Battery diagnosis method, device, equipment and storage medium based on charging pile

Abstract

The present invention provides a battery diagnosis method, device, equipment and storage medium based on a charging pile, wherein the method comprises: before charging the target battery, the charging pile applies three pulse current signals of preset frequencies, and extracts the fundamental components of the voltage and current signals through the battery management system, calculates the equivalent circuit model parameters of the battery, and generates the initial state diagnosis result. During the charging process, a small square wave pulse current is applied to the battery according to the initial diagnosis result, the real-time fundamental component is extracted, the battery impedance value is calculated and the impedance change curve is generated. An adaptive charging control strategy is generated according to the diagnosis result, the impedance curve and the circuit model parameters, and its change parameters are recorded, and finally the target state diagnosis result is generated. The present invention accurately diagnoses the battery state, dynamically adjusts the charging strategy, optimizes the charging process, improves the charging efficiency, and reduces battery loss, which helps to extend the battery life and improve the charging efficiency.

Description

Technical Field

[0001] The present invention relates to the field of charging technology, and in particular to a battery diagnosis method, device, equipment and storage medium based on a charging pile. Background Art

[0002] With the widespread use of electric bicycles, batteries, as their core components, directly affect the vehicle's endurance and service life. The electric bicycle batteries currently on the market are mainly lithium batteries, and their performance and health status will gradually decline with the increase in the number of charge and discharge cycles. Therefore, how to monitor the battery status in real time, accurately diagnose the battery health, and extend the battery life by optimizing the charging strategy has become an important topic of concern in the industry.

[0003] Most existing battery diagnosis methods rely on the basic current and voltage data provided by the Battery Management System (BMS) to determine the health status of the battery through simple voltage and current curve analysis. However, this method has low diagnostic accuracy when dealing with complex problems such as changes in internal battery impedance and capacity decay. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing battery diagnosis method has low diagnostic accuracy when dealing with complex problems such as battery internal impedance changes and capacity decay;

[0005] A first aspect of the present invention provides a battery diagnosis method based on a charging pile, and the battery diagnosis method based on a charging pile comprises:

[0006] Before charging the target battery of the electric bicycle, three pulse current signals of preset frequencies are applied to the target battery through the charging pile, and the first fundamental wave component of the voltage signal and the current signal of the target battery is extracted through the battery management system of the electric bicycle;

[0007] Calculating an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generating an initial state diagnosis result of the target battery according to the equivalent circuit model parameter;

[0008] When charging the target battery, according to the initial state diagnosis result, a charging current superimposed with a small-amplitude square wave pulse is applied to the target battery, and a second fundamental component of a voltage signal and a current signal is extracted through the battery management system;

[0009] Calculating the real-time impedance value of the target battery according to the second fundamental wave component to obtain a battery impedance change curve, and generating an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters;

[0010] The charging pile is controlled to charge the target battery according to the adaptive charging control strategy, and the change parameters of the adaptive charging control strategy when the target battery is charged are recorded, and the target state diagnosis result of the target battery is generated according to the change parameters.

[0011] Optionally, in a first implementation of the first aspect of the present invention, before charging the target battery of the electric bicycle, applying three pulse current signals of preset frequencies to the target battery through the charging pile, and extracting the first fundamental component of the voltage signal and current signal of the target battery through the battery management system of the electric bicycle includes:

[0012] Before charging the target battery of the electric bicycle, pulse current signals of three preset frequencies, low frequency, medium frequency and high frequency, are applied to the target battery to obtain corresponding voltage response signals;

[0013] The battery management system of the electric bicycle performs bandpass filtering on the voltage signal and the current signal of each frequency according to the voltage response signal and the pulse current signal, and performs Fourier transform on the voltage signal and the current signal after the bandpass filtering to obtain spectrum information at three preset frequencies;

[0014] According to the spectrum information, the amplitude and phase information of the first fundamental wave components of the voltage signal and the current signal at three preset frequencies are extracted, and according to the amplitude and phase information of the first fundamental wave components, the first fundamental wave components of the voltage signal and the current signal of the battery at three frequencies are constructed.

[0015] Optionally, in a second implementation of the first aspect of the present invention, calculating an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generating an initial state diagnosis result of the target battery according to the equivalent circuit model parameter includes:

[0016] Calculating the complex impedance value of the target battery at each preset frequency according to the first fundamental wave components at the three preset frequencies, and determining the discrete frequency impedance characteristic of the target battery according to the complex impedance value;

[0017] Performing nonlinear least squares fitting on the discrete frequency impedance characteristics to obtain equivalent circuit model parameters of the target battery;

[0018] A mathematical model of the target battery is constructed according to the equivalent circuit model parameters, and the behavior of the target battery under different working conditions is simulated according to the mathematical model to obtain the initial state diagnosis result of the target battery.

[0019] Optionally, in a third implementation manner of the first aspect of the present invention, when charging the target battery, applying a charging current superimposed with a small-amplitude square wave pulse to the target battery according to the initial state diagnosis result includes:

[0020] Calculating the safe charging current upper limit of the target battery according to the battery capacity and internal resistance information in the initial state diagnosis result to obtain the main charging current amplitude, and calculating the square wave pulse amplitude of the small-amplitude square wave pulse according to a preset percentage of the main charging current amplitude;

[0021] Based on the equivalent circuit model parameters in the initial state diagnosis result, the frequency range of the square wave pulse is determined, and the optimal excitation frequency is selected according to the discrete frequency impedance characteristic to obtain the square wave pulse frequency;

[0022] The main charging current amplitude, square wave pulse frequency and square wave pulse amplitude are synthesized to generate a composite charging current waveform to obtain a charging current control signal;

[0023] A charging current with a small-amplitude square wave pulse superimposed thereon is applied to the target battery by the charging pile according to the charging current control signal.

