Charging remaining time determination method and determination device based on fuzzy reasoning

Abstract

The invention provides a charging remaining time determining method and device based on fuzzy reasoning, and the method comprises the steps: obtaining a state parameter of a power battery, carrying out the fuzzification of the state parameter through a membership function corresponding to the state parameter, and obtaining the membership corresponding to the state parameter; then, performing fuzzy reasoning on the charging remaining time of the power battery based on the membership degree corresponding to the state parameter by using a pre-constructed fuzzy reasoning rule to obtain a fuzzy reasoning result; and finally, performing defuzzification processing on the fuzzy reasoning result to obtain a charging remaining time determined value of the power battery. By monitoring the actual operation data of the power battery and adopting fuzzy reasoning, the charging remaining time estimation effect is improved, the internal characteristics of the battery do not need to be deeply understood, the modeling and maintenance cost is reduced, meanwhile, the change of the battery state can be quickly responded, and more accurate remaining time estimation is provided.

Description

Technical Field

[0001] The present application relates to the technical field of battery management for new energy vehicles, and in particular to a method and device for determining the remaining charging time based on fuzzy reasoning. Background Art

[0002] With the increasing popularity of pure electric vehicles, user expectations for their functionality and user experience are also increasing. During the charging process, users urgently need real-time information on the remaining charging time to effectively plan their travel arrangements. The remaining charging time is the estimated time it will take for an electric vehicle to complete charging from its current state. Its calculation requires a comprehensive consideration of multiple parameters, including the power of the charging device, the current battery charge and capacity, the charging mode, and temperature.

[0003] However, current techniques for estimating remaining charging time still face several pressing challenges: First, the estimation accuracy is insufficient, making it difficult to adapt to complex operating conditions; second, there is a significant conflict between model complexity and real-time performance; and third, existing models generally lack effective characterization of battery aging effects. Due to the complex and variable operating conditions of electric terminals, influenced by multiple factors such as ambient temperature, charging station performance, terminal thermal management capabilities, battery temperature rise characteristics, and charging strategies, current remaining charging time calculation models and algorithms still face challenges in ensuring accuracy. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method and device for determining the remaining charging time based on fuzzy reasoning. By monitoring the actual operating data of the power battery, fuzzy reasoning can better adapt to complex operating conditions, reduce estimation errors, and improve the remaining charging time estimation effect. In addition, fuzzy reasoning is performed only using the state parameters of the power battery, eliminating the need for a deep understanding of the battery's internal characteristics, reducing modeling and maintenance costs, and can quickly respond to changes in the battery state to provide a more accurate remaining time estimate.

[0005] In a first aspect, an embodiment of the present application provides a method for determining the remaining charging time based on fuzzy reasoning, the method comprising: Acquire state parameters of the power battery, and perform fuzzy processing on the state parameters using a membership function corresponding to the state parameters to obtain a membership function corresponding to the state parameters; Using pre-established fuzzy inference rules, fuzzy inference is performed on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter to obtain a fuzzy inference result; Defuzzification is performed on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

[0006] Furthermore, when fuzzy reasoning is performed on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter, the remaining charging time determination method further includes: For each fuzzy reasoning rule, determine whether the fuzzy reasoning rule has rule weight adjustment logic; If so, the weight of the fuzzy inference rule is adjusted based on the rule weight adjustment logic and the state parameter.

[0007] Furthermore, after obtaining the fuzzy inference result, the method for determining the remaining charging time further includes: An aging compensation factor of the power battery is calculated, and a product of the aging compensation factor and the fuzzy reasoning result is determined as a fuzzy reasoning result.

[0008] Furthermore, the aging compensation factor is calculated by the following formula:

[0009] in, represents the aging compensation factor, represents the health status parameter of the power battery, Indicates the increase in internal resistance of the power battery within a preset time period, Indicates the nominal internal resistance of the power battery.

