Battery pack thermal consistency evaluation method of energy storage system and related device

Abstract

The invention relates to the technical field of electric power energy storage, and provides a battery pack thermal consistency evaluation method of an energy storage system and a related device, and the method comprises the steps: constructing a thermal consistency coefficient representing the temperature state of a battery pack in a single charging or discharging stage according to the temperature of the battery pack at different positions in the energy storage system in the charging and discharging process; obtaining a charging weight coefficient and a discharging weight coefficient of the to-be-evaluated battery pack according to the charging current and the discharging current of the to-be-evaluated battery pack under the charging and discharging working conditions; and according to the charging weight coefficient, the thermal consistency coefficient in the charging stage, the discharging weight coefficient and the thermal consistency coefficient in the discharging stage of the to-be-evaluated battery pack, obtaining a thermal consistency result of the to-be-evaluated battery pack under the charging and discharging working conditions. According to the invention, a thermal consistency quantification method considering calculation efficiency and evaluation precision is constructed in an intelligent battery management system of a large-scale energy storage system, and thermal anomaly real-time detection and risk early warning of a battery pack level are realized.

Description

Technical Field

[0001] The present invention relates to the field of electric energy storage technology, and in particular to a method and related device for evaluating thermal consistency of a battery pack of an energy storage system. Background Art

[0002] Large-scale energy storage systems are critical infrastructure for grid peak regulation and renewable energy consumption. In large-scale energy storage systems, uneven heat distribution generated by electrochemical reactions in battery packs during charging and discharging can easily lead to localized overheating, thermal runaway, and even chain reactions, threatening the safety of the energy storage system. Therefore, real-time monitoring of battery pack thermal consistency and providing early warning of abnormalities are core requirements for ensuring the safe operation of energy storage systems.

[0003] Existing battery management systems (BMS) mainly obtain battery surface temperature data through temperature sensors, and then rely on single-cell thermal field reconstruction technology to use limited temperature data to invert the three-dimensional thermal field distribution inside the battery. Typical implementation solutions include inversion algorithms based on heat conduction equations and data-driven thermal field interpolation methods to achieve the evaluation of battery thermal consistency.

[0004] The drawbacks of existing thermal consistency assessment methods are: First, thermal field reconstruction requires high computing resources and requires solving partial differential equations or large-scale matrix operations. The limited computing resources of the edge-side intelligent BMS cannot support the real-time operation of complex algorithms. Second, existing methods rely on dense sensors. However, due to cost and installation limitations, temperature sensors in actual large-scale energy storage systems are usually sparsely deployed and cannot cover the entire thermal field of the battery pack, which in turn leads to a serious decrease in inversion accuracy. Furthermore, existing methods only focus on the thermal field analysis of single cells and do not consider the heat transfer coupling effect at the module / battery pack level, making it difficult to reflect the risk of system-level thermal imbalance.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] In view of this, the present invention provides a battery pack thermal consistency assessment method and related devices for an energy storage system, so as to construct a thermal consistency quantification method that takes into account both computational efficiency and assessment accuracy in the intelligent battery management system of a large-scale energy storage system, thereby realizing real-time detection of thermal anomalies and risk warning at the battery pack level.

[0007] According to one aspect of the present invention, a method for evaluating the thermal consistency of a battery pack of an energy storage system is provided, comprising: constructing a thermal consistency coefficient characterizing the temperature state of the battery pack in a single charging or discharging stage based on the temperatures of battery packs at different locations in the energy storage system during the charging and discharging process; obtaining, based on the charging current and discharging current of the battery pack to be evaluated under a charging and discharging condition, the coefficient of variation of the charging stage and the discharging stage of the battery pack to be evaluated based on the charging and discharging condition; obtaining, based on the coefficient of variation, a charging weight coefficient and a discharging weight coefficient of the battery pack to be evaluated based on the charging and discharging condition, wherein the charging weight coefficient is negatively correlated with the coefficient of variation of the charging stage, and the discharging weight coefficient is negatively correlated with the coefficient of variation of the discharging stage; obtaining a thermal consistency result of the battery pack to be evaluated under the charging and discharging condition based on the charging weight coefficient, the thermal consistency coefficient of the charging stage, the discharging weight coefficient, and the thermal consistency coefficient of the discharging stage of the battery pack to be evaluated based on the charging and discharging condition.

