A method and device for detecting lithium battery loss

Abstract

The present invention relates to a method and device for detecting the loss of a lithium battery, which obtains the basic information of the current lithium battery based on the lithium battery model, and obtains a first mapping table in which the SOH change is associated with the SOC change in different SOH intervals under the normal loss state of the lithium battery; obtains a second mapping table in which the abnormal power consumption data of the lithium battery is associated with the abnormal power consumption behavior based on the lithium battery model; S3. Real-time monitors the power consumption data of the lithium battery, and obtains the SOC change data and SOH change data of the current power consumption behavior based on the BMS system; after the lithium battery completes a power consumption behavior, it is judged whether the SOC change data and SOH change data within the current power consumption behavior are within the mapping range of the first mapping table. If not, the current abnormal power consumption data in the second mapping table is marked and recorded and sent to the user account. The present invention can trace and analyze the abnormal power consumption data of users, which is conducive to users developing healthy power consumption habits, thereby reducing the use cost of the whole vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and particularly to a method and device for detecting the loss of lithium batteries. Background Art

[0002] For new energy vehicles, the cost of their lithium batteries accounts for about 1 / 3 of the total vehicle cost. Depending on the type of lithium battery, the complete charge-discharge cycle times are also different. Under theoretical conditions, lithium iron phosphate batteries generally can reach 3500 times or even 5000 times of charge-discharge cycles, and ternary lithium batteries usually can reach about 2500 times of charge-discharge cycles. However, this is a rough reference value obtained based on ideal conditions through a large number of laboratory tests and long-term data accumulation. In actual use, the impact of a complete charge-discharge of a new energy vehicle lithium battery on SOH is restricted by various factors, usually about 0.05% - 1%. The restricted factors include charging methods, ambient temperature during use, charge-discharge rate, driving habits, and the effectiveness of the battery management system, etc. For example, deep charge-discharge (charging from 0% to 100% or discharging from 100% to 0%) has a greater impact on battery life. If the discharge depth is controlled within 50%, the cycle life will increase significantly. In addition, high or low temperature environments, frequent fast charging, intense driving, etc. will all accelerate battery loss and reduce the charge-discharge cycle times of the battery.

[0003] Currently, although users can understand the health status of the battery in real time through the BMS system, when users' abnormal power consumption behaviors lead to abnormal battery loss, it is impossible to trace the source, which is not conducive to users developing healthy power consumption habits and further affects the use cost of the entire vehicle. Summary of the Invention

[0004] Glossary of Terms:

[0005] SOH (State of Health) characterizes the health status of a lithium battery and is used to evaluate the health degree of the battery relative to the brand-new state;

[0006] SOC (State of Charge) is the state of charge, which is an important indicator used to measure the remaining power of a lithium battery and is usually expressed as a percentage;

[0007] BMS (Battery Management System) is a battery management system, which is a crucial core component in battery systems such as lithium batteries and is mainly used to monitor, manage, and protect the battery pack.

[0008] In order to solve the above problems of the prior art, the present invention provides a method and device for detecting the charge and discharge of a lithium battery, which can trace the source when users' abnormal power consumption behaviors exceed normal battery loss, and is conducive to users developing healthy power consumption habits.

[0009] To achieve the above object, in a first aspect, the technical solution adopted by the present invention is: a method for detecting the loss of a lithium battery, including:

[0010] S1. Obtain the basic information of the current lithium battery based on the lithium battery model, and obtain a first mapping table that associates the SOH change with the SOC change within different SOH intervals under the normal loss state of the lithium battery;

[0011] S2. Obtain a second mapping table that associates the abnormal power consumption data of the lithium battery with the abnormal power consumption behavior based on the lithium battery model;

[0012] S3. Real-time monitor the power consumption data of the lithium battery, and obtain the SOC change data and SOH change data of the current power consumption behavior based on the BMS system. One power consumption behavior is defined as the power consumption behavior between the end of the previous charge and the end of the current charge;

[0013] After the lithium battery completes one power consumption behavior, determine whether the SOC change data and SOH change data within the current power consumption behavior are within the mapping range of the first mapping table. If not, mark and record the current abnormal power consumption data in the second mapping table, record the current abnormal power consumption data and its corresponding abnormal power consumption behavior in the database based on the second mapping table, and send them to the user account.

