Method for evaluating health state of charging device and device thereof, and electronic device

Abstract

The application discloses a kind of charging equipment health state evaluation method and its device, electronic equipment, it is related to new energy technology field, wherein, the evaluation method includes: the actual parameter value of the component parameter of target charging equipment is obtained, based on actual parameter value, using preset nonlinear model determines the benchmark value and increment value of different component parameters, for different components under each circuit, based on benchmark value and increment value, determine the health value of each different component, based on the health value of each different component, determine the health state of target charging equipment.The application solves the technical problem that the accuracy of related art for evaluating the health state of charging equipment is low.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and more specifically, to a method and apparatus for assessing the health status of charging equipment, as well as electronic equipment. Background Technology

[0002] With the increasing number of electric vehicles, the safety issues of electric vehicle charging are becoming increasingly prominent. Improper charging can cause damage to people, vehicles, and the surrounding environment. As a component of electric vehicle charging, the aging of components in the power converter system of charging equipment, such as electrolytic capacitors and power switching devices, can lead to equipment performance degradation and increase safety hazards.

[0003] Currently, power converter condition monitoring technology mainly focuses on aging condition analysis of electrolytic capacitors and power switching devices, exploring their aging characteristics through parameter identification methods. The aging characteristics of electrolytic capacitors are manifested as capacitance decay and increased equivalent series resistance (ESR). Algorithms such as least squares, support vector machines, and particle swarm optimization are used to estimate capacitance values, achieving component-level health diagnosis while considering component aging and changes in operating conditions. Insulated-gate bipolar transistors (IGBTs) and MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors), as power switching devices in power converters, can have their aging process monitored and evaluated by analyzing MOSFET aging characteristic parameters, including static parameters such as threshold voltage and on-resistance, and dynamic parameters such as the duration and voltage of the Miller plateau.

[0004] However, system-level aging condition assessment requires not only focusing on the performance changes of individual components, but also taking a holistic approach and considering the interactions between components. Therefore, there is an urgent need for a comprehensive method to assess the overall aging condition of power converter systems in order to improve the accuracy of assessing the health status of charging equipment.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides a method, apparatus, and electronic device for assessing the health status of charging devices, in order to at least address the technical problem of low accuracy in assessing the health status of charging devices in related technologies.

[0007] According to one aspect of the present invention, a method for assessing the health status of a charging device is provided, comprising: obtaining actual parameter values ​​of component parameters of a target charging device, wherein the target charging device includes: multiple circuits, each circuit including at least: multiple power switching components and multiple electrolytic capacitor components; determining, based on the actual parameter values, a preset nonlinear model to determine baseline and incremental values ​​of different component parameters, wherein the preset nonlinear model includes at least: a first preset nonlinear model and a second preset nonlinear model, the first preset nonlinear model being used to determine the baseline and incremental values ​​of the power switching component parameters under each circuit, and the second preset nonlinear model being used to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters under each circuit; determining the health value of each different component under each circuit based on the baseline and incremental values; and determining the health status of the target charging device based on the health values ​​of each different component.

[0008] Furthermore, before determining the baseline and incremental values ​​of different component parameters using a preset nonlinear model based on actual parameter values, the process includes: collecting parameter values ​​of component parameters from multiple charging devices; constructing a first preset nonlinear model based on all parameter values, and constructing a second preset nonlinear model based on all parameter values; and constructing a preset nonlinear model based on the first preset nonlinear model and the second preset nonlinear model.

[0009] Further, the step of constructing a first preset nonlinear model based on all parameter values ​​includes: for all power switching components in each circuit, determining the baseline value variable, incremental variable, and dispersed value variable of the power switching component parameters corresponding to the power switching components; constructing a first preset formula based on all parameter values, baseline value variables, incremental variables, and dispersed value variables; transforming the first preset formula to obtain a first transformed formula; calculating the average value of all parameter values ​​to obtain the first actual average value of the power switching components; determining a first objective function based on the first transformed formula and the first actual average value; determining first preset constraints, and constructing a first preset nonlinear model based on the first objective function and the first preset constraints.

[0010] Furthermore, the first transformation formula includes the following steps: determining first preset constraints based on multiple different first preset variables, including: sorting all parameter values ​​to obtain the maximum and minimum actual values ​​of the power switching components; determining a first preset range of variation for each first preset variable and determining a first range of variation for the dispersion value variable, wherein the dispersion value variable includes at least a maximum dispersion value and a minimum dispersion value, and the first range of variation includes at least a first range of variation for the maximum dispersion value and a first range of variation for the minimum dispersion value; and determining the first preset constraints based on all first preset ranges of variation, the maximum actual value, the minimum actual value, the first range of variation for the maximum dispersion value, and the first range of variation for the minimum dispersion value.

[0011] Further, the step of constructing a second preset nonlinear model based on all parameter values ​​includes: for all electrolytic capacitor components in each circuit, determining the baseline value variable, incremental variable, and dispersed value variable of the electrolytic capacitor component parameters corresponding to the electrolytic capacitor components; constructing a second preset formula based on all parameter values, baseline value variables, incremental variables, and dispersed value variables; transforming the second preset formula to obtain a second transformed formula; calculating the average value of all parameter values ​​to obtain the second actual average value of the electrolytic capacitor components; determining a second objective function based on the second transformed formula and the second actual average value; determining second preset constraints, and constructing a second preset nonlinear model based on the second objective function and the second preset constraints.

