Systems and methods for managing battery data

Abstract

A system and method for managing battery data are provided. The objective is to propose a mechanism to improve the efficiency and reliability of data management in a wireless battery management system (wBMS) while effectively compressing the large amounts of data generated from battery modules, optimizing storage space, allowing distributed storage, and ensuring the integrity of the compressed data. To this end, a configuration is provided that compresses battery data measured from each module of a battery pack, generates a chain hash for the compressed data of each module to ensure the integrity of the battery data, and stores the compressed data of each module in memory in a distributed manner through chain hash-based data structuring.

Description

Technical Field

[0001] This disclosure relates to systems and methods for managing battery data. Background Technology

[0002] With the growing global demand for sustainable transportation, the electric vehicle (EV) industry is experiencing rapid growth, placing a strong emphasis on the importance of high-performance battery technology. Lithium-ion batteries, a key component of EVs, have seen significant improvements in energy density, lifespan, and safety through continuous technological innovation. However, lithium-ion batteries remain complex systems requiring effective management and monitoring. Battery management systems (BMS) play a crucial role in optimizing the performance and extending the lifespan of these expensive battery packs. In recent years, with the development of wireless communication technology, wireless battery management systems (wBMS) have gained increasing attention, offering advantages such as reduced manufacturing costs and improved reliability by minimizing complex wiring within the battery pack.

[0003] The growing concern about the environmental impact of EV batteries throughout their entire lifecycle (from production to recycling) has led to new regulations, such as the EU's Battery Passport scheme. These require detailed information on the battery's carbon footprint, raw material sources, recyclability, and other aspects of production, use, and recycling, making the systematic collection, storage, and management of battery data increasingly important. Furthermore, advancements in battery technology are emphasizing the modularity and scalability of battery packs, further increasing the need for flexible and efficient Battery Management Systems (BMS). Consequently, advanced wBMS technologies, capable of efficiently compressing and securely storing data, are becoming a key factor in determining the future competitiveness of the EV industry and are attracting the attention of automakers and battery companies.

[0004] The information disclosed herein in this Background section is intended to enhance understanding of the background of this disclosure, and therefore may contain information that does not constitute related (or prior art). Summary of the Invention

[0005] This disclosure relates to a system and method for managing battery data, which improves the data management efficiency and reliability of a wireless battery management system (wBMS), while effectively compressing large amounts of data generated from battery modules, optimizing storage space, allowing distributed storage, and also ensuring the integrity of the compressed data.

[0006] However, the purposes intended to be achieved by this disclosure are not limited to those described herein, and other purposes not described will be readily apparent to those skilled in the art from the following description.

[0007] According to one aspect of this disclosure, a system for managing battery data is provided, the system comprising: a memory; and a processor configured to compress battery data measured from each module of a battery pack, generate a chain hash for the compressed data of each module to ensure the integrity of the battery data, and store the compressed data of each module in the memory in a distributed manner through chain hash-based data structuring.

[0008] Battery data may include at least one of the following for each module: voltage, temperature, and current.

[0009] The processor can use run-length encoding to compress consecutive identical values ​​of battery data in the form of (count, value).

[0010] The processor can use Adaptive Hybrid Coding (AHE) to analyze the variability of battery data, dynamically set thresholds, and use the set thresholds to compress the battery data. Adaptive Hybrid Coding is a combination of delta coding and run-length coding.

[0011] The processor can compress consecutive identical values ​​of battery data using run-length encoding based on the difference between the current value and the previous value, and compress changed values ​​of battery data using delta encoding.

[0012] The processor can convert compressed data into a string, add the previous hash value to the string to generate a string that combines with the previous hash value, and apply the Secure Hash Algorithm (SHA)-256 to the generated string that combines with the previous hash value to generate a hash calculation result as a chain hash.

[0013] The processor can set the previous hash value of the first module in the battery pack to a preset initial value.

[0014] The processor can construct chain-hash-based data from the compressed data of each module using a JavaScript Object Notation (JSON) data schema, based on the battery passport requirements.

[0015] The processor can use machine learning-based anomaly detection algorithms to detect data that deviates from normal patterns in battery data in real time and output warning signals.

