess the battery energy state health from two perspectives, thereby more reasonably estimating the remaining driving range of the electric vehicle and making the estimation results more accurate.

Claims

1. A method for assessing the energy state health of a battery, characterized in that, The method comprises the following steps: obtaining energy state health data, wherein the energy state health data is the ratio of the energy that can be discharged by the battery under different aging state fixed rate conditions to the energy that can be discharged by the battery under the same rate condition in the initial stage of the battery; obtaining the charging energy of the target vehicle battery under different temperatures, and obtaining the ratio of the charging energy to the theoretical charging energy; using a preset algorithm to evaluate the energy state health data and the ratio of the charging energy to the theoretical charging energy, and obtaining an evaluation result.

2. The method of claim 1, wherein, The energy state health data is obtained according to the test data of a plurality of experimental batteries, wherein the test data of the plurality of experimental batteries comprises: the correlation data of storage time and battery aging degree under different preset temperatures and preset SOC conditions; and under the premise of reaching the same battery aging degree, a storage time proportion coefficient is obtained based on the ratio of the storage time corresponding to the preset temperature and preset SOC to the storage time corresponding to the standard temperature and standard SOC. The standard storage time is obtained by multiplying the storage time under different temperatures and different SOC obtained when the target vehicle is powered on by the corresponding storage time proportion coefficient, and the historical cumulative storage time is calculated from the standard storage time, and the energy state health data is calculated based on the historical cumulative storage time and the correlation data of storage time and battery aging degree under standard temperature and standard SOC conditions.

3. The method of claim 1, wherein, The energy state health data is obtained according to the test data of a plurality of experimental batteries, wherein the test data of the plurality of experimental batteries comprises: the correlation data of charge-discharge times and battery aging degree under different preset temperatures; and under the premise of reaching the same battery aging degree, a charge-discharge times proportion coefficient is obtained based on the ratio of the charge-discharge times corresponding to the preset temperature to the charge-discharge times corresponding to the standard temperature. The standard charge-discharge times are obtained by multiplying the charge-discharge times under different temperatures obtained when the target vehicle is powered on by the corresponding charge-discharge times proportion coefficient, and the historical cumulative charge-discharge times are calculated from the standard charge-discharge times, and the energy state health data is calculated based on the historical cumulative charge-discharge times and the correlation data of charge-discharge times and battery aging degree under standard temperature conditions.

4. The method according to claim 2 or 3, characterized in that, The test data of the plurality of experimental batteries is collected and calculated by connecting the battery circuit of the target vehicle.

5. The method of claim 1, wherein, The method comprises the following steps: detecting and obtaining the actual temperature and the starting SOC at each charging time, and obtaining the cut-off SOC and the actual charging capacity of this time; comparing the actual charging capacity of this time with the theoretical required capacity corresponding to the starting SOC under the same temperature charged to the cut-off SOC, to obtain the ratio of the charging energy to the theoretical charging energy.

6. The method of claim 1, wherein, The method comprises the following steps: The preset neural network is trained to obtain the influence weight of the energy state health degree data and the ratio of the charging energy to the theoretical charging energy on the battery energy state health degree evaluation; The preset algorithm is constructed by using the influence weight; The preset algorithm is used to evaluate the energy state health degree data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained.

7. The method of claim 1, wherein, The evaluation result is obtained by using the preset algorithm to evaluate the energy state health degree data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained. If the ratio of the charging energy to the theoretical charging energy meets the preset condition, the energy state health degree data is evaluated by using the preset algorithm, and the evaluation result is obtained.

8. A battery state of energy health assessment device, comprising: It includes: The energy state health degree data is obtained by using the preset algorithm to evaluate the energy state health degree data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained. The energy state health degree data is obtained by using the preset algorithm to evaluate the energy state health degree data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained. It includes:

9. An electronic device, comprising: The energy state health degree data is obtained by using the preset algorithm to evaluate the energy state health degree data and the ratio of the charging energy to the theoretical charging energy, and the evaluation result is obtained. It includes:

10. A computer-readable storage medium, characterized in that, The processor, the storage medium and the bus, the storage medium stores the machine readable instructions executable by the processor, the processor and the storage medium communicate through the bus, the processor executes the machine readable instructions to execute the method of any one of claims 1-7. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to execute the method of any one of claims 1-7.

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