[0012] Furthermore, the second transformation formula includes the following steps: determining the second preset constraints based on multiple different second preset variables, including: sorting all parameter values ​​to obtain the maximum and minimum actual values ​​of the electrolytic capacitor components; determining the second preset range of variation for each second preset variable and the second range of variation for the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the second range of variation includes at least the second range of variation for the maximum dispersion value and the second range of variation for the minimum dispersion value; and determining the second preset constraints based on all second preset ranges of variation, the maximum actual value, the minimum actual value, the second range of variation for the maximum dispersion value, and the second range of variation for the minimum dispersion value.

[0013] Furthermore, for each different component in each circuit, the step of determining the health value of each different component based on the reference value and the incremental value includes: calculating the relative incremental value based on the reference value and the incremental value; and determining the health value of each different component based on the relative incremental value.

[0014] Furthermore, the step of determining the health status of the target charging device based on the health values ​​of each different component includes: calculating the average health value of all components to obtain the health mean; and determining the health status of the target charging device based on the health mean.

[0015] According to another aspect of the present invention, an apparatus for assessing the health status of a charging device is also provided, comprising: a first acquisition unit, configured to acquire actual parameter values ​​of component parameters of a target charging device, wherein the target charging device includes: multiple circuits, each circuit including at least: multiple power switching components and multiple electrolytic capacitor components; a first determination unit, configured to determine, based on the actual parameter values, a preset nonlinear model to determine the baseline values ​​and incremental values ​​of different component parameters, wherein the preset nonlinear model includes at least: a first preset nonlinear model and a second preset nonlinear model, the first preset nonlinear model being used to determine the baseline values ​​and incremental values ​​of the power switching component parameters under each circuit, and the second preset nonlinear model being used to determine the baseline values ​​and incremental values ​​of the electrolytic capacitor component parameters under each circuit; a second determination unit, configured to determine the health value of each different component under each circuit based on the baseline values ​​and incremental values; and a third determination unit, configured to determine the health status of the target charging device based on the health values ​​of each different component.

[0016] Furthermore, the device for assessing the health status of charging equipment includes: a first acquisition module, used to acquire parameter values ​​of component parameters of multiple charging devices before determining the baseline and incremental values ​​of different component parameters using a preset nonlinear model based on actual parameter values; a first construction module, used to construct a first preset nonlinear model based on all parameter values, and construct a second preset nonlinear model based on all parameter values; and a second construction module, used to construct a preset nonlinear model based on the first preset nonlinear model and the second preset nonlinear model.

[0017] Further, the first construction module includes: a first determination submodule, used to determine the baseline value variable, incremental variable, and dispersed value variable of the power switch component parameters corresponding to all power switch components in each circuit; a first construction submodule, used to construct a first preset formula based on all parameter values, baseline value variables, incremental variables, and dispersed value variables; a first transformation submodule, used to transform the first preset formula to obtain a first transformation formula; a first calculation submodule, used to calculate the average value of all parameter values ​​to obtain the first actual average value of the power switch components; a second determination submodule, used to determine a first objective function based on the first transformation formula and the first actual average value; and a third determination submodule, used to determine a first preset constraint condition and construct a first preset nonlinear model based on the first objective function and the first preset constraint condition.

[0018] Furthermore, the first transformation formula includes multiple different first preset variables, and the third determining submodule includes: a first sorting submodule, used to sort all parameter values ​​to obtain the maximum and minimum actual values ​​of the power switching components; a fourth determining submodule, used to determine the first preset variation range of each first preset variable and the first variation range of the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the first variation range includes at least the first variation range of the maximum dispersion value and the first variation range of the minimum dispersion value; and a fifth determining submodule, used to determine the first preset constraint conditions based on all the first preset variation ranges, the maximum actual value, the minimum actual value, the first variation range of the maximum dispersion value, and the first variation range of the minimum dispersion value.

[0019] Further, the first construction module includes: a sixth determining submodule, used to determine the baseline value variable, incremental variable, and dispersed value variable of the electrolytic capacitor component parameters corresponding to all electrolytic capacitor components in each circuit; a second construction submodule, used to construct a second preset formula based on all parameter values, baseline value variables, incremental variables, and dispersed value variables; a second transformation submodule, used to transform the second preset formula to obtain a second transformation formula; a second calculation submodule, used to calculate the average value of all parameter values ​​to obtain the second actual average value of the electrolytic capacitor components; a seventh determining submodule, used to determine a second objective function based on the second transformation formula and the second actual average value; and an eighth determining submodule, used to determine the second preset constraint conditions and construct a second preset nonlinear model based on the second objective function and the second preset constraint conditions.

[0020] Furthermore, the second transformation formula includes multiple different second preset variables, and the eighth determining submodule includes: a second sorting submodule, used to sort all parameter values ​​to obtain the maximum and minimum actual values ​​of the electrolytic capacitor components; a ninth determining submodule, used to determine the second preset variation range of each second preset variable and the second variation range of the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the second variation range includes at least the second variation range of the maximum dispersion value and the second variation range of the minimum dispersion value; and a tenth determining submodule, used to determine the second preset constraint conditions based on all second preset variation ranges, the maximum actual value, the minimum actual value, the second variation range of the maximum dispersion value, and the second variation range of the minimum dispersion value.

[0021] Furthermore, the second determining unit includes: a first calculation module, used to calculate a relative incremental value based on a reference value and an incremental value; and a first determining module, used to determine the health value of each different component based on the relative incremental value.

