Method for detecting state of health of vehicle battery and charging system of vehicle battery

Abstract

The invention provides a vehicle battery health state detection method and a vehicle battery charging system, and relates to the technical field of electric vehicle battery management.The method comprises the steps that after a vehicle is connected into a charging pile, when a preset condition is met, EIS detection is started; obtaining a power supply detected by the EIS based on a preset EMS energy scheduling strategy, wherein the power supply comprises a photovoltaic system, an energy storage system and a power grid; in the frequency sweeping mode, selecting a plurality of frequency points in the characteristic frequency band based on a self-adaptive sampling strategy, and applying alternating current excitation signals of the plurality of frequency points to the vehicle battery; acquiring response data of the vehicle battery under EIS detection; a state of health of the vehicle battery is calculated based on the response data. According to the method provided by the invention, the battery health state can be accurately and rapidly detected in a light storage and charging scene.

Description

Technical Field

[0001] This application relates to the field of electric vehicle battery management technology, and in particular to a method for detecting the health status of a vehicle battery and a charging system for a vehicle battery. Background Technology

[0002] With the rapid development of the new energy vehicle industry, integrated photovoltaic, energy storage and charging (i.e., photovoltaic system, energy storage system and electric vehicle charging pile) charging stations have significant advantages in energy utilization efficiency and operational economy due to their ability to effectively integrate photovoltaic power generation, energy storage system and electric vehicle charging functions, and have become an important development direction for future charging infrastructure.

[0003] However, in the photovoltaic-storage-charging scenario, battery SOH (State of Health) detection faces many challenges. It lacks the ability to identify the internal aging mechanism of the battery and does not meet the real-time requirements of fast charging scenarios in charging stations, making it impossible to achieve accurate and rapid detection of battery health status. Summary of the Invention

[0004] This application provides a method for detecting the health status of a vehicle battery and a charging system for a vehicle battery, which helps to achieve accurate and rapid detection of battery health status in photovoltaic, energy storage and charging scenarios.

[0005] Firstly, this application provides a method for detecting the health status of a vehicle battery, including: After the vehicle is connected to the charging station, EIS detection is initiated when preset conditions are met; The power supply for EIS detection is obtained based on the preset EMS energy dispatch strategy. The power supply sources include photovoltaic systems, energy storage systems and the power grid. In frequency sweep mode, multiple frequency points are selected in the characteristic frequency band based on an adaptive sampling strategy, and AC excitation signals of multiple frequency points are applied to the vehicle battery. Collect vehicle battery response data under EIS testing; The health status of the vehicle battery is calculated based on the response data.

[0006] In one possible implementation, the preset EMS energy dispatch strategy includes: When the change in the output power of the photovoltaic system is less than or equal to the first threshold, the photovoltaic system is selected as the power source; or When the output power of the photovoltaic system changes by more than a first threshold, an energy storage system is selected as the power source; or When the output power of the photovoltaic system is less than the second threshold and the state of charge of the energy storage system is less than or equal to the third threshold, the power grid is selected as the source of power supply.

[0007] In one possible implementation, the voltage ripple of the power supply is less than or equal to the ripple threshold, and the frequency accuracy of the power supply is within a preset range.

[0008] One possible implementation includes the following preset conditions: The vehicle battery is determined to be in a stable state and the state of charge of the vehicle battery is greater than or equal to the state of charge threshold.

[0009] One possible implementation involves determining that the vehicle battery is in a stable state, including: When the magnitude of the change in the current of the vehicle battery is less than or equal to the current threshold, and the magnitude of the change in the voltage of the vehicle battery is less than or equal to the voltage threshold, the vehicle battery is considered to be in a stable state.

[0010] One possible implementation includes an adaptive sampling strategy: Based on the stability of the power supply or the sensitivity of frequency points to battery aging or the quality of response data detected by EIS, the density of selected frequency points is adjusted.

[0011] In one possible implementation, based on the stability of the power supply detected by EIS, the sensitivity of frequency points to battery aging, or the quality of the response data, the density of frequency points is adjusted, including: When the change in the output power of the photovoltaic system is less than or equal to the fourth threshold, increase the density of the frequency points selected in the characteristic frequency range; otherwise, decrease the density of the frequency points selected in the characteristic frequency range. Increase the density of selected frequency points in the target frequency band, which is a frequency band in the characteristic frequency range that is strongly correlated with battery aging; or When the signal-to-noise ratio of the response data is greater than or equal to the signal-to-noise ratio threshold, increase the density of the selected frequency points in the characteristic frequency band; otherwise, decrease the density of the selected frequency points in the characteristic frequency band.

[0012] In one possible implementation, the response data includes voltage and current response data generated by the vehicle battery under AC excitation signal; the health status of the vehicle battery is calculated based on the response data, including: Based on voltage response data and current response data, impedance data corresponding to each frequency point among multiple frequency points is obtained; Parameter fitting is performed based on impedance data and a pre-built equivalent circuit model to extract key parameters of battery health status. By inputting key parameters of battery health status into a pre-built battery health status calculation model, the health status of the vehicle battery can be obtained.