[0016] According to another aspect of this disclosure, a method for managing battery data is provided, the method comprising the steps of: compressing battery data measured from each module of a battery pack by a processor; generating a chain hash for the compressed data of each module by the processor to ensure the integrity of the battery data; and storing the compressed data of each module in a distributed manner by the processor through chain hash-based data structuring. Attached Figure Description

[0017] The following accompanying drawings illustrate embodiments of the present disclosure, and together with the detailed description of the present disclosure, aspects and features of the present disclosure are further described. Therefore, the present disclosure should not be construed as limited to the drawings: Figure 1 This is a block diagram of a system for managing battery data according to embodiments of the present disclosure; Figure 2 This is a diagram illustrating in detail the battery data compression and distributed storage mechanism of a system for managing battery data according to embodiments of the present disclosure; Figure 3 This is a flowchart of a method for managing battery data according to an embodiment of the present disclosure; Figure 4 This is a flowchart illustrating the process of compressing data using an Adaptive Hybrid Coding (AHE) algorithm according to embodiments of the present disclosure; and Figure 5 This is a flowchart of the process of generating a chain hash according to an embodiment of the present disclosure. Detailed Implementation

[0018] In the following description, some embodiments of this disclosure will be described in detail with reference to the accompanying drawings. The terms or words used in this specification and claims should not be construed as limited to their conventional or dictionary meanings, but should be interpreted based on the principle that the inventor may be his / her own lexicographer to appropriately define the concepts of the terms so as to best interpret his / her disclosure in a way consistent with the technical concept of this disclosure.

[0019] The embodiments described in this specification and the constructions shown in the accompanying drawings are merely some of the embodiments of this disclosure and do not represent all technical concepts, aspects, and features of this disclosure. Therefore, it should be understood that various equivalents and modifications may exist to replace or modify the embodiments described herein at the time of filing this application.

[0020] It will be understood that when an element or layer is referred to as being "on," "connected to," or "bonded to" another element or layer, it can be directly on, directly connected to, or directly bonded to the other element or layer, or one or more intermediary elements or intermediary layers may exist. When an element or layer is referred to as being "directly on," "directly connected to," or "directly bonded to" another element or layer, no intermediary element or intermediary layer exists. For example, when a first element is described as being "bonded" or "connected" to a second element, the first element can be directly bonded to or connected to the second element, or the first element can be indirectly bonded to or connected to the second element via one or more intermediary elements.

[0021] In the figures, the dimensions of various elements, layers, etc., may be exaggerated for clarity. The same reference numerals denote the same elements. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. Furthermore, when describing embodiments of this disclosure, the use of "may" refers to "one or more embodiments of this disclosure." When expressions such as "at least one of..." and "any one of..." are placed after a list of elements, they modify the entire list of elements, not individual elements within that list. When a list of elements A, B, and C is specified using phrases such as "at least one of A, B, and C", "at least one of A, B, or C", "at least one selected from the group A, B, and C", or "at least one selected from A, B, and C", the phrase may refer to any suitable combination (or subset) of A, B, and C, and all suitable combinations (or subsets), such as A, B, C, A and B, A and C, B and C, or A and B and C. As used herein, the term "use" and its variations may be considered synonymous with the term "utilize" and its variations, respectively. As used herein, the terms "substantially," "about," and similar terms are used as approximate terms rather than terms of degree and are intended to explain the inherent biases of measurements or calculations that will be recognized by one of ordinary skill in the art.

[0022] It will be understood that although the terms “first,” “second,” “third,” etc., may be used herein to describe various elements, components, regions, layers, and / or portions, these elements, components, regions, layers, and / or portions should not be limited by these terms. These terms are used to distinguish one element, component, region, layer, or portion from another element, component, region, layer, or portion. Therefore, the first element, first component, first region, first layer, or first portion discussed herein may be referred to as a second element, second component, second region, second layer, or second portion without departing from the teachings of the exemplary embodiments.

[0023] For ease of description, spatial relative terms such as “below,” “under,” “down,” “above,” “above,” etc., are used herein to describe the relationship between one element or feature and another element(s) shown in the figures. It will be understood that, in addition to the orientation depicted in the figures, the spatial relative terms are intended to also cover different orientations of the device during use or operation. For example, if the device in the figures is flipped, an element described as “below” or “under” other elements or features will subsequently be oriented “above” or “above” said other elements or features. Thus, the term “below” can encompass both above and below orientations. The device may be otherwise oriented (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein should be interpreted accordingly.