[0022] Furthermore, the third determining unit includes: a second calculation module, used to calculate the average health value of all components to obtain the average health value; and a second determining module, used to determine the health status of the target charging device based on the average health value.

[0023] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for assessing the health status of a charging device as described above.

[0024] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described methods for assessing the health status of a charging device.

[0025] In this invention, the actual parameter values ​​of the components of the target charging device are obtained. Based on the actual parameter values, a preset nonlinear model is used to determine the baseline and incremental values ​​of different component parameters. For different components under each circuit, the health value of each different component is determined based on the baseline and incremental values. Based on the health value of each different component, the health status of the target charging device is determined, thereby solving the technical problem of low accuracy in assessing the health status of charging devices in related technologies.

[0026] In this invention, by collecting the actual parameter values ​​of each component in multiple circuits of the target charging device, and using a first preset nonlinear model to determine the baseline and incremental values ​​of the power switch component parameters in each circuit, and using a second preset nonlinear model to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters in each circuit, the health value of each different component can be calculated based on the baseline and incremental values. Then, based on the health value of each different component, the health status of the target charging device is calculated, thus realizing a comprehensive assessment and monitoring of the aging status of the power converter of the charging device. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0028] Figure 1 This is a flowchart of an optional method for assessing the health status of a charging device according to an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of an optional MOSFET health surface according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of the health status of an optional aluminum electrolytic capacitor according to an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the relationship between an optional external feature quantity and the overall health of a charging device according to an embodiment of the present invention.

[0032] Figure 5 This is a schematic diagram of an optional charging device health status assessment device according to an embodiment of the present invention;

[0033] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) according to an embodiment of the present invention for an evaluation method of the health status of a charging device. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] It should be noted that all related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) collected and involved in this invention are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data comply with the relevant laws, regulations, and standards of the relevant regions, necessary confidentiality measures have been taken, and it does not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0037] In this invention, by establishing an overall aging status assessment model for charging equipment, the actual aging status of MOSFETs and electrolytic capacitors can be determined. Then, based on the proposed health definitions of MOSFETs and aluminum electrolytic capacitors, the overall health of the charging equipment can be obtained. Furthermore, conversion efficiency can be used as an external characteristic quantity of the aging status of MOSFETs and aluminum electrolytic capacitors. A mathematical model of conversion efficiency and overall health of the charging equipment under different output voltages can be established. In other words, by analyzing the external characteristic quantity of conversion efficiency, the health status of internal components of the equipment can be indirectly reflected, thereby realizing intelligent monitoring of the operating status of the charging equipment.

[0038] The present invention will now be described in detail with reference to various embodiments.

[0039] Example 1

[0040] According to an embodiment of the present invention, an embodiment of a method for assessing the health status of a charging device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0041] Figure 1 This is a flowchart of an optional method for assessing the health status of a charging device according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0042] Step S101: Obtain the actual parameter values ​​of the components of the target charging device. The target charging device includes multiple circuits, and each circuit includes at least multiple power switch components and multiple electrolytic capacitor components.

[0043] In this embodiment, the target charging device includes multiple circuits (such as a front-end circuit and a back-end circuit). Each circuit includes at least multiple power switching components and multiple electrolytic capacitor components. For example, the charging module of model EVR500-7500 (i.e. the target charging device) has two aluminum electrolytic capacitor components in each of the front-end and back-end circuits. The charging module has a total of 14 power MOSFETs (i.e., power switching components), of which the front-end circuit has 6 MOSFETs and the back-end circuit has 8 MOSFETs.

[0044] In this embodiment, the actual parameter values ​​of the power switch components and the aluminum electrolytic capacitor components of the target charging device are obtained. The power switch component parameters may include: gate threshold voltage parameters and on-resistance parameters, and the aluminum electrolytic capacitor parameters may include: capacitance parameters and equivalent series resistance parameters.

[0045] Step S102: Based on the actual parameter values, a preset nonlinear model is used to determine the baseline and incremental values ​​of different component parameters. The preset nonlinear model includes at least a first preset nonlinear model and a second preset nonlinear model. The first preset nonlinear model is used to determine the baseline and incremental values ​​of the power switch component parameters under each circuit, and the second preset nonlinear model is used to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters under each circuit.

[0046] Optionally, the actual parameter values ​​of both MOSFETs and aluminum electrolytic capacitors are affected by both factory dispersion (parameter fluctuations during component manufacturing) and aging. Therefore, it is necessary to calculate the factory baseline values ​​of the parameters of MOSFETs and aluminum electrolytic capacitors using mathematical methods. Since the changes in component parameters may not be linear during the aging process, a preset nonlinear model (a pre-built model, including a first preset nonlinear model and a second preset nonlinear model) can be used to determine the baseline and incremental values ​​of different component parameters based on the actual parameter values ​​of the aforementioned components.

[0047] In this embodiment, a first preset nonlinear model can be used to determine the baseline and incremental values ​​of the gate threshold voltage and the on-resistance, and a second preset nonlinear model can be used to calculate the baseline and incremental values ​​of the equivalent series resistance.

[0048] Step S103: For each different component in each circuit, determine the health value of each different component based on the reference value and the incremental value.

[0049] In this embodiment, for different components in each circuit, a preset nonlinear model can be used to calculate the baseline and incremental values ​​of the parameters of different components. Based on the baseline and incremental values, the health value of each different component is determined. By calculating the health value of each different component, its health status can be intuitively judged and potential risks can be identified.

