Battery health state assessment method, system and device and readable storage medium

Abstract

The invention provides a battery health state assessment method, system and device and a readable storage medium, and the method comprises the steps: an edge calculation module determines feature parameters of an equivalent circuit model of a power battery based on an equivalent circuit model feature parameter calculation algorithm; the edge calculation module generates a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC residual capacity of the power battery; and the cloud server determines the actual SOH capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the SOC-OCV curve of the battery. By adopting the method, the evaluation accuracy of the health state of the battery is improved, and the health state of the battery is accurately and effectively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of battery control, and in particular to a battery health status assessment method, system, device and readable storage medium. Background Art

[0002] In the prior art, the equivalent cycle method is mainly used to evaluate the SOH battery health status of power batteries. The equivalent cycle method is generally applied to the vehicle side. During the research and development stage of power batteries, a corresponding relationship curve between the SOH of the power battery and the number of charging cycles will be developed through the charge and discharge cycle data of the entire life cycle. This curve data is then written into the vehicle-side controller, and the current SOH is mapped out through the current number of cycles of the power battery. However, it was found in the study that the problem with the equivalent cycle method is that due to different user habits, the attenuation trend of the power battery is also different, which leads to differences in the SOH-charging cycle number relationship curves of different power batteries. Therefore, there is a large error in using only the relationship curve obtained in the laboratory to calculate the SOH of all power batteries, and this error will continue to increase with the attenuation of the SOH, thereby reducing the accuracy of the battery health status assessment and failing to accurately and effectively assess the battery health status. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a battery health status assessment method, system, device and readable storage medium to improve the accuracy of battery health status assessment and achieve accurate and effective battery health status assessment.

[0004] In a first aspect, an embodiment of the present application provides a battery health status assessment method, which is applied to a battery health status assessment system, the system including an edge computing module and a cloud server, and the method including:

[0005] The edge computing module determines characteristic parameters of the equivalent circuit model of the power battery based on an equivalent circuit model characteristic parameter calculation algorithm;

[0006] The edge computing module generates a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery;

[0007] The cloud server determines the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

[0008] Optionally, the method further comprises:

[0009] The edge computing module writes the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve into a fixed signal;

[0010] The edge computing module transmits the fixed signal to the T-BOX vehicle networking system through the CAN controller local area network bus;

[0011] The edge computing module uploads the fixed signal to the cloud server through the T-BOX.

[0012] Optionally, the method further comprises:

[0013] The cloud server analyzes the fixed signal to obtain the battery SOC-OCV curve;

[0014] The cloud server corrects the battery SOC-OCV curve based on the historical SOC-OCV curve of the power battery.

[0015] Optionally, before the edge computing module determines the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm, the method further includes:

[0016] The edge computing module constructs an equivalent circuit model of the power battery.

[0017] Optionally, the equivalent circuit model includes an ohmic internal resistance, a polarization resistance, a capacitor and an analog power supply, and the polarization resistance and the capacitor are connected in parallel and in series with the ohmic internal resistance and the analog power supply.

[0018] Optionally, generating a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery includes:

[0019] Obtaining a power-on voltage value and a power-on SOC of the power battery each time it is powered on after being at rest;

[0020] Generate a number of SOC-OCV scatter points according to the power-on voltage value and the power-on SOC at each power-on;

[0021] The battery SOC-OCV curve is obtained by curve fitting of several SOC-OCV scattered points.

[0022] Optionally, determining the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve includes:

[0023] According to the characteristic parameters of the equivalent circuit model, the open circuit voltage of the power battery is calculated using a recursive least squares algorithm;

[0024] Calculating the actual SOC of the power battery according to the battery SOC-OCV curve and the open circuit voltage of the power battery;

[0025] The SOH of the power battery is calculated according to the actual SOC of the power battery and the rated capacity of the power battery.

[0026] In a second aspect, an embodiment of the present application provides a battery health status assessment system, the system comprising an edge computing module and a cloud server;

[0027] The edge computing module is used to determine the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm;

[0028] The edge computing module is used to generate a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery;

[0029] The cloud server is used to determine the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

[0030] Optionally, the edge computing module is used to write the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve into a fixed signal;

[0031] The edge computing module is used to transmit the fixed signal to the T-BOX vehicle networking system through the CAN controller local area network bus;

[0032] The edge computing module is used to upload the fixed signal to the cloud server through the T-BOX.

