An electric vehicle battery management detection system and method
Patent Information
- Application Number
- CN202611231175.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-14
- Publication Date
- 2026-09-25
AI Technical Summary
[0005]本发明的目的在于提供一种电动汽车电池管理检测系统及方法，以解决现有电池管理检测技术在工况切换时的状态评估，以及基于固定阈值判定的控制响应优化方面存在的不足，从而实现内阻异常、发热异常与健康状态变化协同识别，以及动态优化分级控制联动；本发明的技术方案包括：
[0029]上述方案中，采集模块对电池包及各电池单体的运行数据进行实时采集并完成时序对齐，参数计算模块基于统一时间戳的数据流结合历史标定参数库计算实时欧姆内阻、内阻偏差值和异常发热率，状态评估模块再结合历史安全评分统计值及最近一次健康状态评估结果生成安全评分，并据此输出风险等级和分级报警信号，控制模块则联动整车控制器和热管理执行部件执行限流、降功率及热管理调节，同时将控制后的温升差值和限流后电流下降幅度回传更新历史安全评分统计值。该方案能够提升多通道数据衔接的稳定性，增强低负荷与高负荷工况下的异常识别准确性，并提高报警与控制联动的针对性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle battery management and fault detection technology, specifically to an electric vehicle battery management and detection system and method. Background Technology
[0002] Existing battery management and monitoring systems are typically used to collect, analyze, and alarm control the operating status of battery packs and individual battery cells.
[0003] While related technologies can monitor battery status by collecting parameters such as cell terminal voltage, branch current, and temperature, the arrival order of different sampling channels can easily become inconsistent during operation. Furthermore, subsequent parameter calculations often rely on historical calibration data and intermediate result caching, leading to unstable connections between data timing alignment, status assessment, and control linkage. Especially during the switching between low-load and high-load conditions, relying solely on fixed thresholds or single characteristics for judgment can easily result in difficulties in coordinating the identification of abnormal internal resistance, abnormal heating, and changes in health status, thus affecting alarm accuracy and the targeted nature of control responses.
[0004] Therefore, battery management and detection solutions in related technologies cannot simultaneously achieve continuous data acquisition, accurate parameter calculation, and real-time linkage effects of hierarchical control. Summary of the Invention
[0005] The purpose of this invention is to provide an electric vehicle battery management detection system and method to address the shortcomings of existing battery management detection technologies in terms of state assessment during operating condition switching and control response optimization based on fixed threshold judgments. This allows for the collaborative identification of internal resistance anomalies, heating anomalies, and changes in health status, as well as dynamic optimization of hierarchical control linkage. The technical solution of this invention includes:
[0006] An electric vehicle battery management and testing system is applied to a battery pack containing a battery pack and at least two individual battery cells, and the system is connected to a vehicle controller and a thermal management execution component, respectively, including a data acquisition module, a parameter calculation module, a status assessment module and a control module;
[0007] The acquisition module is connected to the battery pack and each of the individual battery cells, and is used to collect operating data in real time and output a data stream with a unified timestamp.
[0008] The parameter calculation module is connected to the acquisition module and is used to calculate the real-time ohmic internal resistance, internal resistance deviation value and abnormal heat generation rate based on the data stream.
[0009] The status assessment module is connected to the parameter calculation module and is used to generate a safety score based on the internal resistance deviation value and the abnormal heating rate, and output a graded alarm signal based on the comparison result of the safety score and the preset dynamic alarm threshold.
[0010] The control module is connected to the status assessment module, the vehicle controller, and the thermal management execution component. It is used to perform current limiting, power reduction, and vehicle thermal management adjustment based on the graded alarm signal, and to send back the temperature rise difference after performing the vehicle thermal management adjustment and the current drop after performing the current limiting operation to the status assessment module.
[0011] A method for testing electric vehicle battery management includes the following steps:
[0012] Step 1: The acquisition module collects the operating data of the battery pack and each individual battery cell in real time. The operating data includes at least the branch current and forms a data stream with a unified timestamp, which is then output to the parameter calculation module.
[0013] Step 2: The parameter calculation module calculates the real-time ohmic internal resistance, internal resistance deviation value and abnormal heat generation rate based on the data stream, and outputs them to the status assessment module.