[0024] Optionally, in a fourth implementation of the first aspect of the present invention, calculating the real-time impedance value of the target battery according to the second fundamental wave component to obtain a battery impedance change curve, and generating an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters includes:

[0025] Calculating the real-time impedance value of the target battery during the charging process according to the second fundamental wave component to obtain a real-time impedance data sequence;

[0026] Performing time series analysis on the real-time impedance data sequence, extracting impedance change trend characteristics in the real-time impedance data sequence, and obtaining a battery impedance change curve;

[0027] According to the battery impedance change curve and the equivalent circuit model parameters in the initial state diagnosis result, the battery terminal voltage, temperature change and charge state change during the charging process are predicted to obtain a battery state prediction result;

[0028] Based on the battery state prediction result and in combination with a preset battery safe operating range, constraint calculations are performed on the charging current and voltage to generate an adaptive charging control strategy.

[0029] Optionally, in a fifth implementation of the first aspect of the present invention, controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording a change parameter of the adaptive charging control strategy when the target battery is charged, and generating a target state diagnosis result of the target battery according to the change parameter includes:

[0030] Controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording the changing parameters of the adaptive charging control strategy when the target battery is charged,

[0031] After the charging of the target battery is completed, the pulse current signals of three preset frequencies applied by the charging pile to the target battery are adjusted according to the change parameters, and the third fundamental wave component of the voltage signal and the current signal of the target battery after the pulse current signal adjustment is extracted by the battery management system;

[0032] The equivalent circuit model parameters of the target battery are recalculated according to the third fundamental wave component, and the target state diagnosis result of the target battery is generated according to the recalculated equivalent circuit model parameters.

[0033] Optionally, in a sixth implementation of the first aspect of the present invention, after controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording a change parameter of the adaptive charging control strategy when the target battery is charged, and generating a target state diagnosis result of the target battery according to the change parameter, it also includes:

[0034] Upload the initial state diagnosis result, target state diagnosis result, and impedance change curve of the target battery to the battery state database of the target battery, and associate them with the unique identifier of the target battery to obtain the current charging file of the target battery;

[0035] According to the current charging file and the historical charging file of the target battery in the battery status database, extract the data of the change of the equivalent circuit model parameters with the number of charge and discharge times to obtain the battery performance attenuation trend;

[0036] Perform time series analysis and nonlinear regression fitting on the battery performance attenuation trend to obtain a prediction result of the remaining battery service life, and add the prediction result of the remaining battery service life to the current charging file.

[0037] A second aspect of the present invention provides a battery diagnostic device based on a charging pile, the battery diagnostic device based on a charging pile comprising:

[0038] A pulse applying module, used for applying three pulse current signals of preset frequencies to the target battery of the electric bicycle through the charging pile before charging the target battery, and extracting the first fundamental component of the voltage signal and the current signal of the target battery through the battery management system of the electric bicycle;

[0039] an initial diagnosis module, configured to calculate an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generate an initial state diagnosis result of the target battery according to the equivalent circuit model parameter;

[0040] A charging start module, used for applying a charging current superimposed with a small-amplitude square wave pulse to the target battery when charging the target battery according to the initial state diagnosis result, and extracting a second fundamental component of a voltage signal and a current signal through the battery management system;

[0041] a charging adaptive control module, configured to calculate the real-time impedance value of the target battery according to the second fundamental wave component, obtain a battery impedance change curve, and generate an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters;

[0042] A target diagnosis module is used to control the charging pile to charge the target battery according to the adaptive charging control strategy, record the change parameters of the adaptive charging control strategy when the target battery is charged, and generate a target state diagnosis result of the target battery according to the change parameters.

[0043] A third aspect of the present invention provides a battery diagnostic device based on a charging pile, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the battery diagnostic device based on the charging pile performs the steps of the above-mentioned battery diagnostic method based on the charging pile.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the steps of the above-mentioned battery diagnosis method based on a charging pile.

[0045] The above-mentioned battery diagnosis method, device, equipment and storage medium based on the charging pile, before charging the target battery, applies three pulse current signals of preset frequencies to the charging pile, and extracts the fundamental components of the voltage and current signals through the battery management system, calculates the equivalent circuit model parameters of the battery, and generates the initial state diagnosis result. During the charging process, a small square wave pulse current is applied to the battery according to the initial diagnosis result, the real-time fundamental component is extracted, the battery impedance value is calculated and the impedance change curve is generated. An adaptive charging control strategy is generated according to the diagnosis results, the impedance curve and the circuit model parameters, and its change parameters are recorded, and finally the target state diagnosis result is generated. The present invention accurately diagnoses the battery status, dynamically adjusts the charging strategy, optimizes the charging process, improves the charging efficiency, and reduces battery loss, which helps to extend the battery life and improve the charging efficiency.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of a first embodiment of a battery diagnosis method based on a charging pile in an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of an embodiment of a battery diagnostic device based on a charging pile in an embodiment of the present invention;

[0050] Figure 3 It is a schematic diagram of an embodiment of a battery diagnostic device based on a charging pile in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0053] To facilitate understanding of this embodiment, a battery diagnosis method based on a charging pile disclosed in an embodiment of the present invention is first introduced in detail. Figure 1 As shown, the method comprises the following steps:

[0054] 101. Before charging the target battery of the electric bicycle, three pulse current signals of preset frequencies are applied to the target battery through the charging pile, and the voltage signal and the first fundamental component of the current signal of the target battery are extracted through the battery management system of the electric bicycle;

[0055] In one embodiment of the present invention, before charging the target battery of the electric bicycle, applying three pulse current signals of preset frequencies to the target battery through the charging pile, and extracting the first fundamental component of the voltage signal and current signal of the target battery through the battery management system of the electric bicycle includes: before charging the target battery of the electric bicycle, applying pulse current signals of three preset frequencies of low frequency, medium frequency and high frequency to the target battery to obtain a corresponding voltage response signal; performing bandpass filtering on the voltage signal and current signal of each frequency according to the voltage response signal and the pulse current signal by the battery management system of the electric bicycle, and performing Fourier transform on the voltage signal and current signal after the bandpass filtering to obtain spectrum information at three preset frequencies; extracting the amplitude and phase information of the first fundamental component of the voltage signal and current signal at the three preset frequencies according to the spectrum information, and constructing the first fundamental component of the voltage signal and current signal of the battery at the three frequencies according to the amplitude and phase information of the first fundamental component.

