Battery test parameter adaptive optimization method and system

Abstract

The present application relates to the technical field of battery testing, and particularly relates to a battery testing parameter adaptive optimization method and system. The method comprises: obtaining historical operation data, performing different use stage arrangement, dynamic load model construction and multi-load test protocol generation processing to obtain a multi-load test protocol field; based on the multi-load test protocol field, performing voltage, current and temperature data acquisition, electrochemical impedance spectrum data recording and internal state information extraction processing to generate a first internal state information field; based on the first internal state information field, performing performance attenuation trend recording, risk probability and residual safety time processing and performance evaluation report generation processing, and based on the performance evaluation report field, performing test parameter constraint condition establishment, updated test parameter processing and test protocol update processing. The present application realizes continuous connection between the test protocol, state evaluation and parameter update, and improves the adaptability, continuity and update efficiency of the battery testing process.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to a method and system for adaptive optimization of battery testing parameters. Background Technology

[0002] In the field of battery testing technology, existing solutions typically revolve around historical operational data acquisition, test parameter presetting, charge / discharge process monitoring, and test result output. These solutions suffer from limitations such as a disconnect between multi-load test protocol generation and actual operating conditions, insufficient integration between electrochemical impedance spectroscopy (EIS) data recording and internal state information extraction, and separation between performance evaluation report generation and test protocol updates. Existing methods often rely on pre-defined test procedures for voltage, current, and temperature data acquisition, followed by manual adjustments or adjustments based on phased test results. In test protocol update scenarios, this often results in insufficient reflection of internal state information and lag in the generation of updated test protocol fields, making it difficult to achieve stable test protocol updates based on EIS data recording and internal state information extraction.

[0003] For the joint processing of electrochemical impedance spectroscopy data recording, internal state information extraction and processing, performance evaluation report generation and test protocol update processing, existing technologies generally suffer from common shortcomings such as separation of data links before and after the process, lack of continuous correspondence between performance degradation trend recording and risk probability and remaining safety time processing, and lack of unified connection between the establishment of test parameter constraints and the processing of updated test parameters. It is difficult to form a consistent process for voltage, current and temperature data acquisition, electrochemical impedance spectroscopy data recording, internal state information extraction, performance evaluation report generation and test protocol update during battery testing. As a result, test protocol updates mainly rely on static settings or single test conclusions, making it difficult to continuously reflect changes in historical operating data and current operating status. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an adaptive optimization method for battery testing parameters, comprising:

[0005] S100: Obtain historical battery operation data, divide the usage stages according to voltage, current, and capacity retention rate, extract load change parameters, build a dynamic load model based on the stages and parameters, set low power, high load, and normal operation test scenarios according to the model, and generate multiple load test protocol fields.

[0006] S200: Collect real-time operating data of voltage, current and temperature according to the multi-load test protocol field, trigger electrochemical impedance spectroscopy acquisition in combination with the test scenario, extract the real part and imaginary part of impedance and correspond to the scenario to obtain the first internal state information field.

[0007] S300: Analyze the capacity decay trend based on the first internal state information field, determine the risk probability and remaining safe time for each test scenario, integrate discharge characteristics and instantaneous response capability, and generate a performance evaluation report field.

[0008] S400. Based on the performance evaluation report fields, the upper and lower limits and constraints of the test parameters are written. The test parameters are corrected by combining the control algorithm with the adaptive adjustment coefficient. The corrected parameters are then replaced with the original test protocol to obtain the updated test protocol fields.

[0009] Furthermore, the process of acquiring historical battery operating data, dividing usage stages according to voltage, current, and capacity retention rate, and extracting load change parameters includes:

[0010] Historical operating data includes voltage, current, temperature, capacity retention rate, internal resistance change rate, and corresponding time sequence records. The historical operating data is a collection of historical records continuously formed according to the cyclic charge and discharge process.

[0011] Historical records in similar operating states are grouped into the same stage record, and discontinuous, missing or abnormally abrupt records are kept separately as abnormal records. Abnormal records are not deleted directly and will not participate in the current round of extraction when extracting load change parameters in subsequent rounds, while retaining their original record positions.

[0012] First, the historical operating data is segmented sequentially according to different usage stages. Then, the current change, duration, and temperature change within each segment are recorded to extract the load change cycle. The load change is extracted according to the current difference and temperature difference between adjacent records within the same stage. Finally, the discharge rate parameter is extracted by combining the changes in capacity retention rate and internal resistance change rate within that stage.

[0013] Furthermore, the process of constructing a dynamic load model based on stages and parameters, and generating multiple load test protocol fields according to the model's settings for low power, high load, and normal operation test scenarios, includes:

[0014] The corresponding relationships between different usage stages, load change parameters, voltage, current, temperature, capacity retention rate, and internal resistance change rate are uniformly organized, and the model parameters are written in. The model parameters include stage corresponding parameters, load change cycle parameters, load change amount parameters, discharge rate parameters, and attenuation record parameters corresponding to capacity retention rate and internal resistance change rate.

[0015] Based on the phase sequence, load change cycle, and discharge rate parameters of each model segment in the dynamic load model field, the execution sequence, duration, and load switching position in each test scenario are determined. Multiple test scenarios are then connected sequentially to form a multi-load test protocol. The multi-load test protocol includes the load change cycle, load change amount, discharge rate parameters, and voltage, current, and temperature data acquisition periods corresponding to each test scenario.

[0016] Furthermore, the process of acquiring real-time operating data of voltage, current, and temperature according to the aforementioned multi-load test protocol fields includes:

[0017] Within each test scenario, the data acquisition and processing unit continuously acquires, sequentially records, and matches the battery's terminal voltage, operating current, and temperature according to the load change cycle and discharge rate parameters, ensuring that the same set of voltage, current, and temperature data carries corresponding test scenario information and acquisition sequence information.

[0018] Furthermore, the process of extracting the real and imaginary parts of the impedance and corresponding them to the scenario, based on the electrochemical impedance spectroscopy acquisition triggered by the test scenario, includes:

[0019] Based on the test scenario switching position in the real-time running data field, an impedance recording trigger command is sent to the test system so that the recording and processing of the real and imaginary parts of impedance are performed separately in the low power state test scenario, the high load test scenario, and the normal running test scenario.

[0020] Upon receiving the impedance recording trigger command, the voltage, current, and temperature records under the corresponding scenario are sequentially associated with the real and imaginary parts of the impedance at the current frequency to form the impedance record corresponding to the scenario. The real part of the impedance is used to characterize the ohmic characteristics of the battery in the current operating stage, and the imaginary part of the impedance is used to characterize the polarization and diffusion changes of the battery in the current operating stage.

[0021] Furthermore, the process of obtaining the first internal status information field includes:

[0022] First, read the test scenario records in the electrochemical impedance spectroscopy data field. Then, compare the real and imaginary parts of the impedance in the same test scenario in sequence to extract the corresponding internal state information. The internal state information includes the charge transfer state, electrolyte diffusion state, and internal state change records corresponding to the current test scenario. Then, match the internal state changes in the current test scenario with the voltage, current, and temperature records in the real-time operation data field and write them into the real-time response record. The real-time response record includes internal state change records in the low charge state test scenario, the high load test scenario, and the normal operation test scenario.

[0023] Furthermore, the process of analyzing capacity decay trends, determining the risk probability and remaining safe time for each test scenario, integrating discharge characteristics and instantaneous response capabilities, and generating performance evaluation report fields includes:

[0024] The internal state change records under different test scenarios are read sequentially, and combined with the voltage, current and temperature changes in the real-time response records, the direction, rate and location of capacity retention rate changes during continuous testing are analyzed to form a continuous trend record segmented along the low charge state test scenario, high load test scenario and normal operation test scenario; when a record with accelerated charge transfer state change and continuous temperature rise appears in the first internal state information field, the record is identified as a key record in the capacity decay trend; when a record with stable electrolyte diffusion state and small voltage and current changes appears, it is identified as a stable record;

[0025] First, key records and stable records are read from the performance degradation trend field, and then compared in the order of test scenarios. When key records appear repeatedly in consecutive rounds, the control module increases the risk probability record for the corresponding scenario. When the proportion of stable records increases in consecutive rounds, the control module extends the remaining safe time record for the corresponding scenario. Risk probability refers to the result after recording the possibility that the current test object will experience abnormal degradation, reduced load capacity, or fluctuations in operating status in subsequent test processes. Remaining safe time refers to the result after processing the time range within which the test object can continue to run according to the existing multi-load test protocol based on the performance degradation trend record of the current round and the change position of the previous and subsequent rounds.