[0014] Step 3: The status assessment module determines the current multiplier based on the branch current in the data stream, and processes the internal resistance deviation value and the abnormal heat generation rate based on the current current multiplier to obtain a safety score.
[0015] Step four: The status assessment module compares the safety score with the preset dynamic alarm threshold. If the score is greater than or equal to the dynamic alarm threshold, a graded alarm signal is output to the control module; if the score is less than the dynamic alarm threshold, the monitoring status is maintained.
[0016] Step 5: After receiving the graded alarm signal, the control module performs current limiting, power reduction, and thermal management adjustments. In the next sampling window, it executes Step 2 and Step 3 again, calculates the difference between the safety score generated in the current sampling window and the next sampling window, and upgrades the alarm level of the graded alarm signal if the difference is less than the preset score reduction threshold.
[0017] Preferably, the data stream acquired by the acquisition module includes sampling timestamps, battery cell terminal voltages, branch currents, surface temperatures, ambient temperatures, and current state of charge.
[0018] Preferably, the parameter calculation module is used to determine and output the internal resistance deviation value based on the current state of charge, the ambient temperature, the battery cell terminal voltage, the branch current, and the preset reference internal resistance.
[0019] Preferably, the parameter calculation module is used to calculate the total heat generation rate based on the rate of change of the surface temperature, and to determine the abnormal heat generation rate based on the total heat generation rate, the branch current, and the real-time ohmic internal resistance.
[0020] Preferably, the state evaluation module is preset with a first current multiplier threshold and a second current multiplier threshold, used to determine the current multiplier based on the branch current and to execute dynamic weight allocation logic based on the current current multiplier; when the current current multiplier is less than the first current multiplier threshold, the weight of the internal resistance deviation value is increased;
[0021] When the current current multiplier is greater than or equal to the second current multiplier threshold, the weight of the abnormal heating rate is increased; when the current current multiplier is greater than or equal to the first current multiplier threshold and less than the second current multiplier threshold, the preset base weight is maintained; and the first current multiplier threshold is less than the second current multiplier threshold.
[0022] Preferably, the dynamic alarm threshold is calculated from historical safety score statistics and health status correction values. The historical safety score statistics are determined by the safety score distribution characteristics within a preset sampling period, and the health status correction values are determined based on the most recent health status assessment results.
[0023] Preferably, the control module is used to control the thermal management execution component and the vehicle controller according to the graded alarm signal. The control module has a first preset weight threshold and a second preset weight threshold. When the weight corresponding to the abnormal heating rate is higher than the first preset weight threshold, the thermal management execution component of the battery pack where the corresponding battery cell is located is controlled to increase the cooling power. When the weight of the internal resistance deviation value is higher than the second preset weight threshold, the current limiting is executed.
[0024] Preferably, the data stream in step one includes the battery cell terminal voltage, branch current, ambient temperature, and current state of charge; in step two, the internal resistance deviation value is determined and output based on the current state of charge, the ambient temperature, the battery cell terminal voltage, the branch current, and the preset reference internal resistance.
[0025] Preferably, in step five, the control module performs current limiting, power reduction, and thermal management adjustments, including:
[0026] Send control commands to the vehicle controller;
[0027] The control module executes control according to a pre-set weight allocation rule. The weight allocation rule specifies the weight of abnormal heat generation rate and the weight of internal resistance deviation value associated with the current current ratio. The weight allocation rule is preset with a first preset weight threshold and a second preset weight threshold: when the weight of abnormal heat generation rate is higher than the first preset weight threshold, the thermal management execution component is controlled to increase the cooling power of the battery pack containing the corresponding battery cell; when the weight of internal resistance deviation value is higher than the second preset weight threshold, current limiting is executed.
