Battery health monitoring system in charging process and wireless charger

Abstract

According to the battery health monitoring system in the charging process and the wireless charger provided by the invention, by receiving the voltage data, the current data and the surface temperature data of the electronic equipment in the charging process of the electronic equipment, the real-time data acquisition of the electronic equipment in the charging process is realized, the convenience and the real-time performance are improved, and the charging efficiency is improved. According to the method, filtering parameters are dynamically adjusted based on environmental interference parameters, data are filtered according to the filtering parameters, noise is better filtered through dynamic adjustment, and the charge state and the open-circuit voltage of the electronic equipment are estimated based on target monitoring data in combination with a particle filtering algorithm. Accurate estimation improves the precision of subsequent health monitoring, and the battery state parameters of the electronic equipment are determined based on the charge state and the open-circuit voltage in combination with the historical battery data of the electronic equipment and by utilizing the health assessment model, so that the real-time assessment of the battery health is realized, the charging strategy is conveniently and timely adjusted, the service life of the battery is prolonged, and the safety is improved.

Description

Technical Field

[0001] This invention relates to the field of battery health monitoring technology, and in particular to a battery health monitoring system and wireless charger during the charging process. Background Technology

[0002] With the widespread use of portable electronic devices, the battery, as the core energy storage unit, directly impacts the device's performance and safety. Currently, battery health monitoring is typically performed after charging is complete or requires interrupting the charging process, making it impossible to monitor the dynamic changes in battery status during charging in real time. Traditional wired charging systems often acquire battery data via wired connections, leading to issues such as interface wear and complex wiring. While wireless charging technology solves the inconvenience of wired connections, existing wireless charging devices generally lack battery health monitoring capabilities during the charging process.

[0003] In existing technologies, electromagnetic interference during charging can affect monitoring signals. Traditional monitoring algorithms are not optimized for wireless charging environments, resulting in large data processing errors. Therefore, there is an urgent need for a system that can monitor battery health status in real time and accurately during wireless charging. Summary of the Invention

[0004] This invention provides a battery health monitoring system and a wireless charger during the charging process to solve the problems mentioned in the background art.

[0005] A battery health monitoring system during charging includes:

[0006] The charging receiver module is used to receive energy to charge electronic devices and to acquire voltage data, current data and surface temperature data of electronic devices.

[0007] The filtering module is used to dynamically adjust the filtering parameters based on environmental interference parameters, and filter voltage data, current data and surface temperature data according to the filtering parameters to obtain target monitoring data;

[0008] The state estimation module is used to estimate the state of charge and open-circuit voltage of the electronic device based on the target monitoring data and in combination with the particle filtering algorithm.

[0009] The health monitoring module is used to determine the battery health index of electronic devices based on the state of charge and open-circuit voltage, combined with historical battery data of the electronic devices, and using a health assessment model.

[0010] Preferably, the charging terminal receiving module includes:

[0011] A receiving unit is used to receive energy from the charging transmitter to charge electronic devices.

[0012] The sensor unit includes a voltage sensor, a current sensor, and a temperature sensor, used to collect voltage data, current data, and surface temperature data of electronic devices in real time during the charging process.

[0013] Preferably, the filtering module includes:

[0014] The data signal analysis unit is used to perform sliding window analysis on the voltage data, current data and surface temperature data to obtain the root mean square error of the voltage data, current data and surface temperature data, and to perform fast Fourier transform on the voltage data, current data and surface temperature data to obtain the spectral entropy and the energy ratio of high-frequency components with frequencies greater than a preset frequency based on the transformation results.

[0015] The interference determination unit is used to uniformly standardize the root mean square error, spectral entropy and high-frequency component energy ratio to obtain standardized values, and then fuse the standardized values ​​based on preset error weights, spectral entropy weights and energy weights to obtain a comprehensive interference intensity value.

[0016] The parameter determination unit is used to determine the step size factor and wavelet transform threshold parameters of the adaptive filtering algorithm based on the comprehensive interference intensity value, specifically:

[0017] When the overall interference intensity value exceeds the first preset threshold, the step size factor of the adaptive filtering algorithm is reduced to the first preset value, and the high-frequency threshold parameter of the wavelet transform is increased.

[0018] When the overall interference intensity value is lower than the second preset threshold, the step size factor of the adaptive filtering algorithm is increased to the second preset value, and the low-frequency threshold parameter of the wavelet transform is decreased.

[0019] When the comprehensive interference intensity value is between the first preset threshold and the second preset threshold, the initial step size factor of the adaptive filtering algorithm remains unchanged, and the initial threshold parameter of the wavelet transform remains unchanged.