[0010] Furthermore, the state parameters include state of charge parameters, health state parameters, charging current, battery temperature and working environment humidity; When the state parameter is a state of charge parameter, the membership function corresponding to the state parameter is a hybrid membership function of a combination of a Z-type and a Gaussian type; When the state parameter is a health state parameter, the membership function corresponding to the state parameter is a triangular distribution membership function; When the state parameter is the charging current, the membership function corresponding to the state parameter is a trapezoidal distribution membership function; When the state parameter is battery temperature, the membership function corresponding to the state parameter is a piecewise linear membership function; When the state parameter is the humidity of the working environment, the membership function corresponding to the state parameter is a Z-type exponential membership function.

[0011] Furthermore, the battery temperature of the power battery is determined by the following steps: Determining the charging current and thermal resistance of the power battery, and obtaining the operating environment temperature of the power battery through a temperature sensor; A battery temperature of the power battery is calculated based on the operating environment temperature, the charging current, the thermal resistance, and a simplified coefficient.

[0012] Furthermore, after obtaining the determined value of the remaining charging time of the power battery, the remaining charging time determination method further includes: The determined value of the remaining charging time is sent to a display screen of a vehicle to which the power battery belongs, so that the determined value of the remaining charging time is displayed on the display screen.

[0013] In a second aspect, an embodiment of the present application further provides a device for determining the remaining charging time based on fuzzy reasoning, the device comprising: A parameter fuzzy processing module is used to obtain the state parameters of the power battery and perform fuzzy processing on the state parameters using the membership function corresponding to the state parameters to obtain the membership corresponding to the state parameters; a fuzzy reasoning module, configured to perform fuzzy reasoning on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter using pre-established fuzzy reasoning rules to obtain a fuzzy reasoning result; The defuzzification processing module is used to perform defuzzification processing on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the method for determining the remaining charging time based on fuzzy reasoning as described above are performed.

[0015] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps of the above-mentioned method for determining the remaining charging time based on fuzzy reasoning.

[0016] An embodiment of the present application provides a method and device for determining the remaining charging time based on fuzzy reasoning. First, the state parameters of a power battery are obtained, and the state parameters are fuzzified using a membership function corresponding to the state parameters to obtain the membership corresponding to the state parameters; then, using pre-constructed fuzzy reasoning rules, fuzzy reasoning is performed on the remaining charging time of the power battery based on the membership corresponding to the state parameters to obtain a fuzzy reasoning result; finally, the fuzzy reasoning result is defuzzified to obtain a determined value of the remaining charging time of the power battery.

[0017] This application uses a combination of multiple battery state parameters to comprehensively estimate the remaining charge time of a power battery. By monitoring the actual operating data of the power battery, fuzzy reasoning can better adapt to complex operating conditions and reduce estimation errors, thereby improving the remaining charge time estimation effect and solving common problems in the current field such as accuracy, adaptability, model complexity, and real-time performance. Furthermore, by using only the battery state parameters for fuzzy reasoning, it does not require a deep understanding of the battery's internal characteristics, reducing modeling and maintenance costs. It can also quickly respond to changes in battery status and provide a more accurate remaining charge time estimate.

[0018] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a method for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application; Figure 2 This is a structural diagram of a device for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application; Figure 3 This is a second structural diagram of a device for determining remaining charging time based on fuzzy reasoning provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of the present application.

[0022] First, the application scenarios to which this application is applicable are introduced. This application can be applied in the field of new energy vehicle battery management technology.

[0023] With the increasing popularity of pure electric vehicles, user expectations for their functionality and user experience are also increasing. During the charging process, users urgently need real-time information on the remaining charging time to effectively plan their travel arrangements. The remaining charging time is the estimated time it will take for an electric vehicle to complete charging from its current state. Its calculation requires a comprehensive consideration of multiple parameters, including the power of the charging device, the current battery charge and capacity, the charging mode, and temperature.

[0024] Research has revealed that current techniques for estimating remaining charging time still face several pressing challenges: First, the estimation accuracy is insufficient, making it difficult to adapt to complex operating conditions; second, there is a significant conflict between model complexity and real-time performance; and third, existing models generally lack effective characterization of battery aging effects. Due to the complex and variable operating conditions of electric terminals, influenced by multiple factors such as ambient temperature, charging station performance, terminal thermal management capabilities, battery temperature rise characteristics, and charging strategies, current models and algorithms for calculating remaining charging time still face challenges in ensuring accuracy.