[0028] Compared with the prior art, the present invention has the following beneficial effects:
[0029] In the above scheme, the acquisition module collects real-time operating data of the battery pack and individual battery cells and performs timing alignment. The parameter calculation module calculates real-time ohmic internal resistance, internal resistance deviation, and abnormal heat generation rate based on a unified timestamp data stream and a historical calibration parameter library. The status assessment module then generates a safety score by combining historical safety score statistics and the most recent health status assessment results, and outputs risk level and graded alarm signals accordingly. The control module, in conjunction with the vehicle controller and thermal management actuators, performs current limiting, power reduction, and thermal management adjustments, while simultaneously updating the historical safety score statistics with the temperature rise difference after control and the current reduction after current limiting. This scheme can improve the stability of multi-channel data connection, enhance the accuracy of anomaly identification under low and high load conditions, and improve the targeting of alarm and control linkage. Attached Figure Description
[0030] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. The drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0031] Figure 1 This is a schematic diagram of a module of an electric vehicle battery management and detection system provided in an embodiment of the present invention.
[0032] Figure 2 This is a logic control flowchart of an electric vehicle battery management and detection method provided in an embodiment of the present invention. Detailed Implementation
[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0034] Please see the appendix Figure 1This embodiment provides an electric vehicle battery management detection system and its corresponding detection method, which aims to realize real-time acquisition, parameter calculation, status evaluation and hierarchical control linkage of the operating status of the battery pack and each individual battery cell; the system is applied to a battery pack containing a battery pack and at least two individual battery cells, and is connected to the vehicle controller and thermal management execution unit respectively, and specifically includes an acquisition module, a parameter calculation module, a status evaluation module and a control module;
[0035] The acquisition module is connected to the battery pack and each individual battery cell, and is used to collect operating parameters in real time and output a data stream with a unified timestamp.
[0036] The parameter calculation module, connected to the acquisition module, is used to calculate the real-time ohmic internal resistance, internal resistance deviation value, and abnormal heat generation rate based on the data stream.
[0037] The status assessment module, connected to the parameter calculation module, is used to generate a safety score based on the internal resistance deviation value and abnormal heating rate, and output a graded alarm signal based on the comparison result of the safety score and the preset dynamic alarm threshold.
[0038] The control module connects the status assessment module, the vehicle controller, and the thermal management execution unit. It is used to perform current limiting, power reduction, and vehicle thermal management adjustment based on the graded alarm signals, and to send back the temperature rise difference after the vehicle thermal management adjustment and the current drop after the current limiting operation to the status assessment module.
[0039] The parameter calculation results are written to the intermediate result cache for direct use by the status assessment module. The status assessment module connects the parameter calculation module and the control module. It is used to obtain the pre-stored historical safety score statistics and the most recent health status assessment results read from the external battery management main control chip. It calculates the safety score based on the internal resistance deviation value, abnormal heat generation rate, historical safety score statistics and the most recent health status assessment results. It compares the safety score with the dynamic alarm threshold and outputs the risk level and graded alarm signal when the safety score is greater than or equal to the dynamic alarm threshold.
[0040] If the threshold is not reached, the monitoring status is maintained. The control module connects the status assessment module, the vehicle controller, and the thermal management execution unit. It is used to send control commands to the vehicle controller according to the risk level and control the thermal management execution unit to perform current limiting, power reduction, and thermal management adjustment. It also sends the temperature rise difference after control and the current drop after current limiting back to the status assessment module to update the historical safety score statistics.
[0041] In application conditions, the acquisition module continuously acquires the terminal voltage, branch current, surface temperature, ambient temperature, and current state of charge of each battery cell, forming a continuous data stream with a unified timestamp. To avoid inconsistencies in the arrival order of different sampling channels affecting subsequent calculations, the acquisition results are time-aligned before entering the parameter calculation module and written into a temporary data record for the current period, which can be directly read by subsequent modules. The current period is a single sampling cycle executed by the acquisition module according to a preset sampling interval. The preset sampling interval is calibrated before the system leaves the factory and pre-stored in the historical calibration parameter library, with a value range of 10ms to 100ms, preferably 50ms consistent with the communication message cycle of the vehicle controller. The current period used by the parameter calculation module to calculate the surface temperature change rate within the current period is consistent with the aforementioned single sampling cycle. The surface temperature change rate is the difference between the surface temperature of the current sampling period and the surface temperature of the previous sampling period divided by the preset sampling interval. In the formula, The surface temperature during the current sampling period. The surface temperature of the previous sampling period. The preset sampling interval is used; the preset sampling period is a continuous backward regression from the current time. A time window consisting of sampling periods The preset number of cycles is set to 100, meaning that the historical safety score statistics are determined by the mean and standard deviation of the safety scores over the most recent 100 sampling periods.