[0020] The filtering unit filters voltage data, current data, and surface temperature data based on the step size factor of the adaptive filtering algorithm and the threshold parameter of wavelet transform to obtain target monitoring data.

[0021] Preferably, the error weight, spectral entropy weight, and energy weight in the interference determination unit are determined in the following ways:

[0022] The historical data acquisition unit is used to acquire the historical weights of the root mean square error, spectral entropy, and high-frequency component energy ratio in the historical comprehensive interference intensity value of electronic devices during historical charging processes.

[0023] The model building unit is used to train a weight allocation model based on the historical weights of the root square error, spectral entropy and the energy ratio of high-frequency components in the historical comprehensive interference intensity value, combined with a deep learning model.

[0024] The weight determination unit is used to input the current root mean square error, spectral entropy, and high-frequency component energy proportion into the weight allocation model to obtain error weight, spectral entropy weight, and energy weight.

[0025] Preferably, the state estimation module includes:

[0026] The equivalent model building unit is used to construct the battery equivalent circuit model of the electronic device based on the target current data and target voltage data in the target monitoring data, combined with other electrical parameter data, and to obtain the state equation and observation equation of the battery equivalent circuit model based on real-time monitoring data.

[0027] The numerical determination unit is used to obtain the initial state of charge and the initial open-circuit voltage based on the state equation and the observation equation.

[0028] The numerical correction unit is used to filter the target temperature data in the target monitoring data based on the particle filter algorithm, and then correct the initial state of charge and initial open-circuit voltage to obtain the state of charge and open-circuit voltage of the electronic device.

[0029] Preferably, the health monitoring module includes:

[0030] The offset determination unit is used to establish a state of charge-open circuit voltage variation curve based on the state of charge and open circuit voltage, determine the first curve offset at different charging temperatures based on the variation curve, and determine the second curve offset at different charging times.

[0031] The model optimization unit is used to obtain the latest historical charging data of the last 50 times from the historical battery data of the electronic device, and optimize the parameters of the health assessment model based on the latest historical charging data to obtain the optimal health assessment model.

[0032] The evaluation unit is used to input the state of charge and open-circuit voltage into the optimal health evaluation model to obtain the predicted capacity decay rate and predicted internal resistance growth rate of the battery of the electronic device.

[0033] The sequence determination unit is used to obtain the first historical curve offset sequence at different charging temperatures and the second historical curve offset sequence at different charging times from the latest 50 historical charging data.

[0034] The correction determination unit is used to determine a first correction coefficient based on a first relationship between a first curve offset and a first historical curve offset sequence, and to determine a second correction coefficient based on a second relationship between a second curve offset and a second historical curve offset sequence.

[0035] The correction unit is used to correct the predicted capacity decay rate and the predicted internal resistance growth rate based on the first correction coefficient and the second correction coefficient to obtain the target capacity decay rate and the target internal resistance growth rate.

[0036] The index determination unit is used to determine the battery health index of electronic devices based on the target capacity decay rate and the target internal resistance growth rate.

[0037] Preferably, the correction determination unit includes:

[0038] The first determining unit is used to obtain the first adjacent difference change pattern of the first historical curve offset sequence and determine whether the change of the first curve offset satisfies the first adjacent difference change pattern.

[0039] If so, set the preset coefficient as the first correction coefficient;

[0040] Otherwise, the first correction coefficient is determined based on the difference between the change in the first curve offset and the change in the first adjacent difference.

[0041] The second determining unit is used to obtain the second adjacent difference change pattern of the second historical curve offset sequence and determine whether the change of the second curve offset satisfies the second adjacent difference change pattern.

[0042] If so, set the preset coefficient as the second correction coefficient;

[0043] Otherwise, the second correction coefficient is determined based on the difference between the change in the second curve offset and the change in the second adjacent difference.

[0044] Preferably, the correction unit includes:

[0045] The acquisition unit is used to acquire the average correction coefficient of the first correction coefficient and the second correction coefficient, and to determine the capacity decay rate correction weight and the internal resistance growth rate correction weight based on historical experience.

[0046] The determination unit is used to correct the predicted capacity decay rate based on the average correction coefficient and the capacity decay rate correction weight to obtain the target capacity decay rate, and to correct the predicted internal resistance growth rate based on the average correction coefficient and the internal resistance growth rate correction weight to obtain the target internal resistance growth rate.

[0047] Preferably, the index determining unit includes:

[0048] An index acquisition unit is used to determine a first health index based on the target capacity decay rate and a second health index based on the target internal resistance growth rate.

[0049] An index selection unit is used to select the smaller of the first health index and the second health index as the battery health index of the electronic device.

[0050] A wireless charger includes a battery health monitoring system during charging, and further includes:

[0051] A battery sensing module is used to sense the battery of an electronic device and trigger a charging command.