[0033] Optionally, the cloud server is used to analyze the fixed signal to obtain the battery SOC-OCV curve;

[0034] The cloud server is used to correct the battery SOC-OCV curve based on the historical SOC-OCV curve of the power battery.

[0035] Optionally, the edge computing module is used to construct an equivalent circuit model of the power battery before determining characteristic parameters of the equivalent circuit model of the power battery based on an equivalent circuit model characteristic parameter calculation algorithm.

[0036] Optionally, the equivalent circuit model includes an ohmic internal resistance, a polarization resistance, a capacitor and an analog power supply, and the polarization resistance and the capacitor are connected in parallel and in series with the ohmic internal resistance and the analog power supply.

[0037] Optionally, generating a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery includes:

[0038] Obtaining a power-on voltage value and a power-on SOC of the power battery each time it is powered on after being at rest;

[0039] Generate a number of SOC-OCV scatter points according to the power-on voltage value and the power-on SOC at each power-on;

[0040] The battery SOC-OCV curve is obtained by curve fitting of several SOC-OCV scattered points.

[0041] Optionally, determining the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve includes:

[0042] According to the characteristic parameters of the equivalent circuit model, the open circuit voltage of the power battery is calculated using a recursive least squares algorithm;

[0043] Calculating the actual SOC of the power battery according to the battery SOC-OCV curve and the open circuit voltage of the power battery;

[0044] The SOH of the power battery is calculated according to the actual SOC of the power battery and the rated capacity of the power battery.

[0045] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the steps of the battery health status assessment method described in any optional implementation manner of the first aspect are performed.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the battery health status assessment method described in any optional implementation manner in the above-mentioned first aspect are executed.

[0047] The technical solution provided by this application includes but is not limited to the following beneficial effects:

[0048] This application adopts a battery health status assessment system, which determines the characteristic parameters of the equivalent circuit model of the power battery through the edge computing module based on the equivalent circuit model characteristic parameter calculation algorithm, and generates the battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery, so as to accurately obtain the relevant parameters of the power battery by utilizing the high-precision characteristics of edge computing, and realize the accurate calculation of characteristic parameters and battery data; then, the cloud server determines the actual capacity percentage of the SOH of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve, and can calculate the SOH of the power battery by applying high computing power and massive historical data in the cloud, so as to realize the analysis and evaluation of the health status of the power battery. By adopting the above scheme, the accuracy of the calculation of the equivalent circuit model parameters and the correction accuracy of the SOC-OCV curve can be guaranteed by utilizing the high-precision characteristics of edge computing and the high computing power of the cloud server, thereby improving the accuracy of the evaluation of the battery health status and realizing accurate and effective evaluation of the battery health status.

[0049] 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

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A flow chart of a battery health status assessment method provided by Embodiment 1 of the present invention is shown;

[0052] Figure 2 A schematic diagram showing a battery terminal voltage and current variation provided by the first embodiment of the present invention;

[0053] Figure 3 A schematic diagram showing a battery terminal voltage change situation provided by the first embodiment of the present invention;

[0054] Figure 4 A flow chart of a battery SOC-OCV curve uploading method provided by the first embodiment of the present invention is shown;

[0055] Figure 5 A flow chart of a battery SOC-OCV curve correction method provided by the first embodiment of the present invention is shown;

[0056] Figure 6A schematic diagram of an equivalent circuit model provided by the first embodiment of the present invention is shown;

[0057] Figure 7 A flow chart of a method for generating a battery SOC-OCV curve provided by the first embodiment of the present invention is shown;

[0058] Figure 8 A flow chart of a method for determining battery SOH provided in Embodiment 1 of the present invention is shown;

[0059] Fig. 9 A schematic diagram of the structure of a battery health status assessment system provided by Embodiment 2 of the present invention is shown;

[0060] Fig.10 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0062] Embodiment 1

[0063] To facilitate understanding of this application, Figure 1 The flowchart of a battery health status assessment method provided in the first embodiment of the present invention is shown to describe the contents of the first embodiment of the present application in detail.

[0064] See also Figure 1 As shown, Figure 1 A flowchart of a battery health status assessment method provided in Embodiment 1 of the present invention is shown, wherein the method comprises steps S101 to S103:

[0065] S101: The edge computing module determines characteristic parameters of an equivalent circuit model of a power battery based on an equivalent circuit model characteristic parameter calculation algorithm.