[0042] After reading the above data stream, the parameter calculation module combines the pre-stored historical calibration parameter library to calculate the real-time ohmic internal resistance, internal resistance deviation value and abnormal heating rate at the individual unit level. The real-time ohmic internal resistance is used to characterize the resistance change of the current unit under the operating conditions, the internal resistance deviation value is used to reflect the degree of deviation of it from the preset reference internal resistance, and the abnormal heating rate is used to reflect the difference between the measured temperature rise and the normal Joule heating.
[0043] In addition to receiving the above intermediate results, the status assessment module also reads the historical safety score statistics and the most recent health status assessment result. The historical safety score statistics are used to reflect the distribution of safety scores within a preset number of cycles, and the most recent health status assessment result is used to correct the current judgment baseline.
[0044] The status assessment module generates a current safety score based on this and compares it with the dynamic alarm threshold. When the safety score reaches or exceeds the dynamic alarm threshold, it outputs the corresponding risk level and graded alarm signal. After receiving the graded alarm signal, the control module sends a control command to the vehicle controller according to the risk level and links the thermal management execution component to perform current limiting, power reduction and thermal management adjustment.
[0045] After the control action is completed, the system re-acquires the temperature rise difference and the current drop after current limiting in the subsequent sampling window, and sends these two results back to the status assessment module to update the historical safety score statistics. If the data collected in the subsequent period is incomplete, the acquisition module will prioritize using the stable record in the previous effective period and mark the record as a continuation value to ensure the processing continuity of the parameter calculation module and the status assessment module.
[0046] The system further elaborates on the parameter calculation and state evaluation process, focusing on the calculation methods of real-time ohmic resistance, abnormal heat generation rate, and dynamic weight allocation.
[0047] The parameter calculation module is used to determine and output the internal resistance deviation value based on the current state of charge, ambient temperature, battery cell terminal voltage, branch current and preset reference internal resistance.
[0048] The process applies to operating ranges below a set load threshold. Within this range, the individual unit terminal voltage is affected by polarization and falls below the polarization interference threshold. First, based on the current state of charge and ambient temperature, the corresponding open-circuit voltage is retrieved from the historical calibration parameter library. Then, the real-time ohmic internal resistance is calculated by combining the individual unit terminal voltage and branch current. The real-time ohmic internal resistance is compared with a preset reference internal resistance to obtain the internal resistance deviation rate, which is then written as the internal resistance deviation value into the pending queue of the state assessment module. The calculation of the real-time ohmic internal resistance is based on an equivalent circuit model, and its calculation logic is as follows:
[0049]
[0050] In the formula, For real-time ohmic internal resistance, Open circuit voltage, This refers to the terminal voltage of a single battery cell. This refers to the branch current; for example, if the open-circuit voltage is obtained from a table... The measured terminal voltage is 3.8V. The voltage is 3.6V, and the branch current is... If the value is 100A, then the real-time internal resistance in ohms is calculated. The resistance is 2mΩ; further, the preset reference internal resistance under the current operating condition is obtained from the historical calibration parameter library. For example, if the resistance is 1.6mΩ, then the internal resistance deviation rate is defined as:
[0051]
[0052] In the formula, Internal resistance deviation rate The internal resistance is preset to a reference value, and the calculated internal resistance deviation value is 25%. This value will be used in the subsequent dynamic weight allocation.
[0053] The parameter calculation module is also used to calculate the total heat generation rate based on the rate of change of surface temperature, and to determine the abnormal heat generation rate based on the total heat generation rate, branch current and real-time ohmic internal resistance. This process is applicable to the range of loads greater than or equal to the set load threshold, within which the surface temperature change is used to determine abnormal heat generation.