[0052] The communication module is used to receive power adjustment commands from electronic devices during the charging process;

[0053] The casing is used to secure wireless chargers and electronic devices.

[0054] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0055] By receiving energy to charge electronic devices and acquiring their voltage, current, and surface temperature data, real-time data acquisition during charging is achieved without interrupting the process, improving convenience and real-time performance. Based on environmental interference parameters, filtering parameters are dynamically adjusted to filter voltage, current, and surface temperature data, resulting in target monitoring data. This addresses the limitations of traditional filtering algorithms with fixed parameters that cannot adapt to changing interference, enabling better noise filtering through dynamic adjustment, improving data accuracy, enhancing the signal-to-noise ratio, and reducing interference impact. Based on the target monitoring data and combined with a particle filtering algorithm, the state of charge (SOC) and open-circuit voltage of the electronic device are estimated, accurately improving the precision of subsequent health monitoring. Furthermore, by combining SOC and open-circuit voltage with historical battery data and utilizing a health assessment model, battery state parameters are determined, enabling real-time battery health assessment rather than monitoring after charging. This facilitates timely adjustments to charging strategies, preventing overcharging or inefficiency, extending battery life, and improving safety.

[0056] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention.

[0057] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a structural diagram of a battery health monitoring system during the charging process according to an embodiment of the present invention;

[0060] Figure 2 This is a structural diagram of the state estimation module described in an embodiment of the present invention;

[0061] Figure 3 This is a product diagram of a wireless charger according to an embodiment of the present invention. Detailed Implementation

[0062] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0063] Example 1:

[0064] This invention provides a battery health monitoring system during the charging process, such as... Figure 1 As shown, it includes:

[0065] The charging receiver module is used to receive energy to charge electronic devices and to acquire voltage data, current data and surface temperature data of electronic devices.

[0066] The filtering module is used to dynamically adjust the filtering parameters based on environmental interference parameters, and filter voltage data, current data and surface temperature data according to the filtering parameters to obtain target monitoring data;

[0067] The state estimation module is used to estimate the state of charge and open-circuit voltage of the electronic device based on the target monitoring data and in combination with the particle filtering algorithm.

[0068] The health monitoring module is used to determine the battery health index of electronic devices based on the state of charge and open-circuit voltage, combined with historical battery data of the electronic devices, and using a health assessment model.

[0069] In this embodiment, historical battery data includes, for example, the number of charge-discharge cycles and historical capacity decay curves.

[0070] In this embodiment, the health assessment model is pre-designed based on historical data from electronic devices.

[0071] In this embodiment, the battery state parameters include capacity decay rate and internal resistance growth rate.

[0072] In this embodiment, the environmental interference parameter is mainly electromagnetic interference.

[0073] In this embodiment, particle filtering is suitable for nonlinear, non-Gaussian systems and is more flexible than traditional Kalman filtering. Especially in complex electromagnetic environments such as wireless charging, it can more accurately estimate the state of charge and open-circuit voltage. These two parameters are important for battery health assessment, and accurate estimation can improve the accuracy of subsequent health monitoring.

[0074] The beneficial effects of the above design scheme are as follows: By receiving energy to charge electronic devices and acquiring voltage, current, and surface temperature data of the electronic devices, real-time data acquisition of the electronic devices can be achieved during the charging process without interrupting charging, improving convenience and real-time performance. By dynamically adjusting filtering parameters based on environmental interference parameters, voltage, current, and surface temperature data are filtered according to the filtering parameters to obtain target monitoring data. This solves the problem that traditional filtering algorithms may have fixed parameters and cannot adapt to changing interference. It achieves better noise filtering through dynamic adjustment, improves data accuracy, enhances the signal-to-noise ratio, and reduces the impact of interference. Based on the target monitoring data and combined with the particle filtering algorithm, the state of charge and open-circuit voltage of the electronic devices are estimated, which accurately improves the accuracy of subsequent health monitoring. Based on the state of charge and open-circuit voltage and combined with the historical battery data of the electronic devices, the battery state parameters of the electronic devices are determined using a health assessment model, enabling real-time assessment of battery health instead of monitoring after charging. This facilitates timely adjustment of charging strategies, avoids overcharging or inefficiency, extends battery life, and improves safety.

[0075] Example 2:

[0076] Based on Embodiment 1, this embodiment of the invention provides a battery health monitoring system during charging, wherein the charging end receiving module includes:

[0077] A receiving unit is used to receive energy from the charging transmitter to charge electronic devices.

[0078] The sensor unit includes a voltage sensor, a current sensor, and a temperature sensor, used to collect voltage data, current data, and surface temperature data of electronic devices in real time during the charging process.