[0066] Specifically, the characteristic parameters include the ohmic internal resistance parameter and polarization resistance parameter of the battery in the equivalent circuit model. Figure 2As shown, Figure 2 A schematic diagram of the voltage and current changes of a battery terminal provided by an embodiment of the present invention is shown, wherein the curves in the coordinate system are the curves of the voltage (Voltage, in V) changing with time (Time), and the curves of the current (Current, in A) changing with time (Time). When a static battery is suddenly discharged, the terminal voltage will drop in a step, then drop slowly, and when the discharge is suddenly stopped, the terminal voltage will rise in a step, and then rise slowly; when a static battery is suddenly charged, the terminal voltage will rise in a step, then rise slowly, and when the charging is suddenly stopped, the terminal voltage will fall in a step, and then fall slowly. In the figure, the voltage corresponding to point B or H is the voltage value after the step, and the corresponding position of point C or G is the moment before charging and discharging is stopped. Since the step change rate of the terminal voltage is very fast, the frequency of the cloud data is difficult to accurately capture this change position, so an edge computing module can be used to identify and process the ultra-high frequency CAN data on the vehicle side.

[0067] Since the terminal voltage changes very quickly, the frequency of the cloud data cannot accurately capture the change position. Therefore, the edge computing module can be used to identify and process the ultra-high frequency CAN data at the vehicle end, calculate the voltage change and current change in the step part, and their ratio can represent the ohmic internal resistance parameter of the power battery. The ohmic internal resistance R0 of the power battery can be calculated by the following expression:

[0068] R0=ΔU / ΔI;

[0069] Among them, ΔU is the voltage change at the step position, and ΔI is the current change at the step position.

[0070] See also Figure 3 As shown, Figure 3 A schematic diagram of a battery terminal voltage change provided by the first embodiment of the present invention is shown, wherein the curve in the coordinate system is a parallel RC circuit charge and discharge curve, U1 is the voltage value before the step, and U0 is the voltage value after the step. Figure 3 The left half is the curve of the battery terminal voltage U changing with time t when the battery is charging. Figure 3 The right half is the curve of the battery terminal voltage U changing with time t when the battery is discharged. When the voltage stabilizes, the intersection of the voltage value and the tangent of the voltage change curve is the time constant τ (τ=R p C p ). Based on this characteristic, combined with the characteristics of the power battery equivalent circuit model, R p With C pFor the parallel circuit part, the time constant is calculated based on the charge and discharge curve characteristics of the RC parallel circuit. Combined with the high-frequency signal in the CAN, the change curve after the voltage step is accurately drawn, and the time constant calculation is completed with the high computing power of edge computing.

[0071] From the voltage value after the step ( Figure 2 The voltage change is the voltage at the moment before charging and discharging stops ( Figure 2 The corresponding position of C or G point in the p C p The voltage value (i.e. Figure 3 Before stopping charging and discharging ( Figure 2 The corresponding position of point C or G in the figure), due to the capacitance C p In the fully charged state, at this moment C p There is no current in the branch, so the polarization resistance R in the RC circuit p It can be calculated by the following formula:

[0072] R p =(U0-U1) / I CorG

[0073] Among them, I CorG express Figure 2 The current corresponding to point C or G in the figure, U1 is the voltage value before the step, and U0 is the voltage value after the step.

[0074] S102: The edge computing module generates a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery.

[0075] Specifically, the first frame of data after the power battery is powered on after being stationary for a long time is read through the vehicle-side edge computing module. At this moment, its voltage value represents the open circuit voltage OCV; at the same time, the OCV and SOC values ​​at this moment are calculated, and the complete OCV-SOC correspondence curve of the power battery in the current state can be accumulated.

[0076] S103: The cloud server determines the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

[0077] Specifically, the cloud server uses a rolling window to obtain segment data of the characteristic parameters of the equivalent circuit model, and uses the recursive least squares method to calculate the OCV at the starting position and the OCV at the ending position of each segment data; then combines the battery SOC-OCV curve to determine the SOC at the starting position and the SOC at the ending position of each segment; finally, the SOH of the power battery is calculated based on the SOC at the starting position and the SOC at the ending position of each segment.