[0050] Step S104: Determine the health status of the target charging device based on the health values ​​of each different component.

[0051] In this embodiment, the health status of the target charging device is determined based on the health values ​​of each different component. For example, the overall health status of the target charging device can be determined using the average health values ​​of the MOSFET and the electrolytic capacitor.

[0052] In summary, firstly, the actual parameter values ​​of different components are measured. Based on these actual parameter values, a first preset nonlinear model is used to evaluate the baseline value and aging increment of the MOSFET parameters, and a second preset nonlinear model is used to evaluate the baseline value and aging increment of the electrolytic capacitor parameters. Then, based on the baseline value and aging increment, the health values ​​of different components under different circuits are determined. Finally, based on the health values ​​of each different component, the health status of the target charging device is determined, thus achieving an accurate assessment of the overall health status of the target charging device. This solves the technical problem of low accuracy in assessing the health status of charging devices in related technologies.

[0053] In order to accurately construct the preset nonlinear model, in the method for evaluating the health status of charging devices provided in Embodiment 1 of this application, the parameter values ​​of the component parameters of multiple charging devices are collected; based on all parameter values, a first preset nonlinear model is constructed, and based on all parameter values, a second preset nonlinear model is constructed; based on the first preset nonlinear model and the second preset nonlinear model, a preset nonlinear model is constructed.

[0054] In this embodiment, parameter values ​​of components from multiple charging devices (such as module 1, module 2, module 3, module 4, etc.) are collected. For example, a static parameter testing platform is used to measure the static parameter values ​​of 10 MOSFETs, including the gate threshold voltage (GTP). ) and on-resistance ( The parameters of the electrolytic capacitor, including capacitance C and equivalent series resistance ESR, are measured using a power analyzer.

[0055] For example, the gate threshold voltage parameter values ​​are shown in Table 1, the on-resistance parameter values ​​are shown in Table 2, and the electrolytic capacitor parameter values ​​are shown in Table 3.

[0056] Table 1

[0057]

[0058] Table 2

[0059]

[0060] Table 3

[0061]

[0062] Optionally, since the VIENNA (three-phase rectifier circuit) rectifier in the front-end circuit of the charging device has three-phase symmetry, the aging status of only the two MOSFETs of phase A of the VIENNA rectifier in the front-end circuit can be evaluated. In Table 1 and Table 2, MOS1-MOS2 correspond to the two MOSFETs in the front-end circuit, and MOS3-MOS10 correspond to the eight MOSFETs in the back-end circuit.

[0063] In this embodiment, a first preset nonlinear model can be constructed based on all parameter values, and a second preset nonlinear model can be constructed based on all parameter values. A preset nonlinear model can be constructed based on the first preset nonlinear model and the second preset nonlinear model.

[0064] To accurately construct the first preset nonlinear model, in the charging device health status assessment method provided in Embodiment 1 of this application, for all power switching components in each circuit, the baseline value variable, incremental variable, and dispersed value variable of the power switching component parameters corresponding to the power switching components are determined; based on all parameter values, baseline value variables, incremental variables, and dispersed value variables, a first preset formula is constructed; the first preset formula is transformed to obtain a first transformed formula; the average value of all parameter values ​​is calculated to obtain the first actual average value of the power switching components; based on the first transformed formula and the first actual average value, a first objective function is determined; a first preset constraint condition is determined, and based on the first objective function and the first preset constraint condition, a first preset nonlinear model is constructed.

[0065] Optionally, since the MOSFETs used in the subsequent circuit of a single charging module are of the same type, it can be assumed that the reference values ​​of the on-resistance of the eight MOSFETs are the same. Since the subsequent circuit includes two sets of LLC (i.e., resonant converter) parallel structures with symmetry, it can be assumed that the increments of the on-resistance of the eight MOSFETs are the same.

[0066] In this embodiment, for all power switch components in each circuit, the base value variable (representing the parameter value of the component under ideal conditions), the incremental variable (quantifying the parameter changes caused by component aging), and the dispersion value variable (reflecting the parameter fluctuations of the component during manufacturing) of the corresponding power switch component parameters are determined.

[0067] In this embodiment, it can be based on all parameter values ​​( ), benchmark variable ( ), incremental variables ( ) and dispersion variable ( ), construct the first preset formula (such as the first preset formula for the on-resistance of the 8 MOSFETs in the subsequent circuit is ). ), can make , The first preset formula is transformed to obtain the first transformed formula:

[0068] ;

[0069] Optionally, because and It has a linear relationship, while the dispersion value It conforms to a normal distribution. When the amount of data is large enough, its average value is zero. In this embodiment, the amount of data is small, so the average value of the dispersion value cannot be considered to be zero. However, the approximate value of the benchmark value to be calculated should make the average value of the dispersion value as close to zero as possible. Therefore, a nonlinear programming model (i.e., the first preset nonlinear model) can be used to find the approximate solution of the benchmark value and the aging increment.

[0070] In this embodiment, the average value of all parameter values ​​is calculated to obtain the first actual average value of the power switch components. Based on the first transformation formula and the first actual average value, the first objective function (z) is determined:

[0071] ;

[0072] It can determine the first preset constraint condition of the first objective function, and construct the first preset nonlinear model based on the first objective function and the first preset constraint condition.