[0026] First, according to the test scenario order in the risk probability and remaining safe time fields, read the risk probability records for each test scenario; then call the relevant records in the real-time running data field and the first internal status information field corresponding to the test scenario to complete the discharge characteristic integration and obtain a comprehensive record reflecting the discharge status of the test object under different test scenarios; then read the real-time response records before and after load switching in the test scenario to complete the instantaneous response capability integration and obtain a comprehensive record reflecting the instantaneous running status of the test object during load switching.

[0027] Furthermore, the process of writing the upper and lower limits and constraints of the test parameters according to the fields of the performance evaluation report includes:

[0028] After reading the performance evaluation report fields, the risk probability record and remaining safety time record are extracted according to the test scenario order. Then, combined with the discharge characteristic integration result and the instantaneous response capability integration result, parameter boundaries are established for the current test object under the low-power state test scenario, the high-load test scenario, and the normal operation test scenario. When the risk probability record is at a high position and the remaining safety time record is shortened, the upper limit of charging current, the upper limit of discharging current, and the upper limit of test duration corresponding to the test scenario are written in the tightening direction. When the risk probability record is at a low position and the instantaneous response capability integration result is stable, the cutoff voltage range and temperature range corresponding to the test scenario are written into the normal boundary. Corresponding constraints are established for the above boundaries to form the test parameter constraint condition field.

[0029] Furthermore, the process of correcting the test parameters by combining control algorithms with adaptive adjustment coefficients, and then replacing the corrected parameters with those of the original test protocol, includes:

[0030] First, the test scenario correspondence in the test parameter constraint field is read, and then the control algorithm is called according to the test scenario. The control algorithm first checks whether the current test parameter exceeds the upper and lower limits of the test parameter, then determines the allowed adjustment direction and order according to the constraint conditions, and then completes the parameter correction by combining the adaptive adjustment coefficient. When the risk probability record corresponding to a certain test scenario is high, the adaptive adjustment coefficient adjusts the charging current, discharging current and test duration by a smaller margin. When the instantaneous response capability integration result corresponding to a certain test scenario is relatively stable, the adaptive adjustment coefficient adjusts the cutoff voltage and temperature range by a normal margin. The updated test parameter field includes the updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, updated test duration and the corresponding test scenario identifier.

[0031] Furthermore, a battery test parameter adaptive optimization system includes: a historical operation data processing module, a dynamic load model construction module, a multi-load test protocol generation module, a real-time operation data acquisition module, an electrochemical impedance spectroscopy data recording module, a first internal state information extraction module, a performance evaluation report generation module, and a test protocol update module; the modules are connected in sequence to implement the method described in any of the above-mentioned embodiments.

[0032] The key innovations of this invention include:

[0033] (1) Obtain historical operating data, organize it for different usage stages, construct dynamic load models and generate multi-load test protocols to obtain multi-load test protocol fields, and drive subsequent voltage, current and temperature data acquisition, electrochemical impedance spectroscopy data recording and internal state information extraction and processing by the multi-load test protocol fields to form a link connecting the historical operating data to the test execution object.

[0034] (2) Based on the multi-load test protocol field, voltage, current and temperature data are collected, electrochemical impedance spectroscopy data is recorded and internal state information is extracted and processed to generate a first internal state information field. The electrochemical impedance spectroscopy data recording and internal state information extraction and processing are continuously organized into the same processing link, and the first internal state information field is used as the input object for subsequent performance degradation trend recording, risk probability and remaining safety time processing and performance evaluation report generation processing.

[0035] (3) Based on the first internal state information field, the performance degradation trend is recorded, the risk probability and remaining safety time are processed, and the performance evaluation report is generated to generate the performance evaluation report field; and based on the performance evaluation report field, the test parameter constraints are established, the updated test parameters are processed, and the test protocol is updated to generate the updated test protocol field, and a continuous correspondence is established between the performance evaluation report field and the updated test protocol field.

[0036] The following are its main beneficial effects:

[0037] (1) In view of the problem that the generation of multi-load test protocol is disconnected from the actual operation status in the existing scheme, by acquiring historical operation data and organizing it for different usage stages, constructing dynamic load models and generating multi-load test protocols, the multi-load test protocol fields are made to maintain a continuous correspondence with the subsequent voltage, current and temperature data acquisition, and the multi-load test protocol fields are no longer set separately from historical operation data.

[0038] (2) To address the problem of insufficient connection between electrochemical impedance spectroscopy data recording and internal state information extraction in the existing scheme, the acquisition of voltage, current and temperature data, electrochemical impedance spectroscopy data recording and internal state information extraction are organized into a continuous processing process to generate the first internal state information field, so that the internal state information extraction is based on the electrochemical impedance spectroscopy data recording and the data link before and after is consistent.

[0039] (3) To address the problem of separating the generation of performance evaluation reports from the update of test protocols in existing solutions, performance degradation trend recording, risk probability and remaining safety time processing and performance evaluation report generation are performed based on the first internal state information field. Then, test parameter constraints are established, updated test parameters are processed and test protocols are updated based on the performance evaluation report field, so that a direct transmission relationship is formed between the performance evaluation report field and the updated test protocol field.

[0040] (4) In view of the problem that the performance degradation trend record and the risk probability and remaining safety time processing in the existing scheme are not continuously corresponding, the performance degradation trend record, risk probability and remaining safety time processing are jointly incorporated into the performance evaluation report generation process. The performance evaluation report fields simultaneously carry the content required for the establishment of internal state information and test parameter constraints, reducing the separation of pre-processing and post-processing.

[0041] (5) In view of the lack of unified connection between the establishment of test parameter constraints and the processing of updated test parameters in the existing scheme, the test parameter constraints are established, the updated test parameters are processed and the test protocol is updated continuously based on the performance evaluation report field. This ensures that the updated test protocol field continuously reflects the changes in historical running data and current running status, and the test protocol update is no longer limited to static settings or single test conclusions. Attached Figure Description

[0042] Figure 1 A flowchart illustrating the adaptive optimization method for battery test parameters provided in this application embodiment;

[0043] Figure 2 This is a block diagram of the battery test parameter adaptive optimization system provided in the embodiments of this application. Detailed Implementation

[0044] Example 1: Refer to Figure 1 This is a flowchart illustrating the adaptive optimization method for battery testing parameters provided in an embodiment of the present invention. The process may include at least steps S100-S400:

[0045] S100: Obtain historical battery operation data, divide the usage stages according to voltage, current, and capacity retention rate, extract load change parameters, build a dynamic load model based on the stages and parameters, set low power, high load, and normal operation test scenarios according to the model, and generate multiple load test protocol fields.

[0046] S200: Collect real-time operating data of voltage, current and temperature according to the multi-load test protocol field, trigger electrochemical impedance spectroscopy acquisition in combination with the test scenario, extract the real part and imaginary part of impedance and correspond to the scenario to obtain the first internal state information field.

[0047] S300: Analyze the capacity decay trend based on the first internal state information field, determine the risk probability and remaining safe time for each test scenario, integrate discharge characteristics and instantaneous response capability, and generate a performance evaluation report field.

[0048] S400. Based on the performance evaluation report fields, the upper and lower limits and constraints of the test parameters are written. The test parameters are corrected by combining the control algorithm with the adaptive adjustment coefficient. The corrected parameters are then replaced with the original test protocol to obtain the updated test protocol fields.

[0049] Step S100 includes at least steps S110-S130:

[0050] S110. Obtain historical operation data, organize it for different usage stages and extract load change parameters to obtain historical operation data fields.

[0051] Specifically, the historical operating data is continuously acquired by the data acquisition and processing unit of the test system during the existing test process. The historical operating data includes at least voltage, current, temperature, capacity retention rate, internal resistance change rate, and corresponding time-series records. Understandably, the historical operating data is not a single acquisition value, but a continuous historical record set formed according to the cyclic charge-discharge process. Voltage, current, and temperature are used to characterize the operating process, while capacity retention rate and internal resistance change rate are used to characterize the battery's degradation state during long-term use.