[0079] The beneficial effects of the above design scheme are: by acquiring the voltage data, current data and surface temperature data of the electronic device during the charging process, real-time data acquisition of the electronic device can be achieved without interrupting the charging process, thus improving convenience and real-time performance.

[0080] Example 3:

[0081] Based on Embodiment 1, this embodiment of the invention provides a battery health monitoring system during charging, wherein the filtering module includes:

[0082] The data signal analysis unit is used to perform sliding window analysis on the voltage data, current data and surface temperature data to obtain the root mean square error of the voltage data, current data and surface temperature data, and to perform fast Fourier transform on the voltage data, current data and surface temperature data to obtain the spectral entropy and the energy ratio of high-frequency components with frequencies greater than a preset frequency based on the transformation results.

[0083] The interference determination unit is used to uniformly standardize the root mean square error, spectral entropy and high-frequency component energy ratio to obtain standardized values, and then fuse the standardized values ​​based on preset error weights, spectral entropy weights and energy weights to obtain a comprehensive interference intensity value.

[0084] The parameter determination unit is used to determine the step size factor and wavelet transform threshold parameters of the adaptive filtering algorithm based on the comprehensive interference intensity value, specifically:

[0085] When the overall interference intensity value exceeds the first preset threshold, the step size factor of the adaptive filtering algorithm is reduced to the first preset value, and the high-frequency threshold parameter of the wavelet transform is increased.

[0086] When the overall interference intensity value is lower than the second preset threshold, the step size factor of the adaptive filtering algorithm is increased to the second preset value, and the low-frequency threshold parameter of the wavelet transform is decreased.

[0087] When the comprehensive interference intensity value is between the first preset threshold and the second preset threshold, the initial step size factor of the adaptive filtering algorithm remains unchanged, and the initial threshold parameter of the wavelet transform remains unchanged.

[0088] The filtering unit filters voltage data, current data, and surface temperature data based on the step size factor of the adaptive filtering algorithm and the threshold parameter of wavelet transform to obtain target monitoring data.

[0089] In this embodiment, the adaptive filtering algorithm is the least mean square algorithm.

[0090] In this embodiment, the threshold parameters of the wavelet transform include high-frequency threshold parameters and low-frequency threshold parameters. The high-frequency threshold parameters are used to control the acquisition of the high-frequency components, and the low-frequency threshold parameters are used to control the acquisition of the low-frequency components.

[0091] In this embodiment, the preset frequency is 10kHz.

[0092] In this embodiment, after the root mean square error, spectral entropy and high-frequency component energy ratio are uniformly standardized, the standardized values ​​of the root mean square error, spectral entropy and high-frequency component energy ratio are all within the range of (0, 1).

[0093] In this embodiment, the sum of the error weight, the spectral entropy weight, and the energy weight is 1.

[0094] In this embodiment, the standardized values ​​are fused to obtain the comprehensive interference intensity value. Specifically, the standardized values ​​of root mean square error, spectral entropy, and high-frequency component energy ratio are multiplied by their corresponding weights, and then the product values ​​are added together.

[0095] The beneficial effects of the above design scheme are as follows: By performing sliding window analysis on the voltage, current, and surface temperature data, the root mean square error, spectral entropy, and high-frequency component energy ratio are obtained, providing a data foundation for determining the comprehensive interference intensity. After uniformly standardizing the root mean square error, spectral entropy, and high-frequency component energy ratio, standardized values ​​are obtained. Based on preset error weights, spectral entropy weights, and energy weights, the standardized values ​​are fused to obtain the comprehensive interference intensity value, realizing weighted quantization of interference intensity and providing a foundation for accurate data filtering. When the comprehensive interference intensity value exceeds a first preset threshold, under strong electromagnetic interference, the filtering parameters are prevented from fluctuating drastically due to excessive step size, reducing false convergence and improving the robustness of the filtering algorithm against sudden strong interference during battery charging. The step size factor of the adaptive filtering algorithm is reduced to the first preset value, and the high-frequency threshold parameter of the wavelet transform is increased to specifically eliminate the distortion of the monitoring signal caused by strong interference, retaining the true battery characteristic signal in the low-frequency band, and providing a basis for subsequent state of charge estimation. A cleaner data foundation is achieved by increasing the step size factor of the adaptive filtering algorithm to the second preset value when the overall interference intensity value is below the second preset threshold. This improves the real-time performance of the filtering algorithm when interference is weak, avoiding signal tracking delays caused by excessively small step sizes. This is particularly suitable for fast charging or scenarios where battery status changes rapidly. Furthermore, the low-frequency threshold parameter of the wavelet transform is reduced, preserving more signal details while reducing noise, balancing noise reduction accuracy and signal fidelity, and preventing the loss of battery status characteristics due to over-filtering. This improves the accuracy of the health assessment model. When the overall interference intensity value is between the first and second preset thresholds, the initial step size factor of the adaptive filtering algorithm and the initial threshold parameter of the wavelet transform remain unchanged. This avoids frequent switching of the filtering strategy when the interference intensity fluctuates slightly around the threshold, improving filtering stability. Ultimately, this solves the problem that traditional filtering algorithms may have fixed parameters and be unable to adapt to changing interference, achieving better noise filtering through dynamic adjustment, improving data accuracy, increasing the signal-to-noise ratio, and reducing the impact of interference.

