Method for controlling battery management system, battery management system, and vehicle

Abstract

The present application relates to a method for controlling a battery management system, comprising: identifying a battery state transition node based on a rate of change of each collected battery signal and a battery state flag bit in the collected battery signal, and storing a signal collected at the identified battery state transition node (S1); and adjusting a control strategy of the battery management system (1) based on the variation of each battery signal in the time window and the signal stored at the battery state transition node (S2). According to the application, the battery management system can identify the battery state conversion node, and adjusts the control strategy based on the change of each battery signal and the fusion calculation result of the plurality of battery signals, so that the battery management system quickly and accurately responds to improve the overall performance of the battery and prolong the service life of the battery.

Description

Technical Field

[0001] The present application relates to the field of battery signal processing, and more particularly to a method for controlling a battery management system, a battery management system, a vehicle including the battery management system according to the present application, and a computer program product for at least assisting in implementing the steps of the method according to the present application. Background Art

[0002] The battery management system analyzes the operating state of the battery in real time by monitoring signals such as the current, voltage, temperature, and capacity of the battery, and controls the charging and discharging process of the battery according to the working state to ensure that the battery operates within a safe range. However, in the case where the battery is in a dynamic working condition, especially during the charge and discharge cycle and capacity reset process of the battery, the existing signal processing technologies of the battery management system are difficult to track the battery signals that change frequently and violently in real time, and it is also difficult to respond quickly and accurately to the changing battery signals.

[0003] Therefore, there is room for improvement in the signal processing method of the current battery management system. Summary of the Invention

[0004] The purpose of the present application is to provide a method for controlling a battery management system, a battery management system, a vehicle including the battery management system according to the present application, and a computer program product to at least partially solve the problems in the prior art.

[0005] According to a first aspect of the present application, there is provided a method for controlling a battery management system, the method may include:

[0006] - identifying battery state transition nodes based on the change rate of each collected battery signal and the battery state flag bit in the collected battery signals, and storing the signals collected at the identified battery state transition nodes; and

[0007] - adjusting the control strategy of the battery management system based on the change amount of each battery signal within a time window and the signals stored at the battery state transition nodes.

[0008] The core concept of this application at least includes: The battery management system can identify the battery state transition nodes based on an accurate timing judgment mechanism, and track and store the signals collected at the battery state transition nodes in real time. Under various complex operating conditions of the battery, the battery management system tracks the changes of each battery signal in real time, and jointly models multiple battery signals by adaptively adjusting the calculation method. Based on the changes of each battery signal and the fusion calculation results of multiple battery signals, a comprehensive evaluation is performed, and the control strategy of the battery management system is adjusted according to the evaluation results. Especially when the charging trigger and reset correction states frequently switch, the battery management system can respond quickly and accurately, thereby improving the overall performance of the battery and extending the service life of the battery.

[0009] According to an optional embodiment of the present application, the change amount of each collected battery signal in a time window can be monitored. When it is detected that the change amount of the battery signal in the time window is greater than the change amount threshold assigned to the battery signal, a fusion index for characterizing the probability of battery state mutation can be calculated based on the change amount of each battery signal in the time window, and the control strategy of the battery management system can be adjusted based on the calculated fusion index. Among them, the control strategy includes, for example, a charging control strategy, a state of charge calculation method, a cooling control strategy, a sampling frequency adjustment strategy, a state of charge correction strategy, a compensation coefficient adjustment strategy, and / or a data storage and tracking strategy, etc.

[0010] According to another optional embodiment of the present application, the control strategy of the battery management system can be adjusted based on the change amount of each battery signal in a time window and the priority of each battery signal by means of a decision tree. Among them, the battery signals include a battery charging current signal, a battery charging voltage signal, a battery capacity signal, a battery state of charge signal, a battery energy density value signal, a battery temperature signal, and / or a battery state flag bit, etc.

[0011] According to another optional embodiment of the present application, when it is detected that the change amount of the battery state of charge signal in the time window is greater than the change amount threshold assigned to the signal, the state of charge calculation method of the battery management system can be adjusted.

