An electrochemical energy storage system energy efficiency and operating life coupling evaluation method and system
By constructing a lifetime assessment model and an energy efficiency coupling model, dynamically correcting parameters and cross-validating them, the problem of the independence between energy efficiency and operational lifetime assessment of electrochemical energy storage systems is solved, enabling more accurate assessment and optimization decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INNER MONGOLIA KEDIAN ELECTRIC CO LTD
- Filing Date
- 2026-01-22
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, the energy efficiency assessment and operational life assessment of electrochemical energy storage systems are independent of each other, which cannot effectively reflect the impact of complex operating conditions on battery performance degradation in actual operation, resulting in insufficient assessment accuracy and prediction bias.
By constructing a life assessment model and an energy efficiency coupling model, the energy efficiency assessment parameters are dynamically adjusted based on operational record data, and cross-validation is performed through multiple coupling verification models to ensure the reliability and accuracy of the assessment results.
It improves the accuracy of energy efficiency assessment and lifetime prediction of electrochemical energy storage systems, provides data support for optimal operation and maintenance decisions, and avoids performance degradation of the model due to long-term operation.
Smart Images
Figure CN122113377A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of electrochemical energy storage system technology, and in particular to a method and system for coupled evaluation of energy efficiency and service life of electrochemical energy storage systems. Background Technology
[0002] Electrochemical energy storage systems play a crucial role in smoothing fluctuations in renewable energy generation and providing grid ancillary services. Their economic efficiency and reliability throughout their entire life cycle are highly dependent on two core indicators: operational energy efficiency and cycle life.
[0003] Currently, industry assessments of these two indicators are typically independent: energy efficiency assessments are mostly based on factory-rated parameters or short-term operating data, using static models to calculate instantaneous efficiency; while lifetime prediction heavily relies on empirical models derived from standard laboratory cycle tests, or simple cumulative throughput / cycle count statistics. This fragmented assessment approach has significant drawbacks: First, it fails to dynamically correlate the complex operating stresses in actual operation (such as dynamic loads, inconsistencies, and changes in ambient temperature) with the battery's internal performance degradation mechanisms, leading to significant discrepancies between lifetime predictions and actual conditions; second, static energy efficiency models cannot reflect the time-varying impact of battery health degradation on energy conversion efficiency, such as changes in losses caused by increased internal resistance. Summary of the Invention
[0004] The purpose of this application is to provide a method and system for coupled evaluation of energy efficiency and service life of electrochemical energy storage systems in order to solve the above-mentioned technical problems, thereby improving the evaluation accuracy of electrochemical energy storage systems and providing data support for optimal operation and maintenance decisions.
[0005] In some embodiments of this application, a lifetime assessment model and an energy efficiency coupling model are constructed based on operation record data. The energy efficiency assessment parameters are dynamically corrected based on the expected state of each lifetime node, thereby improving the accuracy of energy efficiency assessment of the electrochemical energy storage system. At the same time, by setting a coupling verification model, the accuracy of the initial lifetime assessment is monitored, thereby achieving a precise assessment of the overall operating status of the electrochemical energy storage system and providing data support for optimal operation and maintenance decisions.
[0006] In some embodiments of this application, multiple coupled verification models are added to cross-validate the evaluation results of each battery sub-component, ensuring the reliability of the output data. At the same time, by periodically analyzing the evaluation record data, the associated evaluation model is dynamically optimized to avoid performance degradation of the model due to long-term operation.
[0007] In some embodiments of this application, a method for coupled evaluation of energy efficiency and operational lifespan of an electrochemical energy storage system is provided, including: Multiple battery sub-components are set according to the equipment parameters of the electrochemical energy storage system, and monitoring data of each battery sub-component is obtained. A coupling assessment strategy is set based on all monitoring data and a preset correlation assessment model, and the assessment results are obtained based on the coupling assessment strategy. Obtain evaluation record data, and determine whether to generate optimization instructions related to the evaluation model based on the evaluation record data; The correlation assessment model includes: Life assessment model and energy efficiency coupling model.
[0008] In some embodiments of this application, the preset correlation evaluation model includes: Retrieve runtime data; Set multiple lifetime mapping indicators and multiple lifetime nodes based on the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; The mapping sub-model for the target lifespan node is set based on the operation record data; The mapping sub-models for each lifetime node are set sequentially; A lifetime assessment model is established based on all the mapping sub-models.