[0073] To accurately determine the first preset constraint, in the method for assessing the health status of charging equipment provided in Embodiment 1 of this application, the first transformation formula includes: sorting all parameter values ​​for multiple different first preset variables to obtain the maximum and minimum actual values ​​of the power switch components; determining the first preset range of change for each first preset variable and determining the first range of change for the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the first range of change includes at least the first range of change for the maximum dispersion value and the first range of change for the minimum dispersion value; and determining the first preset constraint based on all first preset ranges of change, the maximum actual value, the minimum actual value, the first range of change for the maximum dispersion value, and the first range of change for the minimum dispersion value.

[0074] In this embodiment, all parameter values ​​are sorted to obtain the maximum actual value of the power switch component. ) and minimum actual value ( The first transformation formula includes multiple different first preset variables (such as a, b), and determines the first preset range of change for each first preset variable (such as...). , ), and determine the first range of variation for the dispersion variable (including the maximum dispersion value ( The first range of variation and the minimum dispersion () The first range of variation, such as , ).

[0075] In this embodiment, since the dispersion value calculated from the maximum actual value does not exceed the maximum possible dispersion value, therefore Since the dispersion value calculated from the minimum actual value does not exceed the minimum possible dispersion value, therefore The first preset constraint condition can be determined based on the above-mentioned dispersion value range and based on all first preset variation ranges, the maximum actual value, the minimum actual value, the first variation range of the maximum dispersion value, and the first variation range of the minimum dispersion value:

[0076] ;

[0077] To accurately construct the second preset nonlinear model, in the charging device health status assessment method provided in Embodiment 1 of this application, for all electrolytic capacitor components in each circuit, the baseline value variable, incremental variable, and dispersed value variable of the electrolytic capacitor component parameters are determined; based on all parameter values, baseline value variables, incremental variables, and dispersed value variables, a second preset formula is constructed; the second preset formula is transformed to obtain a second transformed formula; the average value of all parameter values ​​is calculated to obtain the second actual average value of the electrolytic capacitor components; based on the second transformed formula and the second actual average value, a second objective function is determined; second preset constraints are determined, and based on the second objective function and the second preset constraints, a second preset nonlinear model is constructed.

[0078] In this embodiment, for all electrolytic capacitor components in each circuit, the baseline value variable, incremental variable, and dispersion value variable of the electrolytic capacitor component parameters are determined. This can be based on all parameter values ​​( ), benchmark variable ( ), incremental variables ( ) and dispersion variable ( ), construct a second preset formula (such as the second preset formula for the ESR of each electrolytic capacitor in the subsequent circuit is ). ), can make , The second preset formula is transformed to obtain the second transformed formula:

[0079] ;

[0080] In this embodiment, the average value of all parameter values ​​is calculated to obtain the second actual average value of the electrolytic capacitor components. Based on the second transformation formula and the second actual average value, the second objective function (z) is determined:

[0081] ;

[0082] It can also determine the second preset constraint conditions of the second objective function, and construct the second preset nonlinear model based on the second objective function and the second preset constraint conditions.

[0083] To accurately determine the second preset constraint, in the method for assessing the health status of charging equipment provided in Embodiment 1 of this application, the second transformation formula includes: sorting all parameter values ​​for multiple different second preset variables to obtain the maximum and minimum actual values ​​of the electrolytic capacitor components; determining the second preset range of variation for each second preset variable, and determining the second range of variation for the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the second range of variation includes at least the second range of variation for the maximum dispersion value and the second range of variation for the minimum dispersion value; and determining the second preset constraint based on all second preset ranges of variation, the maximum actual value, the minimum actual value, the second range of variation for the maximum dispersion value, and the second range of variation for the minimum dispersion value.

[0084] In this embodiment, all parameter values ​​are sorted to obtain the maximum actual value of the electrolytic capacitor component. ) and minimum actual value ( The second transformation formula includes multiple different second preset variables (such as a, b), determines the second preset range of change for each second preset variable, and determines the second range of change for the dispersion value variable (such as...). , ), and determine the second range of variation for the dispersion variable (including the maximum dispersion value ( The second range of variation and the minimum dispersion () The second range of variation, such as , ).

[0085] In this embodiment, consistent with the power switch components described above: the dispersion value calculated from the maximum actual value does not exceed the maximum possible dispersion value, therefore The dispersion value calculated from the minimum actual value does not exceed the minimum possible dispersion value, therefore The second preset constraint can be determined based on the above dispersion value range and based on each second preset variation range, the maximum actual value, the minimum actual value, the second variation range of the maximum dispersion value, and the second variation range of the minimum dispersion value.

[0086] ;

[0087] In order to accurately determine the health value of each different component, in the method for assessing the health status of charging equipment provided in Embodiment 1 of this application, a relative incremental value is calculated based on a baseline value and an incremental value; and the health value of each different component is determined based on the relative incremental value.

[0088] Optionally, for MOSFETs, the health value is associated with the relative aging increment of the gate threshold voltage and on-resistance; for electrolytic capacitors, the health value is associated with the relative aging increment of the capacitance value decay percentage and ESR.

[0089] In this embodiment, for different components, a relative incremental value (i.e., relative incremental value = incremental value / baseline value) can be calculated based on the baseline value and the incremental value. Based on the relative incremental value, the health value (i.e., health level) of each different component can be determined.

