A SOC estimation method, system and device based on high computing power platform

Abstract

The present invention discloses a SOC estimation method, system, and device based on a high-computing-power platform, relating to the field of battery management technology. The method comprises: using a high-computing-power platform to obtain a set of operating data of a target battery system and determine the remaining available capacity; based on the operating data set, determining the current charging end time and the corresponding SOC value; if the target battery system is in a fully charged state, marking the SOC value at the current charging end time as the fully charged SOC value; if it is not in a fully charged state, obtaining the open-circuit voltage after the last charge, and correcting the SOC value after the last charge with the goal of minimizing the open-circuit voltage error, thereby calculating the corrected SOC value at the current charging end time; and calculating the SOC value within a second preset time period based on the corrected SOC value at the current charging end time and the remaining available capacity. The present invention achieves high-precision SOC estimation and correction throughout the entire life cycle and all scenarios.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a SOC estimation method, system and device based on a high computing power platform. Background Art

[0002] The battery's state of charge (SOC) represents the ratio of the battery's current charge to its available capacity. It plays a key role in how car owners measure their mileage and make charging decisions. Current industry technology ensures an SOC estimation error of less than 8%, and with technological advancements, some technologies can ensure an SOC error of less than 5%. However, when the battery is aged, the capacity is uneven, or it has not been fully charged for a long time, the SOC can easily have an error far greater than 8%, and even errors as high as 50%. This can cause the SOC display to remain above 50%, resulting in sudden battery cell undervoltage alarms, SOC dropping to 0% for a short period of time, vehicle power being limited, or even stalling. These problems occur due to the low computing power of the battery management system (BMS), which is unable to identify aging, inconsistencies, and large SOC deviations due to incomplete charging. This is especially true for battery systems using lithium iron phosphate as the positive electrode material. Because the voltage platform of lithium iron phosphate batteries is relatively flat, it is difficult to accurately identify the SOC state, and the probability of the above problems occurring is significantly higher. Summary of the Invention

[0003] The purpose of the present invention is to provide a SOC estimation method, system and device based on a high computing power platform to achieve high-precision SOC estimation and correction in all scenarios throughout the entire life cycle.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] In a first aspect, the present invention provides a SOC estimation method based on a high computing power platform, comprising:

[0006] Using a high computing power platform to obtain an operating data set of the target battery system; the operating data set includes voltage, current and time values ​​at each operating moment within a first preset time period;

[0007] determining a remaining available capacity of the target battery system based on the set of operating data;

[0008] Determining a current charging end time and a corresponding SOC value based on the operating data set;

[0009] If the target battery system is in a fully charged state at the current charging end time, the SOC value at the current charging end time is marked as the fully charged SOC value;

[0010] If the target battery system is not in a fully charged state at the current charging end time, obtaining the open circuit voltage after the last charge, and correcting the SOC value after the last charge with the goal of minimizing the open circuit voltage error to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient;

[0011] Calculating a corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and the corresponding correction coefficient;

[0012] Based on the corrected SOC value at the current charging end time and the remaining available capacity, the SOC value within a second preset time period is calculated; the start time of the second preset time period is the current charging end time.

[0013] In a second aspect, the present invention provides a SOC estimation system based on a high computing power platform, comprising:

[0014] An operating data acquisition module is used to acquire an operating data set of the target battery system using a high computing power platform; the operating data set includes voltage, current and time values ​​at each operating moment within a first preset time period;

[0015] an available capacity determination module, configured to determine a remaining available capacity of the target battery system based on the set of operating data;

[0016] a charging end determination module, configured to determine a current charging end time and a corresponding SOC value based on the operating data set;

[0017] A first full charge marking module is configured to mark the SOC value at the current charging end time as a fully charged SOC value if the target battery system is in a fully charged state at the current charging end time;

[0018] a correction module, configured to, if the target battery system is not in a fully charged state at the current charging end time, obtain an open circuit voltage after the last charge, and correct the SOC value after the last charge with the goal of minimizing the open circuit voltage error, so as to determine a corrected SOC value at the last charging end time and a corresponding correction coefficient;

[0019] A second full charge marking module is configured to calculate a corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and a corresponding correction coefficient;

[0020] The SOC estimation module is configured to estimate the SOC value within a second preset period based on the corrected SOC value at the current charging end time and the remaining available capacity; the starting time of the second preset period is the current charging end time.