[0008] In some embodiments, the thermal consistency coefficient is calculated as follows:

[0009]

[0010] Where n is the number of charging segments in a charging phase or the number of discharging segments in a discharging phase, and is the start and end time of the jth charging or discharging segment; N is the number of temperature sensors in the energy storage system, T(i,t) is the measured temperature of temperature sensor i at time t, T(i,0) is the initial temperature of temperature sensor i, and Δt is the sampling time interval of the temperature sensor; is the maximum temperature measured by all temperature sensors at time t, is the lowest temperature measured by all temperature sensors at time t.

[0011] In some embodiments, the different locations are locations of different temperature sensors.

[0012] In some embodiments, the coefficient of variation is calculated as follows:

[0013]

[0014] Wherein, std(I) is the standard deviation of the charging current vector or the discharging current vector, and mean(I) is the mean of the charging current or the discharging current.

[0015] In some embodiments, the calculation formula of the charging weight coefficient is:

[0016]

[0017] The calculation formula of the discharge weight coefficient is:

[0018]

[0019] Among them, w charge is the charging weight coefficient, w discharge is the discharge weight coefficient, CV charge is the coefficient of variation during the charging phase, CV discharge is the coefficient of variation during the discharge phase.

[0020] In some embodiments, the calculation formula for the thermal consistency of the battery pack to be evaluated under the charge and discharge conditions is:

[0021] TCC P1C1 =w charge ×TCC charge +w discharge ×TCC discharge ;

[0022] Among them, TCC P1C1 To evaluate the thermal consistency of the battery pack P1 under the charge and discharge condition C1, TCC charge TCC is the thermal consistency coefficient of the battery pack P1 to be evaluated during the charging phase of the charge and discharge condition C1. discharge is the thermal consistency coefficient of the battery pack P1 to be evaluated in the discharge phase of the charge and discharge condition C1.

[0023] According to another aspect of the present invention, a battery pack thermal consistency assessment device for an energy storage system is provided, for implementing the battery pack thermal consistency assessment method described in any of the above embodiments, the battery pack thermal consistency assessment device comprising: a thermal consistency construction module configured to construct a thermal consistency coefficient characterizing the temperature state of the battery pack in a single charging or discharging stage based on the temperature of the battery packs at different locations in the energy storage system during the charging and discharging process; a variation coefficient acquisition module configured to obtain, based on the charging current and discharging current of the battery pack to be evaluated under a charging and discharging condition, the variation coefficients of the charging stage and the discharging stage of the battery pack to be evaluated; a weight coefficient acquisition module configured to obtain, based on the variation coefficients, a charging weight coefficient and a discharging weight coefficient of the battery pack to be evaluated based on the charging and discharging condition, wherein the charging weight coefficient is negatively correlated with the variation coefficient of the charging stage, and the discharging weight coefficient is negatively correlated with the variation coefficient of the discharging stage; and a thermal consistency detection module configured to obtain a thermal consistency result of the battery pack to be evaluated under the charging and discharging condition based on the charging weight coefficient of the battery pack to be evaluated, the thermal consistency coefficient of the charging stage, the discharging weight coefficient, and the thermal consistency coefficient of the discharging stage.

[0024] According to another aspect of the present invention, a computer device is provided, comprising: a processor; a memory, wherein the memory stores executable instructions; wherein when the executable instructions are executed by the processor, the battery pack thermal consistency assessment method described in any of the above embodiments is implemented.

[0025] According to another aspect of the present invention, a computer-readable storage medium is provided for storing a program, wherein when the program is executed by a processor, the method for evaluating thermal consistency of a battery pack according to any of the above embodiments is implemented.

[0026] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the battery pack thermal consistency evaluation method described in any of the above embodiments is implemented.