[0009] In some embodiments of this application, the preset correlation evaluation model further includes: Multiple energy efficiency evaluation indicators are set based on operational record data; Establish a basic energy efficiency model based on all energy efficiency assessment indicators; Based on the lifetime node sequence A, ai is sequentially set as the modified lifetime node; Generate energy efficiency correlation data for corrected lifetime nodes based on operational record data; An auxiliary sub-strategy for correcting lifetime nodes is set based on energy efficiency correlation data; The auxiliary sub-strategy includes: an energy efficiency compensation strategy and a coupled verification model; Configure auxiliary sub-strategies for each lifespan node in sequence; An energy efficiency coupling model is established based on the basic energy efficiency model and all auxiliary sub-strategies.
[0010] In some embodiments of this application, the setting of the coupling evaluation strategy includes: Multiple feedback time points can be preset; Establish a sequence B of battery components, B = (b1, b2, ..., bb2) i …b m ), where b iLet be the i-th battery sub-component; m is the number of battery sub-components; b is set sequentially according to the battery sub-component sequence B. i For target sub-component; Set evaluation sub-strategies for target sub-components; The evaluation sub-strategies for each battery sub-component are set sequentially, and the coupling evaluation strategy is set based on all the evaluation sub-strategies; The evaluation sub-strategies for the target sub-component include: Obtain monitoring data of the target sub-component at the current feedback time point; Generate lifespan assessment packages and energy efficiency assessment packages based on monitoring data; The expected remaining life of the target sub-component is determined based on the life assessment model and life assessment package. A Level 1 energy efficiency model for target sub-components is set based on their expected remaining lifespan. The evaluation results of the target sub-components are generated based on the Level 1 energy efficiency model and the energy efficiency assessment package.
[0011] In some embodiments of this application, determining the expected remaining lifetime of the target sub-component includes: Based on the lifetime node sequence A, a is set sequentially. i To compare lifetime nodes; The mapping sub-model for the comparison lifetime nodes is set as the target mapping model; Generate a fit assessment value based on the target mapping model and lifetime assessment package; The lifetime assessment package and the fit assessment values for each lifetime node are generated sequentially. Set the lifetime node corresponding to the maximum value among all matching evaluation values as the associated lifetime node of the target sub-component; The expected remaining lifetime of the target sub-component is set based on the associated lifetime node.
[0012] In some embodiments of this application, the evaluation sub-result of generating the target sub-component includes: Real-time reference values for each energy efficiency assessment indicator of the target sub-component are generated based on the energy efficiency assessment package; The energy efficiency deviation value c of the target sub-component is generated based on the Level 1 energy efficiency model; c=[ (g i *s' i -s i) ]; Where θ1 represents the number of energy efficiency assessment indicators; s i s' is the real-time reference value for the i-th energy efficiency evaluation index in the target sub-component; i g is the anchor reference value for the i-th energy efficiency assessment indicator; i It is the compensation coefficient for setting the i-th energy efficiency assessment index based on the Level 1 energy efficiency model; Generate a verification data packet for the target sub-component according to the first-level energy efficiency model and the energy efficiency evaluation package; Set the coupling verification model in the first-level energy efficiency model as the target verification model; Generate an evaluation confidence value d according to the target verification model and the verification data packet; Preset an evaluation confidence value threshold D1; If d > D1, output the evaluation sub-result; If d < D1, generate an evaluation correction instruction and output the evaluation sub-result according to the evaluation correction instruction.
[0013] In some embodiments of the present application, the judgment of whether to generate an optimization instruction for the associated evaluation model includes: Obtain evaluation record data according to the preset update time node; Set the corrected evaluation values for each life node according to the evaluation record data; Preset a corrected evaluation value threshold F1; If F1 < f i , (i = 1, 2... n), generate a first-level optimization instruction for the i-th life node; where f i is the corrected evaluation value of the i-th life node; n is the number of life nodes.
[0014] In some embodiments of the present application, an electrochemical energy storage system energy efficiency and operation life coupling evaluation system is provided, including: A central control unit for setting multiple battery sub-components according to the device parameters of the electrochemical energy storage system; A monitoring unit including multiple monitoring sub-modules, and the monitoring unit is used to obtain the monitoring data of each battery sub-component; The central control unit includes: A first processing module for setting a coupling evaluation strategy according to all the monitoring data and the preset associated evaluation model; The first processing module is also used to obtain an evaluation result according to the coupling evaluation strategy; A second processing module for obtaining evaluation record data and judging whether to generate an optimization instruction for the associated evaluation model according to the evaluation record data; The first processing module is also used to establish an associated evaluation model; The associated evaluation model includes: a life evaluation model and an energy efficiency coupling model.
