Intelligent self-diagnosis evaluation and risk early warning method and system for storage battery

Abstract

The invention relates to the technical field of storage battery self-diagnosis evaluation and risk early warning, and particularly discloses an intelligent self-diagnosis evaluation and risk early warning method and system for a storage battery. Comprising the steps of diagnosis window signal generation, transient discharge state starting, current compensation instruction generation, stable compensation current formation, gain load adjustment, performance data packet generation, real-time safety monitoring and storage battery comprehensive health assessment. A transient discharge state is started after a stable load period is identified, actual discharge current is sampled at high frequency, dynamic stable compensation current is generated, discharge duration, a single voltage curve and a temperature change rate are synchronously acquired, a multi-dimensional performance data packet is generated, and a safety threshold value is compared in real time to trigger a diagnosis termination mechanism. And finally, by associating the actual discharge capacity with the voltage dispersion, the drop rate and the temperature rise amplitude, issuing an evaluation report containing a capacity conclusion and predictive risk early warning. Accurate assessment and risk pre-management and control of the health state of the storage battery are realized.

Description

Technical Field

[0001] This invention belongs to the field of battery self-diagnosis assessment and risk warning technology, and relates to a method and system for intelligent self-diagnosis assessment and risk warning of batteries. Background Technology

[0002] In critical sectors such as communications, data centers, power, and rail transportation, batteries in DC systems serve as the last line of defense ensuring the continuous and stable operation of core equipment during mains power outages. Their health directly impacts the safety and reliability of the entire system. However, battery performance gradually deteriorates over time and with changes in the usage environment; capacity decay and decreased individual cell consistency are common phenomena. Therefore, accurately and promptly assessing the true health status of online batteries and anticipating potential failure risks has long been a core challenge and key technical problem in power system operation and maintenance management. In particular, standardized testing without affecting normal power supply has remained a technological bottleneck that the industry has been seeking to overcome.

[0003] Currently, the battery testing methods commonly used in the industry are mainly divided into two categories: offline and online. Offline methods are the most traditional and direct, involving periodically isolating the battery pack completely from the system and connecting it to a dedicated external constant current discharge device for verification discharge tests. The actual capacity is calculated by measuring the discharge time and current. Online methods attempt to evaluate the battery without interrupting system operation. These methods include measuring the internal resistance of individual cells using an internal resistance tester, or simply shutting down the charging module and allowing the battery to discharge briefly under actual load to observe voltage changes. These methods play a role in their respective application scenarios and constitute the basic means of current battery maintenance.

[0004] However, these traditional methods all have significant drawbacks. While offline discharge testing yields accurate results, the process is extremely cumbersome, requiring system power interruption, posing significant maintenance risks and workload, and involving long testing cycles, making real-time dynamic monitoring of battery status impossible. Internal resistance testing, though convenient, has weak correlation between its measurement results and the actual battery capacity, making it particularly difficult to accurately determine early battery degradation. Simple online discharge methods, on the other hand, suffer from inconsistent discharge currents due to fluctuating and irregular system loads. This makes standardized testing conditions impossible, resulting in poor repeatability and low accuracy of the final evaluation results, making them unreliable for judgment and potentially even causing shocks to the battery due to sudden load changes. Summary of the Invention

[0005] In view of this, in order to solve the problems mentioned in the background technology, a method and system for intelligent self-diagnosis assessment and risk warning of storage batteries is proposed.

[0006] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention provides a method for intelligent self-diagnosis assessment and risk warning of a storage battery, including: S1, generating a diagnostic window signal: acquiring the real-time load current of a DC system, calculating the fluctuation rate of the real-time load current within a preset time period, identifying a stable load period based on the fluctuation rate and a preset fluctuation threshold and duration, and generating a limited diagnostic window signal.

[0007] S2, Transient Discharge State Activation: Responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, causing the system to enter the transient discharge state.

[0008] S3. Current Compensation Command Generation: Under transient discharge conditions, the actual discharge current of the battery is sampled at high frequency, and a real-time current compensation command reflecting the difference between the actual discharge current and the target value of the standard discharge current is calculated.

[0009] S4. Stable compensation current formation: Based on the real-time current compensation command, the output current of the charging module is adjusted in a closed loop to form a dynamic stable compensation current.

[0010] S5. Gain-based load adjustment: When the difference in the real-time current compensation command is negative and continues for more than a preset time, the secondary non-critical load devices in the system are activated and linked to generate a gain-based load adjustment command.