[0021] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute a SOC estimation method based on a high computing power platform.

[0022] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0023] The present invention discloses an SOC estimation method, system and device based on a high computing power platform. The method determines the remaining available capacity of a target battery system based on the high computing power platform, and uses it as the basis for subsequent SOC changes to solve the SOC error problem caused by aging and imbalance; determines the current charging end time and the corresponding SOC value based on the operation data set; if the target battery system is in a fully charged state at the current charging end time, the corresponding SOC value is marked as the fully charged SOC value; if it is not in a fully charged state at the current charging end time, the open circuit voltage after the last charging is obtained, and with the minimum open circuit voltage error as the goal, the SOC value after the last charging is corrected to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient, thereby solving the SOC error problem caused by long-term non-full charging; finally, based on the corrected SOC value at the last charging end time and the corresponding correction coefficient, the corrected SOC value at the current charging end time is calculated, and then the SOC value within the second preset time period is calculated. The present invention can solve the problem of extremely large SOC errors in existing technical solutions under conditions of battery aging, capacity imbalance, and long-term lack of full charging, and can achieve high-precision SOC estimation in all scenarios throughout the entire life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 Schematic diagram of the flow of the SOC estimation method based on a high computing power platform of the present invention;

[0026] Figure 2 Schematic diagram of an example of the present invention;

[0027] Figure 3 Schematic diagram of the SOC estimation system based on a high computing power platform of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0029] To date, existing SOC estimation methods are all based on BMS hardware, lacking data storage capabilities or the ability to use historical data to correct the current SOC. They primarily employ Kalman filtering, ampere-hour integration, and special voltage point correction methods, which cannot avoid system capacity anomalies and significant SOC errors when the battery is not fully charged for extended periods. This can easily lead to vehicles breaking down due to high SOC. Therefore, the present invention provides an SOC estimation method, system, and device based on a high-computing-power platform. The high-computing-power platform receives information such as the current, voltage, and time of the battery system, and then replaces the battery management system (BMS) with the high-computing-power platform to perform SOC estimation.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Example 1

[0032] like Figure 1 As shown, the present invention provides a SOC estimation method based on a high computing power platform, comprising:

[0033] Step 100 uses a high-computing-power platform to acquire a target battery system's operating data set. Specifically, the data is the historical operating data stored by the BMS on the high-computing-power platform. The operating data set includes the voltage, current, and time values ​​at each operating moment within a first preset time period. The voltage can be the highest / lowest cell voltage value of all cells in the target battery system at each operating moment, or the voltage value of the cell with the lowest capacity. The high-computing-power platform can be a high-computing-power device such as a vehicle computer system or an onboard computer.

[0034] Step 200: Determine the remaining available capacity of the target battery system based on the operating data set; specifically, design a battery SOH estimation system to estimate the remaining available capacity SOH of the battery using the operating data set stored in the high computing power platform. sys .

[0035] Step 300: Determine the current charging end time and the corresponding SOC value based on the operating data set.

[0036] In step 400, if the target battery system is fully charged at the current charging end time, the SOC value at the current charging end time is marked as the fully charged SOC value. The target battery system being fully charged means that the battery has reached the upper limit of charging, and the result can be directly provided by the target battery system.

[0037] In step 500, if the target battery system is not fully charged at the current charging end time, the open circuit voltage after the last charge is obtained, and the SOC value after the last charge is corrected with the goal of minimizing the open circuit voltage error to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient.

[0038] The process of determining the open circuit voltage after the last charge specifically includes:

[0039] (1) Starting from the current charging end time t3, the current charging start time t2 is determined by moving forward in chronological order.

[0040] (2) Starting from the current charging start time t2, the time sequence is forwarded to determine the last charging end time t1.

[0041] (3) Based on the period between the last charge end time t1 and the current charge start time t2, determine the open circuit voltage of the target battery system after the last charge. Specifically, a battery model (which can be an equivalent circuit model, a single particle electrochemical model, a pseudo two-dimensional electrochemical model, etc.) is designed, and the open circuit voltage is calculated using an identification algorithm. Further, the algorithm for determining the open circuit voltage after the last charge (i.e., the identification algorithm) includes a least squares identification algorithm, a Kalman filter algorithm, an H-infinity algorithm, and an intelligent machine learning optimization algorithm. In practical applications, all algorithms that can identify OCV can be used.