[0013] In one possible implementation, key parameters of battery health include charge transfer resistance and diffusion impedance.

[0014] In one possible implementation, the frequency range of the characteristic frequency band is 0.1Hz-1kHz.

[0015] Secondly, this application provides a vehicle battery charging system, including an EMS, a vehicle battery, and a charging pile. The vehicle battery is connected to the charging pile and is used to perform EIS detection and return response data under EIS detection to the charging pile. The charging pile is used for the vehicle battery health status detection method as described in the first aspect, including: after the vehicle is connected to the charging pile, initiating EIS detection when preset conditions are met; obtaining the power supply for EIS detection based on a preset EMS energy scheduling strategy; in frequency sweep mode, selecting multiple frequency points in a characteristic frequency band based on an adaptive sampling strategy and applying AC excitation signals of multiple frequency points to the vehicle battery; collecting the vehicle battery's response data under EIS detection; calculating the vehicle battery's health status based on the response data; and the EMS is used for energy scheduling of the power supply for EIS detection, the power supply source including a photovoltaic system, an energy storage system, and the power grid.

[0016] The beneficial effects of this application are as follows: This application provides a method for detecting the health status of a vehicle battery and a charging system for the vehicle battery. After the vehicle is connected to a charging pile, EIS (Electronic Energy Storage) detection is initiated when preset conditions are met. The EMS (Electronic Energy Management System) provides power for the EIS detection based on a preset energy scheduling strategy. In frequency sweep mode, the charging pile selects multiple frequency points in a characteristic frequency band based on an adaptive sampling strategy, applies AC excitation signals at multiple frequency points to the vehicle battery, and collects the response data of the vehicle battery under EIS detection. The health status of the vehicle battery is calculated based on the response data. By providing a stable frequency source for EIS testing through the energy scheduling of the EMS, the accuracy of the detection signal is ensured. During EIS detection, selecting multiple frequency points in a characteristic frequency band to apply AC excitation signals to the vehicle battery reduces time consumption and improves detection efficiency compared to full-band scanning, meeting the real-time requirements of fast charging scenarios in charging stations. This helps to achieve accurate and rapid detection of battery health status in photovoltaic-storage-charging scenarios. Attached Figure Description

[0017] Figure 1 A schematic diagram of the structure of a vehicle battery charging system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the vehicle battery health status detection method provided in an embodiment of this application. Detailed Implementation

[0018] In this embodiment of the application, unless otherwise stated, the character " / " indicates that the preceding and following objects are in an OR relationship. For example, A / B can represent A or B. "AND / OR" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A AND / OR B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0019] It should be noted that the terms "first" and "second" used in the embodiments of this application are used only for distinguishing descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor should they be construed as indicating or implying order.

[0020] In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. Furthermore, "at least one of the following" or similar expressions refer to any combination of these items, which may include any combination of a single item or a plurality of items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C can be an element itself or a set containing one or more elements.

[0021] In this application, terms such as "exemplary," "in some embodiments," and "in another embodiment" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner.

[0022] In the embodiments of this application, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. Similarly, in the embodiments of this application, "communication" and "transmission" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. For example, transmission can include sending and / or receiving, and can be a noun or a verb.

[0023] In the embodiments of this application, the term "equal to" can be used in conjunction with "greater than" to apply to technical solutions employing the condition of "greater than", and can also be used in conjunction with "less than" to apply to technical solutions employing the condition of "less than". It should be noted that when "equal to" is used with "greater than", it cannot be used with "less than"; and when "equal to" is used with "less than", it cannot be used with "greater than".

[0024] Currently, battery health status detection mainly relies on two types of technologies: one is based on empirical models of voltage and current, which is widely used in battery management systems of various charging stations; the other is based on EIS (Electrochemical Impedance Spectroscopy), which is mostly used in offline laboratory testing scenarios. EIS is a core testing technology for analyzing the characteristics of electrochemical systems (such as batteries). It applies a series of small-amplitude AC excitation signals of different frequencies to the electrochemical system, measures the impedance response of the electrochemical system to signals of different frequencies, and finally obtains the "frequency-impedance" spectrum, i.e., the electrochemical impedance spectrum.

[0025] However, in related technologies, charging stations rely on empirical models of voltage and current to detect battery health, which suffers from low accuracy and difficulty in reflecting the internal aging mechanisms of the battery. While electrochemical impedance spectroscopy (EIS) is used to detect battery health, it requires offline laboratory operation, and full-band scanning typically takes more than 30 minutes, failing to meet the real-time requirements of fast-charging scenarios in charging stations. Furthermore, in photovoltaic-storage-charging scenarios, photovoltaic power generation is volatile, conflicting with the traditional EIS testing's reliance on a stable power source, easily leading to test signal distortion. Directly using the grid to power EIS testing would increase operating costs.

[0026] Based on the above problems, this application proposes a method for detecting the health status of vehicle batteries, which helps to achieve accurate and rapid detection of battery health status in photovoltaic, energy storage and charging scenarios.

[0027] Now combined Figure 1 and Figure 2 The health status detection method and charging system for vehicle batteries provided in the embodiments of this application will be described.