[0054] The parameter calculation module calculates the surface temperature change rate within the current cycle. At this time, the calculation of the total heat generation rate requires the use of the comprehensive heat capacity coefficient and the convective heat transfer coefficient constant. These comprehensive heat capacity coefficient and convective heat transfer coefficient constant are obtained by the standard charge-discharge thermal cycle test bench calibration before the system leaves the factory and are pre-stored in the historical calibration parameter library.
[0055] Specifically, the parameter calculation module multiplies the surface temperature change rate with the comprehensive heat capacity coefficient to obtain the initial heat storage power, and combines the current surface temperature with the ambient temperature to compensate for the heat dissipation power loss. The heat dissipation power loss is calculated based on the product of the convective heat transfer coefficient constant and the temperature difference pre-stored in the historical calibration parameter library, and the sum of the two is used as the total heat generation rate.
[0056] For example, if the overall thermal capacity coefficient of a single unit is calibrated to be 1000 J / K, the current surface temperature change rate is 0.05 K / s, and the initial heat storage power is calculated to be 50 W; at the same time, the heat dissipation power compensation due to the temperature difference is evaluated to be 10 W, then the total heat generation rate is 60 W; the parameter calculation module calculates a Joule heating rate of 40 W based on the branch current of 200 A and the real-time ohmic internal resistance of 1 mΩ, and subtracts the Joule heating rate from the total heat generation rate to obtain an abnormal heating rate of 20 W, thereby quantifying the degree of abnormal heating caused by non-Joule effects;
[0057] If the surface temperature channel is missing in a certain period, the temperature record of the previous valid period will be read first to participate in the rate of change calculation, and that period will be marked as data to be reviewed in order to avoid interruption of the abnormal heating rate calculation.
[0058] The status assessment module has a first current multiplier threshold and a second current multiplier threshold, which are used to determine the current multiplier based on the branch current and to execute dynamic weight allocation logic based on the current current multiplier; when the current current multiplier is less than the first current multiplier threshold, the weight of the internal resistance deviation value is increased.
[0059] When the current current ratio is greater than or equal to the second current ratio threshold, the weight of the abnormal heating rate is increased; when the current current ratio is greater than or equal to the first current ratio threshold but less than the second current ratio threshold, the preset base weight is maintained; and the first current ratio threshold is less than the second current ratio threshold; the weight adjustment result directly affects the safety score calculation, that is, the final score is calculated by superimposing the base score with the product of each abnormal feature and its dynamic weight, without the need to build an independent judgment logic table, reducing the redundant transmission of intermediate state data;
[0060] For example, when a single cell is in a discharge condition where the current rate is less than the first threshold, the system can dynamically increase the weight of the internal resistance deviation value from the usual 0.4 to 0.7, while proportionally reducing the weight of the abnormal heating rate, in order to increase the influence weight of the internal resistance deviation value when it is below the first current rate threshold in the final safety score; on the control link, if the safety score reaches the dynamic alarm threshold, the control module sends a torque limit request to the vehicle controller according to the risk level, and links the thermal management execution component to perform current limiting, power reduction and thermal management adjustment;
[0061] If the difference in safety scores before and after the next sampling window does not show a decreasing trend after recalculation, the control module retains the current alarm level and sends back the temperature rise difference and the current drop after current limiting to the status evaluation module to update the historical safety score statistics. Since the heat conduction process has a physical hysteresis effect, directly evaluating the thermal management effect in the adjacent next sampling window will cause timing logic conflicts. Therefore, the above feedback mechanism implements separate processing based on different physical time delays: for the electrical feedback of the current drop after current limiting, the current sampling frequency is maintained and read in real time.
[0062] For the temperature rise difference after control, a preset thermal inertia delay compensation is introduced. Temperature rise data is only extracted for effective feedback when the duration after control occurs crosses the minimum effective response window determined by the thermal capacity coefficient. The minimum effective response window is obtained in advance by the system through charge-discharge thermal cycle calibration test, and is usually taken as 1.5 to 2 times the thermal time constant of the battery cell. This ensures the physical rationality of the timing alignment of multi-dimensional parameters. Through the above timing decoupling process, the dynamic alarm threshold can be continuously corrected according to the historical score distribution of the real characterization and the latest health status assessment results, avoiding long-term fixation on a single reference value.