[0042] With the goal of minimizing the open-circuit voltage error, the SOC value after the last charge is corrected to determine the corrected SOC value at the end of the last charge and the corresponding correction coefficient, specifically including:

[0043] The open circuit voltage error is optimized and calculated using the following formula:

[0044] J=std(OCV 12 -U oc12 ).

[0045] OCV 12 (k) = f(θ(k)).

[0046]

[0047] Where J represents the value of the open circuit voltage error, std() represents the standard deviation function, and OCV 12 Indicates the corrected open circuit voltage, U oc12 represents the open circuit voltage obtained by battery model identification after the last charge, f() is the SOC-OCV fitting function, which is one of the Gaussian function, polynomial function, hyperbolic tangent function, interpolation function and smoothing function; SOC begin is the SOC value at the end of the last charge, with a maximum value of 100 and a minimum value of 0; λ is the correction coefficient, which is a fixed value in a single solution process; Q(1) represents the battery capacity value at the first moment, which refers to the last charge end time t1, and Q(1) is the set value, usually set to Q n ; Q(k) represents the battery capacity value at the kth moment, which is calculated based on the integration of time and current ampere-hours; Q n Indicates rated capacity.

[0048] When calculating SOC begin , λ, can be solved based on the data of the time period t1-t2 at the same time, or can be solved using the corresponding data from the full charge time t0 earlier than time t1 to the time t0' before charging after time t0. At this time, SOC begin Knowing that it is 100, we only need to solve λ separately. Bringing the solved λ into the time period t1-t2, we only need to solve SOC begin .

[0049] Step 600 : Calculate the corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and the corresponding correction coefficient.

[0050] Specifically, the SOC is obtained by solving begin , λ, calculate θ at the current charging start time t2 according to the θ(k) formula above, marked as θ t2 , calculate the actual SOC value at time t3 according to the following formula, marked as SOC t3 , that is, the calculation formula of the corrected SOC value at the end of charging is:

[0051]

[0052] Among them, Q charge Indicates the charging capacity value during the time period from t2 to t3.

[0053] In one embodiment, the method further comprises:

[0054] (1) Calculate the SOC error value based on the current SOC value at the end of charging and the corrected SOC value at the end of charging; according to the formula Error_SOC = SOC'-SOC t3 Calculate the SOC error value; SOC' represents the currently displayed SOC value, that is, the SOC value at the moment charging ends; Error_SOC is the SOC error value.

[0055] (2) When the SOC error value is within a preset error threshold range, the SOC value at the current charging end time is marked as the output SOC value.

[0056] (3) When the SOC error value is not within the preset error threshold range, the corrected SOC value at the current charging end time is marked as the output SOC value, and the target battery system is error corrected according to the SOC error value. Specifically, the Error_SOC can be spread over the subsequent time period according to a certain error correction rule, so that the Error_SOC is gradually corrected to 0.

[0057] Step 700 : Based on the corrected SOC value at the current charging end time and the remaining available capacity, calculate the SOC value within a second preset period; the start time of the second preset period is the current charging end time.

[0058] Step 700 specifically includes: calculating the SOC value within the second preset time period according to the following formula:

[0059]

[0060] Wherein, SOC(k) represents the SOC value of the battery at the kth moment in the target battery system, SOC(k-1) represents the SOC value of the battery at the k-1th moment in the target battery system, I(k) represents the current of the battery at the kth moment in the target battery system, t(k) represents the time at the kth moment, and t(k-1) represents the time at the k-1th moment.

[0061] like Figure 2 As shown in FIG, in a specific example, a target battery system, a battery management system (BMS) and a high computing power platform are included. The target battery system is connected to the BMS and communicates with each other. The BMS manages the battery system through the information collected from the battery system. The high computing power platform is connected to the BMS system and communicates with each other. The BMS transmits the collected parameters of the battery system, such as voltage, current, SOC, time, etc., to the high computing power platform. The high computing power platform calculates the remaining available capacity (SOC) of the battery system based on the parameters transmitted by the BMS. sys) estimates the SOC value at the end of charging, determines whether it is the fully charged SOC value, and performs corresponding corrections and calculations. It then transmits the remaining available capacity, the fully charged SOC value, or the corrected SOC value at the end of charging back to the BMS, and triggers a correction based on the SOC error value. The BMS then makes corresponding adjustments based on the received correction-triggering data. This completes the initial correction and estimation.