[0078] Battery SOH is used to describe the performance and remaining life of the battery. It is an indicator to measure the health of the battery relative to its new state. It is usually expressed as a percentage. 100% represents a new battery. As the battery is used and the number of cycles increases, the SOH will gradually decrease, indicating that the battery's performance and remaining life are decreasing. Therefore, determining the SOH of the power battery can determine the health status of the power battery. The higher the SOH value of the power battery, the higher its health status.

[0079] In an alternative embodiment, see Figure 4 As shown, Figure 4 A flow chart of a battery SOC-OCV curve uploading method provided in the first embodiment of the present invention is shown, wherein the method further includes steps S401 to S403:

[0080] S401: The edge computing module writes the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve into a fixed signal.

[0081] S402: The edge computing module transmits the fixed signal to the T-BOX vehicle networking system through the CAN controller local area network bus.

[0082] S403: The edge computing module uploads the fixed signal to the cloud server via the T-BOX.

[0083] Specifically, the interaction scenario between the edge computing module and the cloud server can be set to a vehicle scenario. In this scenario, the characteristic parameters of the equivalent circuit model and the upload trigger method of the battery SOC-OCV curve are as follows: the calculation scenario of the equivalent circuit model parameters is relatively complex, and it is necessary to satisfy the existence of a voltage step and the voltage curve to complete the entire process of smooth recovery under the influence of the RC circuit. Considering the complexity of the driving process, the charging scenario is set as the calculation scenario of the equivalent circuit model parameters, and it is required that after the charging reaches the cut-off SOC, the vehicle still stays for a short time to restore the voltage to a stable state; the edge computing module triggers the preparation stage of the entire algorithm by identifying the charging status signal; triggers the data storage of the algorithm to start by identifying the change of the current signal; and triggers the end of the data storage of the algorithm through the change of the voltage signal and the relay power-on status signal (when the voltage signal no longer changes and the relay power-on state is 0, that is, the vehicle is not started, the data storage ends and the algorithm is executed; if the relay power-on state signal changes to 1 first, that is, the vehicle starts before the voltage state has returned to a stable state, the data storage ends and the algorithm is not executed, and the data cache is cleared at the same time). The drawing of the OCV-SOC curve requires capturing more SOC positions. Considering the limitations of the data storage capacity of the edge computing module, a method of calculating and uploading at any time is proposed here. When the calculation conditions are met, the OCV and the corresponding SOC values ​​are uploaded to the cloud, and the cloud uses interpolation to supplement the data of missing points; as the number of uploaded results from the vehicle increases, the cloud continuously iterates and updates the curve so that it can always reflect the current status of the power battery.

[0084] In an alternative embodiment, see Figure 5 As shown, Figure 5 A flow chart of a battery SOC-OCV curve correction method provided by the first embodiment of the present invention is shown, wherein the method further includes steps S501 to S502:

[0085] S501: The cloud server analyzes the fixed signal to obtain the battery SOC-OCV curve.

[0086] S502: The cloud server corrects the battery SOC-OCV curve based on the historical SOC-OCV curve of the power battery.

[0087] Specifically, considering the frequency of cloud data, the first frame signal after power-on in the cloud data may have a short time difference from the actual first frame signal after power-on. During this time difference, its voltage value is significantly different from the actual open circuit voltage, which will cause errors in the OCV-SOC relationship. Correcting the OCV-SOC relationship curve through the edge computing module can greatly reduce the error.

[0088] In an optional embodiment, before the edge computing module determines the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm, the method further includes:

[0089] An equivalent circuit model of the power battery is constructed.

[0090] Specifically, the present application adopts a first-order RC model as the equivalent circuit model of the power battery.

[0091] In an optional embodiment, the equivalent circuit model includes an ohmic internal resistance, a polarization resistance, a capacitor and an analog power supply, and the polarization resistance and the capacitor are connected in parallel and in series with the ohmic internal resistance and the analog power supply.

[0092] For details, see Figure 6 As shown, Figure 6 A schematic diagram of an equivalent circuit model provided by the first embodiment of the present invention is shown, wherein the equivalent circuit model includes an ohmic internal resistance R0, a polarization resistance R p , capacitor C p And analog power supply U OCV (its value is open circuit voltage), polarization resistance R p and capacitor C p Connected in parallel as a whole, with the ohmic internal resistance R0 and the analog power supply U OCV connected in series, I is the working current of the battery during charging and discharging, and U is the working voltage of the battery during charging and discharging; when the battery is charging, the current flows out from the negative electrode of the simulated power supply; when the battery is discharging, the current flows into the negative electrode of the simulated power supply.