[0052] Furthermore, the different usage stages are the stage results after organizing the operating states corresponding to historical operating data. These different usage stages include at least the operating stage corresponding to low power conditions, the operating stage corresponding to high load conditions, and the normal operating stage. The organization process is performed by the data acquisition and processing unit according to the order of voltage changes, current changes, and capacity retention rate changes in the historical records. Historical records in similar operating states are grouped into the same stage record, while discontinuous, missing, or abnormally abrupt records are separately retained as abnormal records. These abnormal records are not directly deleted but are not included in the current round of load change parameter extraction. Their original record positions are retained, facilitating the test system's review of the same batch of historical operating data after subsequent test protocol updates.

[0053] Furthermore, the load change parameters are a set of parameters extracted from the different usage stages that reflect the battery's load change process. This parameter set includes at least the load change cycle, load change amount, and discharge rate parameters. In specific implementation, the data acquisition and processing unit first segments the historical operating data sequentially according to different usage stages, then records the current change, duration, and temperature change within each segment to extract the load change cycle. Subsequently, it extracts the load change amount based on the current difference and temperature difference between adjacent records within the same stage. Then, it extracts the discharge rate parameters by combining the changes in capacity retention rate and internal resistance change rate within that stage. For multiple consecutive high-load records of the same batch of batteries, the data acquisition and processing unit retains multiple load change parameters in chronological order, rather than compressing them into a single average value, facilitating the subsequent dynamic load model's writing of the actual change process under different usage stages. As an engineering embodiment, before the power battery module enters the bench test, the data acquisition and processing unit can first import the historical operating data generated from the previous cycle of charging and discharging, then separately organize the voltage drop process under low charge conditions, the current rise process under high load conditions, and the change process corresponding to capacity retention rate, thereby forming load change parameters that can be directly accessed.

[0054] After completing the above processing, the testing system merges the organized records of different usage stages with the extracted load change parameters and writes them into the historical operation data field. The historical operation data field, as the output of this step, includes the organized historical operation data, the correspondence between different usage stages, and the load change parameters, and is fed into S120 as input to the "historical operation data field" in the natural flow. The historical operation data field also provides a foundational source for the subsequent construction of the dynamic load model and establishes a preceding reference relationship between the real-time operation data field and the electrochemical impedance spectroscopy data field in S200.

[0055] S120. Based on the historical operation data fields, perform dynamic load model construction and model parameter writing processing to obtain dynamic load model fields.

[0056] Specifically, the dynamic load model is an operational description model built upon the historical operational data fields. This model does not simply record historical results, but rather represents a unified and organized model structure that establishes the correspondences between different usage stages, load change parameters, voltage, current, temperature, capacity retention rate, and internal resistance change rate. Furthermore, the dynamic load model can be jointly constructed by the control module and the data acquisition and processing unit. The data acquisition and processing unit is responsible for extracting the stage sequence relationships from the historical operational data fields, while the control module is responsible for writing the load change cycle, load change amount, and discharge rate parameters within the same stage into the model parameter positions. The model parameters constitute the basic parameter set of the dynamic load model, including at least stage-corresponding parameters, load change cycle parameters, load change amount parameters, discharge rate parameters, and attenuation record parameters corresponding to capacity retention rate and internal resistance change rate. Through this writing method, the dynamic load model can retain a complete correspondence of "how the load changes, how the attenuation changes, and how the temperature changes for the same battery under different usage stages."

[0057] In practice, the control module first reads the records of different usage stages from the historical operation data fields, and establishes model segments according to the stage records corresponding to low power status, high load status, and normal operation stages. Then, it writes the load change cycle, load change amount, and discharge rate parameters corresponding to each model segment into the corresponding positions. Next, it writes the voltage, current, temperature, capacity retention rate, and internal resistance change rate within the same model segment as associated records. Understandably, the model parameter writing is not a one-time static write, but rather an overwrite or append update performed each time new historical operation data fields are imported for the same batch of test objects. When a new stage record appears in the historical operation data fields, the control module writes the new stage record into the dynamic load model; when only the original stage record is updated in the historical operation data fields, the control module synchronously updates the model parameters based on the original parameters, retaining the previous round of write records for easy version comparison during subsequent test protocol updates. In actual engineering scenarios, when a power battery module experiences high load conditions and its internal resistance change rate continues to rise in the first two rounds of testing, the control module will retain the model segment corresponding to this type of high load condition as an independent model segment in the dynamic load model, without mixing it with the normal operation phase, so that subsequent test scenario settings can be directly developed for this model segment.

[0058] After constructing the dynamic load model and writing the model parameters, the test system obtains the dynamic load model field. This field includes at least the dynamic load model itself, the model parameter writing results, and the correspondence between stages and parameters. The dynamic load model field is sent to S130 as the output of this step, serving as the direct input to the "Dynamic Load Model Field" in S130. This field also provides a scenario basis for the subsequent voltage, current, and temperature data acquisition in S200, and provides a preliminary stage reference for the performance degradation trend analysis in S300.

[0059] S130. Based on the dynamic load model fields, perform test scenario setting and multi-load test protocol generation processing to obtain multi-load test protocol fields.

[0060] Specifically, the test scenario setting is based on the dynamic load model fields, configuring the operating scenarios that the subsequent test system will execute. The test scenarios include at least a low-power state test scenario, a high-load test scenario, and a normal operation test scenario. Further, the control module determines the execution order, duration, and load switching position within each test scenario according to the stage order, load change cycle, and discharge rate parameters of each model segment in the dynamic load model fields. The multi-load test protocol is a set of test protocols formed by sequentially connecting multiple test scenarios. The multi-load test protocol includes at least the load change cycle, load change amount, discharge rate parameters, and voltage, current, and temperature data acquisition periods corresponding to each test scenario. Understandably, the multi-load test protocol does not directly repeat fixed test steps, but rather generates scenarios based on the stage differences already written in the dynamic load model fields. Different multi-load test protocols can be generated for the same test object under different historical operating data field conditions.

[0061] In practice, the control module first calls the model segment corresponding to the low-power state from the dynamic load model field, and sets the starting position and load change cycle of the low-power state test scenario; then it calls the model segment corresponding to the high-load state, and sets the load change amount and discharge rate parameters of the high-load state test scenario; finally, it calls the model segment corresponding to the normal operation phase, forming a normal operation test scenario that connects with the previous two. Within each test scenario, the control module writes the model parameters into the test protocol configuration item and generates a multi-load test protocol that can be directly executed by the test system. When both the high-load state and a significant increase in the internal resistance change rate are recorded simultaneously in the dynamic load model field, the control module improves the calling order of the high-load state test scenario in the multi-load test protocol and shortens the switching interval between adjacent scenarios; when the dynamic load model field shows that the normal operation phase is stable, the control module keeps the duration of the normal operation test scenario unchanged.

[0062] Furthermore, after the multi-load test protocol is generated, it is written to the parameter configuration location. The parameter configuration includes at least the test scenario sequence, load change cycle, load change amount, discharge rate parameter, and the call location required for subsequent voltage, current, and temperature data acquisition in S210. As a complete engineering embodiment, after importing the dynamic load model fields, the control module of a certain power battery module first establishes a low-charge state test scenario, then establishes a high-load condition test scenario, and subsequently establishes a normal operation test scenario, combining the three into a multi-load test protocol. This multi-load test protocol is directly sent to the test system for execution. Upon entering S210, the data acquisition and processing unit performs voltage, current, and temperature data acquisition according to the multi-load test protocol fields.

[0063] After completing the above processing, the test system obtains the multi-load test protocol field. This multi-load test protocol field is the final output of this main step, containing the test scenario setting results, multi-load test protocol content, and parameter configuration writing results, and is directly input as the "multi-load test protocol field" in subsequent step S210. This multi-load test protocol field establishes a direct correspondence with the real-time running data field of step S200, forms a corresponding relationship with the performance evaluation report field of step S300, and is called again as a write-back object during the test protocol update process of step S400.