[0062] In summary, the present invention avoids the problem of excessive SOC errors caused by abnormal battery system available capacity and long-term incomplete charging by building a battery model to estimate the remaining available battery capacity and designing different correction methods for fully charged and partially charged conditions, thereby ensuring accurate SOC estimation under abnormal conditions. The present invention can solve the problem of large SOC errors in existing technical solutions when the battery system capacity is abnormal or when the battery is not fully charged for a long time, and can be implemented in vehicle-side applications through an on-board high-computing power platform.

[0063] Example 2

[0064] like Figure 3 As shown, in order to implement the technical solution in Example 1 and achieve the corresponding functions and technical effects, this embodiment also provides an SOC estimation system based on a high computing power platform, including:

[0065] An operation data acquisition module is used to acquire an operation data set of a target battery system using a high computing power platform; the operation data set includes voltage, current and time values ​​at each operation moment within a first preset time period.

[0066] The available capacity determination module is configured to determine the remaining available capacity of the target battery system based on the operating data set.

[0067] The charging end determination module is used to determine the current charging end time and the corresponding SOC value based on the operation data set.

[0068] The first full charge marking module is configured to mark the SOC value at the current charging end time as a full charge SOC value if the target battery system is in a fully charged state at the current charging end time.

[0069] a correction module for obtaining the open circuit voltage after the last charge if the target battery system is not in a fully charged state at the current charging end time, and correcting the SOC value after the last charge with the goal of minimizing the open circuit voltage error to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient.

[0070] The second full charge marking module is used to calculate the corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and the corresponding correction coefficient.

[0071] The SOC estimation module is configured to estimate the SOC value within a second preset period based on the corrected SOC value at the current charging end time and the remaining available capacity; the starting time of the second preset period is the current charging end time.

[0072] Example 3

[0073] This embodiment provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the SOC estimation method based on a high computing power platform of embodiment 1. Optionally, the electronic device may be a server.

[0074] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the SOC estimation method based on a high computing power platform of embodiment 1.

[0075] Compared with the prior art, the present invention also has the following advantages:

[0076] 1) The present invention calculates the remaining available capacity of the battery based on the battery model and estimates the SOC accordingly, thereby avoiding the problem of large SOC errors caused by reduced system capacity due to battery aging or imbalance problems.

[0077] 2) The present invention adopts different SOC correction methods in sequence according to the full charge and partial charge status at the end of charging, thereby avoiding the problem of increased SOC error caused by long-term partial charge.

[0078] 3) The high computing power platform of the present invention has high computing power and storage functions, and communicates with the BMS. Through the collaboration of the two, SOC estimation can be achieved more efficiently and accurately.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0080] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A SOC estimation method based on a high computing power platform, characterized in that: Methods include: Use a high-computing platform to obtain the target battery system's operating data set; The operation data set includes voltage, current and time values ​​at each operation moment within a first preset time period; determining a remaining available capacity of the target battery system based on the set of operating data; Determining a current charging end time and a corresponding SOC value based on the operating data set; If the target battery system is in a fully charged state at the current charging end time, the SOC value at the current charging end time is marked as the fully charged SOC value; If the target battery system is not in a fully charged state at the current charging end time, the open circuit voltage after the last charge is obtained, and the SOC value after the last charge is corrected with the goal of minimizing the open circuit voltage error to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient, specifically including: The open circuit voltage error is optimized and calculated using the following formula: J=std(OCV 12 -U oc12 ); OCV 12 (k)=f(θ(k)); Where J represents the value of the open circuit voltage error, std() represents the standard deviation function, and OCV 12 Indicates the corrected open circuit voltage, U oc12 It represents the open circuit voltage obtained by battery model identification after the last charge, f() is the SOC-OCV fitting function, SOC begin is the SOC value at the end of the last charge, λ is the correction coefficient; Q(1) represents the battery capacity value at the first moment, where the first moment refers to the end of the last charge; Q(k) represents the battery capacity value at the target kth moment; Q n Indicates rated capacity; Calculating a corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and the corresponding correction coefficient; Based on the corrected SOC value at the current charging end time and the remaining available capacity, the SOC value within a second preset time period is calculated; the start time of the second preset time period is the current charging end time.