[0014] The beneficial effect of the present invention is that when the relationship between the SOC change and the SOH change of the lithium battery exceeds the predetermined association relationship of the first mapping table during the user's vehicle use, the abnormal power consumption data of the user will be traced, and the abnormal power consumption behavior associated with the abnormal power consumption data will be analyzed based on the second mapping table and sent to the user, which is beneficial for the user to develop a healthy power consumption habit and thus reduce the use cost of the whole vehicle.

[0015] Optionally, before S1, it includes: calculating the complete charge and discharge cycle times of the lithium battery in different SOH intervals under the same test environment based on a machine learning algorithm, and obtaining the ratio of the complete charge and discharge cycle times of each SOH interval;

[0016] After S3, it further includes: calculating the complete charge and discharge cycle times consumed in the SOH interval where the current abnormal power consumption behavior of the lithium battery is located based on the SOC change data and SOH change data in the SOH interval where the current abnormal power consumption behavior of the lithium battery is located; and estimating and counting the available complete charge and discharge cycle times in the current SOH interval and subsequent SOH intervals based on the SOH data of the lithium battery and the ratio of the complete charge and discharge cycle times of each SOH interval of the lithium battery of this model.

[0017] As can be seen from the above description, based on the power consumption data of the current abnormal power consumption behavior, the number of available complete charge and discharge cycles in the future can be estimated, enabling the user to intuitively understand the consequences brought about by the current abnormal power consumption behavior. Moreover, since the number of complete charge and discharge cycles of lithium batteries varies in different SOH intervals, considering the influence of this factor, the impact brought about by the current abnormal power consumption behavior can be estimated based on the ratio of the number of complete charge and discharge cycles in each SOH interval, ensuring the accuracy of the calculation of the subsequent available complete charge and discharge cycles.

[0018] Optionally, the basic information of the lithium battery includes: battery category and model, capacity, voltage, energy density, power density, cycle life, internal resistance, self-discharge rate, operating temperature range, size and shape, safety parameters.

[0019] Optionally, the abnormal power consumption data includes: abnormal power consumption interval data, abnormal power consumption temperature data, abnormal power consumption current data, abnormal power consumption voltage data.

[0020] Optionally, the machine learning algorithms include: decision tree algorithm, random forest algorithm, support vector machine algorithm, and neural network algorithm.

[0021] As can be seen from the above description, the user can select a suitable machine learning algorithm according to actual needs to calculate the number of complete charge and discharge cycles of the lithium battery in different SOH intervals.

[0022] To achieve the above object, in a second aspect, the technical solution adopted by the present invention is: a detection device for lithium battery loss, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps in the above detection method for lithium battery loss are implemented.

[0023] Among them, for the technical effects corresponding to a detection device for lithium battery loss provided in the second aspect, refer to the relevant descriptions of a detection method for lithium battery loss provided in the first aspect. Description of the Drawings

[0024] Figure 1 It is a flowchart of a detection method for lithium battery loss in Embodiment 1 of the present invention;

[0025] Figure 2 It is a schematic diagram of the second mapping table in Embodiment 1 of the present invention;

[0026] Figure 3 It is a schematic structural diagram of a detection device for lithium battery loss in Embodiment 2 of the present invention;

[0027] Description of the Reference Numerals

[0028] 1. A detection device for lithium battery loss; 2. A memory; 3. A processor. Detailed implementation mode

[0029] To better explain the present invention for easy understanding, the present invention will be described in detail below in conjunction with the accompanying drawings through specific implementation modes. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0030] Embodiment 1

[0031] Please refer to Figure 1 and Figure 2 As shown, a detection method for lithium battery loss includes:

[0032] S0. Based on machine learning algorithms, for lithium batteries of different models in the same test environment, which is an ideal test environment, calculate the complete charge and discharge cycle times of lithium batteries in different SOH intervals, and obtain the ratio of the complete charge and discharge cycle times of each SOH interval.

[0033] Among them, the machine learning algorithms include: decision tree algorithm, random forest algorithm, support vector machine algorithm, and neural network algorithm.