[0028] Figure 1 This is a schematic diagram of the structure of a vehicle battery charging system provided in an embodiment of this application. Figure 1As shown, the vehicle battery charging system includes an EMS (Energy Management System), a vehicle battery, and a charging pile. After the vehicle connects to the charging pile, EIS detection is initiated when preset conditions are met. The EMS performs energy scheduling on the power supply for EIS detection. The power supply sources include photovoltaic systems, energy storage systems, and the power grid. Specifically, the EMS provides detection power for EIS detection based on a preset energy scheduling strategy. In frequency sweep mode, the charging pile selects multiple frequency points in a characteristic frequency band based on an adaptive sampling strategy and applies AC excitation signals at multiple frequency points to the vehicle battery. After performing EIS detection, the vehicle battery returns response data under EIS detection to the charging pile. After acquiring the response data, the charging pile calculates the health status of the vehicle battery based on the response data. It is understood that the charging pile can be used to implement the vehicle battery health status detection method provided in this application embodiment.

[0029] In some embodiments, after a vehicle is connected to a charging pile, the charging pile is connected to the vehicle's BMS (Battery Management System) via a CAN bus. The BMS collects data such as battery voltage, current, temperature, and SOC (State of Charge) in real time and transmits the data to the charging pile's detection module. The charging pile receives the data and performs battery status judgment, data processing, and SOH calculation.

[0030] Considering the need for "fast charging and real-time performance" in photovoltaic-storage-charging scenarios (typically requiring EIS testing to be completed within 5 minutes), data processing and SOH calculation are preferentially performed locally at the charging station to avoid data transmission delays and ensure testing efficiency and real-time performance. In other cases, such as when it is necessary to aggregate data from multiple charging stations or to perform long-term aging trend analysis on vehicle batteries, the SOH results can be uploaded to an edge computing platform or cloud platform for data analysis.

[0031] like Figure 2 As shown, Figure 2 The flowchart of the vehicle battery health status detection method provided in this application embodiment is shown, and specifically includes the following steps: Step S21: After the vehicle is connected to the charging pile, EIS detection is started when the preset conditions are met.

[0032] After the electric vehicle's charging gun is physically connected to the charging pile, the subsequent battery status detection and EIS detection processes are triggered as prerequisites for the detection. Connecting the vehicle to the charging pile serves two purposes: firstly, it establishes a physical connection, enabling the electric vehicle to connect to the charging pile hardware and triggering the subsequent detection process; secondly, it establishes a data communication link, providing a data channel for the subsequent collection of parameters such as battery voltage and current.

[0033] The reason for choosing to perform EIS online testing during vehicle charging is that the charging process is a period when the vehicle is stationary and the battery is in a relatively stable thermal state, making it the most suitable time for precise measurements. Furthermore, during the charging process (especially the constant current charging phase), the battery's voltage, current, and temperature change gradually over a short period of time, and the amplitude of the AC excitation signal applied by the EIS test is very small, causing minimal disturbance to the vehicle battery's charging process.

[0034] To improve the stability and accuracy of EIS testing, EIS testing should only be performed when the vehicle battery meets preset conditions.

[0035] In some optional embodiments, the preset conditions include: determining that the vehicle battery is in a stable state and / or that the state of charge (SOC) of the vehicle battery is greater than or equal to a SOC threshold. Therefore, EIS detection is initiated when the vehicle battery is determined to be in a stable state; or when the vehicle battery's SOC is determined to be greater than or equal to the SOC threshold; or when both the vehicle battery is determined to be in a stable state and its SOC is greater than or equal to the SOC threshold are met simultaneously.

[0036] Determining whether the battery is in a "static and stable" condition (such as no significant charging or discharging current or small voltage fluctuations) is to ensure that the battery is stable during EIS testing and to reduce the interference of dynamic conditions on impedance measurement.

[0037] The applicant's research revealed that the SOC range of a vehicle battery between 30% and 70% is the core period for fast charging in a photovoltaic-energy storage-charging scenario. Users are highly sensitive to charging time, and the battery is in a dynamic charge-discharge transition state. The lithium-ion distribution is uneven, and the impedance characteristics are easily affected by current fluctuations, which reduces the accuracy of EIS detection. Therefore, EIS detection is not actively triggered within this range, but is only passively initiated in special scenarios such as "user actively selects battery health diagnosis" or "system detects abnormal battery fluctuations".

[0038] When the initial SOC of the vehicle battery is ≤30%, i.e., at a low SOC, the lithium-ion concentration inside the battery is low, and the chemical potential of the electrode materials changes drastically, leading to significant impedance fluctuations, unstable data, and a low signal-to-noise ratio. When the SOC is ≥70%, the battery's electrochemical characteristics are more stable, and the detection accuracy is higher. Therefore, when the initial SOC of the vehicle battery is ≤30%, EIS detection is not initiated immediately, but rather delayed until the SOC of the vehicle battery reaches ≥70% during the charging process.