[0064] In summary, the technical effects of this step are as follows: By sequentially constructing historical operational data fields, dynamic load model fields, and multi-load test protocol fields, the test protocol no longer uses a single fixed test method, but is generated in a scenario-based manner according to different usage stages and load variation parameters. In this way, a continuous correspondence is established between the multi-load test protocol and subsequent real-time operational data fields, electrochemical impedance spectroscopy data fields, and performance evaluation report fields. When the test protocol is updated, it can return to the existing dynamic load model fields from this step for rewriting.

[0065] Step S200 includes at least steps S210-S230:

[0066] S210: Obtain the multi-load test protocol field, perform voltage, current and temperature data acquisition and processing, and obtain the real-time running data field.

[0067] Specifically, the multi-load test protocol field originates from the multi-load test protocol generation process in the preceding S130. This field includes at least the test scenario sequence, load change cycle, load change amount, discharge rate parameter, and parameter configuration writing result. When the test system enters this step, the control module reads the multi-load test protocol field and sends the test scenario sequence to the data acquisition and processing unit. The voltage, current, and temperature data acquisition and processing refers to continuously acquiring, sequentially recording, and scene-corresponding the battery's terminal voltage, operating current, and temperature according to the load change cycle and discharge rate parameter within each test scenario.

[0068] Specifically, the data acquisition and processing unit first establishes the first round of acquisition records when the low power state test scenario starts, executes the second round of acquisition records when the high load test scenario switches, and executes subsequent acquisition records during the continuous period of the normal operation test scenario, so that the same set of voltage, current and temperature data all carry the corresponding test scenario information and acquisition sequence information.

[0069] Understandably, the real-time operational data is not a simple stack of raw values, but rather a collection of operational records associated with the test scenario. Voltage reflects potential changes during charging and discharging, current reflects the operating state during load changes, and temperature reflects thermal changes during operation. For records that experience data acquisition interruptions, sudden changes, or exceed the current test scenario during testing, the data acquisition and processing unit separately marks these records and retains them at the end of the current round's record, without interrupting subsequent acquisition processes. This ensures that the main record and abnormal records within the same test scenario remain revisitable.

[0070] In an operational engineering embodiment, after the power battery module is connected to the testing system, the control module first calls the low-charge state test scenario. The data acquisition and processing unit continuously acquires voltage, current, and temperature according to the multi-load test protocol fields. When the test scenario switches to the high-load test scenario, the data acquisition and processing unit synchronously increases the acquisition frequency and continues to record the changes in voltage, current, and temperature before and after the switch. When entering the normal operation test scenario, the data acquisition and processing unit restores the normal acquisition frequency, thereby obtaining continuous operation records covering multiple test scenarios. After completing the voltage, current, and temperature data acquisition and processing, the testing system writes the scenario-corresponding operation records into the real-time operation data field. The real-time operation data field includes voltage records, current records, temperature records, test scenario correspondence, and abnormal record locations. It serves as the direct input to the "real-time operation data field" in S220 and as the preliminary operation basis in the subsequent performance degradation trend analysis in S300.

[0071] S220. Based on the real-time running data field, perform impedance real and imaginary part recording processing to obtain electrochemical impedance spectroscopy data field.

[0072] Specifically, the real-time operating data field is output by S210. The voltage, current, and temperature records in the real-time operating data field, together with the test scenario sequence, constitute the input source for this step. The processing of the real and imaginary impedance parts refers to recording the impedance changes of the battery at different frequencies within the test scenario corresponding to the real-time operating data field, and writing the real and imaginary impedance parts at each frequency according to the test scenario. The electrochemical impedance spectroscopy data is a set of frequency-corresponding records composed of the real and imaginary impedance parts. The real impedance part is used to characterize the ohmic characteristics of the battery in the current operating stage, and the imaginary impedance part is used to characterize the polarization and diffusion changes of the battery in the current operating stage.

[0073] Furthermore, the control module sends an impedance recording trigger command to the test system based on the test scenario switching position in the real-time operation data field, so that the recording and processing of the real and imaginary parts of impedance are performed separately in the low-power state test scenario, the high-load test scenario, and the normal operation test scenario; after receiving the impedance recording trigger command, the data acquisition and processing unit associates the voltage, current, and temperature records under the corresponding scenario with the real and imaginary parts of impedance under the current frequency in sequence, and forms the impedance record corresponding to the scenario.

[0074] Understandably, the core minimum set in this step consists of the real-time running data field, the real part of the impedance, and the imaginary part of the impedance. The real-time running data field is an essential input, and the real and imaginary parts of the impedance are essential recording objects. The number of frequencies recorded at different frequencies and the recording times are extensions that can be adjusted according to the test system configuration. When the temperature recording changes significantly within a test scenario, the control module can call for more intensive recording of the real and imaginary parts of the impedance; when the voltage and current recordings remain stable within a test scenario, the control module can maintain the established recording rhythm according to the current multi-load test protocol fields.

[0075] In one engineering embodiment, after the power battery module completes the real-time operating data field recording in a high-load test scenario, the test system immediately performs impedance real and imaginary part recording for that scenario; subsequently, impedance real and imaginary part recording is performed again in a normal operating test scenario, and the records from both test scenarios are stored in the same round of electrochemical impedance spectroscopy data. After processing, the test system obtains an electrochemical impedance spectroscopy data field, which includes at least the impedance real part record, impedance imaginary part record, and the correspondence with the real-time operating data field corresponding to the test scenario. The electrochemical impedance spectroscopy data field serves as the direct input to the "electrochemical impedance spectroscopy data field" in S230, and simultaneously forms an upstream support relationship with the first internal state information field in subsequent S310.

[0076] S230. Based on the electrochemical impedance spectroscopy data field, internal state information is extracted and real-time response is recorded to obtain the first internal state information field.

[0077] Specifically, the electrochemical impedance spectroscopy (EIS) data field comes from S220, and the records of the real impedance, the records of the imaginary impedance, and the correspondence between the test scenarios in the EIS data field together constitute the input of this step. The internal state information refers to the battery's internal operating state record extracted from the relationship between the changes in the real and imaginary impedance, including at least the charge transfer state, the electrolyte diffusion state, and the internal state change record corresponding to the current test scenario. The extraction of the internal state information is completed by the control module. The control module first reads the test scenario records in the EIS data field, and then compares the real and imaginary impedance under the same test scenario in sequence to extract the corresponding internal state information.

[0078] Furthermore, the real-time response record processing refers to, after the internal state information is extracted, matching the internal state changes in the current test scenario with the voltage, current, and temperature records in the real-time operating data fields, and writing this information into the real-time response record. The real-time response record reflects the immediate correspondence of internal state information in the current test scenario, specifically including internal state change records in low-power test scenarios, internal state change records in high-load test scenarios, and internal state change records in normal operating test scenarios.

[0079] Understandably, the extraction of internal state information is not performed independently of the operation process, but rather forms a continuous link together with the real-time operation data field of S210: first, the multi-load test protocol field triggers the acquisition of voltage, current and temperature data, then the real-time operation data field triggers the recording of the real and imaginary parts of impedance, and finally the electrochemical impedance spectroscopy data field extracts the internal state information and completes the real-time response recording.

[0080] For abnormal impedance real part records or abnormal impedance imaginary part records appearing in the same test scenario, the control module retains these records by corresponding them to the abnormal record positions in the current real-time running data field, and continues to extract the internal state information corresponding to the remaining normal records, thereby maintaining the continuous operation of the entire test process. In an operable engineering embodiment, when the power battery module shows a record of continuously increasing imaginary impedance in a high-load test scenario, the control module corresponds the impedance real part record and impedance imaginary part record in this scenario with the voltage, current, and temperature records at the same time position, extracts the charge transfer state and electrolyte diffusion state in this scenario, and writes them into the first internal state information field; when the test scenario is switched to a normal running test scenario, the control module continues to perform the same internal state information extraction and real-time response recording processing, thereby obtaining a first internal state information field covering multiple test scenarios.

[0081] After processing, the test system outputs a first internal state information field. The first internal state information field includes at least internal state information, real-time response records, and the correspondence with the electrochemical impedance spectroscopy data field and the real-time running data field. It serves as the direct input to the "first internal state information field" in S310 and provides preliminary state basis for subsequent risk probability and remaining safety time processing in S320, performance evaluation report generation processing in S330, and upper and lower limit writing of test parameters in S410.