[0027] The beneficial effects of the present invention compared with the prior art include at least:

[0028] The present invention is aimed at intelligent BMS systems with limited computing resources, and designs a battery pack-level thermal consistency assessment method based on the battery heat generation mechanism, taking into account both real-time and accuracy. The present invention analyzes the heat generation mechanism, combines the spatiotemporal distribution characteristics of sparse sensor data, and performs simple calculations based on the temperature data of battery packs at different positions in the energy storage system to derive the thermal consistency coefficient of the battery pack. Then, combined with the coefficient of variation that reflects the stability of the charge and discharge currents, the thermal consistency coefficients of the battery pack in the charging and discharging stages are calculated to obtain the thermal consistency results of the battery pack under charge and discharge conditions. In specific implementation, the present invention first quantifies the temperature difference based on the temperature data of the battery pack through segmented statistics and normalization processing; secondly, it introduces an adaptive weight distribution mechanism to enhance the detection sensitivity under irregular conditions such as charge, discharge and overcharge, and finally forms a temperature consistency coefficient detection with millisecond response, which can provide real-time and accurate thermal consistency assessment results according to the actual charge and discharge conditions, realize low computing power deployment of edge intelligent BMS, and support the safe operation and maintenance of large-scale energy storage systems.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the description, serve to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and it is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0031] Figure 1A schematic diagram showing the steps of a method for evaluating thermal consistency of a battery pack of an energy storage system according to an embodiment of the present invention;

[0032] Figure 2 、 Figure 3 and Figure 4 A schematic diagram showing current changes of the energy storage system under three charging and discharging conditions according to an embodiment of the present invention is shown;

[0033] Figure 5 Schematic diagram showing thermal consistency evaluation results of different battery packs under three charge and discharge conditions according to an embodiment of the present invention;

[0034] Figure 6 A schematic diagram showing a module of a device for evaluating thermal consistency of a battery pack of an energy storage system according to an embodiment of the present invention is shown;

[0035] Figure 7 A schematic structural diagram of a computer device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in many forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to make this disclosure more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art.

[0037] The accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] In addition, the processes shown in the drawings are merely exemplary and do not necessarily include all steps. For example, some steps may be decomposed, some steps may be combined or partially combined, and the actual execution order may change according to actual circumstances.

[0039] It should be noted that, in the absence of conflict, the embodiments of the present invention and features in different embodiments may be combined with each other.

[0040] Figure 1 The main steps of the method for evaluating the thermal consistency of the battery pack of the energy storage system in the embodiment of the present invention are shown in FIG. Figure 1 As shown, the method for evaluating the thermal consistency of a battery pack of an energy storage system provided by an embodiment of the present invention mainly includes the following steps.

[0041] Step S110, based on the temperature of the battery packs at different positions in the energy storage system during the charging and discharging process, construct a thermal consistency coefficient that characterizes the temperature state of the battery pack in a single charging or discharging stage. Wherein, different positions refer to the locations of different temperature sensors. Based on the thermal characteristics of batteries and the evolution law of thermal runaway, the present invention proposes to use a lightweight thermal consistency coefficient (TCC) as a statistical indicator, which can construct a thermal consistency quantification method that takes into account both computing efficiency and evaluation accuracy in the intelligent battery management system (BMS) of large-scale energy storage systems, and realize real-time detection of thermal anomalies and risk warnings at the battery pack level.

[0042] Step S120 , based on the charging current and discharging current of the battery pack under a charge-discharge condition, obtain the coefficient of variation of the battery pack under the charge-discharge condition in the charging phase and the discharging phase. The coefficient of variation can reflect the stability of the charge-discharge current.

[0043] In step S130, based on the coefficient of variation, the charging and discharging weighting factors of the battery pack to be evaluated are obtained based on the charging and discharging conditions. The charging weighting factor is negatively correlated with the coefficient of variation during the charging phase, and the discharging weighting factor is negatively correlated with the coefficient of variation during the discharging phase. A larger coefficient of variation indicates a more unstable current state, so the weighting factors are designed to be negatively correlated with the coefficient of variation.