[0012] According to another optional embodiment of the present application, when it is detected that the change amount of the battery charging current signal in the time window is greater than the change amount threshold assigned to the signal, the charging control strategy of the battery management system can be adjusted.

[0013] According to another optional embodiment of the present application, when it is detected that the change amount of the battery temperature signal in the time window is greater than the change amount threshold assigned to the signal, the cooling control strategy of the battery management system can be adjusted.

[0014] According to another alternative embodiment of the present application, the signal acquisition frequency of the battery management system can be adjusted based on a plurality of battery signals collected by the battery management system, in particular, based on the change rate of the plurality of battery signals collected by the battery management system and the battery state flag bits in the collected battery signals. Wherein, when the change rates of the collected battery signals are all less than a preset change rate threshold, a low-frequency sampling frequency of the battery management system can be adopted; when a key state determination flag bit in the battery state flag bits is monitored, a high-frequency sampling frequency of the battery management system can be adopted; when the change rate of the collected signals is greater than or equal to the preset change rate threshold and no key state determination flag bit is monitored in the battery state flag bits, an intermediate-frequency sampling frequency of the battery management system can be adopted.

[0015] According to another alternative embodiment of the present application, when a state trigger flag bit in the collected battery signals is monitored, a battery state conversion node can be identified based on the change rate of the battery signals, wherein the battery state conversion node includes, for example, a charging trigger node, a capacity reset node, and / or an open-circuit voltage inflection point, etc.

[0016] According to another alternative embodiment of the present application, when a charging trigger flag bit in the collected signals is monitored, a charging trigger node can be identified based on the change rate of the battery signals within a time window with respect to the current sampling moment; when an equalization correction flag bit in the collected signals is monitored, a capacity reset node can be identified based on the change rate of the battery signals within a time window with respect to the current sampling moment; when the second derivative of the open-circuit voltage of the battery with respect to time approaches zero, the open-circuit voltage inflection point of the battery can be identified.

[0017] According to another alternative embodiment of the present application, the calculation method of the state of charge of the battery can be adjusted based on the change amount of each battery signal within a time window and the battery state conversion node. Wherein, when no capacity reset node is identified, a low-precision capacity calculation method can be adopted to calculate the state of charge of the battery; when a capacity reset node is identified, a high-precision capacity calculation method is adopted to calculate the state of charge of the battery, and when it is monitored that the change amount of the signal within the time window is greater than the change amount threshold assigned to the signal, the stored state of charge of the battery can be corrected using the calculated state of charge of the battery.

[0018] According to another optional embodiment of the present application, a sliding time window can be set based on the response time constant of the battery management system, and / or the battery signal stabilization time, the calculation accuracy requirement, and / or the signal sampling frequency, and the maximum and minimum values of each collected battery signal within each sliding time window are determined and analyzed to monitor the change amount of each collected battery signal within each sliding time window.

[0019] According to another optional embodiment of the present application, in the case where the deviation between the signal estimation value calculated based on the collected battery signal and the collected battery signal is greater than a preset threshold, a correction coefficient related to the battery signal can be calculated based on the historical data of the collected battery signal, and the correction coefficient is introduced during the calculation of the battery signal.

[0020] According to a second aspect of the present application, a battery management system is provided, and the battery management system may include the following components:

[0021] - A signal sampling unit configured to collect battery signals; and

[0022] - A control unit configured to execute the method according to the present application.

[0023] According to a third aspect of the present application, a vehicle is provided, and the vehicle may include the battery management system according to the present application.

[0024] According to a fourth aspect of the present application, a computer program product, such as a computer-readable program carrier, includes or stores computer program instructions, and when the computer program instructions are executed by a processor, at least assist in implementing the steps of the method according to the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The principles, features, and advantages of the present application can be better understood by describing the present application in more detail with reference to the accompanying drawings. The drawings show:

[0026] Figure 1 A flowchart showing the operation of a method for controlling a battery management system according to an exemplary embodiment of the present application; and

[0027] Figure 2 A schematic diagram showing a vehicle equipped with a battery management system according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical problems to be solved, the technical solutions, and the beneficial technical effects of the present application more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and multiple exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the protection scope of the present application.