2. The SOC estimation method based on a high computing power platform according to claim 1, characterized in that: The process of determining the open circuit voltage after the last charge specifically includes: Starting from the current charging end time, the current charging start time is determined according to the chronological order; Starting from the current charging start time, count forward in chronological order to determine the last charging end time; An open circuit voltage of the target battery system after the last charge is determined based on a period between the last charge end time and the current charge start time.

3. The SOC estimation method based on a high computing power platform according to claim 1, characterized in that: The algorithm for determining the open circuit voltage after the last charge includes a least squares identification algorithm, a Kalman filter algorithm, an H-infinity algorithm or an intelligent machine learning optimization algorithm.

4. The SOC estimation method based on a high computing power platform according to claim 1, characterized in that: Calculating the SOC value within the second preset time period based on the corrected SOC value at the current charging end time and the remaining available capacity specifically includes: The SOC value within the second preset period is calculated according to the following formula: Wherein, SOC(k) represents the SOC value of the battery at the kth moment in the target battery system, SOC(k-1) represents the SOC value of the battery at the k-1th moment in the target battery system, I(k) represents the current of the battery at the kth moment in the target battery system, t(k) represents the time at the kth moment, t(k-1) represents the time at the k-1th moment, SOH sys Indicates the remaining available capacity, Q n Indicates rated capacity.

5. The SOC estimation method based on a high computing power platform according to claim 1, characterized in that: The method also includes: Calculating an SOC error value based on the SOC value at the current charging end time and the corrected SOC value at the current charging end time; When the SOC error value is within a preset error threshold range, marking the SOC value at the current charging end time as the output SOC value; When the SOC error value is not within a preset error threshold range, the corrected SOC value at the current charging end time is marked as the output SOC value, and error correction is performed on the target battery system according to the SOC error value.

6. The SOC estimation method based on a high computing power platform according to claim 1, characterized in that: The SOC-OCV fitting function is one of a Gaussian function, a polynomial function, a hyperbolic tangent function, an interpolation function and a smoothing function.

7. A SOC estimation system based on a high computing power platform, characterized in that: The system includes: An operating data acquisition module is used to acquire an operating data set of a target battery system using a high computing power platform; The operation data set includes voltage, current and time values ​​at each operation moment within a first preset time period; an available capacity determination module, configured to determine a remaining available capacity of the target battery system based on the set of operating data; a charging end determination module, configured to determine a current charging end time and a corresponding SOC value based on the operating data set; A first full charge marking module is configured to mark the SOC value at the current charging end time as a fully charged SOC value if the target battery system is in a fully charged state at the current charging end time; A correction module is configured to obtain the open circuit voltage after the last charge if the target battery system is not in a fully charged state at the current charging end time, and correct the SOC value after the last charge with the goal of minimizing the open circuit voltage error to determine the corrected SOC value at the last charging end time and the corresponding correction coefficient, specifically including: The open circuit voltage error is optimized and calculated using the following formula: J=std(OCV 12 -U oc12 ); OCV 12 (k)=f(θ(k)); Where J represents the value of the open circuit voltage error, std() represents the standard deviation function, and OCV 12 Indicates the corrected open circuit voltage, U oc12 It represents the open circuit voltage obtained by battery model identification after the last charge, f() is the SOC-OCV fitting function, SOC begin is the SOC value at the end of the last charge, λ is the correction coefficient; Q(1) represents the battery capacity value at the first moment, where the first moment refers to the end of the last charge; Q(k) represents the battery capacity value at the target kth moment; Q n Indicates rated capacity; A second full charge marking module is configured to calculate a corrected SOC value at the current charging end time based on the corrected SOC value at the last charging end time and a corresponding correction coefficient; The SOC estimation module is configured to estimate the SOC value within a second preset period based on the corrected SOC value at the current charging end time and the remaining available capacity; the starting time of the second preset period is the current charging end time.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the SOC estimation method based on a high computing power platform as described in any one of claims 1 to 6.

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