[0011] S6. Performance Data Package Generation: During the maintenance of dynamic stable compensation current, the discharge duration of the battery pack, the dynamic curve of individual cell voltage, and the temperature change rate calculated from the battery temperature difference are collected and integrated simultaneously to generate a multi-dimensional dynamic performance data package.

[0012] S7. Real-time security monitoring: Real-time comparison of dynamic parameters in multi-dimensional dynamic performance data packets with preset multi-level security thresholds. Once any dynamic parameter touches its corresponding security boundary, a diagnostic stop and recovery command is immediately generated.

[0013] S8. Comprehensive Battery Health Assessment: After the diagnosis is completed or the diagnosis is stopped or the recovery command is issued, a comprehensive battery health assessment report is released, which includes capacity conclusions and predictive risk warnings, by correlating the actual discharge capacity with the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package.

[0014] The second aspect of the present invention provides a battery intelligent self-diagnosis assessment and risk warning system, comprising: a diagnostic window signal generation module, which acquires the real-time load current of the DC system, calculates the fluctuation rate of the real-time load current within a preset time period, identifies stable load periods based on the fluctuation rate and a preset fluctuation threshold and duration, and generates a limited diagnostic window signal.

[0015] The transient discharge state activation module responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, causing the system to enter the transient discharge state.

[0016] The current compensation command generation module samples the actual discharge current of the battery at high frequency during transient discharge and calculates a real-time current compensation command that reflects the difference between the actual discharge current and the target value of the standard discharge current.

[0017] The stable compensation current generating module adjusts the output current of the charging module in a closed loop according to the real-time current compensation command to generate a dynamic stable compensation current.

[0018] The gain-adjustable load adjustment module activates and links secondary non-critical load devices in the system to generate gain-adjustable load adjustments when the difference between the real-time current compensation command and the current compensation command is negative and continues for more than a preset time.

[0019] The performance data package generation module synchronously collects and integrates the discharge duration of the battery pack, the dynamic curve of the individual cell voltage, and the temperature change rate calculated from the battery temperature difference during the maintenance of the dynamic stable compensation current, in order to generate a multi-dimensional dynamic performance data package.

[0020] The real-time security monitoring module compares the dynamic parameters in the multi-dimensional dynamic performance data package with the preset multi-level security thresholds in real time. Once any dynamic parameter touches its corresponding security boundary, it immediately generates a diagnostic stop and recovery command.

[0021] The battery comprehensive health assessment module, after the diagnosis is completed or the diagnosis is stopped or resumed, publishes a comprehensive battery health assessment report containing capacity conclusions and predictive risk warnings by correlating the actual discharge capacity with the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package.

[0022] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention starts the test by intelligently identifying the stable load window and cleverly isolates the impact of actual load fluctuations on the battery by using the dynamic compensation current of the charging module. The entire diagnostic process does not require disconnecting the battery from the system, avoiding the business interruption risk and heavy manual operation brought about by traditional offline testing, ensuring the continuity of core business and the overall reliability of the power supply system, realizing online standardized diagnosis of the battery without interrupting the power supply of critical loads, and greatly improving the safety and convenience of operation and maintenance.

[0023] (2) This invention significantly improves the accuracy and repeatability of online diagnostic results by constructing an equivalent virtual constant current discharge environment. Its core lies in the fact that, regardless of changes in the actual external load, the system can always precisely clamp the net discharge current acting on the battery to a preset industry standard value through a real-time compensation mechanism. This ensures that the conditions for online testing are completely consistent with standard offline verification discharge testing, thereby guaranteeing the accuracy of capacity calculation, making the evaluation results authoritative and comparable, and providing reliable data support for battery lifecycle management.

[0024] (3) This invention overcomes the limitations of single capacity assessment and provides a multi-dimensional, three-dimensional comprehensive health assessment and predictive risk warning. The system not only calculates the accurate actual capacity, but also simultaneously collects and analyzes key dynamic indicators such as the consistency of the voltage curves of individual cells, the rate of voltage drop, and the temperature rise characteristics during standardized discharge. Through correlation analysis of these data, the system can gain insight into the equilibrium state within the battery pack and identify lagging cells with early degradation, thereby issuing a forward-looking risk warning before the capacity falls below the warning line.