[0082] In summary, this step sequentially connects the multi-load test protocol field, real-time running data field, electrochemical impedance spectroscopy data field, and first internal state information field. This allows the testing process to move beyond simple data acquisition and instead record the running data within the test scenario and correlate it with changes in the internal state. Compared to methods that only record voltage, current, and temperature, this step adds a continuous correspondence between the real and imaginary parts of impedance and the internal state information, providing a directly accessible state basis for subsequent performance degradation trend analysis and test protocol updates.

[0083] Step S300 includes at least steps S310-S330:

[0084] S310. Obtain the first internal status information field, perform capacity decay trend analysis and performance decay trend recording processing to obtain the performance decay trend field.

[0085] Specifically, the first internal state information field originates from S230, and includes at least internal state information, real-time response records, and their correspondence with the electrochemical impedance spectroscopy data field and the real-time operation data field. The capacity decay trend analysis refers to sequentially reading the internal state change records under different test scenarios and, in conjunction with the voltage, current, and temperature changes in the real-time response records, analyzing the direction, rate, and location of change in capacity retention during continuous testing.

[0086] Understandably, the capacity decay trend is not the result of a single capacity test, but rather a continuous trend record formed segment by segment along the low-power state test scenario, the high-load test scenario, and the normal operation test scenario. Furthermore, after reading the first internal state information field, the control module first arranges the internal state information of the same test object in successive rounds of testing in chronological order, and then maps the voltage changes, current changes, and temperature changes in the real-time response record one-to-one with the internal state changes in the corresponding stages, thereby identifying the decay position of the capacity retention rate in different test scenarios.

[0087] When a record in the first internal state information field shows accelerated charge transfer state changes accompanied by a continuous temperature rise, the control module identifies this record as a key record in the capacity decay trend. Conversely, when a record in the first internal state information field shows stable electrolyte diffusion and small voltage and current changes, the control module identifies this record as a stable record in the capacity decay trend. The performance decay trend recording process is a continuous write operation performed after the capacity decay trend analysis is completed. Specifically, the capacity decay trend and the change records in the internal state information are written together to a unified recording location, forming a trend record that reflects the performance change process. The performance decay trend field is thus formed, and it includes at least the capacity decay trend record, the corresponding internal state change record, and its correspondence with the test scenario.

[0088] In a feasible engineering embodiment, after the power battery module undergoes multiple rounds of testing, the control module reads the internal state change records under high-load test scenarios and the internal state change records under normal operation test scenarios from the first internal state information field. Then, combining these with the voltage, current, and temperature changes of the corresponding rounds, a set of continuous capacity degradation trend records is obtained and written into the performance degradation trend field. This performance degradation trend field serves as the direct input to the "performance degradation trend field" in S320, while retaining the preceding and following correspondence with S210, S220, and S230, and providing a state basis for subsequent risk probability and remaining safety time processing.

[0089] S320. Based on the performance degradation trend field, perform risk probability and remaining safe time processing to obtain the risk probability and remaining safe time fields.

[0090] Specifically, the performance degradation trend field is output by S310. The capacity degradation trend record, internal state change record, and test scenario correspondence in the performance degradation trend field together constitute the input source for this step. The risk probability refers to the result of recording the possibility that the current test object will experience abnormal degradation, reduced load capacity, or fluctuating operating status in subsequent testing processes, based on the continuous degradation records already formed in the performance degradation trend field. The remaining safety time refers to the result of processing the time range within which the test object can continue to run according to the existing multi-load testing protocol, based on the performance degradation trend record of the current round and the position of the change in performance degradation trends between previous and subsequent rounds.

[0091] Furthermore, when executing this step, the control module first reads key records and stable records from the performance degradation trend field, and then compares them according to the test scenario order. When key records repeatedly appear in consecutive rounds, the control module increases the risk probability record in the corresponding scenario; when the proportion of stable records increases in consecutive rounds, the control module extends the remaining safety time record in the corresponding scenario. Understandably, the core minimum set in this step is the performance degradation trend field, risk probability, and remaining safety time. The performance degradation trend field is an essential input, and risk probability and remaining safety time are essential processing results. The subdivisions of risk probability and remaining safety time within each test scenario can be expanded according to the test system configuration. In specific implementation, the control module does not rely solely on a single degradation record, but rather includes performance degradation trend records from low-power state test scenarios, high-load test scenarios, and normal operation test scenarios in its processing scope, thus enabling the risk probability and remaining safety time fields to cover the entire set of multi-load test protocols.

[0092] In one engineering embodiment, when a power battery module continuously exhibits a faster capacity degradation trend in a high-load test scenario, and the number of stable records in the same round of regular operation test scenarios decreases, the control module writes the risk probability corresponding to the high-load test scenario to a higher position and shortens the remaining safe time of the test object before writing it to the same field. When stable records recover in subsequent rounds, the control module updates the remaining safe time. After processing, the test system obtains the risk probability and remaining safe time fields, which include risk probability records, remaining safe time records, and their correspondence with the performance degradation trend field. These fields serve as direct inputs to the "risk probability and remaining safe time fields" in S330 and provide preliminary judgment basis for writing the upper and lower limits of test parameters and establishing constraints in subsequent S410.

[0093] S330. Based on the risk probability and remaining safe time fields, perform discharge characteristic integration, instantaneous response capability integration, and performance evaluation report generation processing to obtain the performance evaluation report field.

[0094] Specifically, the risk probability and remaining safe time fields are derived from S320, and the risk probability record and remaining safe time record constitute the basic input for this step. The discharge characteristic integration refers to organizing the voltage changes, current changes, capacity decay trends, and test scenario sequence already formed during the preceding tests to obtain a comprehensive record reflecting the discharge state of the test object under different test scenarios. The instantaneous response capability integration refers to organizing the internal state changes, temperature changes, and load switching positions in the real-time response records to obtain a comprehensive record reflecting the instantaneous operating state of the test object during load switching. Further, when executing this step, the control module first reads the risk probability record for each test scenario according to the test scenario sequence in the risk probability and remaining safe time fields; then it calls the relevant records in the real-time operating data field and the first internal state information field corresponding to that test scenario to complete the discharge characteristic integration; subsequently, it reads the real-time response records before and after load switching within that test scenario to complete the instantaneous response capability integration.

[0095] Understandably, the performance evaluation report is not a single conclusion record, but a comprehensive report that integrates risk probability, remaining safe time, discharge characteristics, and instantaneous response capability. The performance evaluation report generation process is collaboratively completed by the control module and the testing system. The control module is responsible for generating report content according to the test scenario, while the testing system is responsible for writing the report content into a unified field and retaining the current round's record position. When the same test object has a high risk probability and a shortened remaining safe time in a high-load test scenario, the performance evaluation report generation process will prioritize writing the integrated discharge characteristics and instantaneous response capability results for that test scenario; when the risk probability is low and the remaining safe time is long in a normal operating test scenario, the corresponding report content will be written in the same order.

[0096] In a feasible engineering embodiment, after the power battery module completes S320, the control module reads the risk probability and remaining safety time fields. Then, it combines the voltage changes, current changes, and real-time response records under high-load test scenarios to form an integrated discharge characteristic result. This is further combined with the load switching location within the scenario to form an integrated instantaneous response capability result, ultimately generating a performance evaluation report for the current round. This performance evaluation report is then further written with content corresponding to the regular operating test scenarios and output as performance evaluation report fields. These performance evaluation report fields include at least the risk probability record, remaining safety time record, integrated discharge characteristic result, integrated instantaneous response capability result, and their correspondence with the test scenario. They serve as direct input to the "performance evaluation report fields" in S410 and are also a core basis for subsequent test parameter upper and lower limit writing, constraint establishment, control algorithm, and adaptive adjustment coefficient processing.

[0097] In summary, this step's technical effects are as follows: It propagates the initial internal status information field downwards to form a performance degradation trend field, then further develops risk probability and remaining safety time fields, and finally generates a performance evaluation report field. This ensures that all preceding data acquisition records, impedance records, and internal status records fall within the same evaluation chain. Compared to methods that rely solely on single-round test results for judgment, this step adds a continuous correspondence between capacity degradation trend, risk probability, remaining safety time, discharge characteristics, and transient response capability, providing a more complete prior basis for subsequent test parameter updates.