[0093] In an alternative embodiment, see Figure 7 As shown, Figure 7 A flow chart of a method for generating a battery SOC-OCV curve provided by the first embodiment of the present invention is shown, wherein the method for generating a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery comprises steps S701 to S703:

[0094] S701: Obtain a power-on voltage value and a power-on SOC each time the power battery is powered on after being left at rest.

[0095] S702: Generate a number of SOC-OCV scatter points according to the power-on voltage value and the power-on SOC at each power-on.

[0096] S703: Perform curve fitting on a number of SOC-OCV scattered points to obtain the battery SOC-OCV curve.

[0097] Specifically, the power-on voltage value and power-on SOC of the power battery at the first moment of power-on after each long-term static state are collected, and the SOC-OCV scatter points at the first moment of each power-on are constructed with SOC as the x-axis coordinate value and OCV as the y-axis coordinate value. The SOC-OCV scatter points are curve-fitted in the coordinate system to obtain the battery SOC-OCV curve.

[0098] In an alternative embodiment, see Figure 8 As shown, Figure 8 A flow chart of a battery SOH determination method provided in Embodiment 1 of the present invention is shown, wherein the actual capacity percentage of the SOH of the power battery is determined according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve, including steps S801 to S803:

[0099] S801: Calculating the open circuit voltage of the power battery using a recursive least squares algorithm according to characteristic parameters of the equivalent circuit model.

[0100] Specifically, a plurality of fragment data are selected from the characteristic parameters of the equivalent circuit model through a rolling window with a preset window size, and the cloud server uses a recursive least squares algorithm to calculate the OCV of the start / end position of each fragment data. Figure 6 The equivalent circuit model of , can get the following equation:

[0101]

[0102] After Laplace transformation and discretization, the following formula can be obtained:

[0103]

[0104] Among them, C p is the capacitance, R p is the polarization resistance, R0 is the ohmic internal resistance, U p is the polarization resistance R p The voltage across the two ends, τ = R p C p , T represents the time frequency of cloud data, I is the working current of the battery during charging and discharging, U is the working voltage of the battery during charging and discharging, and U OCV is the open circuit voltage, k is the current moment, k is the moment before the current moment, U(k) represents the working voltage of the battery during charging and discharging at moment k, U(k-1) represents the working voltage of the battery during charging and discharging at moment k-1, I(k-1) represents the working current of the battery during charging and discharging at moment k-1, U OCV (k) represents the open circuit voltage at time k.

[0105] Combining the characteristic parameters of the equivalent circuit model and the current and voltage data uploaded from the cloud, the recursive least squares algorithm is used for convergence fitting to obtain the target result: the open circuit voltage at the start / end position of each segment of data.

[0106] S802: Calculate the actual SOC of the power battery according to the battery SOC-OCV curve and the open circuit voltage of the power battery.

[0107] Specifically, the SOC value at the start / end position of each segment data is determined from the open circuit voltage at the start / end position of each segment data according to the open circuit voltage at the start / end position of each segment data.

[0108] S803: Calculate the SOH of the power battery according to the actual SOC of the power battery and the rated capacity of the power battery.

[0109] Specifically, the ampere-hour integration of the segments is performed, and the SOC values ​​at the start / end positions of the segment data are combined to obtain the power battery SOH. The specific calculation formula is as follows:

[0110]

[0111] Wherein: i(t) is the change of current over time in the segment data, △SOC is the absolute value of the difference between the SOC at the starting position and the SOC at the ending position of the segment data, and Q0 is the rated capacity of the power battery.

[0112] In practical applications, the above-mentioned battery health status assessment system can be used to achieve accurate estimation of SOH in vehicle-cloud collaboration during the charging process. The specific application scenario is that the vehicle is charging at a charging pile, and the online correction of SOH is completed after charging is completed. The specific application principles and processes include: Vehicle-side actions at the moment of charging: During the charging process, current, voltage and other related signals are transmitted to the edge computing module via CAN high frequency, and the edge computing module monitors the changing trends of voltage and current through algorithms; when the charging cut-off SOC is reached, the charging gun stops power output, the current drops instantly, and the voltage value also shows a sudden downward trend. At this time, the edge computing module starts to record data from the moment before the step point; when the voltage value no longer changes, the edge computing module stops recording, starts calculating specific parameters, and passes the results to the T-BOX component via CAN, and T-BOX uploads the signal to the cloud; after the cloud receives the updated parameters, it reads the historical data of the day, selects data fragments with good data quality and no frame loss, and starts the SOH correction operation.