[0044] Step S140 obtains the thermal consistency results of the battery pack under charge and discharge conditions based on the charging weight coefficient, the thermal consistency coefficient during the charging phase, the discharging weight coefficient, and the thermal consistency coefficient during the discharging phase. To address the irregular charging and discharging characteristics that may occur during the operation of large-scale energy storage systems, a weighted average of the thermal consistency coefficients can be calculated, using the current stability of the charge and discharge processes as a weight, to evaluate the thermal consistency of the battery pack over a single charge and discharge cycle.

[0045] Through the above steps, the present invention can introduce a rapid thermal consistency assessment method that combines the battery thermal mechanism with the actual operating conditions of the system in the intelligent battery management system of a large-scale energy storage system. The method of the present invention innovatively proposes to use the thermal consistency coefficient (TCC) as a thermal state characterization statistic, which can construct a thermal state assessment model through sparsely deployed temperature sensor data to quantify the temperature distribution consistency of the battery pack. On this basis, the present invention introduces the current fluctuation coefficient to perform weighted fusion on the thermal consistency coefficient of the charging and discharging stages, so as to achieve adaptive adjustment of the assessment results to dynamic working conditions, thereby achieving real-time early warning of the thermal imbalance risk of the energy storage system at a low computing resource cost.

[0046] In some embodiments, the thermal consistency coefficient is calculated using the formula (1a) as follows:

[0047]

[0048] Where n is the number of charging segments in a charging phase or the number of discharging segments in a discharging phase, and is the start and end time of the jth charging or discharging segment; N is the number of temperature sensors in the energy storage system, T(i,t) is the measured temperature of temperature sensor i at time t, T(i,0) is the initial temperature of temperature sensor i, and Δt is the sampling interval of the temperature sensor; is the maximum temperature measured by all temperature sensors at time t, is the lowest temperature measured by all temperature sensors at time t.

[0049] The calculation formula of the above thermal consistency coefficient constructs a multi-dimensional temperature feature matrix, integrates the spatiotemporal distribution characteristics of the temperature data in the battery pack and the statistical characteristics of the thermal behavior of the dynamic charging and discharging process, and establishes a thermal state characterization model at the battery pack level.

[0050] The following explains how the thermal consistency coefficient (TCC) can be used as a direct and effective indicator for diagnosing thermal imbalance or abnormal behavior of energy storage systems.

[0051] The battery energy conservation equation is shown in formula (2a):

[0052]

[0053] Among them, Q g Indicates the heat generated during battery charging and discharging, Q cool is the heat dissipated by the cooling system, Q cond is the heat transferred to other measurement points, m is the mass of the battery, C p is the specific heat capacity. In formula (2b), I represents the charge and discharge current, R0 is the internal resistance, is the entropy change coefficient (which changes with battery state of charge (SoC) and temperature T):

[0054]

[0055] The heat dissipation of the cooling system is described by formula (2c), where h is the heat transfer coefficient, A cool is the effective cooling area, T ∞ is the cooling medium temperature:

[0056] Q cool =hA cool (TT ∞ ); (2c)

[0057] The heat conduction between the temperature measurement points is given by formula (2d), where k is the thermal conductivity, Acond is the effective conduction area, and L is the effective conduction length:

[0058]

[0059] All parameters in formulas (2a) to (2d) can be calculated using accurate temperature data, such as directly obtaining the temperature field distribution through simulation, or reconstructing temperature data through densely arranged thermocouples, so as to accurately evaluate the thermal consistency of the battery pack. However, the cost of building a battery simulation model or integrating an infrared camera in an industrial scenario is high. Therefore, in order to statistically evaluate thermal consistency using temperature data collected by sparse thermocouples, the present invention proposes a thermal consistency coefficient (TCC) to quantify the temperature consistency of the battery pack during the charge and discharge cycle using the above formula (1a). The left side of formula (2a) represents the rate of change of temperature over time. In order to quantitatively calculate this parameter using a limited number of temperature measurement points, formula (1a) uses As a statistical indicator, it reflects the temperature difference of batteries at different locations over time. According to formula (2a), under the condition of consistent thermal boundary conditions, the thermal response and temperature rise of the battery pack should maintain spatial uniformity, and its consistency is defined by the parameters in formulas (2b) to (2d).