[0025] Based on this, an embodiment of the present application provides a method for determining the remaining charging time based on fuzzy reasoning to improve the estimation effect of the remaining charging time, reduce modeling and maintenance costs, and at the same time be able to quickly respond to changes in battery status and provide a more accurate remaining time estimation.

[0026] See also Figure 1 , Figure 1 This is a flow chart of a method for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application. Figure 1 As shown in , the method for determining the remaining charging time provided in the embodiment of the present application includes: S101 , obtaining state parameters of a power battery, and performing fuzzy processing on the state parameters using a membership function corresponding to the state parameters to obtain a membership function corresponding to the state parameters.

[0027] It should be noted that fuzzy reasoning is a reasoning method based on fuzzy logic, used to deal with uncertainty and ambiguity in the real world. Fuzzy reasoning is based on fuzzy set theory. Elements in a fuzzy set have a certain membership, which indicates the degree to which the element belongs to the set. Fuzzification refers to converting clear and precise input values ​​into membership in a fuzzy set using a membership function. Membership is a core concept in fuzzy numbers, used to quantify the degree to which an element belongs to a fuzzy set. A membership function is a function that describes the membership of an element in a fuzzy set and has a range of [0, 1]. For example, the temperature of 25°C might have a membership of 0.8 in the fuzzy set "warm," 0.3 in "hot," and 0 in "cold."

[0028] Regarding the above step S101, in a specific implementation, when charging the power battery of the new energy vehicle, the state parameters of the power battery in the new energy vehicle are obtained, and the state parameters are fuzzy processed using the membership function corresponding to the state parameters to obtain the membership corresponding to the state parameters.

[0029] Specifically, according to the embodiments provided herein, the power battery's state parameters include the state of charge (SOC), state of health (SOH), charging current (Charging Current), battery temperature (Temp), and operating environment humidity (Humidity). These state parameters serve as input variables for subsequent fuzzy reasoning. Specifically, a non-invasive current sensor is used to collect the power battery's charging current, and a humidity sensor is used to collect the power battery's operating environment humidity.

[0030] Specifically, the battery temperature of the power battery is determined by the following steps: I: Determine the charging current and thermal resistance of the power battery, and obtain the operating environment temperature of the power battery through a temperature sensor.

[0031] Regarding the above step I, during specific implementation, the charging current and thermal resistance of the power battery are determined, and the operating environment temperature of the power battery is obtained through a temperature sensor.

[0032] II: Calculating the battery temperature of the power battery based on the operating environment temperature, the charging current, the thermal resistance, and a simplified coefficient.

[0033] Regarding step II above, in specific implementation, based on the working environment temperature, charging current, thermal resistance, and simplified coefficient of the combined effect of heat capacity and thermal resistance obtained in the above step, the battery temperature of the power battery is calculated by Kalman filtering. Specifically, the battery temperature of the power battery is calculated by the following formula:

[0034] in, Indicates the battery temperature of the power battery, Indicates the working environment temperature of the power battery. A simplified coefficient representing the combined effect of thermal capacitance and thermal resistance, Indicates the charging current of the power battery. Indicates the thermal resistance of the power battery.

[0035] Specifically, according to the embodiments provided in this application, when the state parameter is the state of charge parameter SOC, the membership function corresponding to the state parameter is a hybrid membership function combining Z-type and Gaussian types. Please refer to Table 1 below, which is an example table of membership conversion for the state of charge parameter SOC provided in an embodiment of this application. As shown in Table 1 below, the value range of the power battery's state of charge parameter SOC is [0%, 100%], its domain is [0, 1], its linguistic variables are set to {low L, medium M, high H}, and a hybrid membership function combining Z-type and Gaussian types is used.

[0036] Table 1. Example table of membership conversion for a state of charge parameter SOC

[0037] When the state parameter is a health state parameter, the membership function corresponding to the state parameter is a triangular distribution membership function. Please refer to Table 2 below, which is an example table of membership conversion of a health state parameter SOH provided in an embodiment of the present application. As shown in Table 2 below, the value range of the health state parameter SOH of the power battery is [0%, 100%], and its domain is [0, 1]. The standardized SOH value, 0 represents complete failure, and 1 represents a brand new battery. Its language variable is set to {low L, medium M, high H}, which corresponds to the change in battery aging from high to low, respectively, and a triangular membership function is used.