[0113] The OCV-SOC curve can also be corrected during the use of the vehicle through the above-mentioned battery health status assessment system. The specific application scenario is that the vehicle is powered on again after a period of rest, at which time the OCV-SOC value is corrected and uploaded to the cloud. The specific application principles and processes include: after the vehicle was last used, it was powered off and parked for a period of time, and the power battery reached a static state; at this time, the user got in the car again and started the vehicle, the power battery suddenly powered on at high voltage, and transmitted the relevant signals to the edge computing module through CAN; the edge computing module reads the first frame of power-on data, obtains the voltage and SOC values, and sends them to the T-BOX component in pairs, and the T-BOX component sends the information to the cloud; after the cloud receives the signal, it matches it with other OCV-SOC values ​​uploaded by this vehicle in the past, and corrects the OCV-SOC curve.

[0114] On the one hand, this application combines the characteristics of the equivalent circuit model and the voltage change law of the power battery charging and discharging process, and innovatively proposes a calculation method for characteristic parameters such as ohmic internal resistance, polarization internal resistance, and time constant. Combined with the high-frequency data on the vehicle side, the accuracy of the parameters is guaranteed. On the other hand, the edge computing module accurately captures the high-frequency data and extracts the corresponding values ​​of the open circuit voltage and SOC after standing. Through the cloud-based data update, interpolation and other methods, the reconstruction and real-time correction of the power battery OCV-SOC curve are realized, ensuring the accuracy of the power battery OCV-SOC curve as the battery status changes. In addition, by identifying the data scenarios required by the algorithm, the vehicle signal is accurately adapted, and by identifying the signal changes such as current, voltage, and relay power-on status, the edge computing module's data storage, algorithm execution and other scenarios are triggered, effectively saving the edge computing module's storage resources and computing power resources. Finally, the characteristic parameters of the equivalent circuit model fed back by the edge computing module are used in conjunction with the recursive least squares algorithm to fit and calculate the current, voltage and other signals on the cloud. This not only improves the inaccurate evaluation of the characteristic parameters of the equivalent circuit model caused by issues such as cloud data frequency and abnormality rate, but also uses the high computing power of the cloud to solve the problem of difficult OCV estimation. The real-time corrected OCV-SOC curve fed back by the vehicle is then used to achieve accurate SOC estimation, ensuring the accuracy of subsequent calculations of the power battery SOH.

[0115] Embodiment 2

[0116] Embodiment 2 of the present invention provides a battery health status assessment system, see Fig. 9 As shown, Fig. 9 A schematic diagram of the structure of a battery health status assessment system provided in Embodiment 2 of the present invention is shown, wherein the system includes an edge computing module 901 and a cloud server 902:

[0117] The edge computing module is used to determine the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm;

[0118] The edge computing module is used to generate a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery;

[0119] The cloud server is used to determine the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

[0120] In an optional embodiment, the edge computing module is used to write the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve into a fixed signal;

[0121] The edge computing module is used to transmit the fixed signal to the T-BOX vehicle networking system through the CAN controller local area network bus;

[0122] The edge computing module is used to upload the fixed signal to the cloud server through the T-BOX.

[0123] In an optional embodiment, the cloud server is used to parse the fixed signal to obtain the battery SOC-OCV curve;

[0124] The cloud server is used to correct the battery SOC-OCV curve based on the historical SOC-OCV curve of the power battery.

[0125] In an optional embodiment, the edge computing module is used to construct an equivalent circuit model of the power battery before determining the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm.

[0126] In an optional embodiment, the equivalent circuit model includes an ohmic internal resistance, a polarization resistance, a capacitor and an analog power supply, and the polarization resistance and the capacitor are connected in parallel and in series with the ohmic internal resistance and the analog power supply.

[0127] In an optional embodiment, generating a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery includes:

[0128] Obtaining a power-on voltage value and a power-on SOC of the power battery each time it is powered on after being at rest;

[0129] Generate a number of SOC-OCV scatter points according to the power-on voltage value and the power-on SOC at each power-on;

[0130] The battery SOC-OCV curve is obtained by curve fitting of several SOC-OCV scattered points.