[0096] Example 4:

[0097] Based on Embodiment 3, this embodiment of the invention provides a battery health monitoring system during the charging process. The specific determination methods for the error weight, spectral entropy weight, and energy weight in the interference determination unit are as follows:

[0098] The historical data acquisition unit is used to acquire the historical weights of the root mean square error, spectral entropy, and high-frequency component energy ratio in the historical comprehensive interference intensity value of electronic devices during historical charging processes.

[0099] The model building unit is used to train a weight allocation model based on the historical weights of root mean square error, spectral entropy and the energy ratio of high-frequency components in the historical comprehensive interference intensity value, combined with a deep learning model.

[0100] The weight determination unit is used to input the current root mean square error, spectral entropy, and high-frequency component energy proportion into the weight allocation model to obtain error weight, spectral entropy weight, and energy weight.

[0101] The beneficial effects of the above design scheme are as follows: by training a deep learning model based on the historical weights of root mean square error, spectral entropy, and the energy proportion of high-frequency components in the historical comprehensive interference intensity value, a weight allocation model is obtained. Based on the current root mean square error, spectral entropy, and energy proportion of high-frequency components, the error weight, spectral entropy weight, and energy weight are obtained by inputting them into the weight allocation model, thus providing a weight basis for the accurate determination of the comprehensive interference intensity value.

[0102] Example 5:

[0103] Based on Embodiment 1, this embodiment of the invention provides a battery health monitoring system during the charging process, such as... Figure 2 As shown, the state estimation module includes:

[0104] The equivalent model building unit is used to construct the battery equivalent circuit model of the electronic device based on the target current data and target voltage data in the target monitoring data, combined with other electrical parameter data, and to obtain the state equation and observation equation of the battery equivalent circuit model based on real-time monitoring data.

[0105] The numerical determination unit is used to obtain the initial state of charge and the initial open-circuit voltage based on the state equation and the observation equation.

[0106] The numerical correction unit is used to filter the target temperature data in the target monitoring data based on the particle filter algorithm, and then correct the initial state of charge and initial open-circuit voltage to obtain the state of charge and open-circuit voltage of the electronic device.

[0107] In this embodiment, after filtering the target temperature data in the target monitoring data based on the particle filter algorithm, the initial state of charge and initial open-circuit voltage are corrected by obtaining the standard state of charge and standard open-circuit voltage at the standard temperature, and then comparing them with 1+α(t-25)+β(t-25). 2Multiplying the calculation results yields the final state of charge and open-circuit voltage, where α and β are temperature coefficients, and t is the real-time temperature data.

[0108] The beneficial effects of the above design scheme are as follows: By constructing an equivalent battery circuit model of the electronic device based on the target current data and target voltage data in the target monitoring data, combined with other electrical parameter data, and obtaining the state equation and observation equation of the battery equivalent circuit model based on real-time monitoring data, the initial state of charge and initial open-circuit voltage are obtained based on the state equation and observation equation, and the initial state of charge and initial open-circuit voltage are obtained after filtering the target temperature data in the target monitoring data based on the particle filter algorithm, the initial state of charge and initial open-circuit voltage are corrected to obtain the state of charge and open-circuit voltage of the electronic device, which improves the accuracy of subsequent health monitoring.

[0109] Example 6:

[0110] Based on Embodiment 1, this embodiment of the invention provides a battery health monitoring system during charging, comprising a health monitoring module including:

[0111] The offset determination unit is used to establish a state of charge-open circuit voltage variation curve based on the state of charge and open circuit voltage, determine the first curve offset at different charging temperatures based on the variation curve, and determine the second curve offset at different charging times.

[0112] The model optimization unit is used to obtain the latest historical charging data of the last 50 times from the historical battery data of the electronic device, and optimize the parameters of the health assessment model based on the latest historical charging data to obtain the optimal health assessment model.