[0024] The terminology used herein is for the purpose of describing embodiments of this disclosure and is not intended to limit the disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “an” are intended to include the plural forms as well. It will be further understood that when the terms “comprising,” “including,” and / or variations thereof are used in this specification, it indicates the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0025] Furthermore, any numerical range disclosed and / or described herein is intended to include all subranges with the same numerical precision contained within the described range. For example, the range “1.0 to 10.0” is intended to include all subranges between the described minimum value 1.0 and the described maximum value 10.0 (and including both the described minimum value 1.0 and the described maximum value 10.0) (i.e., a minimum value equal to or greater than 1.0 and a maximum value equal to or less than 10.0), such as 2.4 to 7.6. Any maximum numerical limit described herein is intended to include all lower numerical limits contained therein, and any minimum numerical limit described in this specification is intended to include all higher numerical limits contained therein. Therefore, in order to clearly describe any subranges contained within the range explicitly described herein, the applicant reserves the right to amend this specification (including the claims).

[0026] Referring to two compared elements, features, etc., as “identical” can mean that they are “substantially identical.” Therefore, the phrase “substantially identical” can include cases with deviations considered low in the art (e.g., 5% or less). Furthermore, when a parameter is said to be uniform in a given region, this can mean that it is uniform in terms of its mean.

[0027] Throughout the specification, unless otherwise stated, each element may be singular or plural.

[0028] When any element is described as being positioned (or located or placed) "above (or below)" or "on (or below)" a component, this can mean that the element is positioned to contact the upper (or lower) surface of the component, or it can mean that other components may be located between the component and any element positioned (or located or placed) on (or below) the component.

[0029] Furthermore, it will be understood that when an element is referred to as being "joined," "linked," or "connected" to another element, the elements can be directly "joined," "linked," or "connected" to each other, or there can be an intermediary element between them through which the element can be "joined," "linked," or "connected" to the other element. Additionally, when a part is referred to as being "electrically joined" to another part, the part can be directly connected to the other part, or there can be an intermediary part between them, such that the part and the other part are indirectly connected to each other.

[0030] Throughout this specification, when “A and / or B” is stated, unless otherwise stated, it means A, B, or A and B. That is, “and / or” includes any or all combinations of the listed items. When “C to D” is stated, unless otherwise stated, it means C or greater and D or less.

[0031] Before describing the embodiments, improvements and ultimate goals of this disclosure will be described.

[0032] As the electric vehicle (EV) industry develops, battery management systems (BMS) are constantly being improved, and several major trends have emerged.

[0033] First, there is an active push to introduce wireless BMS (wBMS) that utilizes wireless communication technology, with the goal of reducing complex wiring inside the battery pack to lower manufacturing costs and improve reliability.

[0034] Second, we are working to integrate artificial intelligence (AI) and machine learning technologies into the BMS to improve the accuracy of battery state prediction and develop optimal charge and discharge strategies.

[0035] Third, research is underway to improve the overall performance and lifespan of battery packs through improvements in cell balancing technology, which is evolving towards more precise management of the state of individual cells.

[0036] Fourth, to enhance battery safety, thermal management systems are being advanced, and technologies for detecting anomalies in their early stages are being actively developed.

[0037] Fifth, we will try to introduce encryption and blockchain technologies to enhance the security of battery data, with a focus on ensuring the integrity and reliability of the data.

[0038] Sixth, in order to increase the modularity and scalability of battery packs, plug-and-play BMS designs are being researched, with the goal of flexibly responding to systems with various battery configurations.

[0039] Seventh, in order to maximize energy efficiency, low-power design techniques are being developed to minimize the power consumption of the BMS itself.

[0040] Finally, in response to new regulations, such as the EU's Battery Passport program, systems are being developed to effectively collect, store, and manage data throughout the battery's lifecycle. These various improvement efforts aim to enhance vehicle performance, safety, and affordability, with the ultimate goal of providing sustainable mobility solutions.

[0041] Figure 1 This is a block diagram of a system for managing battery data according to embodiments of the present disclosure, and Figure 2 This is a diagram illustrating in detail the battery data compression and distributed storage mechanism of a system for managing battery data according to embodiments of the present disclosure.

[0042] Reference Figure 1 and Figure 2 The system 100 for managing battery data according to an embodiment may include a memory 110 and a processor 120, and in an embodiment, each of components 110 and 120 may form a battery pack together with the battery managed by the wBMS.