[0038] Table 2. Example of membership conversion for a health status parameter SOH

[0039] When the state parameter is charging current, the membership function corresponding to the state parameter is a trapezoidal distribution membership function. Please refer to Table 3 below, which shows an example of a membership conversion table for charging current provided in an embodiment of the present application. As shown in Table 3 below, 100% of the maximum charging current of the power battery corresponds to the upper limit of the allowable charging current of the calibration parameter power battery (current range 0A-300A), with a domain of [0, 1] and linguistic variables set to {small S, medium M, large L}, using a trapezoidal distribution membership function.

[0040] Table 3. Example of membership conversion for a charging current

[0041] When the state parameter is battery temperature, the membership function corresponding to the state parameter is a piecewise linear membership function. Please refer to Table 4 below, which shows an example membership conversion table for battery temperature provided in an embodiment of the present application. As shown in Table 4, 100% of the maximum allowable operating temperature of the power battery corresponds to the upper limit of the allowable operating temperature of the calibration parameter power battery (temperature range -35°C to 55°C). Its domain is [0, 1], and its linguistic variables are set to {Extremely Low ZL, Low L, Medium F, High H, Extremely High ZH}, using a piecewise linear membership function.

[0042] Table 4: Example of membership conversion for a battery temperature

[0043] When the state parameter is operating environment humidity, the membership function corresponding to the state parameter is a Z-type exponential membership function. Please refer to Table 5 below for an example membership conversion table for operating environment humidity, provided in an embodiment of the present application. As shown in Table 5, the maximum allowable operating humidity of 100% for the power battery corresponds to the upper limit of the operating humidity of the calibration parameter power battery (humidity range 0% to 100%), with the domain being [0, 1] and the linguistic variables being set to {Low, Medium, High}. A Z-type membership function is used.

[0044] Table 5: Example table of membership conversion for a working environment humidity

[0045] S102 , using pre-constructed fuzzy inference rules, and based on the membership degree corresponding to the state parameter, performing fuzzy inference on the remaining charging time of the power battery to obtain a fuzzy inference result.

[0046] Fuzzy inference rules, based on fuzzy set theory, extend traditional mathematical logic to handle inferences involving imprecise premises and approximate conclusions. They are typically constructed using a series of relational terms, such as if-then, else, also, end, or, and so on.

[0047] For the above step S102, during the specific implementation, a series of fuzzy rules for fuzzy reasoning of the remaining charging time of the power battery are pre-established based on experimental data and expert experience. Then, using the fuzzy reasoning rules, fuzzy reasoning is performed on the remaining charging time of the power battery based on the membership corresponding to the state parameters obtained in the above step S101 to obtain the fuzzy reasoning result. Please refer to Table 6 below, which is an example table of membership conversion of fuzzy reasoning results provided in an embodiment of the present application. As shown in Table 6 below, the value range of the remaining charging time is [0, 120], its domain is [0, 1], and its language variable is set to {extremely long ZL, long L, medium F, short TF, extremely short CF}.

[0048] Table 6 An example table of membership conversion of fuzzy inference results

[0049] Here, as an example, when the fuzzy inference rule is IF SOC is LOW AND SOH is POOR AND Charging Current is SMALL AND Temp is COLD AND Humidity is HIGH THEN Remaining Time is VERY LONG (0.9), it means that when the state of charge parameters are low, the state of health parameters are poor, the charging current is small, the battery temperature is low, and the operating environment humidity is high, then the output fuzzy inference result is 0.9. When the fuzzy inference rule is F SOC isMEDIUM AND SOH is EXCELLENT AND Charging Current is LARGE AND Temp is WARMAND Humidity is LOW THEN Remaining Time is SHORT (0.3), it means that when the state of charge parameters are medium, the state of health parameters are excellent, the charging current is large, the battery temperature is warm, and the operating environment humidity is low, then the output fuzzy inference result is 0.3.