[0063] Further details on the dynamic alarm threshold, control strategy, and method execution steps in the system and method, with a focus on explaining the triggering logic of threshold correction, alarm linkage, and control actions;
[0064] Among them, the dynamic alarm threshold is calculated from the historical safety score statistics and the health status correction value. The historical safety score statistics are determined by the safety score distribution characteristics within the preset sampling period, and the health status correction value is determined based on the most recent health status assessment result.
[0065] After generating a safety score, the status assessment module does not directly use a fixed alarm boundary. Instead, it combines the mean and standard deviation of the safety scores within a preset number of cycles to form a historical safety score statistical value. Then, it adds a compensation coefficient determined by the most recent health status assessment result to obtain a dynamic alarm threshold. This allows the threshold to be corrected according to the current health status, avoiding threshold mismatch after long-term use.
[0066] In the specific calculation logic, the system extracts historical security score records within a preset number of cycles, such as the most recent 100 cycles, and calculates the average security score. Standard deviation of safety rating Dynamic alarm threshold The calculation rules are as follows:
[0067]
[0068] In the formula, The confidence coefficient is... This is a correction value for health status; for example, a value of 3 to cover 99.7% of the normal fluctuation range; if It is 50 points. It is 5 points. And the most recent health status assessment result was within the normal set range, making If the value is 0, then the dynamic alarm threshold is set to 65 points;
[0069] The control module is used to control the thermal management actuator and the vehicle controller according to the graded alarm signal. The control module has a first preset weight threshold and a second preset weight threshold. When the weight corresponding to the abnormal heat rate is higher than the first preset weight threshold, the thermal management actuator of the battery pack where the corresponding battery cell is located is controlled to increase the cooling power.
[0070] When the weight of the internal resistance deviation value is higher than the second preset weight threshold, current limiting is executed. It should be noted that the above weight threshold comparison is the control action selection logic executed by the control module after receiving the graded alarm signal. That is, only when the safety score generated by the state assessment module is greater than or equal to the dynamic alarm threshold, and the control module has received the graded alarm signal of the risk level corresponding to the safety score, will the control module compare the weight corresponding to the abnormal heating rate with the first preset weight threshold and the weight corresponding to the internal resistance deviation value with the second preset weight threshold, so as to determine the priority action type between increasing cooling power and executing current limiting. Among them, the weight corresponding to the abnormal heating rate and the weight corresponding to the internal resistance deviation value are dynamically weighted by the state assessment module according to the current current ratio and sent to the control module along with the graded alarm signal. The current current ratio is only used to adjust the weight ratio of the two abnormal features in the safety score. The triggering of the control action is based on the comparison result of the safety score and the dynamic alarm threshold. Specifically, when the weight value corresponding to the abnormal heating rate is greater than the first preset weight threshold, the control module prioritizes issuing adjustment instructions to the thermal management execution component to improve the cooling capacity of the corresponding battery module.
[0071] When the weight value corresponding to the internal resistance deviation value is greater than the second preset weight threshold, a torque limiting request is sent to the vehicle controller first, and the unit load is reduced in conjunction with the current limiting strategy. The above two types of control actions are not triggered independently, but are triggered in a coordinated manner according to the risk level and weight result, so that control resources can be concentrated on the current dominant abnormal feature.
[0072] In terms of method execution, the data stream in step one includes the battery cell terminal voltage, branch current, ambient temperature, and current state of charge; in step two, based on the current state of charge, ambient temperature, battery cell terminal voltage, branch current, and preset reference internal resistance, the internal resistance deviation value is determined and output; if a cell's branch current data shows a short-term anomaly in the current cycle, the valid segment within the current cycle is used first in the calculation, and the deviation mark of that cycle is retained for verification during subsequent health status assessment;
[0073] In the control steps, step five, in which the control module performs current limiting, power reduction, and thermal management adjustments, includes: sending control commands to the vehicle controller; the control module executing control according to a pre-set weight allocation rule, which specifies the weight of abnormal heating rate and the weight of internal resistance deviation value associated with the current current rate, and the weight allocation rule has a first preset weight threshold and a second preset weight threshold: when the weight of abnormal heating rate is higher than the first preset weight threshold, the thermal management execution component is controlled to increase the cooling power of the battery pack containing the corresponding battery cell; when the weight of internal resistance deviation value is higher than the second preset weight threshold, current limiting is executed;
[0074] When the weight assigned to the internal resistance deviation value is higher than the second preset weight threshold, current limiting control is executed; if the recalculation result in the next sampling window shows that the risk has not decreased, the alarm level is maintained or upgraded, and the current control strategy is continued until the safety score falls back below the dynamic alarm threshold.