[0034] S1. Obtain the basic information of the current lithium battery based on the lithium battery model, and obtain the first mapping table of the association between SOH change and SOC change in different SOH intervals under the normal loss state of the lithium battery; the basic information of the lithium battery includes: battery category and model, capacity, voltage, energy density, power density, cycle life, internal resistance, self-discharge rate, working temperature range, size and shape, safety parameters.

[0035] Among them, for how to define the normal loss state of a lithium battery, different models of batteries have different definition criteria. In real scenarios, the battery is affected by various factors such as temperature, charge and discharge rate, and usage habits. Therefore, the impact of SOC change on SOH is restricted by multiple factors, and the SOC change in different SOH intervals also affects the SOH change. When determining the first mapping table, by collecting the SOC change and SOH change data in different SOH intervals during the normal vehicle use process of a large number of batteries of the same model under normal use scenarios, the SOC floating interval per unit SOH change in different SOH intervals is obtained, and then the correlation between SOH change and SOC change in different SOH intervals under the normal loss state of this model of lithium battery is determined. Machine learning algorithms can be used to train these data, and combined with denoising algorithms (such as principal component analysis algorithm, wavelet transform algorithm, etc.) to improve the training accuracy to obtain this first mapping table;

[0036] Among them, the battery states in different SOH intervals are different. The effective usage interval of the SOH of a lithium battery is 80% to 100%. Taking a lithium iron phosphate battery as an example, if the SOH is divided into four intervals: 100% to 95%, 95% to 90%, 95 to 85, and 85 to 80%. In the SOH interval of 100% - 95%, the battery is in a brand-new or nearly brand-new state at this stage. The internal chemical substances are highly active, the structure of the electrode material is complete, the migration of lithium ions is smooth, and the internal resistance of the battery is small. Under ideal usage conditions, for every 1% decrease in SOH in this interval, it may require about 50 - 80 complete charge and discharge cycles. From 100% to 95%, about 250 - 400 complete charge and discharge cycles can be carried out.

[0037] In the SOH interval of 95% - 90%, the battery begins to show a certain degree of aging, but the overall performance is still good. There are slight structural changes and active material shedding in the electrode material, and the internal resistance increases slightly. For every 1% decrease in SOH, it may require about 40 - 60 complete charge and discharge cycles. From 95% to 90%, about 200 - 300 complete charge and discharge cycles can be carried out.

[0038] In the SOH interval of 90% - 85%: The battery aging intensifies. More active material sheds from the electrode material, the structural change is obvious, the internal resistance further increases, the chemical reaction inside the battery becomes more complex, and the side reactions increase. For every 1% decrease in SOH, it may require about 30 - 50 complete charge and discharge cycles. Therefore, from 90% to 85%, about 150 - 250 complete charge and discharge cycles can be carried out.

[0039] In the SOH range of 85% to 80%: The degree of battery aging is relatively deep, the performance of the electrode material and electrolyte declines significantly, the internal resistance is large, the lithium-ion migration ability decreases, and the overall performance and charge-discharge efficiency of the battery are significantly reduced. For every 1% decrease in SOH, only about 20 - 40 complete charge-discharge cycles may be required. From 85% to 80%, about 100 - 200 complete charge-discharge cycles can be performed.

[0040] Of course, the above range division can be made according to the actual working conditions. The more the number of divided ranges, the higher the accuracy. The above range division is only for illustrative purposes.

[0041] S2. Obtain a second mapping table that associates the abnormal power consumption data of the lithium battery with abnormal power consumption behaviors based on the lithium battery model. The second mapping table can be referred to Figure 2 as shown;

[0042] Among them, the abnormal power consumption data includes: abnormal power consumption interval data, abnormal power consumption temperature data, abnormal power consumption current data, and abnormal power consumption voltage data;