[0029] Figure 1 The working flowchart of a method for controlling a battery management system according to an exemplary embodiment of the present application is shown. The following exemplary embodiments describe the method according to the present application in more detail.

[0030] As Figure 1 shown, the method may include steps S1 and S2. In step S1, a battery state transition node may be identified based on the change rate of each collected battery signal and the battery state flag bit in the collected battery signals, and the signals collected at the identified battery state transition node may be stored. Here, the signal sampling unit 11 of the battery management system 1 collects battery signals at a certain signal sampling frequency, and the battery signals may include, for example, battery charging current signals, battery charging voltage signals, battery capacity signals, state of charge signals of the battery, battery energy density value signals, battery temperature signals, and / or battery state flag bits, etc. In the case where a state trigger flag bit in the collected battery signals is monitored, a battery state transition node may be identified based on the change rate of the battery signals, where the battery state transition node is a time node at which the battery management system 1 must particularly process the collected battery signals in the control flow, and it may include, for example, a charging trigger node, a capacity reset node, and / or an open-circuit voltage inflection point, etc. Exemplarily, in the case where a charging trigger flag bit in the collected signals is monitored, a charging trigger node may be identified based on the change rate of the battery signals within a time window with respect to the current sampling moment (for example, a time interval of 3 s before and after the current sampling moment); in the case where an equalization correction flag bit in the collected signals is monitored, a capacity reset node may be identified based on the change rate of the battery signals within a time window with respect to the current sampling moment (for example, a time interval of 1 s before and after the current sampling moment); in the case where the second derivative of the open-circuit voltage of the battery with respect to time approaches zero, an open-circuit voltage inflection point of the battery may be identified. When a battery state transition node is identified, the signals collected at the identified battery state transition node may be automatically traced and stored for subsequent signal analysis and optimization.

[0031] In step S2, the control strategy of the battery management system 1 can be adjusted based on the change amount of each battery signal within the time window and the signals stored at the battery state transition node. Here, the change amount of each collected battery signal within the time window can be monitored. This time window is especially a sliding time window, which can eliminate signal jitter and spike errors and smooth the discontinuity in the boundary processing of the battery signal. The sliding time window can be set based on the response time constant of the battery management system 1. For example, when the response period of the battery management system is 1 s, a time window of 3 s to 5 s can be set; the sliding time window can also be set based on the battery signal stabilization time. For example, the sliding variance of each battery signal stabilization time is obtained, and the minimum window required for stable fluctuation is determined based on the obtained sliding variance; the sliding time window can also be set based on the calculation accuracy requirement. For example, during high-precision calculation, a time window containing 5 to 10 signal sampling points is set; the sliding time window can also be set based on the signal sampling frequency of the signal sampling unit 11. For example, for a signal sampling frequency of 10 Hz, a time window of 0.5 s to 2 s can be set so that the time window contains 5 to 20 signal sampling points. Thus, the maximum and minimum values of each collected battery signal within each divided sliding time window can be determined and analyzed to monitor the change amount of each collected battery signal within the sliding time window.

[0032] Here, a change amount threshold belonging to the battery signal can be set for each type of battery signal. The change amount threshold can be set based on the battery signal variance or the historical average value of the collected battery signals of this type. When it is monitored that the change amount of the battery signal within the time window is greater than the change amount threshold belonging to the battery signal, a fusion index I for characterizing the probability of sudden change in the battery state can be calculated based on the change amount of each battery signal within the time window. f ,and based on the calculated fusion index I f adjust the trigger logic of the control strategy of the battery management system 1. Here, multiple types of battery signals - including battery charging current signal, battery charging voltage signal, battery capacity signal, state of charge signal of the battery, battery energy density signal value signal, and / or battery temperature signal, etc. - are simultaneously incorporated into the calculation framework, and the fusion index I is calculated, for example, in the following manner. f :