[0056] Specifically, before charging the target battery of the electric bicycle, the charging pile first applies three pulse current signals of preset frequencies to the target battery. These preset frequencies include low frequency, medium frequency and high frequency, which are usually selected in the range of 0.1Hz to 1kHz. The low frequency signal is used to capture the diffusion characteristics of the battery, the medium frequency signal reflects the charge transfer process, and the high frequency signal corresponds to the ohmic characteristics of the battery. The charging pile generates these pulse current signals through the built-in current source module, and the signal duration of each frequency is long enough to ensure that the battery response reaches a stable state. When these pulse current signals are applied to the target battery, the battery will generate a corresponding voltage response signal. The battery management system (BMS) of the electric bicycle is responsible for collecting these voltage response signals and the original pulse current signals. The BMS uses a high-precision analog-to-digital converter (ADC) to sample these signals, and the sampling rate is at least twice the highest preset frequency to meet the requirements of the Nyquist sampling theorem. After collecting the original signal, the BMS performs bandpass filtering on the voltage signal and current signal of each frequency respectively. The center frequency of the bandpass filter is set to the corresponding preset frequency, and the bandwidth is narrow enough to filter out noise and other frequency components, but wide enough to retain the basic characteristics of the signal. The filter can be implemented as a digital filter, such as a Butterworth filter or a Chebyshev filter, and its order and bandwidth are adjusted according to specific needs. After the bandpass filtering process, the BMS performs Fourier transform on the filtered voltage and current signals. The Fourier transform can be implemented by the fast Fourier transform (FFT) algorithm to convert the time domain signal to the frequency domain. The selection of the number of FFT points needs to consider the balance between computational complexity and frequency resolution, and usually a power of 2, such as 1024 or 2048 points, is selected. Through FFT, the system obtains the spectrum information at three preset frequencies, including the amplitude spectrum and the phase spectrum. From the obtained spectrum information, the system extracts the amplitude and phase information of the first fundamental component of the voltage signal and the current signal at the three preset frequencies. Specifically, in the spectrum of each preset frequency, the frequency point with the largest amplitude is found, and the amplitude corresponding to the point is the amplitude of the first fundamental component, and the corresponding phase angle is the phase information of the first fundamental component. This process is performed on the voltage signal and the current signal separately. Finally, based on the extracted amplitude and phase information of the first fundamental component, the system constructs the first fundamental component of the voltage signal and current signal of the target battery at three frequencies. The construction process is actually a signal reconstruction process, using the extracted amplitude and phase information to generate the corresponding time domain signal through inverse Fourier transform or directly using a sine function. These reconstructed signals represent the basic response characteristics of the battery at each preset frequency, eliminating the influence of high-order harmonics and noise.

[0057] 102. Calculate an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generate an initial state diagnosis result of the target battery according to the equivalent circuit model parameter;

[0058] In one embodiment of the present invention, the equivalent circuit model parameters of the target battery are calculated according to the first fundamental wave component, and the initial state diagnosis result of the target battery is generated according to the equivalent circuit model parameters, including: calculating the complex impedance value of the target battery at each preset frequency according to the first fundamental wave component at the three preset frequencies, and determining the discrete frequency impedance characteristics of the target battery according to the complex impedance values; performing nonlinear least squares fitting on the discrete frequency impedance characteristics to obtain the equivalent circuit model parameters of the target battery; constructing a mathematical model of the target battery according to the equivalent circuit model parameters, and simulating the behavior of the target battery under different working conditions according to the mathematical model, and the initial state diagnosis result of the target battery.

[0059] Specifically, after obtaining the first fundamental wave component of the target battery at three preset frequencies, the system first calculates the complex impedance value of the target battery at each preset frequency. This process is achieved by dividing the first fundamental wave component of the voltage at each frequency point by the corresponding first fundamental wave component of the current. Specifically, the system uses the Euler formula to convert the voltage and current fundamental wave components in polar coordinate form into complex form, and then performs complex division operations. In this way, for each preset frequency, a complex impedance value can be obtained, in which the real part represents the resistive component and the imaginary part represents the reactive component. These complex impedance values ​​constitute the discrete frequency impedance characteristics of the target battery. Next, the system performs nonlinear least squares fitting on these discrete frequency impedance characteristics to obtain the equivalent circuit model parameters of the target battery. This step uses the Randles equivalent circuit model, which includes a series resistor R0, a parallel resistor R1, a double layer capacitor C1, and a Warburg impedance W. During the fitting process, the system compares the measured complex impedance value with the impedance value predicted by the theoretical model, and minimizes the sum of square errors between the two by iteratively adjusting the model parameters. This optimization problem can be solved using the Levenberg-Marquardt algorithm or other nonlinear optimization algorithms. In order to improve the efficiency and accuracy of the fitting, the system will set reasonable initial values ​​and constraints, such as setting the range of parameter values ​​based on prior knowledge. After the fitting is completed, the system obtains the equivalent circuit model parameters of the target battery, including the specific values ​​of R0, R1, C1 and W. These parameters fully describe the electrochemical characteristics of the battery, where R0 reflects the ohmic resistance of the electrolyte and the electrode, R1 and C1 characterize the charge transfer process and the double layer effect, and W represents the diffusion process of ions in the electrode. Based on these equivalent circuit model parameters, the system further constructs a mathematical model of the target battery. This mathematical model is a dynamic system, usually expressed as a set of differential equations or state space equations. The model contains state variables such as the battery's state of charge (SOC), temperature, current and voltage, as well as the equivalent circuit parameters just obtained. The construction process of the mathematical model involves converting the equivalent circuit into a mathematical expression and considering the relationship between each parameter and factors such as SOC and temperature. After the mathematical model is constructed, the system uses the model to simulate the behavior of the target battery under different working conditions. During the simulation process, the system sets different input conditions, such as different charge and discharge current curves, ambient temperature changes, etc., and then solves the equations of the mathematical model to obtain the battery's response under these conditions. The simulation results include time series data of key parameters such as battery voltage, SOC changes, and temperature changes. By analyzing these simulation results, the system generates the initial state diagnosis results of the target battery. The diagnosis process includes several key steps: First, the system compares the battery performance indicators obtained by simulation with the preset standards to evaluate whether the battery's capacity, internal resistance, and power characteristics are within the normal range.Secondly, the system analyzes the response characteristics of the battery under different working conditions, such as charging and discharging efficiency, thermal management capabilities, etc., to determine the overall performance of the battery.