[0050] Please refer to Table 7 below, which is an example table of a fuzzy rule library for fuzzy reasoning provided in an embodiment of the present application. As shown in Table 7 below, whether the fuzzy rule is met is determined based on the condition part of the fuzzy rule, and the modular reasoning result is output based on the conclusion part of the fuzzy rule.

[0051] Table 7 Example of a fuzzy rule base for fuzzy reasoning

[0052] As an optional embodiment, when fuzzy reasoning is performed on the remaining charging time of the power battery based on the degree of membership corresponding to the state parameter in the above step S102, the method for determining the remaining charging time provided in the present application further includes: A: For each fuzzy inference rule, determine whether the fuzzy inference rule has rule weight adjustment logic.

[0053] Regarding step A above, during the specific implementation, for each fuzzy inference rule, determine whether the fuzzy inference rule has rule weight adjustment logic. Continuing with the example in Table 7 above, the fuzzy inference rule numbered R2 has rule weight adjustment logic, that is, the weight is increased to 1.0 when the working environment humidity is greater than 80%.

[0054] B: If so, the weight of the fuzzy inference rule is adjusted based on the rule weight adjustment logic and the state parameter.

[0055] Regarding step B above, during implementation, if it is determined that the fuzzy inference rule has rule weight adjustment logic, the weight of the fuzzy inference rule is adjusted based on the rule weight adjustment logic and the state parameter. Continuing with the example in step B above, if the operating environment humidity in the power battery state parameter is greater than 80%, the weight of the fuzzy inference rule is adjusted to 1.0.

[0056] As an optional embodiment, after obtaining the fuzzy inference result, the method for determining the remaining charging time provided in the present application further includes: An aging compensation factor of the power battery is calculated, and a product of the aging compensation factor and the fuzzy reasoning result is determined as a fuzzy reasoning result.

[0057] Regarding the above steps, in specific implementation, after obtaining the fuzzy inference result in step S102, the aging compensation factor for the power battery is calculated, and the aging compensation factor is multiplied by the fuzzy inference result, with the product being used as the fuzzy inference result for the power battery. Here, the aging compensation factor is used to adjust the membership value output by the fuzzy inference rule. During the rule output stage, the original output membership value of the rule is multiplied by , thereby dynamically correcting the fuzzy inference result. Since the membership of the input variable only reflects the degree to which it belongs to the fuzzy set, and the membership of the rule output needs to consider the impact of battery aging on charging time, this method not only ensures that the fuzzy inference process fully considers the effects of battery aging, but also improves the accuracy of the remaining charging time estimation.

[0058] Specifically, the aging compensation factor is calculated by the following formula:

[0059] in, represents the aging compensation factor, represents the health status parameter of the power battery, Indicates the increase in internal resistance of the power battery within a preset time period, Indicates the nominal internal resistance of the power battery.

[0060] S103 , performing defuzzification processing on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

[0061] Here, defuzzification is the key step to convert the fuzzy output set into specific numerical values.

[0062] Regarding step S103 above, after obtaining the fuzzy inference result, the fuzzy inference result is defuzzified to obtain the determined value of the remaining charging time of the power battery. As an example, defuzzification can use the centroid method, maximum membership method, etc. to convert the fuzzy output of the remaining charging time obtained by fuzzy inference into a precise numerical output. Alternatively, a hybrid defuzzification strategy can be used, combining the centroid method (primary strategy) with sliding window mean filtering (auxiliary strategy) to obtain the determined value of the remaining charging time.

[0063] As an optional embodiment, after obtaining the determined value of the remaining charging time of the power battery, the method for determining the remaining charging time provided in this application further includes: The determined value of the remaining charging time is sent to a display screen of a vehicle to which the power battery belongs, so that the determined value of the remaining charging time is displayed on the display screen.

[0064] Regarding the above steps, in a specific implementation, after obtaining the determined value of the remaining charging time of the power battery, the determined value of the remaining charging time is transmitted to the display screen of the vehicle to which the power battery belongs, so that the determined value of the remaining charging time is displayed on the display screen. The determined value of the remaining charging time can also be displayed on the display screen of the charging device, or the result can be transmitted to a relevant user terminal via a communication interface, which is not specifically limited in this application.