[0015] In some embodiments of the present application, the establishment of the associated evaluation model includes: Obtain operation record data; Set multiple life mapping indicators and multiple life nodes according to the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; The mapping sub-model for the target lifespan node is set based on the operation record data; The mapping sub-models for each lifetime node are set sequentially; Establish a lifetime assessment model based on all mapping sub-models; Multiple energy efficiency evaluation indicators are set based on operational record data; Establish a basic energy efficiency model based on all energy efficiency assessment indicators; Based on the lifetime node sequence A, ai is sequentially set as the modified lifetime node; Generate energy efficiency correlation data for corrected lifetime nodes based on operational record data; An auxiliary sub-strategy for correcting lifetime nodes is set based on energy efficiency correlation data; The auxiliary sub-strategy includes: an energy efficiency compensation strategy and a coupled verification model; Configure auxiliary sub-strategies for each lifespan node in sequence; An energy efficiency coupling model is established based on the basic energy efficiency model and all auxiliary sub-strategies.
[0016] In some embodiments of this application, the first processing module is further configured to: Multiple feedback time points can be preset; Establish a sequence of battery sub-components B, B=(b1, b2, ..., bi, ..., bm), where bi is the i-th battery sub-component and m is the number of battery sub-components; Based on the sequence of battery sub-components B, bi is sequentially set as the target sub-component; Set evaluation sub-strategies for target sub-components; The evaluation sub-strategies for each battery sub-component are set sequentially, and the coupling evaluation strategy is set based on all the evaluation sub-strategies; The evaluation sub-strategies for the target sub-component include: Obtain monitoring data of the target sub-component at the current feedback time point; Generate lifespan assessment packages and energy efficiency assessment packages based on monitoring data; The expected remaining life of the target sub-component is determined based on the life assessment model and life assessment package. A Level 1 energy efficiency model for target sub-components is set based on their expected remaining lifespan. The evaluation results of the target sub-components are generated based on the Level 1 energy efficiency model and the energy efficiency assessment package.
[0017] Compared with existing technologies, the beneficial effects of the coupled evaluation method and system for energy efficiency and operational life of an electrochemical energy storage system proposed in this application are as follows: Based on operational record data, a lifetime assessment model and an energy efficiency coupling model are constructed. The energy efficiency assessment parameters are dynamically adjusted based on the expected state at each lifetime node to improve the accuracy of energy efficiency assessment of the electrochemical energy storage system. At the same time, by setting up a coupling verification model, the accuracy of the initial lifetime assessment is monitored, so as to achieve a precise assessment of the overall operating status of the electrochemical energy storage system and provide data support for optimal operation and maintenance decisions.
[0018] By adding multiple coupled verification models to cross-validate the evaluation results of each battery sub-component, the reliability of the output data is ensured. At the same time, by regularly analyzing the evaluation record data, the correlation evaluation model is dynamically optimized to avoid performance degradation of the model due to long-term operation. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating a method for coupled evaluation of energy efficiency and operational life of an electrochemical energy storage system according to an embodiment of this application. Detailed Implementation
[0020] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.
[0021] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.
[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0023] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.
[0024] like Figure 1 As shown in the preferred embodiment of this application, a method for coupled evaluation of energy efficiency and operational life of an electrochemical energy storage system includes: S101: Set multiple battery sub-components according to the equipment parameters of the electrochemical energy storage system, and obtain monitoring data of each battery sub-component; S102: Set a coupling evaluation strategy based on all monitoring data and a preset correlation evaluation model, and obtain the evaluation results based on the coupling evaluation strategy; S103: Obtain evaluation record data and determine whether to generate optimization instructions related to the evaluation model based on the evaluation record data; The correlation assessment model includes: Life assessment model and energy efficiency coupling model.
[0025] Specifically, multiple battery sub-modules are set according to the structural parameters of the electrochemical energy storage system. Each battery sub-module represents an independent group of batteries, and the number of battery sub-modules is equal to the number of battery packs in the electrochemical energy storage system.
[0026] Specifically, the pre-defined correlation evaluation model includes: Retrieve runtime data; Set multiple lifetime mapping indicators and multiple lifetime nodes based on the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; The mapping sub-model for the target lifespan node is set based on the operation record data; The mapping sub-models for each lifetime node are set sequentially; A lifetime assessment model is established based on all the mapping sub-models.