[0060] 103. When charging the target battery, according to the initial state diagnosis result, a charging current superimposed with a small-amplitude square wave pulse is applied to the target battery, and the second fundamental component of the voltage signal and the current signal is extracted through the battery management system;

[0061] In one embodiment of the present invention, when charging the target battery, applying a charging current superimposed with a small-amplitude square wave pulse to the target battery according to the initial state diagnosis result includes: calculating the upper limit of the safe charging current of the target battery according to the battery capacity and internal resistance information in the initial state diagnosis result to obtain the main charging current amplitude, and calculating the square wave pulse amplitude of the small-amplitude square wave pulse according to a preset percentage of the main charging current amplitude; determining the frequency range of the square wave pulse based on the equivalent circuit model parameters in the initial state diagnosis result, and selecting the optimal excitation frequency according to the discrete frequency impedance characteristic to obtain the square wave pulse frequency; synthesizing the main charging current amplitude, the square wave pulse frequency and the square wave pulse amplitude to generate a composite charging current waveform to obtain a charging current control signal; applying a charging current superimposed with a small-amplitude square wave pulse to the target battery through the charging pile according to the charging current control signal.

[0062] Specifically, when charging the target battery, the system first calculates the upper limit of the safe charging current based on the battery capacity and internal resistance information in the initial state diagnosis result. This calculation process takes into account the rated capacity of the battery, the current state of charge (SOC), the internal resistance value, and the characteristics of the battery chemistry type. Usually, the system uses a safety factor to ensure that the charging current does not exceed the battery's tolerance. For example, for lithium-ion batteries, the safe charging current is usually no more than 0.5C to 1C (C represents the rated capacity of the battery). The calculated upper limit of the safe charging current is the main charging current amplitude. Next, the system calculates the amplitude of the small amplitude square wave pulse based on a preset percentage of the main charging current amplitude. This preset percentage is usually set between 5% and 10% to ensure that the square wave pulse does not significantly affect the main charging process while providing sufficient signal strength for real-time impedance measurement. For example, if the main charging current is 10A and the preset percentage is 5%, the amplitude of the square wave pulse will be 0.5A. Then, the system determines the frequency range of the square wave pulse based on the equivalent circuit model parameters in the initial state diagnosis result. This step takes into account the RC time constant of the battery, which is the product of R1 and C1, and the characteristics of the Warburg impedance W. Typically, the system selects a frequency range that covers the main dynamic characteristics of the battery, such as from 0.1Hz to 100Hz. Within this range, the system selects the best excitation frequency based on the discrete frequency impedance characteristics. The selection criteria for the best excitation frequency include factors such as the highest signal-to-noise ratio and the most sensitive impedance changes. The system can use optimization algorithms, such as genetic algorithms or particle swarm optimization, to determine the best pulse frequency. After determining the main charging current amplitude, square wave pulse frequency, and square wave pulse amplitude, the system performs signal synthesis on these parameters to generate a composite charging current waveform. This synthesis process involves creating a basic DC charging current waveform (main charging current) and then superimposing a small amplitude square wave pulse on it. The frequency of the square wave pulse is determined by the previously determined optimal excitation frequency, and the amplitude is the previously calculated pulse amplitude. This composite waveform is the charging current control signal. After generating the charging current control signal, the control system of the charging pile converts this signal into an actual current output. This process usually involves digital signal processing (DSP) and power electronic conversion. First, the DSP module converts the control signal in digital form into an analog signal. This analog signal is then fed into the power control circuit, such as a pulse width modulation (PWM) controller. The PWM controller generates the corresponding gate drive signal to control the switching state of power semiconductor devices (such as IGBTs or MOSFETs). The switching operation of power semiconductor devices enables precise control of current. By adjusting the duty cycle of the switch, the system can accurately control the current size and waveform output to the battery. At the same time, the current sensor continuously monitors the actual output current and feeds the measurement results back to the control system to form a closed-loop control to ensure that the actual output current is consistent with the set control signal.In addition, the charging pile is also equipped with protection circuits, such as overcurrent protection, overvoltage protection, and temperature protection, to ensure that the charging current can be cut off in time in any abnormal situation to protect the safety of the battery and charging equipment. These protection circuits work in conjunction with the main control system to respond quickly when an abnormality is detected. The process of extracting the second fundamental component of the voltage signal and current signal through the battery management system is similar to the process of obtaining the first fundamental component, so this method will not be repeated.