[0039] Based on this, this application proposes a method to determine whether the EIS detection trigger condition is met based on the collected SOC data of the vehicle battery, i.e., whether the SOC is greater than or equal to the state of charge threshold. Optionally, the state of charge threshold is greater than or equal to 70%.

[0040] This application breaks away from the fixed pattern of "EIS detection starting immediately upon plugging in the charging gun" by setting up a dynamic SOC-triggered detection mechanism. It intelligently selects the detection period based on the vehicle battery's SOC. For example, detection is triggered directly when SOC ≥ 70%; when SOC ≤ 30%, it is delayed until the later stages of charging. This avoids time conflicts between EIS detection and the fast charging process, meeting the real-time requirements of fast charging scenarios at charging stations. Simultaneously, it leverages the more stable electrochemical characteristics of batteries at high SOC levels to improve detection accuracy.

[0041] In some embodiments, after the vehicle is connected to the charging pile, when it is determined that the vehicle battery is in a stable state and the SOC of the vehicle battery is ≥70%, EIS detection is initiated. This ensures that the battery state is stable during EIS detection, reduces the interference of dynamic operating conditions on impedance measurement, and at the same time, takes advantage of the fact that the battery electrochemical characteristics are more stable under high SOC to improve the accuracy of EIS detection, making the obtained response data more accurate, thereby improving the accuracy of SOH calculation.

[0042] Optionally, if the preset conditions are not met, battery status data (voltage, current, SOC, etc.) will be continuously collected until the preset conditions are met, at which point EIS detection will be initiated.

[0043] In some embodiments, determining that the vehicle battery is in a stable state includes: determining that the vehicle battery is in a stable state when the magnitude change of the current of the vehicle battery is less than or equal to a current threshold and the magnitude change of the voltage of the vehicle battery is less than or equal to a voltage threshold.

[0044] When the amplitude change of the vehicle battery current is less than or equal to the current threshold, and the amplitude change of the vehicle battery voltage is less than or equal to the voltage threshold, it indicates that the battery current and voltage fluctuations are small, and the vehicle battery is considered to be in a stable state. For example, when the amplitude change (i.e., fluctuation) of the battery current is ≤0.1A and the amplitude change (i.e., fluctuation) of the voltage is ≤50mV, the vehicle battery is considered to be in a stable state.

[0045] Step S22: Obtain the power supply detected by EIS based on the preset EMS energy dispatch strategy.

[0046] The sources of power supply include photovoltaic systems, energy storage systems, and the power grid.

[0047] Before EIS testing, the testing hardware connecting the EMS system and the charging pile ensures real-time communication of status data and battery parameters from the photovoltaic system, energy storage system, and power grid. In a photovoltaic-energy storage-charging scenario, the power supply for the charging pile (including the power supply for EIS testing) comes from the photovoltaic system, energy storage system, or power grid. The EMS, through energy dispatching, ensures that the input voltage of the charging pile is sufficiently stable during EIS testing, laying the foundation for generating a high-quality AC excitation signal.

[0048] In some optional embodiments, the preset EMS energy dispatch strategy includes: When the change in the output power of the photovoltaic system is less than or equal to the first threshold, the photovoltaic system is selected as the power source; or When the output power of the photovoltaic system changes by more than a first threshold, an energy storage system is selected as the power source; or When the output power of the photovoltaic system is less than the second threshold and the state of charge of the energy storage system is less than or equal to the third threshold, the power grid is selected as the source of power supply.

[0049] The EMS energy dispatch logic in a photovoltaic-storage-charging scenario is as follows: Based on the real-time status of the photovoltaic system, energy storage system, and power grid, the system decides on the power source for EIS testing (e.g., prioritizing stable photovoltaic or energy storage power sources) to ensure the stability of the testing power supply while also considering operational economy. The photovoltaic system is prioritized as the power source. When photovoltaic output fluctuates significantly, the system switches to a "curtailment + energy storage auxiliary" power supply mode to provide a stable frequency source for EIS testing, thereby ensuring the accuracy of the detection signal and maximizing the utilization of clean energy to improve the energy efficiency of the photovoltaic-storage-charging system. The power grid serves only as a backup power source. Power grid supplementation is only activated when photovoltaic output is insufficient or the remaining energy storage capacity is too low to ensure the continuity of power supply for EIS testing.

[0050] Specifically, within a preset time interval (e.g., 1 minute, 10 minutes, etc.), when the photovoltaic output fluctuation is ≤10%, that is, when the change in the output power of the photovoltaic system is less than or equal to the first threshold (i.e., 10%), the photovoltaic system is selected as the source of power supply; when the photovoltaic output fluctuation is >10%, that is, when the change in the output power of the photovoltaic system is greater than the first threshold, the energy storage system is selected as the source of power supply; when the output power of the photovoltaic system is less than the second threshold (e.g., minimum photovoltaic output) and the SOC of the energy storage system is less than or equal to the third threshold (e.g., 20%), the power grid is selected as the source of power supply.

[0051] It is understood that the first threshold, the second threshold, and the third threshold in this application can be set by a technician according to the specific circumstances, and this application does not impose any restrictions.