[0113] The evaluation unit is used to input the state of charge and open-circuit voltage into the optimal health evaluation model to obtain the predicted capacity decay rate and predicted internal resistance growth rate of the battery of the electronic device.

[0114] The sequence determination unit is used to obtain the first historical curve offset sequence at different charging temperatures and the second historical curve offset sequence at different charging times from the latest 50 historical charging data.

[0115] The correction determination unit is used to determine a first correction coefficient based on a first relationship between a first curve offset and a first historical curve offset sequence, and to determine a second correction coefficient based on a second relationship between a second curve offset and a second historical curve offset sequence.

[0116] The correction unit is used to correct the predicted capacity decay rate and the predicted internal resistance growth rate based on the first correction coefficient and the second correction coefficient to obtain the target capacity decay rate and the target internal resistance growth rate.

[0117] The index determination unit is used to determine the battery health index of electronic devices based on the target capacity decay rate and the target internal resistance growth rate.

[0118] The beneficial effects of the above design scheme are as follows: By establishing a state of charge-open circuit voltage variation curve based on the state of charge and open circuit voltage, determining the first curve offset at different charging temperatures, and determining the second curve offset at different charging times, the characteristic drift of the battery under different environmental conditions such as temperature fluctuations and differences in charging time can be quantitatively characterized. This enables real-time capture of the impact of temperature changes on the battery. Furthermore, by obtaining the latest 50 historical charging data from the battery's historical data, and optimizing the parameters of the health assessment model based on this latest historical charging data, the optimal health assessment model is obtained. This solves the problem of assessment bias caused by changes in battery aging characteristics in traditional static models. To address the issue of battery health discrepancies, a dual calibration mechanism is established by acquiring the first historical curve offset sequence at different charging temperatures and the second historical curve offset sequence at different charging times from the latest 50 historical charging data. Based on the first relationship between the first curve offset and the first historical curve offset sequence, a first correction coefficient is determined. Based on the second relationship between the second curve offset and the second historical curve offset sequence, a second correction coefficient is determined. This avoids the cross-influence of environmental interference and aging characteristics when evaluating single parameters, ensuring the accuracy of capacity decay rate and internal resistance growth rate. It enables real-time assessment of battery health, rather than monitoring only after charging, facilitating timely adjustment of charging strategies, avoiding overcharging or inefficiency, extending battery life, and improving safety.

[0119] Example 7:

[0120] Based on Embodiment 6, this embodiment of the invention provides a battery health monitoring system during charging, wherein the correction determination unit includes:

[0121] The first determining unit is used to obtain the first adjacent difference change pattern of the first historical curve offset sequence and determine whether the change of the first curve offset satisfies the first adjacent difference change pattern.

[0122] If so, set the preset coefficient as the first correction coefficient;

[0123] Otherwise, the first correction coefficient is determined based on the difference between the change in the first curve offset and the change in the first adjacent difference.

[0124] The second determining unit is used to obtain the second adjacent difference change pattern of the second historical curve offset sequence and determine whether the change of the second curve offset satisfies the second adjacent difference change pattern.

[0125] If so, set the preset coefficient as the second correction coefficient;

[0126] Otherwise, the second correction coefficient is determined based on the difference between the change in the second curve offset and the change in the second adjacent difference.

[0127] In this embodiment, since the optimal health assessment model is based on historical data, the prediction results conform to the first adjacent difference change law and the second adjacent difference change law of the first historical curve offset sequence. When the actual situation does not follow the above laws, it needs to be corrected.

[0128] In this embodiment, the preset coefficient is 0.01.

[0129] The beneficial effects of the above design scheme are: by determining the first correction coefficient based on the first relationship between the first curve offset and the first historical curve offset sequence, and by determining the second correction coefficient based on the second relationship between the second curve offset and the second historical curve offset sequence, the influence caused by the discrepancy between the model prediction and the actual situation is eliminated, ensuring the correctness of the target capacity decay rate and the target internal resistance growth rate.

[0130] Example 8:

[0131] Based on Embodiment 6, this embodiment of the invention provides a battery health monitoring system during charging, wherein the correction unit includes:

[0132] The acquisition unit is used to acquire the average correction coefficient of the first correction coefficient and the second correction coefficient, and to determine the capacity decay rate correction weight and the internal resistance growth rate correction weight based on historical experience.

[0133] The determination unit is used to correct the predicted capacity decay rate based on the average correction coefficient and the capacity decay rate correction weight to obtain the target capacity decay rate, and to correct the predicted internal resistance growth rate based on the average correction coefficient and the internal resistance growth rate correction weight to obtain the target internal resistance growth rate.