[0063] 104. Calculate the real-time impedance value of the target battery according to the second fundamental wave component, obtain the battery impedance change curve, and generate an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters;

[0064] In one embodiment of the present invention, the real-time impedance value of the target battery is calculated according to the second fundamental component to obtain a battery impedance change curve, and an adaptive charging control strategy is generated according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters, including: calculating the real-time impedance value of the target battery during the charging process according to the second fundamental component to obtain a real-time impedance data sequence; performing time series analysis on the real-time impedance data sequence, extracting impedance change trend characteristics in the real-time impedance data sequence, and obtaining a battery impedance change curve; predicting the battery terminal voltage, temperature change and charge state change during the charging process according to the battery impedance change curve and the equivalent circuit model parameters in the initial state diagnosis result to obtain a battery state prediction result; based on the battery state prediction result, combined with a preset battery safe operating range, constraining the charging current and voltage to generate an adaptive charging control strategy.

[0065] Specifically, during the charging process, the system first calculates the real-time impedance value of the target battery based on the second fundamental component. This process is similar to the impedance calculation in the initial state assessment, but it is performed on the data continuously collected during the charging process. Specifically, the system uses the small-amplitude square wave pulse superimposed on the charging current as the excitation signal, and calculates the impedance value in complex form by measuring the second fundamental component of the battery voltage response. This calculation process uses Fourier transform or lock-in amplifier technology to extract the amplitude and phase information of the fundamental component from the collected voltage and current signals, and then obtains the impedance value through complex division. The system repeats this calculation process at fixed time intervals (for example, every second or every few seconds) to obtain a real-time impedance data sequence. This sequence contains information about the change of battery impedance over time during the charging process. In order to extract meaningful information from this data sequence, the system performs time series analysis on it. This analysis process includes multiple steps: first, preprocess the raw data, such as removing outliers and smoothing; then, use techniques such as moving average or exponential smoothing to reduce the impact of short-term fluctuations; then, apply trend analysis algorithms, such as linear regression or polynomial fitting, to identify long-term change trends; finally, use more complex time series analysis methods, such as autoregressive integrated moving average (ARIMA) models, to capture the dynamic characteristics of impedance changes. Through these analyses, the system extracts the trend characteristics of impedance changes, including the overall growth or decline trend of impedance, the rate of change, and periodic fluctuations, thereby obtaining the battery impedance change curve. This curve not only reflects the current state of the battery, but also contains dynamic information about the change in battery performance. Next, the system combines this battery impedance change curve with the equivalent circuit model parameters in the initial state diagnosis results to construct a dynamic battery model. This model is an extension of the previous static equivalent circuit model, adding time-varying parameters to reflect the changes in battery characteristics during charging. For example, the system can use algorithms such as Kalman filters or particle filters to update model parameters in real time so that it can accurately reflect the current state of the battery. Based on this dynamic model, the system predicts the battery terminal voltage, temperature changes, and state of charge (SOC) changes during the charging process. The prediction process involves solving a set of differential equations or difference equations that describe the changes in the internal state of the battery over time and charging current. The prediction results include the changing trends of battery voltage, temperature, and SOC over a period of time in the future (such as the next few minutes or tens of minutes). The system then compares these prediction results with the preset battery safe operating range. The safe operating range is usually provided by the battery manufacturer and includes upper and lower voltage limits, maximum allowable temperature, maximum charging current, etc. If the prediction results show that the battery state may exceed the safe range, the system will trigger the corresponding adjustment mechanism. Based on the comparison of the prediction results and the safe operating range, the system performs constraint calculations on the charging current and voltage.This calculation process takes into account multiple factors: first, ensure that the charging current and voltage do not cause the battery to exceed the safe operating range; second, complete the charging as quickly as possible within the safe range; third, consider the need to maximize the battery life and avoid long-term damage to the battery due to excessive fast charging. The system can use optimization algorithms, such as model predictive control (MPC) or dynamic programming, to calculate the optimal charging curve. Finally, the system generates an adaptive charging control strategy. This strategy is not a fixed charging curve, but a dynamically adjusted control scheme. It includes real-time adjustment rules for charging current, charging cutoff conditions, and response strategies in different situations. For example, if it is predicted that the battery temperature is about to reach the upper limit, the strategy may include operations such as reducing the charging current or suspending charging. A key feature of this adaptive charging control strategy is its persistence and adaptability. The strategy is continuously updated as the charging process progresses, and is adjusted based on the latest battery status prediction and actual measurement results. This ensures that the charging process can always be maintained in the optimal and safest state, while also being able to cope with dynamic changes in battery characteristics and the impact of the external environment.

[0066] 105. Control the charging pile to charge the target battery according to the adaptive charging control strategy, record the change parameters of the adaptive charging control strategy when the target battery is charged, and generate a target state diagnosis result of the target battery according to the change parameters.

[0067] In one embodiment of the present invention, controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording the changing parameters of the adaptive charging control strategy when the target battery is charged, and generating the target state diagnosis result of the target battery according to the changing parameters include: controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording the changing parameters of the adaptive charging control strategy when the target battery is charged, after the charging of the target battery is completed, adjusting the pulse current signals of three preset frequencies applied by the charging pile to the target battery according to the changing parameters, and extracting the third fundamental component of the voltage signal and current signal of the target battery after the pulse current signal is adjusted through the battery management system; recalculating the equivalent circuit model parameters of the target battery according to the third fundamental component, and generating the target state diagnosis result of the target battery according to the recalculated equivalent circuit model parameters.