[0065] The method for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application first obtains the state parameters of the power battery, and uses the membership function corresponding to the state parameters to fuzzy process the state parameters to obtain the membership corresponding to the state parameters; then, using pre-constructed fuzzy reasoning rules, fuzzy reasoning is performed on the remaining charging time of the power battery based on the membership corresponding to the state parameters to obtain a fuzzy reasoning result; finally, the fuzzy reasoning result is defuzzified to obtain a determined value of the remaining charging time of the power battery.

[0066] This application uses a combination of multiple battery state parameters to comprehensively estimate the remaining charge time of a power battery. By monitoring the actual operating data of the power battery, fuzzy reasoning can better adapt to complex operating conditions and reduce estimation errors, thereby improving the remaining charge time estimation effect and solving common problems in the current field such as accuracy, adaptability, model complexity, and real-time performance. Furthermore, by using only the battery state parameters for fuzzy reasoning, it does not require a deep understanding of the battery's internal characteristics, reducing modeling and maintenance costs. It can also quickly respond to changes in battery status and provide a more accurate remaining charge time estimate.

[0067] See also Figure 2 、 Figure 2 , Figure 2 This is one of the structural diagrams of a device for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application. Figure 3 This is a second structural diagram of a device for determining the remaining charging time based on fuzzy reasoning provided in an embodiment of the present application. Figure 2 As shown in , the charging remaining time determination device 200 includes: The parameter fuzzy processing module 201 is used to obtain the state parameters of the power battery and perform fuzzy processing on the state parameters using the membership function corresponding to the state parameters to obtain the membership corresponding to the state parameters; The fuzzy reasoning module 202 is configured to perform fuzzy reasoning on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter using pre-established fuzzy reasoning rules to obtain a fuzzy reasoning result; The defuzzification processing module 203 is configured to perform defuzzification processing on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

[0068] Furthermore, when the fuzzy reasoning module 202 is used to perform fuzzy reasoning on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter, the fuzzy reasoning module 202 is further used to: For each fuzzy reasoning rule, determine whether the fuzzy reasoning rule has rule weight adjustment logic; If so, the weight of the fuzzy inference rule is adjusted based on the rule weight adjustment logic and the state parameter.

[0069] See also Figure 3 The device 200 for determining the remaining charging time further includes an adjustment module 204. After obtaining the fuzzy inference result, the adjustment module 204 is configured to: An aging compensation factor of the power battery is calculated, and a product of the aging compensation factor and the fuzzy reasoning result is determined as a fuzzy reasoning result.

[0070] Furthermore, the adjustment module 204 is further configured to calculate the aging compensation factor using the following formula:

[0071] in, represents the aging compensation factor, represents the health status parameter of the power battery, Indicates the increase in internal resistance of the power battery within a preset time period, Indicates the nominal internal resistance of the power battery.

[0072] Furthermore, the state parameters include state of charge parameters, health state parameters, charging current, battery temperature and working environment humidity; When the state parameter is a state of charge parameter, the membership function corresponding to the state parameter is a hybrid membership function of a combination of a Z-type and a Gaussian type; When the state parameter is a health state parameter, the membership function corresponding to the state parameter is a triangular distribution membership function; When the state parameter is the charging current, the membership function corresponding to the state parameter is a trapezoidal distribution membership function; When the state parameter is battery temperature, the membership function corresponding to the state parameter is a piecewise linear membership function; When the state parameter is the humidity of the working environment, the membership function corresponding to the state parameter is a Z-type exponential membership function.

[0073] Furthermore, the parameter fuzzy processing module 201 is further configured to determine the battery temperature of the power battery through the following steps: Determining the charging current and thermal resistance of the power battery, and obtaining the operating environment temperature of the power battery through a temperature sensor; A battery temperature of the power battery is calculated based on the operating environment temperature, the charging current, the thermal resistance, and a simplified coefficient.

[0074] See also Figure 3The device 200 for determining the remaining charging time further includes a data sending module 205. After obtaining the determined value of the remaining charging time of the power battery, the data sending module 205 is configured to: The determined value of the remaining charging time is sent to a display screen of a vehicle to which the power battery belongs, so that the determined value of the remaining charging time is displayed on the display screen.