[0090] For example, Figure 2 This is a schematic diagram of an optional MOSFET health surface according to an embodiment of the present invention, such as... Figure 2 As shown, the x-axis is (Gate threshold voltage) relative aging increment, y-axis is (On-resistance) relative aging increment, z-axis represents the health of the power MOSFET (i.e., power components), surface expression is: That is, when the gate threshold voltage relative to the aging increment is 0 and the on-resistance relative to the aging increment is 15%, the health level is 60; when the gate threshold voltage relative to the aging increment is 15% and the on-resistance relative to the aging increment is 0, the health level is also 60; when both are 0, the health level is 100. Therefore, if either of them exceeds the failure threshold (15%), the health level of the MOSFET will be lower than 60.

[0091] For example, Figure 3 This is a schematic diagram of an optional aluminum electrolytic capacitor health surface according to an embodiment of the present invention, such as... Figure 3 As shown, the x-axis represents the percentage decay of C (capacitance value), the y-axis represents the relative aging increment of ESR (equivalent series resistance), and the z-axis represents the health of the aluminum electrolytic capacitor. The surface expression is as follows: That is, when the capacitance value decays by 20% and the ESR relative aging increment is 0, the health level is 60; when the capacitance value decays by 0% and the ESR relative aging increment is 20%, the health level is also 60; when both are 0, the health level is 100. Therefore, if either of them exceeds the failure threshold (20%), the health level of the aluminum electrolytic capacitor will be lower than 60.

[0092] In this embodiment, a health rating between 60 and 100 means that no parameter of the MOSFET or electrolytic capacitor has reached the failure threshold, i.e., a healthy state.

[0093] In the embodiment, since each circuit includes two electrolytic capacitors, the second preset nonlinear model calculates the baseline and incremental values ​​of the ESR of each electrolytic capacitor in different circuits. Therefore, for each circuit, the average value of the health of the two electrolytic capacitors can be calculated to obtain the health of the electrolytic capacitors in each circuit.

[0094] For example, the health status of each component is shown in Table 4.

[0095] Table 4

[0096]

[0097] In order to accurately determine the health status of the target charging device, in the health status assessment method of the charging device provided in Embodiment 1 of this application, the average health values ​​of all components are calculated to obtain the average health value; based on the average health value, the health status of the target charging device is determined.

[0098] In this embodiment, the average health of all components can be used as the overall health of the target charging device. According to Table 4, the overall health of modules 1-4 are 67.3853, 66.3383, 67.2567, and 67.6638, respectively. The calculation results show that the relationship of the overall health of the four charging modules is Module 4 > Module 1 > Module 3 > Module 2.

[0099] In some alternative embodiments, conversion efficiency (the ability to convert input electrical energy into output electrical energy) can measure the performance of a charging device and can serve as an external characteristic quantity characterizing the aging state of MOSFETs and electrolytic capacitors. A mathematical model can be established between this external characteristic quantity (conversion efficiency at different output voltages) and the overall health of the charging device. Figure 4 This is a schematic diagram of the relationship between an optional external characteristic quantity and the overall health of a charging device according to an embodiment of the present invention, such as... Figure 4As shown, the x-axis represents the output voltage (V), the y-axis represents the overall health of the module, and the z-axis represents the conversion efficiency. The conversion efficiency of the charging module increases with the improvement of the overall health. The overall health of the charging device can be evaluated by the conversion efficiency. The conversion efficiency calculated by the above mathematical model can also be compared with the actual conversion efficiency to verify the accuracy of the mathematical model.

[0100] In this embodiment of the invention, by establishing an overall aging status assessment model (i.e., a preset nonlinear model) for the MOSFETs and aluminum electrolytic capacitors in the charging device, baseline and incremental values ​​of the parameters can be obtained. Based on the baseline and incremental values, relative incremental values ​​can be obtained. Then, by constructing a surface expression for health and relative incremental values, the relative aging increment is mapped to the health value, realizing an intuitive assessment of the component health status. Furthermore, a mathematical model of conversion efficiency and overall health of the charging device under different output voltages can be constructed. In the absence of baseline values ​​for component parameters, the overall health of the charging device can be assessed through conversion efficiency, thereby accurately assessing the actual aging status of the components and achieving the technical effect of comprehensively assessing the overall health of the charging device.

[0101] The following is a detailed description with reference to another embodiment.

[0102] Example 2

[0103] The device for assessing the health status of a charging device provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0104] Figure 5 This is a schematic diagram of an optional charging device health status assessment apparatus according to an embodiment of the present invention, such as... Figure 5 As shown, the device for assessing the health status of the charging equipment may include: a first acquisition unit 50, a first determination unit 51, a second determination unit 52, and a third determination unit 53.

[0105] The first acquisition unit 50 is used to acquire the actual parameter values ​​of the component parameters of the target charging device. The target charging device includes multiple circuits, and each circuit includes at least multiple power switch components and multiple electrolytic capacitor components.

[0106] The first determining unit 51 is used to determine the baseline and incremental values ​​of different component parameters based on the actual parameter values ​​using a preset nonlinear model. The preset nonlinear model includes at least a first preset nonlinear model and a second preset nonlinear model. The first preset nonlinear model is used to determine the baseline and incremental values ​​of the power switch component parameters under each circuit, and the second preset nonlinear model is used to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters under each circuit.

[0107] The second determining unit 52 is used to determine the health value of each different component based on the reference value and the incremental value for each different component under each circuit.

[0108] The third determining unit 53 is used to determine the health status of the target charging device based on the health values ​​of each different component.