[0027] Specifically, the operational record data includes experimental data from accelerated aging tests of battery packs of the same specifications and type in the current electrochemical energy storage system, as well as historical operational data related to life assessment and energy efficiency assessment collected from other electrochemical energy storage systems (such as charge and discharge current, voltage, temperature, number of charge and discharge cycles, cumulative throughput, and historical maintenance records).
[0028] Specifically, lifetime mapping metrics include, but are not limited to: charge / discharge rate, depth of discharge, state of charge change curve, temperature distribution within the battery cluster, average value, average rate, cumulative throughput energy, and equivalent cycle count, etc., which fluctuate with the lifetime degradation of the electrochemical energy storage system.
[0029] Specifically, multiple lifespan nodes are set within the expected lifespan of the battery in the electrochemical energy storage system, and expected parameters corresponding to each lifespan mapping index in the target lifespan node are generated by analyzing the operation record data, thereby constructing a mapping sub-model.
[0030] Specifically, the expected lifespan of the battery (the number of usable cycles from the start of the battery to the end of its expected lifespan) can be evenly divided to generate multiple lifespan sub-cycles based on time sequences. The expected remaining usable cycles corresponding to the midpoint of a single lifespan sub-cycle can be set as a lifespan node.
[0031] Specifically, the pre-defined correlation evaluation model also includes: Multiple energy efficiency evaluation indicators are set based on operational record data; Establish a basic energy efficiency model based on all energy efficiency assessment indicators; Based on the lifetime node sequence A, ai is sequentially set as the modified lifetime node; Generate energy efficiency correlation data for corrected lifetime nodes based on operational record data; An auxiliary sub-strategy for correcting lifetime nodes is set based on energy efficiency correlation data; The auxiliary sub-strategies include: energy efficiency compensation strategy and coupled verification model; Configure auxiliary sub-strategies for each lifespan node in sequence; An energy efficiency coupling model is established based on the basic energy efficiency model and all auxiliary sub-strategies.
[0032] Specifically, the energy efficiency assessment indicators include energy efficiency, voltage efficiency, and coulombic efficiency. Through quantification, the reference values of each energy efficiency assessment indicator are made to be within the same range. The higher the reference value of each energy efficiency assessment indicator, the higher the operating energy efficiency of the corresponding battery sub-component.
[0033] Specifically, anchor reference values for each energy efficiency evaluation indicator are set based on the operation record data (i.e., the best reference values for battery sub-components under ideal conditions), and a basic energy efficiency model is set based on all anchor reference values.
[0034] Specifically, energy efficiency correlation data involves filtering relevant energy efficiency records at the correction lifespan node from the operational record data. By analyzing the energy efficiency correlation data at the correction lifespan node, correction coefficients are set for each energy efficiency evaluation indicator, and each correction coefficient is less than 1.
[0035] Specifically, an energy efficiency compensation strategy for corrected lifetime nodes is generated based on all correction coefficients. By setting the energy efficiency compensation strategy for corrected lifetime nodes, interference with the accuracy of energy efficiency assessment due to the lifetime degradation of electrochemical energy storage systems can be avoided.
[0036] Specifically, by analyzing energy efficiency correlation data, the maximum deviation value of each energy efficiency assessment index at the corrected lifespan node is generated (i.e., the difference between the real-time reference value and the expected reference value of the energy efficiency assessment index). The expected reference value is the product of the anchored reference value of the energy efficiency assessment index and the correction coefficient. A coupled verification model for the lifespan node is constructed based on all maximum deviation values. The accuracy of the initial lifespan assessment can be determined using the coupled verification model and the real-time reference values of each energy efficiency assessment index.
[0037] It is understood that in the above embodiments, multiple coupled verification models are added to cross-validate the evaluation results of each battery sub-component, ensuring the reliability of the output data. At the same time, by regularly analyzing the evaluation record data, the associated evaluation model is dynamically optimized to avoid performance degradation of the model due to long-term operation.
[0038] In a preferred embodiment of this application, a coupling evaluation strategy is set, including: Multiple feedback time points can be preset; Establish a sequence B of battery components, B = (b1, b2, ..., bb2) i …b m ), where b i Let be the i-th battery sub-component; m is the number of battery sub-components; b is set sequentially according to the battery sub-component sequence B. i For target sub-component; Set evaluation sub-strategies for target sub-components; The evaluation sub-strategies for each battery sub-component are set sequentially, and the coupling evaluation strategy is set based on all the evaluation sub-strategies; The evaluation sub-strategies for the target sub-component include: Obtain monitoring data of the target sub-component at the current feedback time point; Generate lifespan assessment packages and energy efficiency assessment packages based on monitoring data; The expected remaining life of the target sub-component is determined based on the life assessment model and life assessment package. A Level 1 energy efficiency model for target sub-components is set based on their expected remaining lifespan. The evaluation results of the target sub-components are generated based on the Level 1 energy efficiency model and the energy efficiency assessment package.