[0060] The charge and discharge current of the energy storage system changes with time, resulting in Q g However, since all battery packs are connected in series, the charge and discharge current I and charge SoC change at the same time, so Q g The fluctuation of is also consistent (Formula (2b)). Similarly, A in Formulas (2c) and (2d) cond , L, T ∞ and T next It depends only on the location of the measurement point. Therefore, the factors that affect the spatial difference of TCC are only local material properties, such as mass m, specific heat capacity C p , thermal conductivity k, and heat transfer coefficient h. Based on this, the spatial difference of TCC can be used as a direct and effective indicator for diagnosing thermal imbalance or abnormal behavior of battery systems.

[0061] After constructing the thermal consistency coefficient (TCC), adaptive calculations can be performed based on the actual operating conditions of the large-scale energy storage system. The operating conditions of the large-scale energy storage system will be dynamically adjusted according to changes in the grid side and the load side. Figures 2 to 4 As shown in the figure, the current changes under three typical charge and discharge cycles are shown. Figure 2 The charge and discharge conditions shown are (C1), Figure 3 The charge and discharge condition 2 (C2) and Figure 4In the third charge / discharge condition (C3), the charge / discharge pattern is irregular and variable, making thermal consistency assessment difficult. However, it can be seen that the current in the discharge state is more stable than the charge current. Therefore, thermal consistency assessment results in the discharge state are more reliable than those in the charge state.

[0062] The present invention samples the weighted average thermal consistency coefficient under the charge and discharge states in a single operating cycle as the final thermal consistency evaluation result of this charge and discharge operating condition. Assume that the vector of the current in the charge and discharge stage is I charge and I discharg , calculate the corresponding coefficient of variation CV charge and CV discharge , where the coefficient of variation is calculated as shown in formula (1b):

[0063]

[0064] Among them, std(I) calculates the standard deviation of the current vector, that is, the standard deviation of the charging current vector or the discharging current vector, and mean(I) calculates the mean of the current, that is, the mean of the charging current or the discharging current.

[0065] The larger the coefficient of variation, the more unstable the current state. Therefore, the inverse of the coefficient of variation is used as the weight. The calculation of the charging weight coefficient and the discharging weight coefficient are shown in formulas (1c) and (1d), respectively:

[0066]

[0067] Among them, w charge is the charging weight coefficient, w discharge is the discharge weight coefficient, CV charge is the coefficient of variation during the charging phase, CV discharge is the coefficient of variation during the discharge phase.

[0068] Finally, the thermal consistency coefficients of the corresponding charge or discharge conditions are weighted averaged to obtain the thermal consistency of the battery pack under charge and discharge conditions. The calculation formula (1e) is:

[0069] TCC P1C1 =w charge ×TCC charge +w discharge ×TCC discharge ; (1e)

[0070] Among them, TCC P1C1 To evaluate the thermal consistency of the battery pack P1 under the charge and discharge condition C1, TCC charge TCC is the thermal consistency coefficient of the battery pack P1 to be evaluated during the charging phase of the charge and discharge condition C1.discharge is the thermal consistency coefficient of the battery pack P1 to be evaluated in the discharge phase of the charge and discharge condition C1.

[0071] Using the above formulas (1a) to (1e), the thermal consistency evaluation results (coefficients) of each battery pack under the charge and discharge conditions C1, C2, and C3 can be calculated, as shown in FIG. Figure 5 As shown. Figure 5 It can be seen that under similar operating conditions, the thermal consistency coefficient proposed in the present invention has good generalization, and the same battery pack exhibits similar thermal consistency under similar but not completely identical operating conditions.