[0033] I f = w1·ΔS1 + w2·ΔS2 + … + w n ·ΔS n ,

[0034] where, ΔS i represents the change amount of the i-th battery signal within the set time window, wi represents the weight of the i-th battery signal, which can be set based on historical data or empirically. In this way, multiple battery signals are integrated into a unified analysis framework, and the calculation results at each time node are ensured to be consistent and highly accurate. When the calculated fusion index I f exceeds a preset threshold, it can be determined that a mutation has occurred in the battery state, and the control strategy of the battery management system 1 needs to be adjusted. The control strategy includes, for example, a charging control strategy, a state-of-charge calculation method, a cooling control strategy, a sampling frequency adjustment strategy, a state-of-charge correction strategy, a compensation coefficient adjustment strategy, and / or a data storage and tracking strategy, etc.

[0035] In addition, the control strategy of the battery management system 1 can also be adjusted based on the change amount of each battery signal within a time window and the priority of each battery signal by means of a decision tree. Here, different priorities can be assigned to each battery signal. For example, the battery signals are arranged in descending order of priority as follows: battery charging current signal, battery state-of-charge signal, battery energy density signal value signal, and battery temperature signal, etc. The battery management system 1 can respond to the change amount of the battery signal within the time window through a decision tree (such as including a rule tree or a lightweight decision model, etc.), especially by first responding to the change amount of the battery signal with a high priority within the time window.

[0036] Exemplarily, when it is monitored that the change amount of the battery state-of-charge signal within the time window is greater than the change amount threshold assigned to the signal, the state-of-charge calculation method of the battery management system 1 can be adjusted. Specifically, the calculation method of the battery state-of-charge can be adjusted based on the change amount of each battery signal within the time window and the battery state transition node. Among them, when the capacity reset node is not identified, a low-precision capacity calculation method can be used to calculate the battery state-of-charge to improve the overall data calculation efficiency; when the capacity reset node is identified, a high-precision capacity calculation method can be used to calculate the battery state-of-charge to improve the data calculation accuracy near the battery state transition node.

[0037] According to the compensation coefficient adjustment strategy of the battery management system, for battery signals with relatively large calculation errors, especially the battery state-of-charge signal and the battery energy density signal value signal, when the deviation between the signal estimated value calculated based on the collected battery signals and the collected battery signals - for example, the deviation between the battery state-of-charge value estimated based on battery voltage, current, temperature, etc. and the collected battery state-of-charge value - is greater than a preset threshold, a correction coefficient related to the battery signal can be calculated based on the historical data of the collected battery signals, and this correction coefficient can be introduced during the calculation of the battery signal.

[0038] Regarding the sampling frequency adjustment strategy, the signal acquisition frequency of the battery management system 1 can be adjusted based on multiple battery signals collected by the battery management system 1. Specifically, the signal acquisition frequency of the battery management system 1 can be adjusted based on the change rate of multiple battery signals collected by the battery management system 1 and the battery state flag bits in the collected battery signals. For example, when the change rates of the collected battery signals are all less than a preset change rate threshold, it can be determined that the battery is currently in a normal charge / discharge state or in an operating condition of sleep / standby (i.e., the collected battery signal is a null value). Thus, the low-frequency sampling frequency of the battery management system 1 can be adopted. When a key state determination flag bit (such as an equalization correction flag bit) in the battery state flag bits is monitored, it can be determined that the battery is in a state of charge correction stage, and thus the high-frequency sampling frequency of the battery management system 1 can be adopted to track the battery signals that change frequently and severely in real time under the dynamic operating conditions of the battery. When the change rate of the collected signal is greater than or equal to the preset change rate threshold and no key state determination flag bit is monitored in the battery state flag bits, for example, during the fast discharge stage or recharge stage of the battery, the medium-frequency sampling frequency of the battery management system 1 can be adopted.

[0039] When it is monitored that the change amount of the battery signal in the time window is greater than the change amount threshold assigned to the signal, the battery management system 1 can adopt a state of charge correction strategy, thereby triggering the correction process of the battery state of charge, and using the calculated battery state of charge to correct the battery state of charge to ensure the continuity and consistency of battery capacity estimation.