[0043] Among them, the abnormal power consumption interval data is usually recorded when the power consumption interval exceeds the threshold (generally set to 7 to 15 days according to different battery types), indicating that the vehicle has been in an idle state for a long time, resulting in self-discharge loss. If the self-discharge of the lithium battery stored for a long time is serious, it may cause the battery voltage to be too low, affecting its performance and life. For example, the abnormal power consumption interval data is: the power consumption interval is one month, then the corresponding abnormal power consumption behavior: the idle time is one month, and there is a long-term idle behavior of the vehicle. In addition, the abnormal power consumption temperature is used to indicate that the temperature is too high or too low during the discharge or charge process of the lithium battery. The normal charging temperature range of lithium iron phosphate batteries is generally 0°C to 45°C, and the discharge temperature range is -20°C to 60°C. Beyond this range, it indicates that the data is abnormal power consumption temperature data. For example, the power consumption temperature data is: the discharge temperature is 65°C, then the corresponding abnormal power consumption behavior: the discharge temperature is too high, and there may be situations such as driving in a high-temperature environment or speeding; and there are two situations for the abnormal power consumption current data, namely abnormal discharge current and abnormal charging current. For example, if there is a large current discharge in the current discharge data of the lithium battery (the large current discharge is 200A to 500A and the duration needs to exceed 1 minute, indicating that there are phenomena such as continuous rapid acceleration, high speed, speeding or heavy load driving), or if there is overcurrent charging, unstable charging current, small current charging or reverse (backflow) charging in the current charging data of the lithium battery, then its abnormal power consumption data will be recorded; and the abnormal power consumption voltage data includes abnormal discharge voltage data and abnormal charging voltage data. The abnormal discharge voltage data includes over-discharge voltage data and battery pack voltage imbalance data. The abnormal charging voltage data includes overcharge voltage data, unstable charging voltage data and under-voltage charging data.

[0044] Of course, the abnormal power consumption data is not limited to the above data, and may also include abnormal charge and discharge depth data, abnormal internal resistance data, etc., which can be supplemented or modified according to actual needs;

[0045] S3. Real-time monitor the power consumption data of the lithium battery, and obtain the SOC change data and SOH change data of the current power consumption behavior based on the BMS system. One power consumption behavior is defined as the power consumption behavior between the end of the previous charge and the end of the current charge;

[0046] After the lithium battery completes one power consumption behavior, determine whether the SOC change data and SOH change data within the current power consumption behavior are within the mapping range of the first mapping table. If not, mark and record the current abnormal power consumption data in the second mapping table, record the current abnormal power consumption data and its corresponding abnormal power consumption behavior in the database based on the second mapping table, and send them to the user account.

[0047] S4. Based on the SOC change data and SOH change data of the SOH interval where the current abnormal power consumption behavior of the lithium battery is located, calculate the number of complete charge and discharge cycles consumed in the SOH interval where the current abnormal power consumption behavior is located; and based on the SOH data of the lithium battery and the proportion of the number of complete charge and discharge cycles in each SOH interval of the lithium battery of this model, estimate and count the available number of complete charge and discharge cycles in the current SOH interval and subsequent SOH intervals.

[0048] Embodiment 2

[0049] Please refer to Figure 3 As shown, a detection device 1 for lithium battery loss includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in Embodiment 1 are implemented.

[0050] Since the system / device described in the above embodiments of the present invention is the system / device adopted for implementing the method in the above embodiments of the present invention, based on the method described in the above embodiments of the present invention, those skilled in the art can understand the specific structure and variations of the system / device, and thus will not be elaborated here. Any system / device adopted by the method in the above embodiments of the present invention falls within the scope of protection of the present invention.

[0051] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, apparatuses, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] It should be noted that in the description of this specification, the description of terms such as "one embodiment", "some embodiments", "embodiment", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0053] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the claims should be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0054] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. A method for detecting the loss of a lithium battery, characterized in that, Including: S1. Obtain the basic information of the current lithium battery based on the lithium battery model, and obtain the first mapping table associating the SOH change with the SOC change within different SOH intervals under the normal loss state of the lithium battery; S2. Obtain the second mapping table associating the abnormal power consumption data of the lithium battery with the abnormal power consumption behavior based on the lithium battery model; S3. Monitor the power consumption data of the lithium battery in real time, and obtain the SOC change data and SOH change data of the current power consumption behavior based on the BMS system. One power consumption behavior is defined as the power consumption behavior between the end of the previous charge and the end of the current charge; After the lithium battery completes one power consumption behavior, determine whether the SOC change data and SOH change data within the current power consumption behavior are within the mapping range of the first mapping table. If not, mark and record the current abnormal power consumption data in the second mapping table, record the current abnormal power consumption data and its corresponding abnormal power consumption behavior in the database based on the second mapping table, and send them to the user account.