[0134] The beneficial effects of the above design scheme are: the predicted capacity decay rate is corrected based on the average correction coefficient and the capacity decay rate correction weight to obtain the target capacity decay rate; the predicted internal resistance growth rate is corrected based on the average correction coefficient and the internal resistance growth rate correction weight to obtain the target internal resistance growth rate; and the internal resistance growth rate and capacity decay rate are corrected based on trend changes and weight design to ensure the accuracy of battery health assessment.

[0135] Example 9:

[0136] Based on Embodiment 6, this embodiment of the invention provides a battery health monitoring system during charging, wherein the index determination unit includes:

[0137] An index acquisition unit is used to determine a first health index based on the target capacity decay rate and a second health index based on the target internal resistance growth rate.

[0138] An index selection unit is used to select the smaller of the first health index and the second health index as the battery health index of the electronic device.

[0139] In this embodiment, when the first health index and the second health index are the same, either one can be selected.

[0140] The beneficial effects of the above design scheme are: by taking the smaller of the first health index and the second health index as the battery health index of the electronic device, the battery's shortcomings are taken into account, ensuring the accuracy of the battery health index, realizing real-time assessment of battery health instead of monitoring only after charging, facilitating timely adjustment of charging strategies, avoiding overcharging or low efficiency, extending battery life, and improving safety.

[0141] Example 10:

[0142] This invention provides a wireless charger, including a battery health monitoring system during charging as described in the embodiment, and further comprising:

[0143] A battery sensing module is used to sense the battery of an electronic device and trigger a charging command.

[0144] The communication module is used to receive power adjustment commands from electronic devices during the charging process;

[0145] The casing is used to secure wireless chargers and electronic devices.

[0146] In this embodiment, the product diagram of the charger is as follows: Figure 3 As shown, it includes a housing and a battery sensing module.

[0147] The beneficial effects of the above design are: by providing a wireless charger with a battery health monitoring system during the charging process, the battery health can be assessed in real time, rather than after charging, which facilitates timely adjustment of the charging strategy, avoids overcharging or inefficiency, extends battery life, and improves safety.

[0148] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this application and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A battery health monitoring system during charging, characterized in that, include: The charging receiver module is used to receive energy to charge electronic devices and to acquire voltage data, current data and surface temperature data of electronic devices. The filtering module is used to dynamically adjust the filtering parameters based on environmental interference parameters, and filter voltage data, current data and surface temperature data according to the filtering parameters to obtain target monitoring data; The state estimation module is used to estimate the state of charge and open-circuit voltage of the electronic device based on the target monitoring data and in combination with the particle filtering algorithm. The health monitoring module is used to determine the battery health index of electronic devices based on the state of charge and open-circuit voltage, combined with historical battery data of the electronic devices, and using a health assessment model.

2. The battery health monitoring system during charging according to claim 1, characterized in that, The charging terminal receiving module includes: A receiving unit is used to receive energy from the charging transmitter to charge electronic devices. The sensor unit includes a voltage sensor, a current sensor, and a temperature sensor, used to collect voltage data, current data, and surface temperature data of electronic devices in real time during the charging process.

3. The battery health monitoring system during charging according to claim 1, characterized in that, The filtering module includes: The data signal analysis unit is used to perform sliding window analysis on the voltage data, current data and surface temperature data to obtain the root mean square error of the voltage data, current data and surface temperature data, and to perform fast Fourier transform on the voltage data, current data and surface temperature data to obtain the spectral entropy and the energy ratio of high-frequency components with frequencies greater than a preset frequency based on the transformation results. The interference determination unit is used to uniformly standardize the root mean square error, spectral entropy and high-frequency component energy ratio to obtain standardized values, and then fuse the standardized values ​​based on preset error weights, spectral entropy weights and energy weights to obtain a comprehensive interference intensity value. The parameter determination unit is used to determine the step size factor and wavelet transform threshold parameters of the adaptive filtering algorithm based on the comprehensive interference intensity value, specifically: When the overall interference intensity value exceeds the first preset threshold, the step size factor of the adaptive filtering algorithm is reduced to the first preset value, and the high-frequency threshold parameter of the wavelet transform is increased. When the overall interference intensity value is lower than the second preset threshold, the step size factor of the adaptive filtering algorithm is increased to the second preset value, and the low-frequency threshold parameter of the wavelet transform is decreased. When the comprehensive interference intensity value is between the first preset threshold and the second preset threshold, the initial step size factor of the adaptive filtering algorithm remains unchanged, and the initial threshold parameter of the wavelet transform remains unchanged. The filtering unit filters voltage data, current data, and surface temperature data based on the step size factor of the adaptive filtering algorithm and the threshold parameter of wavelet transform to obtain target monitoring data.