[0075] To eliminate the conflict of response time differences between different underlying physical variables, the process of judging whether the risk has decreased based on the difference in safety scores before and after the above control adopts a comprehensive verification logic that combines transient electrical response and hysteretic thermal response. Considering that the electrical change caused by current limiting is a transient process within the preset electrical transient assessment period, while activating the cooling capacity of the corresponding battery module to achieve the temperature conduction of the entire pack is a physical evolution with preset thermal delay properties, the system decouples and distinguishes electrical and thermal terms when recalculating.
[0076] For the internal resistance risk score caused by current limiting, the electrical reading value of the next sampling window is directly used for updating and correction; while for the thermodynamic risk dominated by abnormal heat generation rate, within its corresponding physical cooling delay threshold, the alarm logic is not directly triggered to upgrade because the surface temperature has not dropped temporarily. Instead, the theoretical trend of expected heat generation reduction is used to replace the measured temperature drop in the intermediate calculation of safety scoring; only when the temperature rise does not decrease after exceeding this preset delay threshold is it determined that the system risk has not decreased and the alarm level is upgraded, thereby constructing a stable and reliable closed-loop control logic.
[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. An electric vehicle battery management and testing system, applied to a battery pack comprising a battery pack and at least two individual battery cells, wherein the system is connected to a vehicle controller and a thermal management execution component, characterized in that, It includes a data acquisition module, a parameter calculation module, a status assessment module, and a control module; The acquisition module is connected to the battery pack and each of the individual battery cells, and is used to collect operating data in real time and output a data stream with a unified timestamp. The parameter calculation module, connected to the acquisition module, is used to calculate the real-time ohmic internal resistance, internal resistance deviation, and abnormal heat generation rate based on the data stream. Specifically, the parameter calculation module retrieves the corresponding open-circuit voltage from the historical calibration parameter library based on the current state of charge and ambient temperature in the data stream, and calculates the real-time ohmic internal resistance according to the following formula: In the formula, Rreal is the real-time ohmic internal resistance, Uocv is the open-circuit voltage, Ut is the single-cell terminal voltage, and Ibranch is the branch current; and the internal resistance deviation rate is calculated according to the following formula, and the internal resistance deviation rate is taken as the internal resistance deviation value: In the formula, ΔR is the internal resistance deviation rate, and Rstd is the preset reference internal resistance under the current working condition obtained from the historical calibration parameter library; The status assessment module, connected to the parameter calculation module, is used to generate a safety score based on the internal resistance deviation value and the abnormal heating rate, and output a graded alarm signal based on the comparison result of the safety score and a preset dynamic alarm threshold; wherein, the dynamic alarm threshold is calculated according to the following formula: In the formula, Smean is the mean of the safety scores within a preset number of cycles, Sdev is the standard deviation of the safety scores within a preset number of cycles, k is the confidence coefficient, and CSOH is the health status correction value determined based on the most recent health status assessment result. The control module is connected to the status assessment module, the vehicle controller, and the thermal management execution component. It is used to perform current limiting, power reduction, and vehicle thermal management adjustment based on the graded alarm signal, and to send back the temperature rise difference after performing the vehicle thermal management adjustment and the current drop after performing the current limiting operation to the status assessment module.