[0025] (4) This invention establishes a fully automated, closed-loop controlled intelligent diagnostic process and incorporates a comprehensive multi-level safety protection mechanism. From automatically finding the optimal testing time to dynamically adjusting the load to meet testing conditions, and then to real-time safety monitoring during the testing process and immediate termination and recovery in case of abnormalities, the entire process requires no manual intervention, greatly reducing the reliance on the professional skills of maintenance personnel. This safety mechanism can monitor core parameters such as battery cell voltage and temperature in real time. Once a safety threshold is reached, the diagnostic process will be terminated immediately and the system will be restored to normal float charging state, ensuring that the testing process itself will not damage the battery or electrical equipment under any circumstances. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0028] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see Figure 1 The first aspect of the present invention provides a method for intelligent self-diagnosis assessment and risk warning of a storage battery, including: S1, generating a diagnostic window signal: acquiring the real-time load current of a DC system, calculating the fluctuation rate of the real-time load current within a preset time period, identifying a stable load period based on the fluctuation rate and a preset fluctuation threshold and duration, and generating a limited diagnostic window signal.

[0031] In a specific embodiment of the present invention, identifying the stable load period and generating a limited diagnostic window signal includes: using a sliding preset time period as a calculation unit, processing the real-time load current of the acquired DC system, calculating its arithmetic mean and standard deviation, and calculating the fluctuation rate of the real-time load current within the preset time period based on these two values.

[0032] The calculated volatility is compared with a preset volatility threshold. When the volatility is continuously lower than the preset volatility threshold and the duration of this state exceeds the set minimum diagnostic time, the current period is determined to be the stable load period.

[0033] Generate a limited diagnostic window signal, which serves as the sole trigger condition for initiating subsequent diagnostic processes.

[0034] It should be noted that this step aims to automatically identify a period of stable current from the continuously changing total load of the DC system, providing an undisturbed and stable environment for subsequent battery diagnostics. The implementation process begins by using a high-precision Hall current sensor installed on the DC system's main bus to continuously collect the total load current value at a set sampling frequency, forming a time-series data stream—the real-time load current of the DC system. Subsequently, the system processes the data stream in a sliding, preset time period, set to 15 minutes. Within each 15-minute calculation unit, the system calculates the arithmetic mean and standard deviation of all current sampling points, and calculates the current volatility based on these two values. When the calculated volatility is continuously below a preset volatility threshold, and this low-volatility state is maintained for a duration exceeding a set minimum diagnostic time, the system determines the current period as an ideal stable load period. Once the determination is successful, the system immediately generates a unique, digitally defined diagnostic window signal. This signal is a high-level logic signal and is sent to the diagnostic control core module as the sole trigger command to initiate all subsequent diagnostic operations.

[0035] It should also be noted that the formula for calculating the volatility of real-time load current within a preset time period is: V = (σ / μ) × 100%, where V represents volatility, a dimensionless percentage value used to quantify the dispersion of current data; μ represents the arithmetic mean of the real-time load current of the DC system collected within the preset time period, in amperes (A); and σ represents the standard deviation of all current samples within the same time period, also in amperes (A). By dividing the standard deviation by the mean, a relative assessment of the volatility is achieved, eliminating the influence of the absolute current dimension and making volatility a universally applicable evaluation indicator.

[0036] It should be further clarified that the real-time load current of a DC system refers to the total current consumed by all electrical devices connected to the DC bus at a given instant. The preset fluctuation threshold is a key parameter used to define whether the load is "stable." This threshold is set based on statistical analysis of long-term load data from several different types of DC systems, ultimately set at 5%. This value effectively eliminates minor, non-faulty load fluctuations during normal operation while capturing truly stable periods. The minimum diagnostic time is typically set at 15 minutes. This value is determined based on statistical analysis of the DC system load fluctuation characteristics. This ensures a complete battery performance assessment is completed within stable load periods while avoiding the risk of the system being exposed to unsteady-state operation due to excessively long diagnostic times, thus balancing diagnostic efficiency and accuracy.

[0037] S2, Transient Discharge State Activation: Responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, causing the system to enter the transient discharge state.

[0038] In a specific embodiment of the present invention, the precise reduction of the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage includes: after receiving a limited diagnostic window signal, locking and recording the current terminal voltage of the battery pack at this moment by using a voltmeter connected in parallel with the battery pack.

[0039] A digital voltage regulation command is sent to the charging module to smoothly reduce its DC output voltage from the current float charge voltage to a target voltage, which is equal to the locked current terminal voltage minus a preset small voltage difference.

[0040] When the battery discharge current is detected to change from zero to a stable positive value, the system is confirmed to have entered the transient discharge state.