[0040] For another example, when it is monitored that the change amount of the battery charging current signal in the time window is greater than the change amount threshold assigned to the signal, the charging control strategy of the battery management system 1 can be adjusted, such as reducing the charging current, disconnecting the charging circuit, etc.

[0041] For another example, when it is monitored that the change amount of the battery temperature signal in the time window is greater than the change amount threshold assigned to the signal and the battery is in a stage of drastic temperature change, the cooling control strategy of the battery management system 1 can be adjusted, such as quickly starting the cooling device of the battery management system 1 with a response delay of less than 100 ms.

[0042] According to an embodiment of the present application, the battery management system can identify battery state transition nodes based on an accurate timing judgment mechanism, and track and store in real time the signals collected at the battery state transition nodes. Under various complex operating conditions of the battery, the battery management system tracks in real time the changes in each battery signal, jointly models multiple battery signals by adaptively adjusting the calculation method, comprehensively evaluates based on the changes in each battery signal and the fusion calculation results of multiple battery signals, and adjusts the control strategy of the battery management system according to the evaluation results. Especially when the charging trigger and reset correction states frequently switch, the battery management system can respond quickly and accurately, thereby improving the overall performance of the battery and extending the battery life.

[0043] In addition, it should be noted that the step numbers described herein do not necessarily represent the order of precedence, but are merely a kind of reference numeral. According to the specific situation, the order can be changed as long as the technical purpose of the present application can be achieved.

[0044] Figure 2 A schematic diagram of a vehicle showing an exemplary embodiment according to the present application. As Figure 2 shown, a battery management system 1 is configured in the vehicle 10, and the battery management system 1 may include the following components:

[0045] - A signal sampling unit 11, which is configured to collect battery signals; and

[0046] - A control unit 12, which is configured to execute the method according to the present application.

[0047] It should be understood that in this text, expressions such as "first", "second", "third", etc. are only for descriptive purposes, and should not be construed as indicating or implying relative importance, nor should they be construed as implicitly specifying the quantity of the indicated technical features.

[0048] If an embodiment includes an "and / or" association between a first feature and a second feature, it should be interpreted as follows: According to one embodiment, the embodiment has both the first feature and the second feature. According to another embodiment, the embodiment has either only the first feature or only the second feature.

[0049] Although specific implementation schemes have been described above, these implementation schemes are not intended to limit the scope of the disclosure of the present application, even when describing a single implementation scheme with respect to a specific feature. The feature examples provided in the disclosure of the present application are for illustrative purposes only, not for limitation, unless otherwise stated. In specific implementations, according to actual needs and where technically feasible, multiple features can be combined with each other. Various substitutions, changes and modifications can also be conceived without departing from the spirit and scope of the present application.

Claims

1. A method for controlling a battery management system (1), the method comprising: Identifying battery state transition nodes based on the change rates of the collected individual battery signals and the battery state flag bits in the collected battery signals, and storing the signals collected at the identified battery state transition nodes; And Adjusting the control strategy of the battery management system (1) based on the change amounts of the individual battery signals within a time window and the signals stored at the battery state transition nodes.

2. The method according to claim 1, wherein, Monitoring the change amounts of the collected individual battery signals within a time window, and in the case where it is monitored that the change amount of a battery signal within the time window is greater than the change amount threshold assigned to the battery signal, calculating a fusion index for characterizing the probability of sudden battery state change based on the change amounts of the individual battery signals within the time window, and adjusting the control strategy of the battery management system (1) based on the calculated fusion index, wherein the control strategy includes, for example, a charging control strategy, a state of charge calculation method, a cooling control strategy, a sampling frequency adjustment strategy, a state of charge correction strategy, a compensation coefficient adjustment strategy, and / or a data storage and tracking strategy.