[0039] Specifically, a single battery sub-component represents a group of independent batteries.
[0040] Specifically, the monitoring data includes real-time parameters of various lifespan mapping indicators for battery sub-components and real-time reference values of various energy efficiency assessment indicators. A lifespan assessment package is generated based on the real-time reference values of all lifespan mapping indicators, and an energy efficiency assessment package is generated based on the real-time reference values of all energy efficiency assessment indicators.
[0041] Specifically, determining the expected remaining lifetime of the target sub-component includes: Based on the lifetime node sequence A, a is set sequentially. i To compare lifetime nodes; The mapping sub-model for the comparison lifetime nodes is set as the target mapping model; Generate a fit assessment value based on the target mapping model and lifetime assessment package; The lifetime assessment package and the fit assessment values for each lifetime node are generated sequentially. Set the lifetime node corresponding to the maximum value among all matching evaluation values as the associated lifetime node of the target sub-component; The expected remaining lifetime of the target sub-component is set based on the associated lifetime node.
[0042] Specifically, the corresponding fit assessment value is generated by calculating the difference between the real-time parameters of each life assessment index in the life assessment package and the expected parameters corresponding to the comparison life nodes. The greater the difference, the smaller the corresponding fit assessment value. The mapping relationship between the two can be set according to historical parameters.
[0043] Specifically, the expected remaining available number of cycles corresponding to the associated lifetime node is set as the expected remaining lifetime of the target sub-component.
[0044] Specifically, a Level 1 energy efficiency model is generated by combining the energy efficiency compensation strategy corresponding to the associated lifetime node with the basic energy efficiency model.
[0045] Specifically, the evaluation sub-results for generating the target sub-component include: Real-time reference values for each energy efficiency assessment indicator of the target sub-component are generated based on the energy efficiency assessment package; The energy efficiency deviation value c of the target sub-component is generated based on the Level 1 energy efficiency model; c=[ (g i *s' i -s i) ]; Where θ1 represents the number of energy efficiency assessment indicators; s i s' is the real-time reference value for the i-th energy efficiency evaluation index in the target sub-component;i is the anchoring reference value for the i-th energy efficiency evaluation index; g i is the compensation coefficient for setting the i-th energy efficiency evaluation index according to the primary energy efficiency model; Generate a verification data packet for the target sub-component according to the primary energy efficiency model and the energy efficiency evaluation package; Set the coupling verification model in the primary energy efficiency model as the target verification model; Generate an evaluation confidence value d according to the target verification model and the verification data packet; Preset an evaluation confidence value threshold D1; If d > D1, output the evaluation sub-result; If d < D1, generate an evaluation correction instruction and output the evaluation sub-result according to the evaluation correction instruction.
[0046] Specifically, set g i *s' i -s i The absolute value of is the verification difference of the i-th energy efficiency evaluation index. Determine whether the verification difference of the current energy efficiency evaluation index is higher than the corresponding maximum deviation value in the coupling verification model in the primary energy efficiency model. If it is higher, generate a secondary difference (the difference between the verification difference and the maximum deviation value) of the current energy efficiency evaluation index according to the verification difference and the maximum deviation value. If it is lower, do not generate the secondary difference of the current energy efficiency evaluation index. Set the evaluation confidence value according to the sum of all secondary differences. The larger the sum of all secondary differences, the smaller the corresponding evaluation confidence value. The mapping relationship between the two can be set according to historical parameters.
[0047] Specifically, the evaluation confidence value threshold can be set according to historical parameters. When the real-time evaluation confidence value is less than the preset evaluation confidence value threshold, it indicates that there is an error in the initial life evaluation, and the life evaluation needs to be重新进行 according to the evaluation correction instruction.
[0048] Specifically, the evaluation sub-result includes the expected remaining life and the current energy efficiency deviation status.
[0049] It can be understood that in the above embodiments, by adding multiple coupling verification models to cross-verify the evaluation results of each battery sub-component, the reliability of the output data is ensured. At the same time, by regularly analyzing the evaluation record data, the associated evaluation model is dynamically optimized to avoid performance degradation of the model due to long-term operation.