[0043] Memory 110 may store at least one instruction executed by processor 120, which will be described herein. Memory 110 may be implemented as a volatile storage medium and / or a non-volatile storage medium. For example, memory 110 may be implemented as read-only memory (ROM) and / or random access memory (RAM).

[0044] In addition, the memory 110 can store data compressed by the processor 120 for each module. For example, the memory 110 can store battery data such as battery voltage, battery temperature, and battery current.

[0045] Processor 120 is the entity that manages battery data and can be implemented as a central processing unit (CPU) or a system-on-a-chip (SoC). Processor 120 can run an operating system (OS) or applications to control software components or multiple hardware components connected to processor 120, and can process various data and perform various calculations. Processor 120 can be configured to execute at least one instruction stored in memory 110 and store the execution result data in memory 110. Simultaneously, processor 120 can be implemented as a battery management system (BMS) or a microcontroller unit (MCU) within a BMS, and, as is known, can be configured to detect the current, voltage, open-circuit voltage (OCV), and temperature of individual battery cells.

[0046] The operation of processor 120 in managing battery data will be described in detail here.

[0047] The processor 120 can measure battery data for each module in the battery pack, such as voltage, temperature, and current. For example, in the case of a battery pack including three modules, the processor 120 can measure battery data for ten time units. Here, the first module can have a voltage of [3.7, 3.7, 3.8, 3.8, 3.8, 3.9, 3.9, 3.9, 3.8, 3.8], a temperature of [25.0, 25.5, 26.0, 26.0, 26.5, 27.0, 27.0, 27.5, 27.5, 27.0], and a current of [2.1, 2.1, 2.0, 2.0, 2.2, 2.2, 2.1, 2.1, 2.0, 2.0].

[0048] Processor 120 can compress the measured battery data of each module in the battery pack. To do this, processor 120 can use run-length encoding to compress consecutive identical values ​​of the battery data in the form of (count, value). In other words, processor 120 can perform compression by the following steps: setting a first value for each measurement item (voltage, temperature, current) of the battery data as a reference value; counting the number of consecutive identical values ​​and storing the battery data in the form of (count, value); and starting a new (count, value) pair when the value changes.

[0049] For example, processor 120 can compress the aforementioned voltage data of the first module [3.7, 3.7, 3.8, 3.8, 3.8, 3.9, 3.9, 3.9, 3.8, 3.8] into [(2, 3.7), (3, 3.8), (3, 3.9), (2, 3.8)]. Furthermore, processor 120 can compress the temperature data of the first module [25.0, 25.5, 26.0, 26.0, 26.5, 27.0, 27.0, 27.5, 27.5, 27.0] into [(1, 25.0), (1, 25.5), (2, 26.0), (1, 26.5), (2, 27.0), (2, 27.5), (1, 27.0)]. In addition, the processor 120 can compress the current data of the first module [2.1, 2.1, 2.0, 2.0, 2.2, 2.2, 2.1, 2.1, 2.0, 2.0] into [(2, 2.1), (2, 2.0), (2, 2.2), (2, 2.1), (2, 2.0)].

[0050] In an embodiment, processor 120 may use adaptive hybrid coding (AHE), which is a combination of delta (incremental) coding and run-length coding, to analyze the variability of battery data, dynamically set thresholds, and use the set thresholds to compress the battery data. For example, processor 120 may use run-length coding to compress consecutive identical values ​​of battery data based on the difference between the current value and the previous value, and use delta coding to compress changing values ​​of battery data.

[0051] The AHE algorithm will be described in detail here. According to the AHE algorithm, firstly (the first operation: dynamic threshold calculation), the variability of the data is analyzed to calculate the dynamic threshold (equation: δ = α × α(ΔX)). Here, δ is the dynamic threshold, α is an adjustment factor (which is typically 0.5), and α(ΔX) is the standard deviation of the difference between consecutive data points. For example, when the original data is [3.7, 3.72, 3.69, 3.71, 3.7, 3.7, 3.68, 3.75, 3.8, 3.79], the AHE algorithm can perform the calculation using this equation, such as ΔX = [0.02, -0.03, 0.02, -0.01, 0, -0.02, 0.07, 0.05, -0.01], α(ΔX) ≈ 0.0308, and δ = 0.5 × 0.0308 ≈ 0.0154.

[0052] Subsequently (second operation: encoding initialization), according to the AHE algorithm, an initial value can be set to begin encoding. In other words, according to the AHE algorithm, the initial value can be set to, for example, the previous value ( pre_value ) = First data point, count ( count ) = 1, and the encoding result ( encoded = []. For example, according to the AHE algorithm, the initial value can be set as follows: pre_value =3.7, count =1, and encoded =[].