[0041] It should be noted that this step begins when the diagnostic control core module receives the unique, limited diagnostic window signal generated in the previous step. Upon receiving this high-level logic signal, the system immediately locks and records the current terminal voltage value of the battery pack using a high-precision voltmeter connected in parallel with the battery pack. This value is typically the system-set float charge voltage. Next, the control module sends a precise digital voltage regulation command to the charging module in the DC system. This command is transmitted via the system's internal communication bus, such as CAN or RS485. The command instructs the charging module to smoothly and linearly reduce its DC output voltage from the current float charge voltage value to a target voltage. This target voltage is equal to the locked current terminal voltage of the battery pack minus a preset small voltage difference. This voltage difference causes the charging module's potential to be lower than the battery pack's potential, thus disrupting the original energy flow balance. Based on the fundamental principle that current flows from high to low potential in a circuit, the battery pack automatically transitions from a charging state to a discharging state, beginning to supply energy to the loads on the system bus, becoming the sole power source in the system. The system continuously monitors the current at the battery output terminal. When it detects that the discharge current changes from zero to a stable positive value, the system confirms that it has successfully entered a transient discharge state in which the battery independently supports the load, and marks the achievement of this state, providing a clear starting condition for the subsequent current control process.

[0042] It should also be noted that the formula for calculating the target voltage in this step is: U_CM_target = U_batt_current - ΔU_offset, where U_CM_target represents the target output voltage that the charging module needs to be adjusted to, in volts (V), U_batt_current represents the current terminal voltage of the battery pack that is measured and locked in real time at the moment the limited diagnostic window signal is received, in volts (V), and its setting value is determined by the type of battery and the system operating requirements, and ΔU_offset represents the preset small voltage difference, in volts (V), which is a key parameter set to ensure that the battery can reliably take over the load from the charging module.

[0043] It should be further explained that the charging module is an energy conversion device whose core function is to convert input AC or DC power into controllable DC power to charge the battery and supply power to the system load. In this method, it must have the ability to precisely adjust its output voltage and current through external digital commands. The current terminal voltage of the battery pack refers to the real-time voltage value between its positive and negative terminals in the float charging state before the diagnostic begins. The data structure is a floating-point number. The preset small voltage difference is a fixed, empirical voltage setting value. The setting is based on ensuring that the voltage difference is sufficient to overcome the voltage drop caused by the line impedance from the charging module to the bus and then to the battery. It is usually set to 0.5 volts based on actual measurements of typical DC system wiring. This value ensures that the discharge is effectively triggered without causing an excessive drop in the system bus voltage, which would affect the normal operation of the load.

[0044] S3. Current Compensation Command Generation: Under transient discharge conditions, the actual discharge current of the battery is sampled at high frequency, and a real-time current compensation command reflecting the difference between the actual discharge current and the target value of the standard discharge current is calculated.

[0045] In a specific embodiment of the present invention, the step of calculating the real-time current compensation command reflecting the difference between the actual discharge current and the target value of the standard discharge current includes: continuously sampling to obtain a time-series data stream of the actual discharge current through a current sensor placed in the battery output circuit.

[0046] Read the preset standard discharge current target value from the system configuration memory.

[0047] The mathematical difference between the actual discharge current and the target value of the standard discharge current is calculated in real time, and the difference is encapsulated into a signal with a positive or negative sign and a specific value to form the real-time current compensation command.

[0048] It's important to note that after the system enters the transient discharge state, the core task of this step is to accurately quantify the difference between the discharge current and the standard target. The system uses a high-frequency current sensor placed in the battery output circuit to continuously sample at a set frequency, thereby obtaining a high-resolution data stream. This data stream represents the real-time monitoring of the battery's actual discharge current. Simultaneously, the diagnostic control module reads a pre-set value from its internal configuration memory—the preset standard discharge current target value. Subsequently, the system's microprocessor mathematically compares the real-time monitored battery discharge current value with the preset standard discharge current target value in real time, calculating the difference. This difference directly reflects the deviation between the actual discharge intensity caused by the current system load on the battery and the ideal test standard. Finally, this calculated difference is converted into a standardized digital signal, forming a real-time current compensation command with a positive or negative sign and a specific numerical value. The sign indicates the direction of compensation, and the magnitude indicates the intensity of compensation.

[0049] It should also be noted that the real-time monitoring of the actual discharge current of the battery is a time-series data obtained from high-frequency sampling, reflecting the instantaneous change in the output current of the battery under transient discharge conditions. The preset standard discharge current target value is a fixed and critical configuration parameter, which is usually set according to internationally or industry-standard battery capacity verification discharge procedures. For example, for a battery with a rated capacity of 200 Ah, its 10-hour rate discharge current is 20 Amps, and this 20 Amps can be set as the preset standard discharge current target value.