[0050] In the preferred embodiment of the present application, determining whether to generate an optimization instruction for the associated evaluation model includes: Obtain evaluation record data according to the preset update time node; Set the corrected evaluation values of each life node according to the evaluation record data; Preset a corrected evaluation value threshold F1; If F1 <f i (i=1,2…n), generate the first-level optimization instructions for the i-th lifetime node; Among them, f i is the corrected evaluation value for the i-th lifetime node; n is the number of lifetime nodes.
[0051] Specifically, the evaluation record data consists of all evaluation process data collected between two adjacent update times. The initial call count (i.e., the number of times the remaining lifespan of the battery sub-component is determined to be at that lifespan node during the initial evaluation) and the correction count (i.e., the number of times the subsequent coupled verification model determines that the initial lifespan evaluation result is incorrect) are generated for each lifespan node. A corrected evaluation value is generated based on the ratio between the correction count and the initial call count; the larger the ratio, the larger the corrected evaluation value. The mapping relationship between the two can be set based on historical parameters.
[0052] Specifically, the correction evaluation value threshold can be set based on historical parameters. When the correction evaluation value is greater than the preset correction evaluation value threshold, it indicates that the evaluation accuracy of the mapping sub-model of the current lifespan node is poor and needs to be optimized and updated according to the first-level optimization instructions.
[0053] It is understandable that in the above embodiments, the correlation evaluation model is dynamically optimized by periodically analyzing and evaluating the recorded data, so as to avoid the performance degradation of the model due to long-term operation.
[0054] Another preferred embodiment of the method for coupled evaluation of energy efficiency and operational lifespan of an electrochemical energy storage system, based on any of the above preferred embodiments, provides a method for coupled evaluation of energy efficiency and operational lifespan of an electrochemical energy storage system, including: The central control unit is used to set multiple battery sub-components according to the equipment parameters of the electrochemical energy storage system; The monitoring unit includes multiple monitoring sub-modules and is used to acquire monitoring data from each battery component. The central control unit includes: The first processing module sets a coupling evaluation strategy based on all monitoring data and a preset correlation evaluation model. The first processing module is also used to obtain evaluation results according to the coupling evaluation strategy; The second processing module is used to acquire evaluation record data and determine whether to generate optimization instructions related to the evaluation model based on the evaluation record data. The first processing module is also used to establish a correlation evaluation model; The correlation assessment models include: life assessment model and energy efficiency coupling model.
[0055] Specifically, the monitoring submodule is preferably a data acquisition device of various types.
[0056] In a preferred embodiment of this application, a correlation evaluation model is established, including: Retrieve runtime data; Set multiple lifetime mapping indicators and multiple lifetime nodes based on the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; The mapping sub-model for the target lifespan node is set based on the operation record data; The mapping sub-models for each lifetime node are set sequentially; Establish a lifetime assessment model based on all mapping sub-models; Multiple energy efficiency evaluation indicators are set based on operational record data; Establish a basic energy efficiency model based on all energy efficiency assessment indicators; Based on the lifetime node sequence A, ai is sequentially set as the modified lifetime node; Generate energy efficiency correlation data for corrected lifetime nodes based on operational record data; An auxiliary sub-strategy for correcting lifetime nodes is set based on energy efficiency correlation data; The auxiliary sub-strategies include: energy efficiency compensation strategy and coupled verification model; Configure auxiliary sub-strategies for each lifespan node in sequence; An energy efficiency coupling model is established based on the basic energy efficiency model and all auxiliary sub-strategies.
[0057] In a preferred embodiment of this application, the first processing module is further configured to: Multiple feedback time points can be preset; Establish a sequence of battery sub-components B, B=(b1, b2, ..., bi, ..., bm), where bi is the i-th battery sub-component and m is the number of battery sub-components; Based on the sequence of battery sub-components B, bi is sequentially set as the target sub-component; Set evaluation sub-strategies for target sub-components; The evaluation sub-strategies for each battery sub-component are set sequentially, and the coupling evaluation strategy is set based on all the evaluation sub-strategies; The evaluation sub-strategies for the target sub-component include: Obtain monitoring data of the target sub-component at the current feedback time point; Generate lifespan assessment packages and energy efficiency assessment packages based on monitoring data; The expected remaining life of the target sub-component is determined based on the life assessment model and life assessment package. A Level 1 energy efficiency model for target sub-components is set based on their expected remaining lifespan. The evaluation results of the target sub-components are generated based on the Level 1 energy efficiency model and the energy efficiency assessment package.