[0068] Specifically, during the charging process, the charging pile charges the target battery according to the adaptive charging control strategy, while continuously recording the change parameters of the strategy. These parameters include the real-time adjustment value of the charging current, the change of the charging voltage, the accumulation of the charging time, and the triggering conditions of the strategy adjustment. The control system of the charging pile stores these parameters in the local database in real time to form a detailed charging process log. After the charging is completed, the system adjusts the three preset frequency pulse current signals applied by the charging pile to the target battery according to the recorded change parameters. Specifically, the system first analyzes the change parameters recorded during the charging process, including the battery's response characteristics to different charging currents, impedance change trends, and temperature changes. Based on this information, the system fine-tunes the three preset frequencies initially set. For low-frequency signals, the system can adjust the frequency according to the battery discharge depth and charging efficiency observed during the charging process. For example, if the low-frequency response characteristics of the battery are found to change significantly during the charging process, the system can reduce the frequency of the low-frequency signal to better capture the diffusion characteristics of the battery. For medium-frequency signals, the system will adjust according to the impedance change trend during the charging process. If the battery's charge transfer impedance is observed to change significantly during the charging process, the system can adjust the frequency of the medium-frequency signal accordingly to more accurately reflect the current charge transfer process. For high-frequency signals, the system mainly adjusts according to the changes in ohmic impedance observed during the charging process. If the internal resistance of the battery changes significantly during the charging process, the system can fine-tune the frequency of the high-frequency signal to more accurately measure the current ohmic impedance. In addition to adjusting the frequency, the system also adjusts the amplitude and duration of the pulse current signal based on the observations during the charging process. For example, if the battery is found to be particularly sensitive to certain frequencies during the charging process, the system can increase the amplitude of the pulse current at these frequencies to obtain a clearer response signal. At the same time, if it is observed that certain dynamic characteristics of the battery take longer to fully manifest, the system can increase the duration of the pulse signal accordingly. This signal adjustment strategy based on charging process data can make the diagnosis process more targeted and efficient. It makes full use of the battery behavior information obtained during the charging process, so that subsequent diagnosis can more accurately reflect the current state of the battery. After adjustment, the charging pile applies a new pulse current signal to the target battery, and the battery management system then extracts the third fundamental component of the voltage signal and current signal of the target battery. This process is technically similar to the method of extracting the first fundamental component during initial diagnosis, involving steps such as signal acquisition, filtering, and Fourier transform, which will not be repeated in this method.

[0069] Furthermore, after controlling the charging pile to charge the target battery according to the adaptive charging control strategy, recording the change parameters of the adaptive charging control strategy when the target battery is charged, and generating the target state diagnosis result of the target battery according to the change parameters, it also includes: uploading the initial state diagnosis result, the target state diagnosis result, and the impedance change curve of the target battery to the battery state database of the target battery, and associating them with the unique identifier of the target battery to obtain the current charging file of the target battery; extracting the change data of the equivalent circuit model parameters with the number of charge and discharge times according to the current charging file and the historical charging file of the target battery in the battery state database to obtain the battery performance attenuation trend; performing time series analysis and nonlinear regression fitting on the battery performance attenuation trend to obtain the prediction result of the remaining service life of the battery, and adding the prediction result of the remaining service life of the battery to the current charging file.

[0070] Specifically, after completing the charging process of the target battery and generating the target state diagnosis result, the system enters the data management and analysis stage. First, the system uploads the initial state diagnosis result, the target state diagnosis result and the impedance change curve recorded during the charging process of the target battery to the battery state database. This upload process involves data formatting, compression and encryption to ensure the integrity and security of the data. The uploaded data is associated with the unique identifier of the target battery, which is usually a serial number assigned by the battery manufacturer or a unique ID automatically generated by the system. Through this association, the system creates a current charging archive of the target battery, which contains complete information of this charging cycle. Next, the system extracts the data on the change of equivalent circuit model parameters with the number of charge and discharge cycles based on the current charging archive and the historical charging archive of the target battery stored in the battery state database. This extraction process includes data query, screening and sorting. The system first retrieves all historical charging records of the battery from the database, and then arranges these records in chronological order to form a time series data set. This data set contains the equivalent circuit model parameters before and after each charge, such as series resistance R0, parallel resistance R1, double layer capacitance C1 and Warburg impedance W. The system analyzes the changing trends of these parameters to obtain the battery performance decay trend. This analysis process involves multiple steps: First, the system preprocesses the raw data, including removing outliers and standardizing data. Then, the system uses methods such as sliding window technology or exponential smoothing to smooth short-term fluctuations and highlight long-term trends. Next, the system applies regression analysis techniques, such as polynomial regression or exponential regression, to fit the changing trends of various parameters with the number of charge and discharge times. Through these steps, the system obtains a mathematical model that describes the change of battery performance with the number of uses. After obtaining the battery performance decay trend, the system conducts more in-depth time series analysis and nonlinear regression fitting. Time series analysis methods include autoregressive integrated moving average (ARIMA) model, seasonal decomposition, etc., which can capture periodic changes and long-term trends in the data. Nonlinear regression fitting uses more complex models, such as exponential decay models or Weibull distribution models, which can generally better describe the nonlinear decay process of battery performance over time. The system uses these advanced analysis methods, combined with prior knowledge of battery chemical characteristics and usage patterns, to build a battery life prediction model. This model takes into account multiple factors, including the number of charge and discharge times, depth, temperature conditions, and usage time. By fitting and extrapolating historical data, the system obtains the prediction result of the remaining service life of the battery. This prediction result usually includes the estimated number of remaining cycles, the estimated time to reach 80% of the rated capacity (usually regarded as the end of the battery life), and other information. Finally, the system adds the prediction result of the remaining service life of the battery to the charging archive. This addition process includes updating database records, recalculating relevant statistical indicators, etc.By correlating the lifetime prediction results with the current charging profile, the system creates a comprehensive battery health status report that not only reflects the current state of the battery but also provides a prediction of future performance changes.