2. The detection method of lithium battery loss according to claim 1, characterized in that, Before S1, it includes: calculating the complete charge and discharge cycle times of the lithium battery within different SOH intervals under the same test environment based on the machine learning algorithm for different models of lithium batteries, and obtaining the ratio of the complete charge and discharge cycle times of each SOH interval; After S3, it further includes: calculating the complete charge and discharge cycle times consumed in the SOH interval where the current abnormal power consumption behavior of the lithium battery is located based on the SOC change data and SOH change data in the SOH interval where the current abnormal power consumption behavior of the lithium battery is located; and estimating and counting the available complete charge and discharge cycle times in the current SOH interval and subsequent SOH intervals based on the SOH data of the lithium battery and the ratio of the complete charge and discharge cycle times of each SOH interval of the lithium battery of this model.

3. The detection method of lithium battery loss according to claim 1, characterized in that The basic information of the lithium battery includes: battery category and model, capacity, voltage, energy density, power density, cycle life, internal resistance, self-discharge rate, operating temperature range, size and shape, safety parameters.

4. The detection method of lithium battery loss according to claim 1, characterized in that, The abnormal power consumption data includes: abnormal power consumption interval data, abnormal power consumption temperature data, abnormal power consumption current data, abnormal power consumption voltage data.

5. The detection method of lithium battery loss according to claim 2, wherein, The machine learning algorithm includes: decision tree algorithm, random forest algorithm, support vector machine algorithm, and neural network algorithm.

6. A detection device for lithium battery loss, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the following steps are implemented: S1. Obtain the basic information of the current lithium battery based on the lithium battery model, and obtain the first mapping table associating the SOH change with the SOC change within different SOH intervals under the normal loss state of the lithium battery; S2. Obtain the second mapping table associating the abnormal power consumption data of the lithium battery with the abnormal power consumption behavior based on the lithium battery model; S3. Monitor the power consumption data of the lithium battery in real time, and obtain the SOC change data and SOH change data of the current power consumption behavior based on the BMS system. One power consumption behavior is defined as the power consumption behavior between the end of the previous charge and the end of the current charge; After the lithium battery completes a power consumption behavior, it is judged whether the SOC change data and SOH change data within the current power consumption behavior are within the mapping range of the first mapping table. If not, the current abnormal power consumption data within the second mapping table is marked and recorded, and the current abnormal power consumption data and its corresponding abnormal power consumption behavior are recorded in the database based on the second mapping table and sent to the user account.

7. The detection device for lithium battery loss according to claim 6, wherein Before the S1, it includes: calculating the complete charge and discharge cycle times of the lithium battery in different SOH intervals under the same test environment based on a machine learning algorithm, and obtaining the proportion of the complete charge and discharge cycle times of each SOH interval. After the S3, it further includes: calculating the complete charge and discharge cycle times consumed in the SOH interval where the current abnormal power consumption behavior of the lithium battery is located based on the SOC change data and SOH change data in the SOH interval where the current abnormal power consumption behavior is located; and estimating and counting the available complete charge and discharge cycle times in the current SOH interval and subsequent SOH intervals based on the SOH data of the lithium battery and the proportion of the complete charge and discharge cycle times of each SOH interval of the lithium battery of this model.

8. The detection device for lithium battery loss according to claim 6, characterized in that, The basic information of the lithium battery includes: battery category and model, capacity, voltage, energy density, power density, cycle life, internal resistance, self-discharge rate, operating temperature range, size and shape, safety parameters.

9. The detection device for lithium battery loss according to claim 6, wherein The abnormal power consumption data includes: abnormal power consumption interval data, abnormal power consumption temperature data, abnormal power consumption current data, abnormal power consumption voltage data.

10. The detection device for lithium battery loss according to claim 7, wherein, The machine learning algorithm includes: decision tree algorithm, random forest algorithm, support vector machine algorithm, and neural network algorithm.

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