[0052] It should be noted that, in this embodiment, the fluctuation range refers to the proportion of change in the output power of the photovoltaic system relative to its rated capacity within a preset time interval. This indicator is used to quantify the instability of photovoltaic power generation. For example, a photovoltaic output fluctuation > 10% within 1 minute means that the change in the output power of the photovoltaic system within 1 minute is greater than 10%, that is, the change in the output power of the photovoltaic system relative to its rated capacity within 1 minute is greater than 10%. For example, for a 1MW photovoltaic power station, "photovoltaic fluctuation > 10%" means that the change in output power relative to its rated capacity is greater than 10%, that is, the change in output power is greater than 100kW.

[0053] This application utilizes EMS energy dispatch to prioritize the use of photovoltaic (PV) power generation, which has the lowest cost. When PV output fluctuates significantly, the energy storage system provides supplementary power, ensuring that vehicle charging and EIS testing are not interrupted or have reduced power due to PV fluctuations. This provides a stable power supply for high-precision EIS testing, ensuring the accuracy of the battery SOH (State of Health) readings. Grid power supplementation is only activated when PV output is insufficient (e.g., at night or on cloudy days) or when the remaining energy storage capacity is too low. The grid serves as a backup power source, ensuring uninterrupted charging and testing services.

[0054] Optionally, the voltage ripple of the power supply is less than or equal to the ripple threshold, and the frequency accuracy of the power supply is within a preset range.

[0055] For example, when photovoltaic output fluctuations exceed 10%, the system switches to a "curtailment + energy storage" power supply mode, with an output voltage ripple of ≤2% and a frequency accuracy requirement of ≤±0.1%. This accuracy ensures minimal deviation at typical applied frequency points (such as 0.1Hz, 10Hz, 100Hz, etc.), avoiding acquisition errors and signal distortion caused by frequency shifts. For instance, if a 1Hz output is required, the actual output can be between 0.999Hz and 1.001Hz (accuracy 0.1%).

[0056] It should be noted that voltage ripple refers to the ratio of the peak-to-peak value of the AC noise component to the DC setpoint; it represents the high-frequency noise and fluctuations output by the power supply itself. Excessive voltage ripple in the power supply will introduce measurement errors, leading to severe distortion of the calculated impedance value. Frequency accuracy characterizes the deviation between the output frequency value and the setpoint. Impedance calculation relies on accurately measuring the phase difference between voltage and current. This phase difference is extremely sensitive to frequency deviation; even a small frequency deviation can cause significant phase calculation errors. Therefore, ensuring the frequency accuracy of the power supply can reduce impedance calculation errors and improve the accuracy of SOH detection.

[0057] Based on the energy dispatching results of the EMS, this application outputs a stable excitation power supply that meets the EIS detection requirements, controls the voltage ripple and frequency accuracy of the output power supply, and provides a reliable signal for subsequent frequency sweeping.

[0058] Step S23: In frequency sweep mode, multiple frequency points are selected in the characteristic frequency band based on the adaptive sampling strategy, and AC excitation signals of multiple frequency points are applied to the vehicle battery.

[0059] After the vehicle is connected to the charging station, when it is determined that the vehicle battery is in a stable state and the SOC of the vehicle battery is ≥70%, the EIS detection is initiated, and the hardware such as the signal generator and impedance measurement unit of the charging station are called into working state.

[0060] In a photovoltaic-storage-charging scenario, the input power of the charging pile is dynamically allocated by the photovoltaic system, energy storage system, and power grid based on an energy dispatch strategy (EMS). This provides the total power for the charging pile's main circuit, control system, and EIS (Electronic Information System) detection. EIS detection requires the charging pile's signal generator to generate a high-precision, low-noise, frequency-controllable micro AC excitation signal, which is then superimposed on the DC charging current. In the charging pile's local controller or impedance measurement unit, the collected raw data undergoes preliminary processing such as filtering and phase-locked amplification to calculate the impedance spectrum or key characteristic parameters.

[0061] Optionally, the characteristic frequency range is 0.1Hz-1kHz. Within this frequency range, impedance information is highly correlated with battery aging, resulting in higher efficiency for EIS detection. The 0.1Hz-1kHz range includes a mid-frequency region of 1Hz-1kHz and a low-frequency region of 0.1Hz-1Hz. The 1Hz-1kHz mid-frequency region primarily reflects the charge transfer process of the battery, while the 0.1Hz-1Hz low-frequency region primarily reflects the diffusion process of lithium ions within the electrode material.

[0062] Optionally, multiple frequency points include 0.1Hz, 1Hz, 10Hz, 100Hz, and 1kHz.

[0063] To enhance the collection of core aging information, this application further proposes to adjust the density of selected frequency points based on an adaptive sampling strategy in characteristic frequency ranges (such as 0.1Hz-1kHz) that are highly correlated with battery aging mechanisms, thereby improving detection efficiency.

[0064] In some optional embodiments, the adaptive sampling strategy includes adjusting the density of selected frequency points based on the stability of the power supply detected by EIS, the sensitivity of frequency points to battery aging, or the quality of response data.