[0072] In summary, the present invention proposes a thermal consistency evaluation method framework suitable for intelligent BMS systems, including a thermal consistency coefficient calculation method based on the definition of battery thermal mechanism, a thermal state evaluation model constructed by integrating multi-dimensional temperature and spatiotemporal characteristics, and a weighted average mechanism designed based on charge and discharge current stability. Specifically, it includes the reading and normalization of raw data from temperature sensors, the extraction of thermal response statistical indicators, the design of adaptive weighting strategies, and the output mechanism of the final thermal consistency evaluation results. The method of the present invention not only takes into account the computing power limitations of edge BMS systems, but also takes into account the differences in thermal responses of energy storage systems under variable working conditions, achieving the unity of real-time, reliability, and system adaptability of thermal anomaly detection, and providing a scientific and effective support means for the thermal management of large-scale energy storage systems.

[0073] Current thermal consistency assessment methods for large-scale energy storage systems face challenges such as high computing resource consumption, insufficient assessment granularity, and slow response speed. These challenges are particularly evident in edge intelligent BMS deployment, which makes real-time deployment difficult and challenging to cope with dynamic operating conditions. The rapid thermal consistency assessment method proposed in this paper constructs a temperature consistency coefficient model based on the battery heat generation mechanism and actual temperature sensor data. This avoids the large number of partial differential calculations and high-density sensor reliance required for traditional thermal field reconstruction, significantly reducing algorithm complexity and data dependency. Furthermore, by introducing the current coefficient of variation as an indicator of charge and discharge stability, the present invention adaptively weights and fuses the thermal consistency results under dynamic operating conditions, making the final output assessment result closer to the system's actual risk level and improving the sensitivity and robustness of the assessment method. The method not only achieves millisecond-level thermal anomaly detection response, adapting to the deployment requirements of edge computing environments, but also effectively characterizes thermal state change trends based on sparse data, providing a real-time, low-cost, and accurate thermal consistency assessment method for large-scale energy storage systems. This helps improve the overall system operational safety and anomaly diagnosis capabilities, providing solid support for energy storage system operation and maintenance strategy optimization and safety management.

[0074] Embodiments of the present invention also provide a battery pack thermal consistency assessment device that can be used to implement the battery pack thermal consistency assessment method described in any of the above embodiments. The features and principles of the battery pack thermal consistency assessment method described in any of the above embodiments can be applied to the following battery pack thermal consistency assessment device embodiments. In the following battery pack thermal consistency assessment device embodiments, the features and principles of battery pack thermal consistency assessment that have already been explained will not be repeated.

[0075] The battery pack thermal consistency evaluation device of the present invention can be deployed in an intelligent BMS (battery management system) of an energy storage system. Figure 6 The main modules of the battery pack thermal consistency evaluation device for the energy storage system according to the embodiment of the present invention are shown in FIG. Figure 6 As shown, the battery pack thermal consistency evaluation device 600 includes: a thermal consistency construction module 610, configured to construct a thermal consistency coefficient characterizing the temperature state of the battery pack in a single charging or discharging stage according to the temperature of the battery packs at different positions in the energy storage system during the charging and discharging process; a variation coefficient acquisition module 620, configured to obtain the variation coefficient of the charging stage and the discharging stage of the battery pack to be evaluated based on the charging current and the discharging current of the battery pack to be evaluated under a charging and discharging condition; a weight coefficient acquisition module 630, configured to obtain the charging weight coefficient and the discharging weight coefficient of the battery pack to be evaluated based on the charging and discharging condition according to the variation coefficient, the charging weight coefficient is negatively correlated with the variation coefficient of the charging stage, and the discharging weight coefficient is negatively correlated with the variation coefficient of the discharging stage; a thermal consistency detection module 640, configured to obtain the thermal consistency of the battery pack to be evaluated under the charging and discharging condition according to the charging weight coefficient of the battery pack to be evaluated based on the charging and discharging condition, the thermal consistency coefficient of the charging stage, the discharge weight coefficient, and the thermal consistency coefficient of the discharge stage.

[0076] The specific principles of each module can be referred to the description of the above-mentioned battery pack thermal consistency evaluation method embodiments, and will not be repeated here.