[0053] Subsequently (the third operation: data looping and encoding), according to the AHE algorithm, the data is looped to calculate the difference (delta Δ) between the current value and the previous value, and the data can be encoded based on this difference. In other words, according to the AHE algorithm, when the absolute value of Δ is δ or less, count Increase. Otherwise, in ( count , prev_value () and (1, Δ) are added encoded after, prev_value It can be updated, and count It can be initialized.

[0054] For example, according to the AHE algorithm, when the current value is 3.72 and Δ is 0.02, |0.02|≤0.0154 does not hold true, therefore encoded =[(1, 3.7), (1, 0.02)], prev_value =3.72, and count =1. Furthermore, according to the AHE algorithm, when the current value is 3.69 and Δ is -0.03, encoded =[(1, 3.7), (1, 0.02), (1, -0.03)], prev_ value =3.69, and count =1. Furthermore, according to the AHE algorithm, when the current value is 3.71 and Δ is 0.02, |0.02|≤0.0154 does not hold true, therefore... encoded =[(1, 3.7), (1, 0.02), (1, -0.03), (1, 0.02)], prev_ value =3.71, and count =1.

[0055] Then (fourth operation: final encoding complete), according to the AHE algorithm, all data is looped through, and then the last group can be added to the encoded result. In other words, according to the AHE algorithm, ( count , prev_value (1, Δ) and (1, Δ) can be added to the encoding. Therefore, the AHE algorithm can output... encoded =[(1, 3.7), (1, 0.02), (1, -0.03), (1, 0.02),(2, 3.7), (1, -0.02), (1, 0.07), (1, 0.05), (1, -0.01)] as the final encoding result.

[0056] Subsequently (the fifth operation: decoding), according to the AHE algorithm, the encoded data can be restored to its original form. This step can be performed as needed. In other words, according to the AHE algorithm, the result array can be initialized ( decoded =[]), then you can loop. encoded An array. In other words, according to the AHE algorithm, when processing ( count , value After that, when value When it is a real number, it can be... value Add to decoded The number of times and count The same amount, otherwise (previous value + value Add to decodedTherefore, the AHE algorithm can output [3.7, 3.72, 3.69, 3.71, 3.7, 3.7, 3.68, 3.75, 3.8, 3.79] as the decoding result.

[0057] Meanwhile, the processor 120 can generate a chain hash for the compressed data of each module of the battery pack to ensure the integrity of the battery data, and can distribute the compressed data of each module of the battery pack in the memory 110 in a distributed manner through data structuring based on the generated chain hash.

[0058] Processor 120 can convert compressed data into a string and add the previous hash value to the string to generate a string combined with the previous hash value. Here, processor 120 can apply the Secure Hash Algorithm (SHA)-256 to the generated string combined with the previous hash value to generate a hash calculation result as a chain hash.

[0059] The processor 120 can set the previous hash value of the first module in the battery pack to a preset initial value. For example, the processor 120 can set the previous hash value of the first module in the battery pack to a "genesis hash" (e.g., "0" x 64).

[0060] As described herein, processor 120 generates chained hashes to ensure the integrity of compressed data, thereby providing an optimal balance between compression efficiency and data reliability.

[0061] Processor 120 can structure the compressed data of each module in the battery pack into chain-hash-based data using a JavaScript Object Notation (JSON) data schema, according to battery passport requirements. In this way, processor 120 can efficiently extract data and generate reports to comply with regulations and effectively manage the results of compression and hashing to prepare the foundation for distributed storage. Processor 120 can maximize the scalability, reliability, and performance of system 100 through distributed storage.

[0062] Processor 120 can use a machine learning-based anomaly detection algorithm (Isolation Forest) to detect data deviating from normal patterns in battery data in real time and output a warning signal. According to an embodiment, processor 120 can learn normal patterns from data from each module over the past month and calculate anomaly scores for the real-time data stream. When the calculated anomaly score exceeds a threshold, processor 120 can send a warning signal to the central system.