[0098] Step S400 includes at least steps S410-S430:

[0099] S410. Obtain the performance evaluation report fields, perform upper and lower limit writing of test parameters and constraint establishment processing to obtain the test parameter constraint fields.

[0100] Specifically, the performance evaluation report fields originate from S330 and include at least the risk probability record, remaining safe time record, integrated discharge characteristic result, integrated instantaneous response capability result, and their correspondence with the test scenario. The upper and lower limits of the test parameters refer to the executable boundaries set for charging current, discharging current, cutoff voltage, temperature range, and test duration before the current test object enters the next round of testing. The constraints refer to the set of conditions after uniformly recording the allowed parameter range, prohibited parameter range, and switching conditions under each test scenario following the writing of the upper and lower limits of the test parameters.

[0101] Specifically, after reading the performance evaluation report fields, the testing system first extracts risk probability records and remaining safety time records according to the test scenario sequence. Then, combining the discharge characteristic integration results and instantaneous response capability integration results, it establishes parameter boundaries for the current test object under low-battery state test scenarios, high-load test scenarios, and normal operation test scenarios. When the risk probability record is at a high position and the remaining safety time record is shortening, the testing system writes the upper limit of charging current, upper limit of discharging current, and upper limit of test duration corresponding to that test scenario in a tightening direction. When the risk probability record is at a low position and the instantaneous response capability integration results are stable, the testing system writes the cutoff voltage range and temperature range corresponding to that test scenario into the normal boundaries. Furthermore, the upper and lower limits of the test parameters are not written at once for a single parameter, but rather the charging and discharging stages are recorded separately, different test scenarios are recorded separately, and each set of parameter boundaries is associated with the corresponding performance evaluation report fields.

[0102] Understandably, the core minimum set in this step consists of the performance evaluation report field, the upper and lower limits of test parameters, and the constraints. The performance evaluation report field is an essential input, while the upper and lower limits of test parameters and the constraints are essential outputs. Preferably, the correspondence between previous and subsequent rounds of writing results for different test objects can be retained for later updates. In an operational engineering embodiment, after the test system reads the performance evaluation report field of a power battery module, it finds that the risk probability record under the high-load test scenario continuously increases, and the remaining safe time record continuously decreases. Therefore, the upper limit of discharge current and the upper limit of test duration under this scenario are written to the lower boundary, while the temperature range is written to the narrower boundary. For the normal operation test scenario, the results of the existing discharge characteristics integration and the instantaneous response capability integration are still written to the normal boundary. After writing is completed, the system establishes corresponding constraints for the above boundaries, forming a test parameter constraint field, and uses this test parameter constraint field as the direct input to the "test parameter constraint field" in S420, while simultaneously forming a coherent field transfer relationship with the preceding S310 to S330.

[0103] S420. Based on the test parameter constraint field, perform control algorithm and adaptive adjustment coefficient processing to obtain the updated test parameter field.

[0104] Specifically, the test parameter constraint field is output by S410, and includes the test scenario correspondence, upper and lower limits of the test parameters, and constraint conditions. The control algorithm refers to the execution rules for adjusting the charging current, discharging current, cutoff voltage, temperature range, and test duration item by item under the constraints of the test parameter upper and lower limits. The adaptive adjustment coefficient refers to the adjustment record that calls upon and corrects the adjustment range of each parameter in the control algorithm based on the risk probability, remaining safe time, discharge characteristics, and instantaneous response capability already recorded in the performance evaluation report field.

[0105] Furthermore, when the test system executes this step, it first reads the test scenario correspondence in the test parameter constraint field, and then calls the control algorithm according to the test scenario. The control algorithm first checks whether the current test parameter exceeds the upper and lower limits of the test parameter, then determines the allowed adjustment direction and allowed adjustment order according to the constraint conditions, and then completes the parameter correction by combining the adaptive adjustment coefficient.

[0106] When the risk probability record corresponding to a certain test scenario is high, the adaptive adjustment coefficient adjusts the charging current, discharging current, and test duration by a smaller margin; when the integrated result of the instantaneous response capability corresponding to a certain test scenario is relatively stable, the adaptive adjustment coefficient adjusts the cutoff voltage and temperature range by a normal margin. Understandably, the adaptive adjustment coefficient does not operate independently of constraints, but is invoked item by item around different test scenarios after the upper and lower limits of test parameters and constraints have been established. The updated test parameter fields are thus formed, and the updated test parameter fields include at least the updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, updated test duration, and the corresponding test scenario identifier. In one engineering embodiment, when the test system invokes the control algorithm for the aforementioned power battery module, it first limits the upper limit of the discharging current based on the test parameter constraint fields in the high-load test scenario, and then reduces the test duration in that scenario in conjunction with the adaptive adjustment coefficient; for normal operation test scenarios, the cutoff voltage and temperature range are kept within normal boundaries, and only the charging current is fine-tuned. After processing, the updated test parameter field is obtained. The updated test parameter field is used as the direct input of the "updated test parameter field" in S430 and continues to serve the next round of test protocol update and parameter configuration writing processing.

[0107] S430. Based on the updated test parameter fields, perform test protocol update and parameter configuration writing processes to obtain the updated test protocol fields.

[0108] Specifically, the updated test parameter fields originate from S420 and already include updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, and updated test duration for different test scenarios. The test protocol update refers to the process of replacing each updated test parameter with the corresponding test scenario according to the original test scenario order in the multi-load test protocol fields. The parameter configuration writing refers to writing the updated parameters corresponding to each test scenario into the execution configuration location of the test system after the test protocol update is completed, ensuring a one-to-one correspondence with the fields in the current round of performance evaluation report.

[0109] Furthermore, the test system first reads the test scenario identifier from the updated test parameter field, then calls the same test scenario record from the original multi-load test protocol field, and writes the updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, and updated test duration into the corresponding positions. For low-power state test scenarios, the test system writes the updated parameters into the execution configuration position of the low-power state test scenario; for high-load test scenarios and normal operation test scenarios, the test system completes the writing in the same way. Understandably, the test protocol update is not a regeneration of a new protocol detached from the preceding fields, but rather a targeted replacement and corresponding writing on the existing structure of the multi-load test protocol field, thereby ensuring that the multi-load test protocol field formed in S100 and the updated test protocol field formed in S400 maintain the same link.

[0110] Furthermore, after the test protocol update and parameter configuration are written, the test system outputs the updated test protocol fields. These updated test protocol fields include at least the updated test scenario sequence, updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, and updated test duration. In subsequent rounds, these fields are transmitted back to S210 as the source of the "multi-load test protocol fields," thus forming a closed-loop operation process from S100 to S400 and back to S200. In an operational engineering embodiment, after completing S420 of a certain power battery module, the test system writes the updated test parameter fields back to the original multi-load test protocol fields, forming a new execution protocol, and then sends this execution protocol to the next round of test tasks. When the next round of testing begins, S210 directly calls the updated test protocol fields to perform voltage, current, and temperature data acquisition. In this way, the test system continuously calls the same protocol link in different rounds of testing and continuously receives new performance evaluation report fields.

[0111] In summary, this step transforms the performance evaluation report fields into test parameter constraint fields, then into updated test parameter fields, and finally into updated test protocol fields. This allows the performance evaluation results to directly enter the next round of test protocol updates. Compared to methods that only provide static evaluations after testing without writing back the test protocol, this step adds a continuous transitive relationship between test parameter upper and lower limits, constraints, control algorithms, adaptive adjustment coefficients, and test protocol updates, enabling the test protocol to be updated round by round based on the results of previous evaluations.