[0050] S4. Stable compensation current formation: Based on the real-time current compensation command, the output current of the charging module is adjusted in a closed loop to form a dynamic stable compensation current.

[0051] In a specific embodiment of the present invention, the closed-loop regulation of the output current of the charging module to form a dynamic stable compensation current includes: when the real-time current compensation command is a positive value, the charging module is instructed to enter a constant current output mode, and the magnitude of its output current is equal to the value of the real-time current compensation command, thereby forming a dynamic stable compensation current.

[0052] By using this dynamic and stable compensation current to handle the portion of the load current that exceeds the standard discharge current target value, the net discharge current flowing out of the battery is precisely reduced and stabilized at the standard discharge current target value.

[0053] It should be noted that this step is the core closed-loop control link of the entire diagnostic method. Its purpose is to precisely clamp the battery discharge current from a fluctuating value affected by the actual load to a constant standard value. The execution process begins when the diagnostic control module receives the real-time current compensation command generated in the previous step. The control module parses the sign and value of the command. When the total system load is higher than the preset standard discharge current target value, the command is a positive value. The control module immediately issues a dynamic current output command to the charging module based on this positive value. The charging module then switches from a low-voltage standby state to a constant current output mode, and its output current is exactly equal to the value of the real-time current compensation command. This current provided by the charging module and the current output by the battery merge on the DC bus to jointly power the system. Through this "filling" method, the charging module takes on the portion of the load current exceeding the standard discharge target value, thereby precisely reducing and stabilizing the net discharge current flowing from the battery at the preset standard discharge current target value. This current dynamically output by the charging module to offset load fluctuations forms a dynamically stable compensation current.

[0054] It should also be noted that the current clamping principle follows the nodal current law of the circuit, as shown in the following formula: I_batt_net = I_load_actual - I_comp_current, where I_batt_net represents the final net discharge current of the battery after compensation, in amperes; I_load_actual represents the total actual load current of the system, in amperes; and I_comp_current represents the compensation current provided by the charging module, the magnitude of which is controlled to be equal to the value of the real-time current compensation command, in amperes. Through this formula, regardless of how I_load_actual fluctuates, as long as I_comp_current can follow its changes in real time, I_batt_net can be guaranteed to remain at a constant target value.

[0055] S5. Gain-based load adjustment: When the difference in the real-time current compensation command is negative and continues for more than a preset time, the secondary non-critical load devices in the system are activated and linked to generate a gain-based load adjustment command.

[0056] In a specific embodiment of the present invention, generating a gain-adjusting load instruction includes: starting a timer and recording the cumulative duration for which the difference between the real-time current compensation instruction and the actual current compensation instruction is negative.

[0057] When the cumulative duration exceeds the preset duration, query the pre-configured list of devices, which includes device identifiers, rated current values, and control addresses.

[0058] Select a suitable combination of secondary non-critical load devices based on the magnitude of the negative value difference, and issue a gain-adjusting load command to drive the intelligent relays of the corresponding devices to close, connecting these devices to the DC system, thereby enabling the compensation mechanism of dynamic stable compensation current to take effect again.

[0059] It should be noted that this step is specifically designed to address situations where the system's own load is insufficient to meet the test standards. When the system is in the discharge control state established in the previous steps, the diagnostic control module continuously assesses the value of the real-time current compensation command. If the calculated difference of this command is negative, it means that the actual discharge current of the real-time monitored battery is lower than the preset standard discharge current target value. In this case, the charging module cannot compensate for a non-existent overload by outputting current, leading to constant current control failure. The system will start a timer. If this negative state persists for more than a preset duration, the system confirms that this is not an instantaneous fluctuation but a genuine low-load state. At this point, the system will automatically activate a contingency plan, namely, linking secondary non-critical load devices within the system. The system will query a pre-configured device list and select one or more devices marked as having no critical impact on the main system's operation and that can be safely remotely controlled. Based on the magnitude of the negative difference, the system calculates the required increase in current and selects an appropriate combination of devices for activation. Subsequently, the system generates and issues a gain-based load adjustment command, which drives the intelligent relays of the corresponding devices to close via the control bus, connecting these devices to the DC system. This action artificially and controllably increases the total load of the system, raising it to near or slightly above the preset standard discharge current target value. This restores the difference in the real-time current compensation command to zero or a positive value, allowing the dynamic stability compensation mechanism of the previous step to take effect again and continue to maintain the constant current discharge of the battery.