[0058] According to the first concept of this application, a lifetime assessment model and an energy efficiency coupling model are constructed based on operation record data. The energy efficiency assessment parameters are dynamically corrected based on the expected state of each lifetime node, thereby improving the accuracy of energy efficiency assessment of the electrochemical energy storage system. At the same time, by setting a coupling verification model, the accuracy of the initial lifetime assessment is monitored, thereby achieving a precise assessment of the overall operating status of the electrochemical energy storage system and providing data support for optimal operation and maintenance decisions.
[0059] According to the second concept of this application, the evaluation results of each battery sub-component are cross-validated by adding multiple coupled verification models to ensure the reliability of the output data. At the same time, the correlation evaluation model is dynamically optimized by regularly analyzing the evaluation record data to avoid performance degradation of the model due to long-term operation.
[0060] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.
Claims
1. A method for coupled evaluation of energy efficiency and operational life of an electrochemical energy storage system, characterized in that, It includes: Set multiple battery sub-components according to the device parameters of the electrochemical energy storage system, and obtain the monitoring data of each battery sub-component; Set the coupling evaluation strategy according to all the monitoring data and the preset correlation evaluation model, and obtain the evaluation result according to the coupling evaluation strategy; Obtain the evaluation record data, and judge whether to generate an optimization instruction for the correlation evaluation model according to the evaluation record data; Among them, the correlation evaluation model includes: The life evaluation model and the energy efficiency coupling model.
2. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 1, characterized in that, The preset correlation evaluation model includes: Obtain the operation record data; Set multiple life mapping indexes and multiple life nodes according to the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; Set the mapping sub-model of the target life node according to the operation record data; Set the mapping sub-models of each life node in turn; Establish a life evaluation model according to all the mapping sub-models.
3. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 2, characterized in that, The preset correlation evaluation model also includes: Set multiple energy efficiency evaluation indexes according to the operation record data; Establish a basic energy efficiency model according to all the energy efficiency evaluation indexes; Set ai as the corrected life node in turn according to the life node sequence A; Generate the energy efficiency correlation data of the corrected life node according to the operation record data; Set the auxiliary sub-strategy of the corrected life node according to the energy efficiency correlation data; The auxiliary sub-strategy includes: the energy efficiency compensation strategy and the coupling verification model; Set the auxiliary sub-strategies of each life node in turn; Establish an energy efficiency coupling model according to the basic energy efficiency model and all the auxiliary sub-strategies.
4. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 3, characterized in that, The setting of the coupling evaluation strategy includes: Preset multiple feedback time nodes; Establish a sequence B of battery components, B = (b1, b2, ..., bb2) i …b m ), where b i Let be the i-th battery sub-component; m is the number of battery sub-components; b is set sequentially according to the battery sub-component sequence B. i For target sub-component; Set the evaluation sub-strategy of the target sub-component; Set the evaluation sub-strategies of each battery sub-component in turn, and set the coupling evaluation strategy according to all the evaluation sub-strategies; Among them, the evaluation sub-strategy of the target sub-component includes: Obtain the monitoring data of the target sub-component at the current feedback time node; Generate a life evaluation package and an energy efficiency evaluation package according to the monitoring data; Judge the expected remaining life of the target sub-component according to the life evaluation model and the life evaluation package; Set the primary energy efficiency model of the target sub-component according to the expected remaining life; Generate the evaluation sub-result of the target sub-component according to the primary energy efficiency model and the energy efficiency evaluation package.
5. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 4, characterized in that, The judgment of the expected remaining life of the target sub-component includes: Based on the lifetime node sequence A, a is set sequentially. i To compare lifetime nodes; Set the mapping sub-model of the comparison life node as the target mapping model; Generate a fitting evaluation value according to the target mapping model and the life evaluation package; Generate the fitting evaluation values of the life evaluation package and each life node in turn; Set the life node corresponding to the maximum value among all the fitting evaluation values as the associated life node of the target sub-component; Set the expected remaining life of the target sub-component according to the associated life node.
6. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 5, characterized in that, The generation of the evaluation sub-result of the target sub-component includes: Generate the real-time reference values of each energy efficiency evaluation index of the target sub-component according to the energy efficiency evaluation package; Generate the energy efficiency deviation value c of the target sub-component according to the primary energy efficiency model; c=[ (g i *s' i -s i) ]; Where θ1 represents the number of energy efficiency assessment indicators; s i s' is the real-time reference value for the i-th energy efficiency evaluation index in the target sub-component; i g is the anchor reference value for the i-th energy efficiency assessment indicator; i It is the compensation coefficient for setting the i-th energy efficiency assessment index based on the Level 1 energy efficiency model; Generate the verification data package of the target sub-component according to the primary energy efficiency model and the energy efficiency evaluation package; Set the coupling verification model in the primary energy efficiency model as the target verification model; Generate the evaluation confidence value d according to the target verification model and the verification data package; Preset the evaluation confidence value threshold D1; If d > D1, output the evaluation sub-result; If d < D1, generate an evaluation correction instruction, and output the evaluation sub-result according to the evaluation correction instruction.
7. The method for coupled evaluation of energy efficiency and operational life of electrochemical energy storage systems as described in claim 6, characterized in that, The determination of whether to generate an optimization instruction for the correlation evaluation model includes: Obtain evaluation record data according to preset update time nodes; The corrected evaluation values for each lifespan node are set based on the assessment record data; Preset correction evaluation value threshold F1; If F1 <f i (i=1,2…n), generate the first-level optimization instructions for the i-th lifetime node; Among them, f i is the corrected evaluation value for the i-th lifetime node; n is the number of lifetime nodes.
8. A coupled evaluation system for energy efficiency and operational life of an electrochemical energy storage system, employing the coupled evaluation method for energy efficiency and operational life of an electrochemical energy storage system as described in any one of claims 1-7, characterized in that, include: The central control unit is used to set multiple battery sub-components according to the equipment parameters of the electrochemical energy storage system; The monitoring unit includes multiple monitoring sub-modules, which are used to acquire monitoring data of each battery component. The central control unit includes: The first processing module sets a coupling evaluation strategy based on all monitoring data and a preset correlation evaluation model. The first processing module is also used to obtain evaluation results according to the coupling evaluation strategy; The second processing module is used to acquire evaluation record data and determine whether to generate optimization instructions related to the evaluation model based on the evaluation record data. The first processing module is also used to establish a correlation evaluation model; The correlation assessment model includes: a life assessment model and an energy efficiency coupling model.
9. The coupled evaluation system for energy efficiency and operational life of an electrochemical energy storage system as described in claim 8, characterized in that, The establishment of the correlation evaluation model includes: Retrieve runtime data; Set multiple lifetime mapping indicators and multiple lifetime nodes based on the operation record data; Establish a lifetime node sequence A, A=(a1, a2, ..., a... i …a n ), where a i Let be the i-th lifetime node; n is the number of lifetime nodes; Set a sequentially according to the lifespan nodes. i For the target lifetime node; The mapping sub-model for the target lifespan node is set based on the operation record data; The mapping sub-models for each lifetime node are set sequentially; Establish a lifetime assessment model based on all mapping sub-models; Multiple energy efficiency evaluation indicators are set based on operational record data; Establish a basic energy efficiency model based on all energy efficiency assessment indicators; Based on the lifetime node sequence A, ai is sequentially set as the modified lifetime node; Generate energy efficiency correlation data for corrected lifetime nodes based on operational record data; An auxiliary sub-strategy for correcting lifetime nodes is set based on energy efficiency correlation data; The auxiliary sub-strategy includes: an energy efficiency compensation strategy and a coupled verification model; Configure auxiliary sub-strategies for each lifespan node in sequence; An energy efficiency coupling model is established based on the basic energy efficiency model and all auxiliary sub-strategies.
10. The coupled evaluation system for energy efficiency and operational life of an electrochemical energy storage system as described in claim 9, characterized in that, The first processing module is also used for: Multiple feedback time points can be preset; Establish a sequence of battery sub-components B, B=(b1, b2, ..., bi, ..., bm), where bi is the i-th battery sub-component and m is the number of battery sub-components; Based on the sequence of battery sub-components B, bi is sequentially set as the target sub-component; Define the evaluation sub-strategy for the target sub-component; The evaluation sub-strategies for each battery sub-component are set sequentially, and the coupling evaluation strategy is set based on all the evaluation sub-strategies; The evaluation sub-strategies for the target sub-component include: Obtain monitoring data of the target sub-component at the current feedback time point; Generate lifespan assessment packages and energy efficiency assessment packages based on monitoring data; The expected remaining life of the target sub-component is determined based on the life assessment model and life assessment package. A Level 1 energy efficiency model for target sub-components is set based on their expected remaining lifespan. The evaluation results of the target sub-components are generated based on the Level 1 energy efficiency model and the energy efficiency assessment package.