[0112] Example 2: Figure 2 A structural block diagram of a battery test parameter adaptive optimization system according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include:

[0113] The historical operation data processing module 01 is used to acquire historical operation data, process it for different usage stages, and extract load change parameters to obtain historical operation data fields. These fields are then sent to the dynamic load model construction module. Specifically, the historical operation data processing module receives historical operation data generated by the battery during the charging, discharging, resting, and load switching stages. This historical operation data includes voltage records, current records, temperature records, runtime records, and corresponding usage stage records. The module merging historical operation data chronologically, removing missing records, merging duplicate records, marking abnormal jump records, and organizing consecutive records within the same usage stage into corresponding data segments. The load change parameter extraction extracts voltage, current, temperature, and duration values ​​corresponding to the load increase, load decrease, load hold, and load switching processes from each data segment, thereby forming the historical operation data fields. These historical operation data fields maintain a correspondence with the usage stage records and are sent to the dynamic load model construction module as input for dynamic load model construction and model parameter writing.

[0114] The dynamic load model construction module 02 receives the historical operating data field, constructs the dynamic load model, writes model parameters, obtains the dynamic load model field, and sends the dynamic load model field to the multi-load test protocol generation module. Specifically, after receiving the historical operating data field, the dynamic load model construction module reads the corresponding voltage change value, current change value, temperature change value, and duration according to different usage stages, and establishes a dynamic load model according to the load switching order. The dynamic load model consists of stage order, load change amplitude, holding time, temperature range, and corresponding operating state. When writing model parameters, the load change parameters, stage duration, temperature constraints, and switching order of each stage are written into the same model record. When the dynamic load model construction module detects stage order conflicts or missing parameters, the corresponding model record maintains the original stage order and registers an anomaly flag, and does not participate in the current model output. The processed model record forms the dynamic load model field, which is sent to the multi-load test protocol generation module for test scenario setting and multi-load test protocol generation.

[0115] The multi-load test protocol generation module 03 receives the dynamic load model fields, sets test scenarios, and generates multi-load test protocols to obtain multi-load test protocol fields. These fields are then sent to the real-time running data acquisition module. Specifically, after receiving the dynamic load model fields, the multi-load test protocol generation module sets test scenarios according to the stage sequence, load change amplitude, holding time, and temperature range recorded therein. The test scenarios include constant load test scenarios, stepped load test scenarios, continuously switching load test scenarios, and high-temperature operation test scenarios. Each test scenario corresponds to a different voltage acquisition cycle, current acquisition cycle, and temperature acquisition cycle. The multi-load test protocol generation involves writing the execution order, acquisition order, load switching time, and recording time corresponding to each test scenario into a protocol record, forming protocol content that corresponds one-to-one with the dynamic load model. The multi-load test protocol fields also maintain the correspondence between the test scenarios and the dynamic load model fields and are sent to the real-time running data acquisition module as the basis for calling voltage, current, and temperature data acquisition.

[0116] The real-time running data acquisition module 04 receives the multi-load test protocol field, acquires voltage, current, and temperature data to obtain a real-time running data field, and sends the real-time running data field to the electrochemical impedance spectroscopy data recording module. Specifically, the real-time running data acquisition module starts the acquisition process according to the test scenario sequence recorded in the multi-load test protocol field, acquiring voltage, current, and temperature data at each load stage. The real-time running data acquisition module synchronously registers the acquisition start time, load switching time, and acquisition end time, so that voltage, current, and temperature data at the same time form corresponding records. During the acquisition process, the real-time running data acquisition module supplements breakpoint records, marks abnormal peak records, and isolates records outside the test scenario range; isolated records are not included in the current real-time running data field. After time merging and stage correspondence, the real-time running data field is formed, and the real-time running data field is sent to the electrochemical impedance spectroscopy data recording module for use by the impedance real and imaginary part records.

[0117] The electrochemical impedance spectroscopy (EIS) data recording module 05 receives the real-time running data field, records the real and imaginary parts of the impedance to obtain the EIS data field, and sends the EIS data field to the first internal state information extraction module. Specifically, the EIS data recording module reads voltage, current, and temperature records under the same test scenario from the real-time running data field and assigns them according to the acquisition time. The real and imaginary impedance records are segmented based on the response differences at each acquisition time, so that different load stages form corresponding real and imaginary impedance records. The EIS data recording module groups the real, imaginary impedance records, corresponding temperature records, and corresponding load stage records under the same test scenario into a group of EIS data, and retains an interruption marker at the corresponding position when a recording interruption or stage missing is detected. The processed data forms the EIS data field, which is sent to the first internal state information extraction module as input for internal state information extraction and real-time response recording.

[0118] The first internal state information extraction module 06 receives the electrochemical impedance spectroscopy data field, extracts internal state information, and records real-time response to obtain a first internal state information field. This first internal state information field is then sent to the performance evaluation report generation module. Specifically, the first internal state information extraction module reads the impedance real part record, imaginary part record, temperature record, and load stage record corresponding to each test scenario from the electrochemical impedance spectroscopy data field and categorizes them according to the same test scenario. The internal state information extraction extracts the capacity state, decay state, polarization state, and response state from the correspondence between changes in the impedance real part, imaginary part, temperature, and load. The real-time response recording writes the changes in each internal state at load switching and hold times into a record. The first internal state information extraction module sorts the internal state information at different times, maintaining the correspondence with the test scenario and acquisition time, thereby forming the first internal state information field. The first internal state information field is sent to the performance evaluation report generation module for use in capacity decay trend analysis, performance decay trend recording, risk probability, and remaining safety time processing.

[0119] The performance evaluation report generation module 07 receives the first internal state information field, performs capacity decay trend analysis, performance decay trend recording, risk probability and remaining safe time processing, discharge characteristic integration, instantaneous response capability integration, and performance evaluation report generation to obtain the performance evaluation report field, and sends the performance evaluation report field to the test protocol update module. Specifically, after receiving the first internal state information field, the performance evaluation report generation module reads the capacity state, decay state, polarization state, and response state in the order of the test scenarios. The capacity decay trend analysis forms a sequential trend record based on the capacity state changes under different test scenarios. The performance decay trend record registers the changes in decay state and response state during continuous testing. The risk probability and remaining safe time processing divides the internal state changes corresponding to each test scenario into intervals and forms corresponding records for high-risk intervals and remaining operational intervals. The discharge characteristic integration merges the discharge process records under different test scenarios into unified content, and the instantaneous response capability integration merges the response status during the load switching phase into unified content. After the merging is completed, a performance evaluation report is generated, forming the fields of the performance evaluation report, and sent to the test protocol update module for writing upper and lower limits of test parameters, establishing constraints, and calling control algorithms and adaptive adjustment coefficients.

[0120] The test protocol update module 08 receives the performance evaluation report fields, writes upper and lower limits for test parameters and establishes constraints, processes control algorithms and adaptive adjustment coefficients, updates the test protocol, and writes parameter configurations to obtain updated test protocol fields. Specifically, the test protocol update module reads the capacity decay trend record, performance decay trend record, risk probability record, remaining safe time record, integrated discharge characteristic content, and integrated instantaneous response capability content from the performance evaluation report fields. It writes upper and lower limits for the voltage range, current range, temperature range, acquisition time, and load switching time of the test parameters and establishes constraints corresponding to each test scenario. The control algorithm and adaptive adjustment coefficient processing adjust the parameter change amplitude, parameter holding time, and load switching sequence for each test scenario according to the constraints, and writes the adjustment results into a new protocol record. After the test protocol update module completes the test protocol update and parameter configuration writing, it generates the updated test protocol field. The updated test protocol field is sent back to the real-time running data acquisition module as the basis for calling the next round of voltage, current and temperature data acquisition, while maintaining the correspondence with the historical running data field, dynamic load model field, multi-load test protocol field, real-time running data field, electrochemical impedance spectroscopy data field, first internal state information field and performance evaluation report field.

Claims

1. An adaptive optimization method for battery testing parameters, characterized in that, include: S100: Obtain historical battery operation data, divide the usage stages according to voltage, current, and capacity retention rate, extract load change parameters, build a dynamic load model based on the stages and parameters, set low power, high load, and normal operation test scenarios according to the model, and generate multiple load test protocol fields. S200: Collect real-time operating data of voltage, current and temperature according to the multi-load test protocol field, trigger electrochemical impedance spectroscopy acquisition in combination with the test scenario, extract the real part and imaginary part of impedance and correspond to the scenario to obtain the first internal state information field. S300: Analyze the capacity decay trend based on the first internal state information field, determine the risk probability and remaining safe time for each test scenario, integrate discharge characteristics and instantaneous response capability, and generate a performance evaluation report field. S400. Based on the performance evaluation report fields, the upper and lower limits and constraints of the test parameters are written. The test parameters are corrected by combining the control algorithm with the adaptive adjustment coefficient. The corrected parameters are then replaced with the original test protocol to obtain the updated test protocol fields.