[0077] The battery pack thermal consistency assessment device of the present invention can construct a thermal consistency quantification solution that takes into account both computational efficiency and assessment accuracy in the intelligent battery management system of a large-scale energy storage system, thereby realizing real-time detection of thermal anomalies and risk warnings at the battery pack level.

[0078] An embodiment of the present invention further provides a computer device comprising a processor and a memory, wherein the memory stores executable instructions. When the executable instructions are executed by the processor, the battery pack thermal consistency evaluation method described in any of the above embodiments is implemented.

[0079] The computer device of the present invention can be deployed in the intelligent BMS (battery management system) of the energy storage system. It can build a thermal consistency quantification solution that takes into account both computing efficiency and evaluation accuracy in the intelligent battery management system of the large-scale energy storage system, and realize real-time detection of thermal anomalies and risk warning at the battery pack level.

[0080] Figure 7 The main structure of the computer device in the embodiment of the present invention is shown in FIG. Figure 7 As shown, the computer device 700 is in the form of a general-purpose computing device. The components of the computer device 700 include but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different platform components (including the storage unit 720 and the processing unit 710), etc. The storage unit 720 stores program code, which can be executed by the processing unit 710, so that the processing unit 710 performs the steps of the battery pack thermal consistency evaluation method described in any of the above embodiments. For example, the processing unit 710 can perform the following steps: Figure 1 Steps shown.

[0081] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit and / or a cache storage unit, and may further include a read-only storage unit. The storage unit 720 may also include a program / utility having one or more program modules, such as, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment.

[0082] Bus 730 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0083] The computer device 700 can also communicate with one or more external devices, which can be one or more of a keyboard, a pointing device, a Bluetooth device, or the like. These external devices enable a user to interact with the computer device 700. The computer device 700 can also communicate with one or more other computing devices, including routers and modems. Such communication can be performed via input / output (I / O) interfaces. Furthermore, the computer device 700 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter. The network adapter can communicate with other modules of the computer device 700 via the bus 730. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the computer device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0084] An embodiment of the present invention further provides a computer-readable storage medium for storing a program, which, when executed, implements the battery pack thermal consistency evaluation method described in any of the above embodiments.

[0085] The storage medium of the present invention can be executed by a processor deployed in an intelligent BMS (battery management system) of an energy storage system, thereby constructing a thermal consistency quantification solution that takes into account both computational efficiency and evaluation accuracy in the intelligent battery management system of a large-scale energy storage system, and realizing real-time detection of thermal anomalies and risk warnings at the battery pack level.

[0086] The storage medium may be a portable compact disk read-only memory and include program code, and may be run on a terminal device, such as a computer. However, the storage medium of the present invention is not limited thereto and may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0087] The storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of readable storage media include, but are not limited to, an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0088] The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable signal medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0089] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device, such as via the Internet using an Internet service provider.

[0090] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the battery pack thermal consistency evaluation method described in any of the above embodiments.

[0091] When the computer program product of the present invention is run on a terminal device deployed in an intelligent BMS (battery management system) of an energy storage system, it can construct a thermal consistency quantification solution that takes into account both computational efficiency and evaluation accuracy in the intelligent battery management system of a large-scale energy storage system, thereby realizing real-time detection of thermal anomalies and risk warnings at the battery pack level.

[0092] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for evaluating thermal consistency of a battery pack of an energy storage system, characterized in that: include: Based on the temperature of battery packs at different locations in the energy storage system during the charging and discharging process, a thermal consistency coefficient is constructed to characterize the temperature state of the battery pack in a single charging or discharging stage; Obtaining, according to the charging current and discharging current of the battery pack to be evaluated under a charge-discharge operating condition, a coefficient of variation of the battery pack to be evaluated in a charging phase and a discharging phase based on the charge-discharge operating condition; Obtaining, based on the coefficient of variation, a charging weight coefficient and a discharging weight coefficient of the battery pack to be evaluated based on the charge and discharge operating conditions, wherein the charging weight coefficient is negatively correlated with the coefficient of variation of the charging stage, and the discharging weight coefficient is negatively correlated with the coefficient of variation of the discharging stage; The thermal consistency result of the battery pack to be evaluated under the charging and discharging conditions is obtained according to the charging weight coefficient of the battery pack to be evaluated based on the charging and discharging conditions, the thermal consistency coefficient of the charging stage, the discharging weight coefficient and the thermal consistency coefficient of the discharging stage.