[0065] In this application, the adaptive sampling strategy refers to a frequency sweeping strategy that dynamically adjusts the sampling point density based on three factors: the sensitivity of frequency points to battery aging information, the power supply stability of the photovoltaic-storage-charging scenario, and the quality of real-time detection data, for the electrochemical impedance spectroscopy (EIS) frequency sweeping process.

[0066] Optionally, based on the stability of the power supply detected by EIS, the sensitivity of frequency points to battery aging, or the quality of the response data, the density of frequency points can be adjusted, including: When the change in the output power of the photovoltaic system is less than or equal to the fourth threshold, increase the density of the frequency points selected in the characteristic frequency range; otherwise, decrease the density of the frequency points selected in the characteristic frequency range. Increase the density of selected frequency points in the target frequency band, which is a frequency band in the characteristic frequency range that is strongly correlated with battery aging; or When the signal-to-noise ratio of the response data is greater than or equal to the signal-to-noise ratio threshold, increase the density of the selected frequency points in the characteristic frequency band; otherwise, decrease the density of the selected frequency points in the characteristic frequency band.

[0067] For example, within a preset time interval (e.g., 1 minute), the change in the output power of the photovoltaic system is represented by ΔP. The fourth threshold is 10%. When ΔP ≤ 10%, the power supply is stable, and the sampling point density is increased in the range of 0.1Hz-1kHz, i.e., the density of the selected frequency points is increased. When ΔP > 10%, the power supply is unstable, and the sampling point density is reduced in the range of 0.1Hz-1kHz, i.e., the density of the selected frequency points is reduced, retaining only the core typical frequency points, such as 0.1Hz, 1Hz, 10Hz, 100Hz, and 1kHz.

[0068] In the 0.1Hz-1kHz frequency band strongly correlated with battery aging (i.e., the target frequency band), the sampling point density is increased. The strong correlation between the target frequency band and the battery can be based on the following criteria: the correlation coefficient between the impedance parameter (e.g., charge transfer resistance) and capacity decay in the selected target frequency band is greater than a preset value (e.g., 0.9); the absolute value of the Pearson correlation coefficient between the impedance parameter and SOH in the selected target frequency band is greater than a preset value (e.g., 0.9); or the physical processes in the selected target frequency band (e.g., charge transfer, diffusion, etc.) directly correspond to the dominant aging mechanism (e.g., SEI growth, active material loss, etc.).

[0069] Optionally, the target frequency band can be the 1Hz-100Hz band. In the 1Hz-100Hz band, SEI thickening and loss of active material directly lead to an increase in charge transfer resistance, and the change is significant. Increase the sampling point density in the 1Hz-100Hz band; for example, in addition to the core typical frequency points of 0.1Hz, 1Hz, 10Hz, 100Hz, and 1kHz, add frequency points of 20Hz, 40Hz, 60Hz, 80Hz, and 90Hz.

[0070] Signal quality is measured using the signal-to-noise ratio (SNR) of the response data. For example, when SNR ≥ 60dB (high quality), the sampling point density is increased in the range of 0.1Hz-1kHz; when SNR < 60dB (low quality), the sampling point density is decreased in the range of 0.1Hz-1kHz.

[0071] This application adopts a "segmented compression" frequency sweeping strategy, focusing on characteristic frequency bands that are highly correlated with battery aging, selecting typical frequency points to apply excitation, and adjusting the density of selected frequency points in the characteristic frequency band based on an adaptive sampling strategy. This shortens the detection time while ensuring key aging information, reducing time consumption and improving detection efficiency compared to full-band scanning.

[0072] In some embodiments, the AC excitation signal in EIS detection can be a voltage signal or a current signal. Optionally, the AC excitation signal is a current signal. Voltage excitation is more sensitive to the internal state of the battery and is easily affected by polarization effects and dynamic changes during testing, leading to fluctuations or distortions in the response current, thus affecting the accuracy of SOH testing. Compared to voltage excitation, current excitation has higher controllability and stability, reducing fluctuations in the excitation signal itself. This allows the impedance data at different frequency points to more accurately reflect the actual electrochemical characteristics of the battery under test, thereby improving the accuracy of SOH detection.

[0073] Step S24: Collect response data of the vehicle battery under EIS detection.

[0074] The response data includes voltage and current response data generated by the vehicle battery under the action of an AC excitation signal.

[0075] Step S25: Calculate the health status of the vehicle battery based on the response data.

[0076] In this application, the charging pile collects the impedance response (amplitude and phase difference of voltage and current) of the battery to different frequency excitations, processes the data through filtering, phase-locked amplification and other technologies, extracts effective impedance information, and then calculates the SOH of the vehicle battery based on the impedance information.

[0077] In some embodiments, calculating the health status of the vehicle battery based on response data includes: Based on voltage response data and current response data, impedance data corresponding to each frequency point among multiple frequency points is obtained; Parameter fitting is performed based on impedance data and a pre-built equivalent circuit model to extract key parameters of battery health status. By inputting key parameters of battery health status into a pre-built battery health status calculation model, the health status of the vehicle battery can be obtained.