[0060] It should also be noted that the preset duration is a time threshold parameter, set to filter out brief, occasional load drops and avoid unnecessary load switching actions. Based on the analysis of load curves in a large number of industrial sites, it is usually set to 30 seconds. Secondary non-critical load equipment refers to electrical equipment in a DC power supply system whose short-term start-stop will not affect the operation of core business, such as redundant cooling fans, backup lighting circuits, or cabinet heating strips.

[0061] S6. Performance Data Package Generation: During the maintenance of dynamic stable compensation current, the discharge duration of the battery pack, the dynamic curve of individual cell voltage, and the temperature change rate calculated from the battery temperature difference are collected and integrated simultaneously to generate a multi-dimensional dynamic performance data package.

[0062] In a specific embodiment of the present invention, the specific process of generating a multi-dimensional dynamic performance data package includes: activating a multi-channel voltage acquisition module, each channel of which is independently connected to each individual cell in the battery pack, to monitor and record the voltage data stream with timestamps with high precision, and to aggregate the data to form a dynamic curve of the individual cell voltage.

[0063] When the constant current discharge state is established, a built-in timer is triggered to record the cumulative discharge duration.

[0064] Obtain the initial temperature at the beginning and the final temperature at the end of a preset time interval.

[0065] The temperature change rate is obtained by dividing the difference between the final temperature and the initial temperature by the preset time interval.

[0066] The discharge duration, individual cell voltage dynamic curves, and temperature change rate are encapsulated in real time to generate a continuously updated multi-dimensional dynamic performance data package.

[0067] It's important to note that, firstly, a built-in timer is triggered the instant the constant current discharge state is established, precisely recording the accumulated time from that moment onward to form the discharge duration. Simultaneously, a multi-channel voltage acquisition module is activated. Each channel of this module is independently connected to the positive and negative terminals of each individual battery cell in the battery pack, monitoring and recording the voltage drop trajectory of each individual battery cell during discharge with high precision at a sampling frequency of once per second. These parallel, timestamped voltage data streams are aggregated to form the dynamic voltage curve of each individual battery cell. While acquiring voltage data, temperature sensors deployed at key temperature measurement points in the battery pack, such as the center of the battery surface and electrode connections, are also simultaneously recording temperature data. The system performs differential calculations on the continuous temperature readings to obtain the rate of temperature change over time, i.e., the temperature change rate. Finally, the system encapsulates and packages these three dimensions of data—the single accumulated discharge duration, the set of curves containing all individual batteries, and the temperature change rate data at key points—in real time, generating a structured, continuously updated, multi-dimensional dynamic performance data package.

[0068] It should also be noted that the preset time interval is set based on the need to balance noise suppression and response sensitivity. Based on actual measurements of the thermal characteristics of lead-acid and lithium batteries, it is usually set to 60 seconds. The dynamic curve of the individual battery voltage is a composite data structure. It is a collection of voltage curves of all individual batteries in the battery pack. Each curve itself is a sequence of data points consisting of timestamps and voltage values. This structure can comprehensively reveal the consistency within the battery pack and whether there are any lagging cells.

[0069] S7. Real-time security monitoring: Real-time comparison of dynamic parameters in multi-dimensional dynamic performance data packets with preset multi-level security thresholds. Once any dynamic parameter touches its corresponding security boundary, a diagnostic stop and recovery command is immediately generated.

[0070] In a specific embodiment of the present invention, the generation of diagnostic stop and resume instructions specifically includes: comparing the latest voltage reading of each individual cell in the dynamic curve of individual cell voltage with a preset discharge cutoff voltage threshold.

[0071] The latest battery surface temperature reading is compared with the preset maximum safe temperature threshold.

[0072] The system bus voltage is compared with the preset minimum operating voltage threshold.

[0073] When any of the above comparison operations shows that the parameters touch the safety boundary, the diagnostic abort and recovery instructions are given the highest execution priority and generated immediately.

[0074] It should be noted that while the system is performing constant current discharge, an independent, high-priority safety monitoring module continuously receives and parses the real-time updated multi-dimensional dynamic performance data packets generated in the previous step. The monitoring module immediately decomposes the dynamic parameters within the data packets and performs a series of parallel, real-time comparison operations. Specifically, it checks each curve in the set of individual cell voltage dynamic curves, comparing the latest voltage reading of each individual cell with a preset discharge cutoff voltage threshold. Simultaneously, it compares the latest battery surface temperature reading with a preset maximum safe temperature threshold. Furthermore, it monitors the bus voltage of the entire system and compares it with a preset minimum operating voltage threshold. These comparisons are continuous; if any dynamic parameter touches the lower or upper limit of its corresponding safety threshold—for example, if the voltage of an individual cell falls below the cutoff voltage, or the battery temperature exceeds the maximum limit—the safety monitoring module immediately and unconditionally generates a diagnostic stop and recovery command. This instruction has the highest execution priority in the system. Once generated, it will forcibly interrupt all ongoing diagnostic processes, command the charging module to immediately abandon the output of compensation current, and quickly restore to the normal float charge voltage state, thereby taking full control of the system load and ensuring the absolute safety of the battery and the entire DC system under any abnormal conditions.