3. The method according to any one of the preceding claims, wherein, Adjusting the control strategy of the battery management system (1) by means of a decision tree based on the change amounts of the individual battery signals within a time window and the priorities of the individual battery signals, wherein the battery signals include a battery charging current signal, a battery charging voltage signal, a battery capacity signal, a battery state of charge signal, a battery energy density value signal, a battery temperature signal, and / or a battery state flag bit; and / or In the case where it is monitored that the change amount of the battery state of charge signal within the time window is greater than the change amount threshold assigned to the signal, adjusting the state of charge calculation method of the battery management system (1); and / or In the case where it is monitored that the change amount of the battery charging current signal within the time window is greater than the change amount threshold assigned to the signal, adjusting the charging control strategy of the battery management system (1); and / or In the case where it is monitored that the change amount of the battery temperature signal within the time window is greater than the change amount threshold assigned to the signal, adjusting the cooling control strategy of the battery management system (1).

4. The method according to any one of the above claims, wherein, Adjusting the signal acquisition frequency of the battery management system (1) based on multiple battery signals collected by the battery management system (1), in particular adjusting the signal acquisition frequency of the battery management system (1) based on the change rates of the multiple battery signals collected by the battery management system (1) and the battery state flag bits in the collected battery signals, wherein in the case where the change rates of the collected battery signals are all less than a preset change rate threshold, the low-frequency sampling frequency of the battery management system (1) is adopted, in the case where a key state determination flag bit in the battery state flag bits is monitored, the high-frequency sampling frequency of the battery management system (1) is adopted, and in the case where the change rate of the collected signals is greater than or equal to the preset change rate threshold and no key state determination flag bit in the battery state flag bits is monitored, the medium-frequency sampling frequency of the battery management system (1) is adopted.

5. The method according to any one of the preceding claims, wherein When a status trigger flag bit in the acquired battery signal is detected, a battery state transition node is identified based on the change rate of the battery signal, where the battery state transition node includes a charge trigger node, a capacity reset node, and / or an open-circuit voltage inflection point; and / or When a charge trigger flag bit in the acquired signal is detected, a charge trigger node is identified based on the change rate of the battery signal within a time window with respect to the current sampling moment; and / or When an equalization correction flag bit in the acquired signal is detected, a capacity reset node is identified based on the change rate of the battery signal within a time window with respect to the current sampling moment; and / or When it is detected that the second derivative of the open-circuit voltage of the battery with respect to time is close to zero, the open-circuit voltage inflection point of the battery is identified.

6. The method according to any one of the preceding claims, wherein, Based on the change amount of each battery signal within the time window and the battery state transition node, the calculation method of the state of charge of the battery is adjusted. Wherein, when the capacity reset node is not identified, a low-precision capacity calculation method is used to calculate the state of charge of the battery, and when the capacity reset node is identified, a high-precision capacity calculation method is used to calculate the state of charge of the battery, and when it is detected that the change amount of the signal within the time window is greater than the change amount threshold assigned to the signal, the stored state of charge of the battery is corrected using the calculated state of charge of the battery.

7. The method according to any one of the preceding claims, wherein, Based on the response time constant of the battery management system (1), and / or the battery signal stabilization time, the calculation accuracy requirement and / or the signal sampling frequency, a sliding time window is set, and the maximum value and the minimum value of each acquired battery signal within each sliding time window are determined and analyzed to monitor the change amount of each acquired battery signal within each sliding time window; and / or When the deviation between the signal estimated value calculated based on the acquired battery signal and the acquired battery signal is greater than a preset threshold, a correction coefficient related to the battery signal is calculated based on the historical data of the collected battery signal, and the correction coefficient is introduced during the calculation of the battery signal.

8. A battery management system (1), the battery management system (1) comprising the following components: A signal sampling unit (11) configured to acquire battery signals; and A control unit (12) configured to execute the method according to any one of the above claims.

9. A vehicle (10), the vehicle (10) comprising the battery management system (1) according to claim 8.

10. A computer program product, such as a computer-readable program carrier, containing or storing computer program instructions, the computer program instructions when executed by a processor at least assist in implementing the steps of the method according to any one of claims 1 to 7.

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