[0078] Specifically, filtering and lock-in amplification techniques are used to remove interference noise in the photovoltaic-storage-charging scenario. Then, based on the filtered voltage and current response data, impedance data (including real and imaginary parts) is extracted for each frequency point. Next, based on an equivalent circuit model, the impedance data is fitted using nonlinear least squares to extract parameters strongly correlated with the state of health (SOH) in the characteristic frequency band—that is, key parameters of the battery's state of health. Optionally, key parameters of the battery's state of health include charge transfer resistance and diffusion impedance. The charge transfer resistance and diffusion impedance are then substituted into a pre-constructed SOH calculation model (such as a linear degradation model) to obtain the SOH.

[0079] Optionally, the calculated SOH value can be output (e.g., displayed on the charging pile display screen, or transmitted to the vehicle BMS or charging station management platform) to provide battery health reference for users or maintenance personnel.

[0080] In some embodiments, the method provided in this application further includes: providing a safety warning based on the calculated battery SOH.

[0081] When the calculated SOH (State of Harm) exceeds the preset SOH threshold (e.g., 70%), it indicates that the battery's use is severely affected in this state, and continued use may bring uncertain risks. At this time, the charging station can output a safety warning signal to the user, reminding them that the battery may have safety hazards, so that the user can check or replace it in time, reduce battery safety risks, and improve battery safety.

[0082] Optionally, safety warning signals can be output to users via communication alarms, such as email, SMS, or push notifications to users' terminal devices, and / or, safety warning signals can be displayed to users through the charging pile's display screen, or alerted to users via sound alarms or visual alarms, such as buzzers or flashing lights.

[0083] In some embodiments, the method provided in this application further includes, before inputting key parameters of the battery state of health into a pre-built battery SOH calculation model: Acquire battery temperature data during EIS testing of the vehicle battery; adjust key parameters of battery health status based on the battery temperature data.

[0084] Battery temperature affects the electrochemical characteristics of the battery (such as impedance, lithium-ion diffusion rate, etc.). The influence of battery temperature needs to be considered in EIS detection and SOH calculation. Therefore, this application further proposes to correct the key parameters of battery health status based on battery temperature data.

[0085] Specifically, battery temperature data is acquired during EIS testing of the vehicle battery, and the average battery temperature is calculated based on this data. A corresponding correction coefficient is then obtained based on the average battery temperature. Different correction coefficients have preset mapping relationships with different temperature ranges. The temperature range to which the average battery temperature belongs can be determined first, and a correction coefficient matching that average battery temperature can be obtained based on the preset mapping relationship. This correction coefficient is then multiplied by key parameters of the battery health status to correct for these key parameters.

[0086] Optionally, battery temperature data can be obtained by a temperature detection module, which can be a digital temperature sensor. To reduce the deviation between the battery body temperature value and the actual battery temperature, the temperature detection module can be installed in the center area of ​​the vehicle battery.

[0087] In this embodiment, by introducing battery temperature data, the key parameters of battery health status are corrected, which helps to improve the accuracy and reliability of EIS detection. When calculating the battery SOH based on the key parameters of battery health status, the detection accuracy of SOH can be improved, and more accurate health management and safety monitoring can be achieved.

[0088] This application addresses the issues of insufficient real-time performance of battery State of Health (SOH) detection in photovoltaic-storage-charging scenarios, susceptibility of power supply to fluctuations in photovoltaic power generation, and low accuracy. It aims to achieve accurate and rapid detection of battery health status. By dynamically triggering detection timing with SOC (direct detection for SOC ≥ 70%, delayed detection until later in the charging process for SOC ≤ 30%), and employing segmented compressed frequency sweeping (focusing on the 0.1Hz-1kHz characteristic frequency band and adaptively selecting frequency points), coupled with EMS energy dispatching of "curtailed solar power + energy storage assistance" to ensure stable power supply for detection, a battery health status calculation model is constructed based on key parameters of battery health status extracted from EIS detection response data. This enables online EIS detection within 5 minutes, accurately assessing SOH while balancing detection accuracy, adaptability to photovoltaic-storage-charging scenarios, and operational economics.

[0089] In summary, the embodiments of this application have the following technical effects: (1) Compared with the detection technology based on the empirical model of voltage and current, the present application adopts the detection technology based on electrochemical impedance spectroscopy, which can accurately reflect the internal aging mechanism of the battery.

[0090] (2) By employing an EMS energy dispatch strategy in a photovoltaic-storage-charging scenario, an automatic switch to a "curtailment + energy storage auxiliary" power supply mode is established when large fluctuations in photovoltaic output interfere with the frequency source output. This provides a stable frequency source for EIS testing, resolving the issue of "distorted EIS excitation frequency due to photovoltaic power generation fluctuations in a photovoltaic-storage-charging scenario," thereby ensuring the accuracy of the detection signal. The EMS energy dispatch provides a stable frequency source for EIS testing, thus ensuring the accuracy of the detection signal.

[0091] (3) During EIS detection, multiple frequency points are selected in the characteristic frequency band to apply AC excitation signals to the vehicle battery. Compared with full-band scanning, this reduces the time consumption, improves the detection efficiency, meets the real-time requirements of fast charging scenarios in charging stations, and helps to achieve accurate and rapid detection of battery health status in the photovoltaic-storage-charging scenario.