[0075] It should also be noted that dynamic parameters are a collective term for the real-time changing data items contained in the multi-dimensional dynamic performance data package. In this step, they specifically refer to the individual cell voltage, battery surface temperature, and system bus voltage. The preset discharge cutoff voltage threshold is a specific parameter among the multi-level safety thresholds. It is set based on the technical specifications provided by the battery manufacturer and aims to prevent irreversible damage to the battery due to over-discharge. It is usually taken as 80% of the rated voltage of the individual cell. The maximum safe temperature threshold is another key safety parameter. It is set based on industry safety standards and the thermal stability of battery materials. It aims to prevent dangers such as thermal runaway caused by overheating of the battery and is generally set to 50°C. The minimum operating voltage threshold is the lower limit of voltage set to ensure that the critical load equipment connected to the system can operate normally. It is set based on the minimum operating voltage requirements in the specifications of these devices and can be set to 48V.

[0076] S8. Comprehensive Battery Health Assessment: After the diagnosis is completed or the diagnosis is stopped or the recovery command is issued, a comprehensive battery health assessment report is released, which includes capacity conclusions and predictive risk warnings, by correlating the actual discharge capacity with the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package.

[0077] In a specific embodiment of the present invention, the publication of a comprehensive battery health assessment report that includes capacity conclusions and predictive risk warnings specifically includes: extracting the final discharge duration from the multidimensional dynamic performance data package, multiplying it by the standard discharge current target value, calculating the actual discharge capacity, and comparing it with the rated capacity of the battery to obtain the actual discharge capacity percentage.

[0078] The actual discharge capacity percentage is compared with the actual discharge capacity percentage range corresponding to each capacity conclusion stored in the database. If the actual discharge capacity percentage falls within the actual discharge capacity percentage range corresponding to a certain capacity conclusion, then that capacity conclusion is taken as the capacity conclusion of the battery.

[0079] It should be noted that the capacity conclusions include, but are not limited to, sufficient capacity, slight capacity decline, moderate capacity decline, and severe capacity decline.

[0080] The dynamic curves of individual cell voltages are processed, and the standard deviation of all individual cell voltage values ​​is calculated at multiple key time points to quantify the individual cell voltage dispersion.

[0081] The predicted risk index of the battery is obtained by weighting and summing the individual cell voltage dispersion, voltage drop rate, and temperature rise amplitude.

[0082] It should be noted that the formula for calculating the predicted risk index of the battery is as follows: Where β represents the predicted risk index of the battery, ε, v and W represent the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude, respectively, ε′, v′ and W′ represent the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude set as references, respectively, α1, α2 and α3 represent the weights of the predicted risk assessment corresponding to the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude, respectively, and α1+α2+α3=1.

[0083] In one specific embodiment of the present invention, regarding the typical weighting of single-cell voltage dispersion, voltage drop rate, and temperature rise amplitude, single-cell voltage dispersion is typically set to the highest weight of 40%, as it directly reflects the internal consistency of the battery pack and has a significant impact on long-term reliability; voltage drop rate is the next highest weight at approximately 35%, as its changes can quickly indicate an increase in battery internal resistance or degradation of the active material of the plates; temperature rise amplitude has a relatively low weight of approximately 25%. This allocation ratio is based on industry experience and failure mode analysis, and the actual system may adjust the priorities according to the battery type and usage scenario.

[0084] The predicted risk index of the battery is compared with the predicted risk index range corresponding to each risk warning level stored in the database. If the predicted risk index of the battery is within the predicted risk index range corresponding to a certain risk warning level, then that risk warning level is taken as the risk warning level of the battery.

[0085] Reference Figure 2 The second aspect of the present invention provides a battery intelligent self-diagnosis assessment and risk warning system, including: a diagnostic window signal generation module, a transient discharge state activation module, a current compensation command generation module, a stable compensation current formation module, a gain load adjustment module, a performance data package generation module, a real-time safety monitoring module, a battery comprehensive health assessment module, and a database.

[0086] It should be noted that the present invention also includes a database for storing the actual discharge capacity ratio range corresponding to each capacity conclusion, and storing the predicted risk index range corresponding to each risk warning level.