[0075] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in FIG, the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0076] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The specific implementation of the steps of the method for determining the remaining charging time based on fuzzy reasoning in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0077] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The specific implementation of the steps of the method for determining the remaining charging time based on fuzzy reasoning in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0078] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0080] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0081] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0082] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0083] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for determining the remaining charging time based on fuzzy reasoning, characterized in that: The method for determining the remaining charging time includes: Acquire state parameters of the power battery, and perform fuzzy processing on the state parameters using a membership function corresponding to the state parameters to obtain a membership function corresponding to the state parameters; Using pre-established fuzzy inference rules, fuzzy inference is performed on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter to obtain a fuzzy inference result; Defuzzification is performed on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

2. The method for determining the remaining charging time according to claim 1, wherein: When fuzzy reasoning is performed on the remaining charging time of the power battery based on the degree of membership corresponding to the state parameter, the remaining charging time determination method further includes: For each fuzzy reasoning rule, determine whether the fuzzy reasoning rule has rule weight adjustment logic; If so, the weight of the fuzzy inference rule is adjusted based on the rule weight adjustment logic and the state parameter.

3. The method for determining the remaining charging time according to claim 1, wherein: After obtaining the fuzzy inference result, the method for determining the remaining charging time further includes: An aging compensation factor of the power battery is calculated, and a product of the aging compensation factor and the fuzzy reasoning result is determined as a fuzzy reasoning result.

4. The method for determining the remaining charging time according to claim 3, wherein: The aging compensation factor is calculated by the following formula: in, represents the aging compensation factor, represents the health status parameter of the power battery, Indicates the increase in internal resistance of the power battery within a preset time period, Indicates the nominal internal resistance of the power battery.

5. The method for determining the remaining charging time according to claim 1, wherein: The state parameters include state of charge parameters, health state parameters, charging current, battery temperature and working environment humidity; When the state parameter is a state of charge parameter, the membership function corresponding to the state parameter is a hybrid membership function of a combination of a Z-type and a Gaussian type; When the state parameter is a health state parameter, the membership function corresponding to the state parameter is a triangular distribution membership function; When the state parameter is the charging current, the membership function corresponding to the state parameter is a trapezoidal distribution membership function; When the state parameter is battery temperature, the membership function corresponding to the state parameter is a piecewise linear membership function; When the state parameter is the humidity of the working environment, the membership function corresponding to the state parameter is a Z-type exponential membership function.

6. The method for determining the remaining charging time according to claim 5, wherein: The battery temperature of the power battery is determined by the following steps: Determining the charging current and thermal resistance of the power battery, and obtaining the operating environment temperature of the power battery through a temperature sensor; A battery temperature of the power battery is calculated based on the operating environment temperature, the charging current, the thermal resistance, and a simplified coefficient.

7. The method for determining the remaining charging time according to claim 1, wherein: After obtaining the determined value of the remaining charging time of the power battery, the remaining charging time determination method further includes: The determined value of the remaining charging time is sent to a display screen of a vehicle to which the power battery belongs, so that the determined value of the remaining charging time is displayed on the display screen.

8. A device for determining remaining charging time based on fuzzy reasoning, characterized in that: The device for determining the remaining charging time comprises: A parameter fuzzy processing module is used to obtain the state parameters of the power battery and perform fuzzy processing on the state parameters using the membership function corresponding to the state parameters to obtain the membership corresponding to the state parameters; a fuzzy reasoning module, configured to perform fuzzy reasoning on the remaining charging time of the power battery based on the membership degree corresponding to the state parameter using pre-established fuzzy reasoning rules to obtain a fuzzy reasoning result; The defuzzification processing module is used to perform defuzzification processing on the fuzzy inference result to obtain a determined value of the remaining charging time of the power battery.

9. An electronic device, characterized in that: include: A processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. When the processor is running, the machine-readable instructions execute the steps of the method for determining the remaining charging time based on fuzzy reasoning as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining the remaining charging time based on fuzzy reasoning according to any one of claims 1 to 7 are executed.

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