[0092] By using segmented compression frequency sweeping technology, the traditional EIS detection mode of sweeping the entire frequency band from 0.01Hz to 10kHz is abandoned. Instead, the focus is on the characteristic frequency band of 0.1Hz to 1kHz that is strongly related to battery aging. An adaptive sampling strategy is adopted, and only typical frequency points are selected for excitation. While ensuring the integrity of key information on battery aging, the EIS detection time is compressed to within 5 minutes, which greatly improves the detection efficiency.

[0093] The above are merely specific embodiments of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application shall be determined by the protection scope of the claims.

Claims

1. A method for detecting the health status of a vehicle battery, characterized in that, The method includes: After the vehicle is connected to the charging station, EIS detection is initiated when preset conditions are met; The power supply detected by the EIS is obtained based on the preset EMS energy dispatch strategy. The power supply sources include photovoltaic systems, energy storage systems and power grids. In frequency sweep mode, multiple frequency points are selected in the characteristic frequency band based on an adaptive sampling strategy, and AC excitation signals of the multiple frequency points are applied to the vehicle battery. Collect the response data of the vehicle battery under the EIS detection; The health status of the vehicle battery is calculated based on the response data.

2. The method according to claim 1, characterized in that, The preset EMS energy dispatch strategy includes: When the change in the output power of the photovoltaic system is less than or equal to a first threshold, the photovoltaic system is selected as the source of the power supply; or When the change in the output power of the photovoltaic system exceeds the first threshold, the energy storage system is selected as the source of the power supply; or When the output power of the photovoltaic system is less than the second threshold and the state of charge of the energy storage system is less than or equal to the third threshold, the power grid is selected as the source of the power supply.

3. The method according to claim 1 or 2, characterized in that, The voltage ripple of the power supply is less than or equal to the ripple threshold, and the frequency accuracy of the power supply is within a preset range.

4. The method according to claim 1, characterized in that, The preset conditions include: The vehicle battery is determined to be in a stable state and the state of charge of the vehicle battery is greater than or equal to the state of charge threshold.

5. The method according to claim 4, characterized in that, The determination that the vehicle battery is in a stable state includes: When the magnitude change of the current of the vehicle battery is less than or equal to the current threshold, and the magnitude change of the voltage of the vehicle battery is less than or equal to the voltage threshold, the vehicle battery is determined to be in a stable state.

6. The method according to claim 1, characterized in that, The adaptive sampling strategy includes: The density of selected frequency points is adjusted based on the stability of the power supply detected by the EIS, the sensitivity of frequency points to battery aging, or the quality of the response data.

7. The method according to claim 6, characterized in that, Adjusting the density of frequency points based on the stability of the power supply or the sensitivity of frequency points to battery aging or the quality of the response data detected by the EIS includes: When the change in the output power of the photovoltaic system is less than or equal to the fourth threshold, the density of the selected frequency points in the characteristic frequency range is increased; otherwise, the density of the selected frequency points in the characteristic frequency range is decreased; or Increase the density of selected frequency points in the target frequency band, which is a frequency band in the characteristic frequency range that is strongly correlated with battery aging; or When the signal-to-noise ratio of the response data is greater than or equal to the signal-to-noise ratio threshold, the density of the selected frequency points in the characteristic frequency band is increased; otherwise, the density of the selected frequency points in the characteristic frequency band is decreased.

8. The method according to claim 1, characterized in that, The response data includes voltage response data and current response data generated by the vehicle battery under the action of the AC excitation signal; The calculation of the vehicle battery health status based on the response data includes: Based on the voltage response data and the current response data, the impedance data corresponding to each of the plurality of frequency points is obtained; Based on the impedance data and the pre-built equivalent circuit model, parameter fitting is performed to extract key parameters of the battery health status. The key parameters of the battery health status are input into a pre-built battery health status calculation model to obtain the health status of the vehicle battery.

9. The method according to claim 8, characterized in that, Key parameters for the battery's health status include charge transfer resistance and diffusion impedance.

10. The method according to claim 1, characterized in that, The frequency range of the characteristic frequency band is 0.1Hz-1kHz.

11. A charging system for a vehicle battery, characterized in that, The system includes an EMS (Energy Management System), a vehicle battery, and a charging pile. The vehicle battery is connected to the charging pile and performs EIS (Energy Information System) detection, returning response data under the EIS detection to the charging pile. The charging pile initiates EIS detection when preset conditions are met after the vehicle is connected to the charging pile. It obtains the power supply for the EIS detection based on a preset EMS energy scheduling strategy. In frequency sweep mode, multiple frequency points are selected in a characteristic frequency band based on an adaptive sampling strategy, and AC excitation signals at these multiple frequency points are applied to the vehicle battery. The system collects the vehicle battery's response data under the EIS detection and calculates the vehicle battery's health status based on the response data. The EMS performs energy scheduling on the power supply for the EIS detection, and the power supply source includes photovoltaic systems, energy storage systems, and the power grid.

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