[0063] According to the embodiments, a run-length encoding-based compression algorithm is used to efficiently compress and store battery data, which improves data storage efficiency. Furthermore, a chain-hash-based distributed storage method reduces the risk of data tampering, enhancing data integrity and security. Additionally, when the battery pack configuration changes, module-specific independent data management facilitates battery pack reconfiguration, providing system flexibility and scalability. Moreover, according to the embodiments, energy efficiency can be maximized through optimized compression algorithms and distributed processing, and regulatory responsiveness can be improved using data structures optimized for battery passport requirements. Furthermore, distributed processing shortens data collection and analysis cycles, allowing for real-time monitoring and response, and minimizing data loss when replacing individual modules, facilitating maintenance.

[0064] As described herein, according to this disclosure, more historical battery data can be stored in the same storage space, which improves the accuracy of long-term performance analysis and prediction, and improves data reliability, which in turn improves the accuracy of battery performance and lifespan prediction. According to this disclosure, various battery configurations can be easily applied, which can shorten product development cycles and reduce BMS power consumption, thereby increasing the overall energy efficiency of the EV. According to this disclosure, the time spent processing data to comply with regulations and the report generation time can be reduced, and the rate of detection and response to abnormal battery states increases, which can improve safety. According to this disclosure, maintenance time is shortened, thus improving vehicle utilization rate (VUR).

[0065] Figure 3 This is a flowchart of a method for managing battery data according to embodiments of the present disclosure. (Refer to...) Figure 3 This disclosure describes data compression and distributed storage mechanisms as a method for managing battery data according to embodiments of the present disclosure. Detailed descriptions of the elements already described herein will be omitted, and their time-series configuration will be described primarily.

[0066] First, refer to Figure 1 and Figure 3 In operation 310, processor 120 can compress battery data measured from each module of the battery pack. This will be referenced here. Figure 4 Describe it.

[0067] Subsequently, in operation 320, processor 120 can generate chained hashes for the compressed data of each module in the battery pack to ensure the integrity of the battery data. This will be referenced here. Figure 5 Describe it.

[0068] Subsequently, in operation 330, processor 120 can store compressed data of each module of the battery pack in a distributed manner through chain hash-based data structuring.

[0069] Figure 4 This is a flowchart of a process for compressing data using the AHE algorithm according to an embodiment of the present disclosure.

[0070] Reference Figure 1 and Figure 4 In operation 410, processor 120 can analyze the variability of battery data to calculate dynamic thresholds.

[0071] Subsequently, in operation 420, the processor 120 can set initial values ​​to begin encoding.

[0072] Subsequently, in operation 430, processor 120 may cycle through the data to calculate the difference (delta Δ) between the current value and the previous value, and encode the data based on the difference.

[0073] Subsequently, in operation 440, after looping through all the data, processor 120 can add the last group to the encoded result.

[0074] Subsequently, in operation 450, processor 120 can restore the encoded data to its original form. Operation 450 can be performed as needed.

[0075] Figure 5 This is a flowchart of the process of generating a chain hash according to an embodiment of the present disclosure.

[0076] Reference Figure 1 and Figure 5 In operation 510, processor 120 can convert compressed data into a string.

[0077] Subsequently, in operation 520, processor 120 may add the previous hash value to the string to generate a string that combines the previous hash value.

[0078] Here, the processor 120 can set the previous hash value of the first module in the battery pack to a preset initial value. For example, the processor 120 can set the previous hash value of the first module in the battery pack to a "genesis hash" (e.g., "0"). 64).

[0079] Subsequently, in operation 530, processor 120 can apply SHA-256 to the generated string combined with the hash value to generate a hash calculation result as a chain hash.

[0080] The embodiments described herein can be implemented as, for example, methods, processes, apparatus, software programs, data streams, and signals. Even when discussed in the context of a single form of implementation (e.g., discussed only as a method), the features in question can be implemented in another form (e.g., apparatus or program). Apparatus can be implemented in suitable forms, such as hardware, software, or firmware. Methods can be implemented in apparatuses such as processors (generally referred to as processing devices, including, for example, computers, microprocessors, integrated circuits, programmable logic devices, etc.). Processors may also include communication devices that facilitate information communication between end users, such as computers, cellular phones, personal digital assistants (PDAs), and other communication devices.

[0081] According to this disclosure, more historical battery data can be stored in the same storage space, thus improving the accuracy of long-term performance analysis and prediction.

[0082] According to this disclosure, data reliability is improved, thereby enabling improvements in battery performance and prediction accuracy.

[0083] According to this disclosure, various battery configurations can be easily adjusted, thereby shortening the product development cycle.