[0109] The aforementioned device for assessing the health status of charging equipment can acquire the actual parameter values ​​of the components of the target charging equipment through the first acquisition unit 50, determine the baseline and incremental values ​​of different component parameters based on the actual parameter values ​​using a preset nonlinear model through the first determination unit 51, determine the health value of each different component based on the baseline and incremental values ​​through the second determination unit 52, and determine the health status of the target charging equipment based on the health values ​​of each different component in each circuit through the third determination unit 53.

[0110] Optionally, the device for assessing the health status of charging equipment includes: a first acquisition module, used to acquire parameter values ​​of component parameters of multiple charging devices before determining the baseline and incremental values ​​of different component parameters using a preset nonlinear model based on actual parameter values; a first construction module, used to construct a first preset nonlinear model based on all parameter values, and construct a second preset nonlinear model based on all parameter values; and a second construction module, used to construct a preset nonlinear model based on the first preset nonlinear model and the second preset nonlinear model.

[0111] Optionally, the first construction module includes: a first determining submodule, used to determine, for each power switching component in each circuit, the base value variable, incremental variable, and dispersed value variable of the power switching component parameters; a first construction submodule, used to construct a first preset formula based on all parameter values, base value variables, incremental variables, and dispersed value variables; a first transformation submodule, used to transform the first preset formula to obtain a first transformed formula; a first calculation submodule, used to calculate the average value of all parameter values ​​to obtain a first actual average value of the power switching components; a second determining submodule, used to determine a first objective function based on the first transformed formula and the first actual average value; and a third determining submodule, used to determine a first preset constraint condition and construct a first preset nonlinear model based on the first objective function and the first preset constraint condition.

[0112] Optionally, the first transformation formula includes multiple different first preset variables, and the third determining submodule includes: a first sorting submodule, used to sort all parameter values ​​to obtain the maximum and minimum actual values ​​of the power switching components; a fourth determining submodule, used to determine the first preset variation range of each first preset variable and the first variation range of the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the first variation range includes at least the first variation range of the maximum dispersion value and the first variation range of the minimum dispersion value; and a fifth determining submodule, used to determine the first preset constraint conditions based on all the first preset variation ranges, the maximum actual value, the minimum actual value, the first variation range of the maximum dispersion value, and the first variation range of the minimum dispersion value.

[0113] Optionally, the first construction module includes: a sixth determining submodule, used to determine the baseline value variable, incremental variable, and dispersed value variable of the electrolytic capacitor component parameters corresponding to all electrolytic capacitor components in each circuit; a second construction submodule, used to construct a second preset formula based on all parameter values, baseline value variables, incremental variables, and dispersed value variables; a second transformation submodule, used to transform the second preset formula to obtain a second transformation formula; a second calculation submodule, used to calculate the average value of all parameter values ​​to obtain the second actual average value of the electrolytic capacitor components; a seventh determining submodule, used to determine a second objective function based on the second transformation formula and the second actual average value; and an eighth determining submodule, used to determine the second preset constraint conditions and construct a second preset nonlinear model based on the second objective function and the second preset constraint conditions.

[0114] Optionally, the second transformation formula includes multiple different second preset variables, and the eighth determining submodule includes: a second sorting submodule, used to sort all parameter values ​​to obtain the maximum and minimum actual values ​​of the electrolytic capacitor components; a ninth determining submodule, used to determine the second preset variation range of each second preset variable and the second variation range of the dispersion value variable, wherein the dispersion value variable includes at least the maximum dispersion value and the minimum dispersion value, and the second variation range includes at least the second variation range of the maximum dispersion value and the second variation range of the minimum dispersion value; and a tenth determining submodule, used to determine the second preset constraint conditions based on all second preset variation ranges, the maximum actual value, the minimum actual value, the second variation range of the maximum dispersion value, and the second variation range of the minimum dispersion value.

[0115] Optionally, the second determining unit includes: a first calculation module, used to calculate a relative incremental value based on a reference value and an incremental value; and a first determining module, used to determine the health value of each different component based on the relative incremental value.

[0116] Optionally, the third determining unit includes: a second calculation module, used to calculate the average health value of all components to obtain the average health value; and a second determining module, used to determine the health status of the target charging device based on the average health value.

[0117] The aforementioned device for assessing the health status of charging equipment may further include a processor and a memory. The first acquisition unit 50, the first determination unit 51, the second determination unit 52, the third determination unit 53, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0118] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the health status of the target charging device can be determined based on the health values ​​of each different component.

[0119] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0120] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for assessing the health status of a charging device as described above.

[0121] When a computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: obtaining the actual parameter values ​​of the component parameters of the target charging device; based on the actual parameter values, using a preset nonlinear model to determine the baseline and incremental values ​​of different component parameters; for different components under each circuit, based on the baseline and incremental values, determining the health value of each different component; and based on the health value of each different component, determining the health status of the target charging device.

[0122] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method for assessing the health status of a charging device.

[0123] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) according to an embodiment of the present invention for evaluating the health status of a charging device. Figure 6As shown, an electronic device may include one or more processors (e.g., Figure 6 The processors 602a, 602b, ..., 602n, etc., may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), and a memory 604 for storing data. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0124] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0126] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit 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 the present invention, in essence, or the part that contributes to the prior art, or all or part 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0131] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the health status of a charging device, characterized in that, include: Obtain the actual parameter values ​​of the component parameters of the target charging device, wherein the target charging device includes: multiple circuits, and each circuit includes at least: multiple power switch components and multiple electrolytic capacitor components; Based on the actual parameter values, a preset nonlinear model is used to determine the baseline and incremental values ​​of different component parameters. The preset nonlinear model includes at least a first preset nonlinear model and a second preset nonlinear model. The first preset nonlinear model is used to determine the baseline and incremental values ​​of the power switch component parameters in each circuit, and the second preset nonlinear model is used to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters in each circuit. For each different component in each circuit, the health value of each different component is determined based on the reference value and the incremental value; The health status of the target charging device is determined based on the health values ​​of each different component.