[0131] In an optional embodiment, determining the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve includes:

[0132] According to the characteristic parameters of the equivalent circuit model, the open circuit voltage of the power battery is calculated using a recursive least squares algorithm;

[0133] Calculating the actual SOC of the power battery according to the battery SOC-OCV curve and the open circuit voltage of the power battery;

[0134] The SOH of the power battery is calculated according to the actual SOC of the power battery and the rated capacity of the power battery.

[0135] Embodiment 3

[0136] Based on the same application concept, see Fig.10 As shown, Fig.10 FIG. 4 shows a schematic diagram of the structure of a computer device provided by Embodiment 3 of the present invention, wherein: Fig.10 As shown, a computer device 1000 provided in Embodiment 3 of the present application includes:

[0137] A processor 1001, a memory 1002 and a bus 1003, wherein the memory 1002 stores machine-readable instructions executable by the processor 1001. When the computer device 1000 is running, the processor 1001 communicates with the memory 1002 via the bus 1003. When the processor 1001 is running, the machine-readable instructions execute the steps of the battery health status assessment method shown in the above-mentioned embodiment 1.

[0138] Embodiment 4

[0139] Based on the same application concept, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the battery health status assessment method described in any one of the above embodiments are executed.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0141] The computer program product for battery health status assessment provided in an embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0142] The voltage sampling device provided in the embodiment of the present invention can be specific hardware on the device or software or firmware installed on the device. The system provided in the embodiment of the present invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the system embodiment can refer to the corresponding contents in the aforementioned method embodiment. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0143] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some communication interface, device or unit, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0146] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0147] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0148] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A battery health status assessment method, characterized in that: Applied to a battery health status assessment system, the system includes an edge computing module and a cloud server, and the method includes: The edge computing module determines characteristic parameters of the equivalent circuit model of the power battery based on an equivalent circuit model characteristic parameter calculation algorithm; The edge computing module generates a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery; The cloud server determines the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

2. The method according to claim 1, characterized in that: The method further comprises: The edge computing module writes the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve into a fixed signal; The edge computing module transmits the fixed signal to the T-BOX vehicle networking system through the CAN controller local area network bus; The edge computing module uploads the fixed signal to the cloud server through the T-BOX.

3. The method according to claim 2, characterized in that The method further comprises: The cloud server analyzes the fixed signal to obtain the battery SOC-OCV curve; The cloud server corrects the battery SOC-OCV curve based on the historical SOC-OCV curve of the power battery.

4. The method according to claim 1, characterized in that: Before the edge computing module determines the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm, the method further includes: The edge computing module constructs an equivalent circuit model of the power battery.

5. The method according to claim 4, characterized in that The equivalent circuit model includes an ohmic internal resistance, a polarization resistance, a capacitor and a simulated power supply. The polarization resistance and the capacitor are connected in parallel and in series with the ohmic internal resistance and the simulated power supply.

6. The method according to claim 1, characterized in that The generating a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery includes: Obtaining a power-on voltage value and a power-on SOC of the power battery each time it is powered on after being at rest; Generate a number of SOC-OCV scatter points according to the power-on voltage value and the power-on SOC at each power-on; The battery SOC-OCV curve is obtained by curve fitting of several SOC-OCV scattered points.

7. The method according to claim 1, characterized in that The determining the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve includes: According to the characteristic parameters of the equivalent circuit model, the open circuit voltage of the power battery is calculated using a recursive least squares algorithm; Calculating the actual SOC of the power battery according to the battery SOC-OCV curve and the open circuit voltage of the power battery; The SOH of the power battery is calculated according to the actual SOC of the power battery and the rated capacity of the power battery.

8. A battery health status assessment system, characterized in that: The system includes an edge computing module and a cloud server; The edge computing module is used to determine the characteristic parameters of the equivalent circuit model of the power battery based on the equivalent circuit model characteristic parameter calculation algorithm; The edge computing module is used to generate a battery SOC-OCV curve based on the power-on voltage value and the power-on SOC remaining capacity of the power battery; The cloud server is used to determine the SOH actual capacity percentage of the power battery according to the characteristic parameters of the equivalent circuit model and the battery SOC-OCV curve.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the battery health status assessment method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the battery health status assessment method as described in any one of claims 1 to 7.

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