[0084] According to this disclosure, the overall energy efficiency of an EV can be improved due to the reduction in power consumption of the battery management system (BMS).

[0085] According to this disclosure, it is possible to reduce the time spent processing data to comply with regulations and the time spent generating reports.

[0086] According to this disclosure, the rate of detection and response to abnormal battery states is increased, which can improve safety.

[0087] According to this disclosure, maintenance time is shortened, thus increasing VUR.

[0088] However, the effects that can be achieved through this disclosure are not limited to those described herein, and those skilled in the art will clearly understand from the detailed description other effects not described.

[0089] Although this disclosure has been described with reference to embodiments and accompanying drawings illustrating aspects thereof, this disclosure is not limited thereto. Various modifications and variations can be made by those skilled in the art within the spirit of this disclosure and the scope of the appended claims and their equivalents.

Claims

1. A system for managing battery data, the system comprising: Memory; as well as The processor is configured to compress battery data measured from each module of the battery pack, generate a chain hash for the compressed data of each module to ensure the integrity of the battery data, and store the compressed data of each module in the memory in a distributed manner through data structuring based on the chain hash.

2. The system according to claim 1, wherein, The battery data includes at least one of the voltage, temperature, and current of each module.

3. The system according to claim 1, wherein, The processor uses run-length encoding to compress consecutive identical values ​​of the battery data in the form of counts and values.

4. The system according to claim 3, wherein, The processor uses adaptive hybrid coding to analyze the variability of the battery data, dynamically sets a threshold, and uses the set threshold to compress the battery data. The adaptive hybrid coding is a combination of delta coding and run-length coding.

5. The system according to claim 4, wherein, The processor compresses consecutive identical values ​​of the battery data using the run-length encoding based on the difference between the current value and the previous value, and compresses changed values ​​of the battery data using the delta encoding.

6. The system according to claim 1, wherein, The processor converts the compressed data into a string, adds the previous hash value to the string to generate a string combined with the previous hash value, and applies a secure hash algorithm to the generated string combined with the previous hash value to generate a hash calculation result as the chain hash.

7. The system according to claim 6, wherein, The processor sets the previous hash value of the first module in the battery pack to a preset initial value.

8. The system according to claim 1, wherein, The processor, according to the battery passport requirements, uses a data model based on JavaScript object representation to construct the compressed data of each module into data based on the chain hash.

9. The system according to claim 1, wherein, The processor uses a machine learning-based anomaly detection algorithm to detect data in the battery data that deviates from the normal pattern in real time and outputs a warning signal.

10. A method for managing battery data, the method comprising the following steps: The processor compresses the battery data measured from each module of the battery pack; The processor generates chain hashes for the compressed data of each module to ensure the integrity of the battery data. as well as The processor stores the compressed data of each module in a distributed manner through data structuring based on the chain hash.

11. The method according to claim 10, wherein, The battery data includes at least one of the voltage, temperature, and current of each module.

12. The method according to claim 10, wherein, The step of compressing the battery data includes using run-length encoding to compress consecutive identical values ​​of the battery data in the form of counts and values.

13. The method according to claim 12, wherein, The step of compressing the battery data further includes: An adaptive hybrid coding approach is used to analyze the variability of the battery data to dynamically set thresholds; the adaptive hybrid coding is a combination of delta coding and the travel coding. The battery data is compressed using the set threshold.

14. The method according to claim 13, wherein, The step of compressing the battery data further includes: The consecutive identical values ​​of the battery data are compressed using the travel encoding; and Based on the difference between the current value and the previous value, the change value of the battery data is compressed using the delta encoding.

15. The method according to claim 10, wherein, The steps for generating the chain hash include: Convert the compressed data into a string; Add the previous hash value to the string to generate a string that combines the previous hash value; and A secure hash algorithm is applied to the generated string that is combined with the previous hash value to generate a hash calculation result as the chain hash.

16. The method according to claim 15, wherein, The step of generating the chain hash includes setting the previous hash value of the first module in the battery pack to a preset initial value.

17. The method according to claim 10, wherein, The step of storing the compressed data in the distributed manner includes: structuring the compressed data of each module into data based on the chain hash using a data schema based on JavaScript object representation, according to the battery passport requirements.

18. The method according to claim 10, wherein, The processor uses a machine learning-based anomaly detection algorithm to detect data in the battery data that deviates from the normal pattern in real time and outputs a warning signal.

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