2. The method for assessing the health status of charging equipment according to claim 1, characterized in that, Before determining the baseline and incremental values ​​of different component parameters using a preset nonlinear model based on the actual parameter values, the process also includes: Collect parameter values ​​of components from multiple charging devices; Based on all the parameter values, a first preset nonlinear model is constructed, and based on all the parameter values, a second preset nonlinear model is constructed. The preset nonlinear model is constructed based on the first preset nonlinear model and the second preset nonlinear model.

3. The method for assessing the health status of charging equipment according to claim 2, characterized in that, The step of constructing the first preset nonlinear model based on all the parameter values ​​includes: For all power switching components in each circuit, determine the base value variable, incremental variable, and dispersion value variable of the power switching component parameters corresponding to the power switching component; Based on all the parameter values, the baseline variable, the incremental variable, and the dispersion variable, a first preset formula is constructed; The first preset formula is transformed to obtain the first transformed formula; The average value of all the parameter values ​​is calculated to obtain the first actual average value of the power switch component. Based on the first transformation formula and the first actual average value, the first objective function is determined; First preset constraints are determined, and based on the first objective function and the first preset constraints, the first preset nonlinear model is constructed.

4. The method for assessing the health status of charging equipment according to claim 3, characterized in that, The first transformation formula includes: a plurality of different first preset variables, and the step of determining the first preset constraints includes: Sort all the parameter values ​​to obtain the maximum and minimum actual values ​​of the power switch components; Determine a first preset range of variation for each of the first preset variables, and determine a first range of variation for the dispersion value variable, wherein the dispersion value variable includes at least a maximum dispersion value and a minimum dispersion value, and the first range of variation includes at least a first range of variation for the maximum dispersion value and a first range of variation for the minimum dispersion value; The first preset constraint condition is determined based on all the first preset variation ranges, the maximum actual value, the minimum actual value, the first variation range of the maximum dispersion value, and the first variation range of the minimum dispersion value.

5. The method for assessing the health status of charging equipment according to claim 2, characterized in that, The step of constructing the second preset nonlinear model based on all the parameter values ​​includes: For all electrolytic capacitor components in each circuit, determine the baseline value variable, incremental variable, and dispersion value variable of the electrolytic capacitor component parameters corresponding to the electrolytic capacitor component. Based on all the parameter values, the baseline variable, the incremental variable, and the dispersion variable, a second preset formula is constructed; The second preset formula is transformed to obtain the second transformed formula; The average value of all the parameter values ​​is calculated to obtain the second actual average value of the electrolytic capacitor components. Based on the second transformation formula and the second actual average value, the second objective function is determined; Determine the second preset constraint condition, and construct the second preset nonlinear model based on the second objective function and the second preset constraint condition.

6. The method for assessing the health status of charging equipment according to claim 5, characterized in that, The second transformation formula includes: multiple different second preset variables, and the step of determining the second preset constraints includes: Sort all the parameter values ​​to obtain the maximum and minimum actual values ​​of the electrolytic capacitor components; Determine a second preset range of variation for each of the second preset variables, and determine a second range of variation for the dispersion value variable, wherein the dispersion value variable includes at least: a maximum dispersion value and a minimum dispersion value, and the second range of variation includes at least: a second range of variation for the maximum dispersion value and a second range of variation for the minimum dispersion value; The second preset constraint is determined based on all the second preset variation ranges, the maximum actual value, the minimum actual value, the second variation range of the maximum dispersion value, and the second variation range of the minimum dispersion value.

7. The method for assessing the health status of charging equipment according to claim 1, characterized in that, For each different component in the circuit, the step of determining the health value of each different component based on the reference value and the incremental value includes: Calculate the relative increment value based on the baseline value and the increment value; Based on the relative increment value, the health value of each of the different components is determined.

8. The method for assessing the health status of charging equipment according to claim 1, characterized in that, The step of determining the health status of the target charging device based on the health values ​​of each different component includes: The average health value of all the components is calculated to obtain the average health value; The health status of the target charging device is determined based on the health average.

9. A device for assessing the health status of a charging device, characterized in that, include: The first acquisition unit is used to acquire the actual parameter values ​​of the component parameters of the target charging device, wherein the target charging device includes: multiple circuits, and each circuit includes at least: multiple power switch components and multiple electrolytic capacitor components; The first determining unit is used to determine the baseline and incremental values ​​of different component parameters based on the actual parameter values ​​using a preset nonlinear model. The preset nonlinear model includes at least a first preset nonlinear model and a second preset nonlinear model. The first preset nonlinear model is used to determine the baseline and incremental values ​​of the power switch component parameters in each circuit, and the second preset nonlinear model is used to determine the baseline and incremental values ​​of the electrolytic capacitor component parameters in each circuit. The second determining unit is used to determine the health value of each different component under each circuit based on the reference value and the incremental value. The third determining unit is used to determine the health status of the target charging device based on the health values ​​of each different component.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for assessing the health status of a charging device as described in any one of claims 1 to 8.

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