2. The method according to claim 1, characterized in that, The process of acquiring historical battery operating data, dividing usage stages according to voltage, current, and capacity retention rate, and extracting load change parameters includes: Historical operating data includes voltage, current, temperature, capacity retention rate, internal resistance change rate, and corresponding time sequence records. The historical operating data is a collection of historical records continuously formed according to the cyclic charge and discharge process. Historical records in similar operating states are grouped into the same stage record, and discontinuous, missing or abnormally abrupt records are kept separately as abnormal records. Abnormal records are not deleted directly and will not participate in the current round of extraction when extracting load change parameters in subsequent rounds, while retaining their original record positions. First, the historical operating data is segmented sequentially according to different usage stages. Then, the current change, duration, and temperature change within each segment are recorded to extract the load change cycle. The load change is extracted according to the current difference and temperature difference between adjacent records within the same stage. Finally, the discharge rate parameter is extracted by combining the changes in capacity retention rate and internal resistance change rate within that stage.

3. The method according to claim 2, characterized in that, The process of building a dynamic load model based on stages and parameters, and generating multiple load test protocol fields according to the model's settings for low power, high load, and normal operation test scenarios, includes: The corresponding relationships between different usage stages, load change parameters, voltage, current, temperature, capacity retention rate, and internal resistance change rate are uniformly organized, and the model parameters are written in. The model parameters include stage corresponding parameters, load change cycle parameters, load change amount parameters, discharge rate parameters, and attenuation record parameters corresponding to capacity retention rate and internal resistance change rate. Based on the phase sequence, load change cycle, and discharge rate parameters of each model segment in the dynamic load model field, the execution sequence, duration, and load switching position in each test scenario are determined. Multiple test scenarios are then connected sequentially to form a multi-load test protocol. The multi-load test protocol includes the load change cycle, load change amount, discharge rate parameters, and voltage, current, and temperature data acquisition periods corresponding to each test scenario.

4. The method according to claim 3, characterized in that, The process of acquiring real-time operating data of voltage, current, and temperature according to the aforementioned multi-load test protocol fields includes: Within each test scenario, the data acquisition and processing unit continuously acquires, sequentially records, and matches the battery's terminal voltage, operating current, and temperature according to the load change cycle and discharge rate parameters, ensuring that the same set of voltage, current, and temperature data carries corresponding test scenario information and acquisition sequence information.

5. The method according to claim 4, characterized in that, The process of triggering electrochemical impedance spectroscopy acquisition based on the test scenario, extracting the real and imaginary parts of the impedance, and mapping them to the corresponding scenario includes: Based on the test scenario switching position in the real-time running data field, an impedance recording trigger command is sent to the test system so that the recording and processing of the real and imaginary parts of impedance are performed separately in the low power state test scenario, the high load test scenario, and the normal running test scenario. Upon receiving the impedance recording trigger command, the voltage, current, and temperature records under the corresponding scenario are sequentially associated with the real and imaginary parts of the impedance at the current frequency to form the impedance record corresponding to the scenario. The real part of the impedance is used to characterize the ohmic characteristics of the battery in the current operating stage, and the imaginary part of the impedance is used to characterize the polarization and diffusion changes of the battery in the current operating stage.

6. The method according to claim 5, characterized in that, The process of obtaining the first internal status information field includes: First, read the test scenario records in the electrochemical impedance spectroscopy data field. Then, compare the real and imaginary parts of the impedance in the same test scenario in sequence to extract the corresponding internal state information. The internal state information includes the charge transfer state, electrolyte diffusion state, and internal state change records corresponding to the current test scenario. Then, match the internal state changes in the current test scenario with the voltage, current, and temperature records in the real-time operation data field and write them into the real-time response record. The real-time response record includes internal state change records in the low charge state test scenario, the high load test scenario, and the normal operation test scenario.

7. The method according to claim 6, characterized in that, The process of analyzing capacity decay trends, determining the risk probability and remaining safe time for each test scenario, integrating discharge characteristics and instantaneous response capabilities, and generating performance evaluation report fields includes: The internal state change records under different test scenarios are read sequentially, and combined with the voltage, current and temperature changes in the real-time response records, the direction, rate and location of capacity retention rate changes during continuous testing are analyzed to form a continuous trend record segmented along the low charge state test scenario, high load test scenario and normal operation test scenario; when a record with accelerated charge transfer state change and continuous temperature rise appears in the first internal state information field, the record is identified as a key record in the capacity decay trend; when a record with stable electrolyte diffusion state and small voltage and current changes appears, it is identified as a stable record; First, key records and stable records are read from the performance degradation trend field, and then compared in the order of test scenarios. When key records appear repeatedly in consecutive rounds, the control module increases the risk probability record for the corresponding scenario. When the proportion of stable records increases in consecutive rounds, the control module extends the remaining safe time record for the corresponding scenario. Risk probability refers to the result after recording the possibility that the current test object will experience abnormal degradation, reduced load capacity, or fluctuations in operating status in subsequent test processes. Remaining safe time refers to the result after processing the time range within which the test object can continue to run according to the existing multi-load test protocol based on the performance degradation trend record of the current round and the change position of the previous and subsequent rounds. First, according to the test scenario order in the risk probability and remaining safe time fields, read the risk probability records for each test scenario; then call the relevant records in the real-time running data field and the first internal status information field corresponding to the test scenario to complete the discharge characteristic integration and obtain a comprehensive record reflecting the discharge status of the test object under different test scenarios; then read the real-time response records before and after load switching in the test scenario to complete the instantaneous response capability integration and obtain a comprehensive record reflecting the instantaneous running status of the test object during load switching.

8. The method according to claim 7, characterized in that, The process of writing the upper and lower limits and constraints of the test parameters according to the fields in the performance evaluation report includes: After reading the performance evaluation report fields, the risk probability record and remaining safety time record are extracted according to the test scenario order. Then, combined with the discharge characteristic integration result and the instantaneous response capability integration result, parameter boundaries are established for the current test object under the low-power state test scenario, the high-load test scenario, and the normal operation test scenario. When the risk probability record is at a high position and the remaining safety time record is shortened, the upper limit of charging current, the upper limit of discharging current, and the upper limit of test duration corresponding to the test scenario are written in the tightening direction. When the risk probability record is at a low position and the instantaneous response capability integration result is stable, the cutoff voltage range and temperature range corresponding to the test scenario are written into the normal boundary. Corresponding constraints are established for the above boundaries to form the test parameter constraint condition field.

9. The method according to claim 8, characterized in that, The process of correcting test parameters using a control algorithm combined with adaptive adjustment coefficients, and then replacing the corrected parameters with those of the original test protocol, includes: First, the test scenario correspondence in the test parameter constraint field is read, and then the control algorithm is called according to the test scenario. The control algorithm first checks whether the current test parameter exceeds the upper and lower limits of the test parameter, then determines the allowed adjustment direction and order according to the constraint conditions, and then completes the parameter correction by combining the adaptive adjustment coefficient. When the risk probability record corresponding to a certain test scenario is high, the adaptive adjustment coefficient adjusts the charging current, discharging current and test duration by a smaller margin. When the instantaneous response capability integration result corresponding to a certain test scenario is relatively stable, the adaptive adjustment coefficient adjusts the cutoff voltage and temperature range by a normal margin. The updated test parameter field includes the updated charging current, updated discharging current, updated cutoff voltage, updated temperature range, updated test duration and the corresponding test scenario identifier.

10. A battery testing parameter adaptive optimization system, characterized in that, include: The system comprises a historical operation data processing module, a dynamic load model construction module, a multi-load test protocol generation module, a real-time operation data acquisition module, an electrochemical impedance spectroscopy data recording module, a first internal state information extraction module, a performance evaluation report generation module, and a test protocol update module; these modules are connected in sequence to implement the method described in any one of claims 1-9.

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