[0071] In this embodiment, before charging the target battery, the charging pile applies three pulse current signals of preset frequencies, and the fundamental components of the voltage and current signals are extracted through the battery management system, the equivalent circuit model parameters of the battery are calculated, and the initial state diagnosis results are generated. During the charging process, a small square wave pulse current is applied to the battery based on the initial diagnosis results, the real-time fundamental component is extracted, the battery impedance value is calculated, and the impedance change curve is generated. An adaptive charging control strategy is generated based on the diagnosis results, the impedance curve, and the circuit model parameters, and its change parameters are recorded to finally generate the target state diagnosis results. The present invention helps to extend the battery life and improve the charging efficiency by accurately diagnosing the battery status, dynamically adjusting the charging strategy, optimizing the charging process, and improving the charging efficiency, while reducing battery loss.

[0072] The above describes the battery diagnosis method based on the charging pile in the embodiment of the present invention. The following describes the battery diagnosis device based on the charging pile in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a battery diagnostic device based on a charging pile includes:

[0073] A pulse applying module 201 is used to apply three pulse current signals of preset frequencies to the target battery of the electric bicycle through the charging pile before charging the target battery, and extract the first fundamental component of the voltage signal and the current signal of the target battery through the battery management system of the electric bicycle;

[0074] An initial diagnosis module 202, configured to calculate an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generate an initial state diagnosis result of the target battery according to the equivalent circuit model parameter;

[0075] A charging start module 203, configured to apply a charging current superimposed with a small-amplitude square wave pulse to the target battery when charging the target battery according to the initial state diagnosis result, and extract a second fundamental component of a voltage signal and a current signal through the battery management system;

[0076] A charging adaptive control module 204, configured to calculate the real-time impedance value of the target battery according to the second fundamental wave component, obtain a battery impedance change curve, and generate an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters;

[0077] The target diagnosis module 205 is used to record the change parameters of the adaptive charging control strategy when the target battery is charged, and generate a target state diagnosis result of the target battery according to the change parameters.

[0078] In an embodiment of the present invention, the battery diagnostic device based on the charging pile runs the above-mentioned battery diagnostic method based on the charging pile. Before charging the target battery, the battery diagnostic device based on the charging pile applies three pulse current signals of preset frequencies to the charging pile, and extracts the fundamental components of the voltage and current signals through the battery management system, calculates the equivalent circuit model parameters of the battery, and generates the initial state diagnosis result. During the charging process, a small square wave pulse current is applied to the battery according to the initial diagnosis result, the real-time fundamental component is extracted, the battery impedance value is calculated, and the impedance change curve is generated. An adaptive charging control strategy is generated according to the diagnosis result, the impedance curve and the circuit model parameters, and its change parameters are recorded, and finally the target state diagnosis result is generated. The present invention accurately diagnoses the battery state, dynamically adjusts the charging strategy, optimizes the charging process, improves the charging efficiency, and reduces battery loss, which helps to extend the battery life and improve the charging efficiency.

[0079] above Figure 2 The battery diagnostic device based on the charging pile in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The battery diagnostic device based on the charging pile in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0080] Figure 3 : is a structural schematic diagram of a battery diagnostic device based on a charging pile provided by an embodiment of the present invention. The battery diagnostic device 300 based on the charging pile may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the battery diagnostic device 300 based on the charging pile. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the battery diagnostic device 300 based on the charging pile to implement the steps of the above-mentioned battery diagnostic method based on the charging pile.

[0081] The charging pile-based battery diagnostic device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The illustrated structure of the charging pile-based battery diagnostic device does not constitute a limitation on the charging pile-based battery diagnostic device provided by the present invention, and may include more or fewer components than illustrated, or a combination of certain components, or a different arrangement of components.

[0082] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the battery diagnosis method based on the charging pile.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0085] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery diagnosis method based on a charging pile, characterized in that: The battery diagnosis method based on the charging pile includes: Before charging the target battery of the electric bicycle, three pulse current signals of preset frequencies are applied to the target battery through the charging pile, and the first fundamental wave component of the voltage signal and the current signal of the target battery is extracted through the battery management system of the electric bicycle; Calculating an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generating an initial state diagnosis result of the target battery according to the equivalent circuit model parameter; When charging the target battery, according to the initial state diagnosis result, a charging current superimposed with a small-amplitude square wave pulse is applied to the target battery, and a second fundamental component of a voltage signal and a current signal is extracted through the battery management system; Calculating the real-time impedance value of the target battery according to the second fundamental wave component to obtain a battery impedance change curve, and generating an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters; The charging pile is controlled to charge the target battery according to the adaptive charging control strategy, and the change parameters of the adaptive charging control strategy when the target battery is charged are recorded, and the target state diagnosis result of the target battery is generated according to the change parameters.

2. The battery diagnosis method based on the charging pile according to claim 1 is characterized in that: Before charging the target battery of the electric bicycle, applying three pulse current signals of preset frequencies to the target battery through the charging pile, and extracting the voltage signal and the first fundamental component of the current signal of the target battery through the battery management system of the electric bicycle includes: Before charging the target battery of the electric bicycle, pulse current signals of three preset frequencies, low frequency, medium frequency and high frequency, are applied to the target battery to obtain corresponding voltage response signals; The battery management system of the electric bicycle performs bandpass filtering on the voltage signal and the current signal of each frequency according to the voltage response signal and the pulse current signal, and performs Fourier transform on the voltage signal and the current signal after the bandpass filtering to obtain spectrum information at three preset frequencies; According to the spectrum information, the amplitude and phase information of the first fundamental wave components of the voltage signal and the current signal at three preset frequencies are extracted, and according to the amplitude and phase information of the first fundamental wave components, the first fundamental wave components of the voltage signal and the current signal of the battery at three frequencies are constructed.