4. The battery health monitoring system during charging according to claim 3, characterized in that, The specific methods for determining the error weight, spectral entropy weight, and energy weight in the interference determination unit are as follows: The historical data acquisition unit is used to acquire the historical weights of the root mean square error, spectral entropy, and high-frequency component energy ratio in the historical comprehensive interference intensity value of electronic devices during historical charging processes. The model building unit is used to train a weight allocation model based on the historical weights of the root square error, spectral entropy and the energy ratio of high-frequency components in the historical comprehensive interference intensity value, combined with a deep learning model. The weight determination unit is used to input the current root mean square error, spectral entropy, and high-frequency component energy proportion into the weight allocation model to obtain error weight, spectral entropy weight, and energy weight.

5. A battery health monitoring system during charging according to claim 1, characterized in that, The state estimation module includes: The equivalent model building unit is used to construct the battery equivalent circuit model of the electronic device based on the target current data and target voltage data in the target monitoring data, combined with other electrical parameter data, and to obtain the state equation and observation equation of the battery equivalent circuit model based on real-time monitoring data. The numerical determination unit is used to obtain the initial state of charge and the initial open-circuit voltage based on the state equation and the observation equation. The numerical correction unit is used to filter the target temperature data in the target monitoring data based on the particle filter algorithm, and then correct the initial state of charge and initial open-circuit voltage to obtain the state of charge and open-circuit voltage of the electronic device.

6. A battery health monitoring system during charging according to claim 1, characterized in that, The health monitoring module includes: The offset determination unit is used to establish a state of charge-open circuit voltage variation curve based on the state of charge and open circuit voltage, determine the first curve offset at different charging temperatures based on the variation curve, and determine the second curve offset at different charging times. The model optimization unit is used to obtain the latest historical charging data of the last 50 times from the historical battery data of the electronic device, and optimize the parameters of the health assessment model based on the latest historical charging data to obtain the optimal health assessment model. The evaluation unit is used to input the state of charge and open-circuit voltage into the optimal health evaluation model to obtain the predicted capacity decay rate and predicted internal resistance growth rate of the battery of the electronic device. The sequence determination unit is used to obtain the first historical curve offset sequence at different charging temperatures and the second historical curve offset sequence at different charging times from the latest 50 historical charging data. The correction determination unit is used to determine a first correction coefficient based on a first relationship between a first curve offset and a first historical curve offset sequence, and to determine a second correction coefficient based on a second relationship between a second curve offset and a second historical curve offset sequence. The correction unit is used to correct the predicted capacity decay rate and the predicted internal resistance growth rate based on the first correction coefficient and the second correction coefficient to obtain the target capacity decay rate and the target internal resistance growth rate. The index determination unit is used to determine the battery health index of electronic devices based on the target capacity decay rate and the target internal resistance growth rate.

7. A battery health monitoring system during charging according to claim 6, characterized in that, The correction determination unit includes: The first determining unit is used to obtain the first adjacent difference change pattern of the first historical curve offset sequence and determine whether the change of the first curve offset satisfies the first adjacent difference change pattern. If so, set the preset coefficient as the first correction coefficient; Otherwise, the first correction coefficient is determined based on the difference between the change in the first curve offset and the change in the first adjacent difference. The second determining unit is used to obtain the second adjacent difference change pattern of the second historical curve offset sequence and determine whether the change of the second curve offset satisfies the second adjacent difference change pattern. If so, set the preset coefficient as the second correction coefficient; Otherwise, the second correction coefficient is determined based on the difference between the change in the second curve offset and the change in the second adjacent difference.

8. A battery health monitoring system during charging according to claim 6, characterized in that, The correction unit includes: The acquisition unit is used to acquire the average correction coefficient of the first correction coefficient and the second correction coefficient, and to determine the capacity decay rate correction weight and the internal resistance growth rate correction weight based on historical experience. The determination unit is used to correct the predicted capacity decay rate based on the average correction coefficient and the capacity decay rate correction weight to obtain the target capacity decay rate, and to correct the predicted internal resistance growth rate based on the average correction coefficient and the internal resistance growth rate correction weight to obtain the target internal resistance growth rate.

9. A battery health monitoring system during charging according to claim 6, characterized in that, The index determination unit includes: An index acquisition unit is used to determine a first health index based on the target capacity decay rate and a second health index based on the target internal resistance growth rate. An index selection unit is used to select the smaller of the first health index and the second health index as the battery health index of the electronic device.

10. A wireless charger, comprising a battery health monitoring system during charging as described in claim 1, characterized in that, Also includes: A battery sensing module is used to sense the battery of an electronic device and trigger a charging command. The communication module is used to receive power adjustment commands from electronic devices during the charging process; The casing is used to secure wireless chargers and electronic devices.

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