2. The battery pack thermal consistency evaluation method according to claim 1, wherein: The calculation formula of the thermal consistency coefficient is: Where n is the number of charging segments in a charging phase or the number of discharging segments in a discharging phase, and is the start and end time of the jth charging or discharging segment; N is the number of temperature sensors in the energy storage system, T(i,t) is the measured temperature of temperature sensor i at time t, T(i,0) is the initial temperature of temperature sensor i, and Δt is the sampling time interval of the temperature sensor; is the maximum temperature measured by all temperature sensors at time t, is the lowest temperature measured by all temperature sensors at time t.

3. The battery pack thermal consistency evaluation method according to claim 2, wherein: The different positions are positions of different temperature sensors.

4. The battery pack thermal consistency evaluation method according to claim 2, wherein: The calculation formula of the coefficient of variation is: Wherein, std(I) is the standard deviation of the charging current vector or the discharging current vector, and mean(I) is the mean of the charging current or the discharging current.

5. The battery pack thermal consistency evaluation method according to claim 4, wherein: The calculation formula of the charging weight coefficient is: The calculation formula of the discharge weight coefficient is: Among them, w charge is the charging weight coefficient, w discharge is the discharge weight coefficient, CV charge is the coefficient of variation during the charging phase, CV discharge is the coefficient of variation during the discharge phase.

6. The battery pack thermal consistency evaluation method according to claim 5, wherein: The calculation formula for the thermal consistency of the battery pack to be evaluated under the charge and discharge conditions is: TCC P1C1 =w charge ×TCC charge +w discharge ×TCC discharge ; Among them, TCC P1C1 To evaluate the thermal consistency of the battery pack P1 under the charge and discharge condition C1, TCC charge TCC is the thermal consistency coefficient of the battery pack P1 to be evaluated during the charging phase of the charge and discharge condition C1. discharge is the thermal consistency coefficient of the battery pack P1 to be evaluated in the discharge phase of the charge and discharge condition C1.

7. A battery pack thermal consistency assessment device for an energy storage system, characterized in that: For implementing the battery pack thermal consistency evaluation method according to any one of claims 1 to 6, the battery pack thermal consistency evaluation device comprises: a thermal consistency building module configured to build a thermal consistency coefficient representing the temperature state of the battery pack in a single charging or discharging stage based on the temperature of the battery pack at different locations in the energy storage system during the charging and discharging process; a coefficient of variation acquisition module configured to obtain, based on the charging current and discharging current of the battery pack to be evaluated under a charge and discharge operating condition, a coefficient of variation of the battery pack to be evaluated in a charge phase and a discharge phase based on the charge and discharge operating condition; a weight coefficient acquisition module configured to obtain, based on the coefficient of variation, a charging weight coefficient and a discharging weight coefficient of the battery pack to be evaluated based on the charging and discharging operating conditions, wherein the charging weight coefficient is negatively correlated with the coefficient of variation of the charging stage, and the discharging weight coefficient is negatively correlated with the coefficient of variation of the discharging stage; The thermal consistency detection module is configured to obtain the thermal consistency result of the battery pack to be evaluated under the charging and discharging conditions based on the charging weight coefficient of the battery pack to be evaluated based on the charging and discharging conditions, the thermal consistency coefficient of the charging stage, the discharge weight coefficient and the thermal consistency coefficient of the discharge stage.

8. A computer device, characterized in that: include: processor; a memory, wherein executable instructions are stored in the memory; Wherein, when the executable instruction is executed by the processor, the battery pack thermal consistency evaluation method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the battery pack thermal consistency evaluation method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the battery pack thermal consistency evaluation method according to any one of claims 1 to 6 is implemented.

Images