2. A method for detecting electric vehicle battery management, executed by the electric vehicle battery management detection system as described in claim 1, characterized in that, Includes the following steps: Step 1: The acquisition module collects the operating data of the battery pack and each individual battery cell in real time. The operating data includes at least the branch current and forms a data stream with a unified timestamp, which is then output to the parameter calculation module. Step 2: The parameter calculation module calculates the real-time ohmic internal resistance, internal resistance deviation value and abnormal heat generation rate based on the data stream, and outputs them to the status assessment module. Step 3: The status assessment module determines the current multiplier based on the branch current in the data stream, and processes the internal resistance deviation value and the abnormal heat generation rate based on the current current multiplier to obtain a safety score. Step four: The status assessment module compares the safety score with the preset dynamic alarm threshold. If the score is greater than or equal to the dynamic alarm threshold, a graded alarm signal is output to the control module; if the score is less than the dynamic alarm threshold, the monitoring status is maintained. Step 5: After receiving the graded alarm signal, the control module performs current limiting, power reduction, and thermal management adjustments. In the next sampling window, it executes Step 2 and Step 3 again, calculates the difference between the safety score generated in the current sampling window and the next sampling window, and upgrades the alarm level of the graded alarm signal if the difference is less than the preset score reduction threshold.
3. The electric vehicle battery management and testing system according to claim 1, characterized in that, The data stream collected by the acquisition module includes sampling timestamps, battery cell terminal voltages, branch currents, surface temperatures, ambient temperatures, and current state of charge.
4. The electric vehicle battery management and testing system according to claim 3, characterized in that, The parameter calculation module is used to determine and output the internal resistance deviation value based on the current state of charge, the ambient temperature, the battery cell terminal voltage, the branch current, and the preset reference internal resistance.
5. The electric vehicle battery management and testing system according to claim 3, characterized in that, The parameter calculation module is used to calculate the total heat generation rate based on the rate of change of the surface temperature, and to determine the abnormal heat generation rate based on the total heat generation rate, the branch current, and the real-time ohmic internal resistance.
6. The electric vehicle battery management and testing system according to claim 3, characterized in that, The state evaluation module is preset with a first current multiplier threshold and a second current multiplier threshold, which are used to determine the current current multiplier based on the branch current and to execute dynamic weight allocation logic based on the current current multiplier. When the current current multiplier is less than the first current multiplier threshold, the weight of the internal resistance deviation value is increased; When the current current ratio is greater than or equal to the second current ratio threshold, the weight of the abnormal heating rate is increased; When the current current multiplier is greater than or equal to the first current multiplier threshold and less than the second current multiplier threshold, the preset base weight is maintained; and the first current multiplier threshold is less than the second current multiplier threshold.
7. The electric vehicle battery management and testing system according to claim 1, characterized in that, The dynamic alarm threshold is calculated from the historical safety score statistics and the health status correction value. The historical safety score statistics are determined by the safety score distribution characteristics within a preset sampling period, and the health status correction value is determined based on the most recent health status assessment result.
8. The electric vehicle battery management and testing system according to claim 6, characterized in that, The control module is used to control the thermal management execution component and the vehicle controller according to the graded alarm signal. The control module has a first preset weight threshold and a second preset weight threshold. When the weight corresponding to the abnormal heating rate is higher than the first preset weight threshold, the thermal management execution component of the battery pack where the corresponding battery cell is located is controlled to increase the cooling power. When the weight of the internal resistance deviation value is higher than the second preset weight threshold, the current limiting is executed.
9. The electric vehicle battery management testing method according to claim 2, characterized in that, The data stream in step one includes the battery cell terminal voltage, branch current, ambient temperature, and current state of charge. In step two, the internal resistance deviation value is determined and output based on the current state of charge, the ambient temperature, the battery cell terminal voltage, the branch current, and the preset reference internal resistance.
10. The electric vehicle battery management testing method according to claim 2, characterized in that, In step five, the control module performs current limiting, power reduction, and thermal management adjustments, including: Send control commands to the vehicle controller; The control module executes control according to a pre-set weight allocation rule. The weight allocation rule specifies the weight of abnormal heat generation rate and the weight of internal resistance deviation value associated with the current current ratio. The weight allocation rule is preset with a first preset weight threshold and a second preset weight threshold: when the weight of abnormal heat generation rate is higher than the first preset weight threshold, the thermal management execution component is controlled to increase the cooling power of the battery pack containing the corresponding battery cell; when the weight of internal resistance deviation value is higher than the second preset weight threshold, current limiting is executed.