3. The battery diagnosis method based on the charging pile according to claim 2 is characterized in that: The calculating the equivalent circuit model parameters of the target battery according to the first fundamental wave component, and generating the initial state diagnosis result of the target battery according to the equivalent circuit model parameters includes: Calculating the complex impedance value of the target battery at each preset frequency according to the first fundamental wave components at the three preset frequencies, and determining the discrete frequency impedance characteristics of the target battery according to the complex impedance values; Performing nonlinear least squares fitting on the discrete frequency impedance characteristics to obtain equivalent circuit model parameters of the target battery; A mathematical model of the target battery is constructed according to the equivalent circuit model parameters, and the behavior of the target battery under different working conditions is simulated according to the mathematical model to obtain the initial state diagnosis result of the target battery.

4. The battery diagnosis method based on the charging pile according to claim 3 is characterized in that: When charging the target battery, applying a charging current superimposed with a small-amplitude square wave pulse to the target battery according to the initial state diagnosis result includes: Calculating the safe charging current upper limit of the target battery according to the battery capacity and internal resistance information in the initial state diagnosis result to obtain the main charging current amplitude, and calculating the square wave pulse amplitude of the small-amplitude square wave pulse according to a preset percentage of the main charging current amplitude; Based on the equivalent circuit model parameters in the initial state diagnosis result, the frequency range of the square wave pulse is determined, and the optimal excitation frequency is selected according to the discrete frequency impedance characteristic to obtain the square wave pulse frequency; The main charging current amplitude, square wave pulse frequency and square wave pulse amplitude are synthesized to generate a composite charging current waveform to obtain a charging current control signal; A charging current with a small-amplitude square wave pulse superimposed thereon is applied to the target battery by the charging pile according to the charging current control signal.

5. The battery diagnosis method based on the charging pile according to claim 1 is characterized in that: The calculating the real-time impedance value of the target battery according to the second fundamental wave component to obtain a battery impedance change curve, and generating an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters includes: Calculating the real-time impedance value of the target battery during the charging process according to the second fundamental wave component to obtain a real-time impedance data sequence; Performing time series analysis on the real-time impedance data sequence, extracting impedance change trend characteristics in the real-time impedance data sequence, and obtaining a battery impedance change curve; According to the battery impedance change curve and the equivalent circuit model parameters in the initial state diagnosis result, the battery terminal voltage, temperature change and charge state change during the charging process are predicted to obtain a battery state prediction result; Based on the battery state prediction result and in combination with a preset battery safe operating range, constraint calculations are performed on the charging current and voltage to generate an adaptive charging control strategy.

6. The battery diagnosis method based on the charging pile according to claim 1 is characterized in that: The step of controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording the change parameters of the adaptive charging control strategy when the target battery is charged, and generating the target state diagnosis result of the target battery according to the change parameters includes: Controlling the charging pile to charge the target battery according to the adaptive charging control strategy, and recording the changing parameters of the adaptive charging control strategy when the target battery is charged, After the charging of the target battery is completed, the pulse current signals of three preset frequencies applied by the charging pile to the target battery are adjusted according to the change parameters, and the third fundamental wave component of the voltage signal and the current signal of the target battery after the pulse current signal adjustment is extracted by the battery management system; The equivalent circuit model parameters of the target battery are recalculated according to the third fundamental wave component, and the target state diagnosis result of the target battery is generated according to the recalculated equivalent circuit model parameters.

7. The battery diagnosis method based on the charging pile according to claim 1 is characterized in that: After controlling the charging pile to charge the target battery according to the adaptive charging control strategy, recording the change parameters of the adaptive charging control strategy when the target battery is charged, and generating the target state diagnosis result of the target battery according to the change parameters, the method further includes: Upload the initial state diagnosis result, target state diagnosis result, and impedance change curve of the target battery to the battery state database of the target battery, and associate them with the unique identifier of the target battery to obtain the current charging file of the target battery; According to the current charging file and the historical charging file of the target battery in the battery status database, extract the data of the change of the equivalent circuit model parameters with the number of charge and discharge times to obtain the battery performance attenuation trend; Perform time series analysis and nonlinear regression fitting on the battery performance attenuation trend to obtain a prediction result of the remaining battery service life, and add the prediction result of the remaining battery service life to the current charging file.

8. A battery diagnostic device based on a charging pile, characterized in that: The battery diagnostic device based on the charging pile includes: A pulse applying module, used for applying three pulse current signals of preset frequencies to the target battery of the electric bicycle through the charging pile before charging the target battery, and extracting the first fundamental component of the voltage signal and the current signal of the target battery through the battery management system of the electric bicycle; an initial diagnosis module, configured to calculate an equivalent circuit model parameter of the target battery according to the first fundamental wave component, and generate an initial state diagnosis result of the target battery according to the equivalent circuit model parameter; a charging start module, used for applying a charging current superimposed with a small-amplitude square wave pulse to the target battery when charging the target battery according to the initial state diagnosis result, and extracting a second fundamental component of a voltage signal and a current signal through the battery management system; a charging adaptive control module, configured to calculate the real-time impedance value of the target battery according to the second fundamental wave component, obtain a battery impedance change curve, and generate an adaptive charging control strategy according to the initial state diagnosis result, the impedance change curve and the equivalent circuit model parameters; A target diagnosis module is used to control the charging pile to charge the target battery according to the adaptive charging control strategy, record the change parameters of the adaptive charging control strategy when the target battery is charged, and generate a target state diagnosis result of the target battery according to the change parameters.

9. A battery diagnostic device based on a charging pile, characterized in that: The battery diagnostic device based on the charging pile includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the battery diagnosis device based on the charging pile executes the steps of the battery diagnosis method based on the charging pile as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the battery diagnosis method based on the charging pile as described in any one of claims 1-7 are implemented.

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