[0087] The diagnostic window signal generation module is connected to the transient discharge state initiation module, the transient discharge state initiation module is connected to the current compensation command generation module, the current compensation command generation module is connected to the stable compensation current forming module, the stable compensation current forming module is connected to the gain load adjustment module, the stable compensation current forming module is connected to the performance data package generation module, the performance data package generation module is connected to the real-time safety monitoring module, both the performance data package generation module and the real-time safety monitoring module are connected to the battery comprehensive health assessment module, and the battery comprehensive health assessment module is connected to the database.

[0088] The diagnostic window signal generation module acquires the real-time load current of the DC system and calculates the fluctuation rate of the real-time load current within a preset time period. Based on the fluctuation rate and the preset fluctuation threshold and duration, it identifies stable load periods and generates a limited diagnostic window signal.

[0089] The transient discharge state activation module responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, so that the system enters the transient discharge state.

[0090] The current compensation command generation module samples the actual discharge current of the battery at high frequency during transient discharge and calculates a real-time current compensation command that reflects the difference between the actual discharge current and the target value of the standard discharge current.

[0091] The stable compensation current generating module adjusts the output current of the charging module in a closed loop according to the real-time current compensation command to generate a dynamic stable compensation current.

[0092] When the difference between the real-time current compensation command and the actual current compensation command is negative and continues for more than a preset time, the gain-adjusting load module activates and links the secondary non-critical load devices in the system to generate a gain-adjusting load command.

[0093] The performance data package generation module synchronously collects and integrates the discharge duration of the battery pack, the dynamic curve of the individual cell voltage, and the temperature change rate calculated from the battery temperature difference during the maintenance of the dynamic stable compensation current, in order to generate a multi-dimensional dynamic performance data package.

[0094] The real-time security monitoring module compares the dynamic parameters in the multi-dimensional dynamic performance data packet with the preset multi-level security thresholds in real time. Once any dynamic parameter touches its corresponding security boundary, it immediately generates a diagnostic stop and recovery command.

[0095] The battery comprehensive health assessment module, after the diagnosis is completed or the diagnosis is stopped or resumed, analyzes the actual discharge capacity and the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package, and publishes a battery comprehensive health assessment report containing capacity conclusions and predictive risk warnings.

[0096] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for intelligent self-diagnosis assessment and risk warning of a storage battery, characterized in that, include: S1. Diagnostic window signal generation: Obtain the real-time load current of the DC system and calculate the fluctuation rate of the real-time load current within a preset time period. Based on the fluctuation rate and the preset fluctuation threshold and duration, identify the stable load period and generate a limited diagnostic window signal. S2, Transient Discharge State Start-up: Responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, so that the system enters the transient discharge state; S3. Current compensation command generation: Under transient discharge conditions, the actual discharge current of the battery is sampled at high frequency, and a real-time current compensation command reflecting the difference between the actual discharge current and the standard discharge current target value is calculated. S4. Stable compensation current formation: Based on the real-time current compensation command, the output current of the charging module is adjusted in a closed loop to form a dynamic stable compensation current. S5. Gain-based load adjustment: When the difference between the real-time current compensation command and the actual current compensation command is negative and continues for more than a preset time, the secondary non-critical load devices in the system are activated and linked to generate a gain-based load adjustment command. S6. Performance data package generation: During the maintenance of dynamic stable compensation current, the discharge duration of the battery pack, the dynamic curve of individual cell voltage, and the temperature change rate calculated from the battery temperature difference are collected and integrated simultaneously to generate a multi-dimensional dynamic performance data package. S7. Real-time security monitoring: Real-time comparison of dynamic parameters in multi-dimensional dynamic performance data packets with preset multi-level security thresholds. Once any dynamic parameter touches its corresponding security boundary, a diagnostic stop and recovery command is immediately generated. S8. Comprehensive Battery Health Assessment: After the diagnosis is completed or the diagnosis is stopped or the recovery command is issued, a comprehensive battery health assessment report is released, which includes capacity conclusions and predictive risk warnings, by correlating the actual discharge capacity with the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package.

2. The intelligent self-diagnosis assessment and risk warning method for storage batteries according to claim 1, characterized in that: The process of identifying stable load periods and generating a defined diagnostic window signal includes: Using a preset time period as the calculation unit, the real-time load current of the DC system is processed, its arithmetic mean and standard deviation are calculated, and the fluctuation rate of the real-time load current within the preset time period is calculated based on these two values. The calculated volatility is compared with a preset volatility threshold. When the volatility is continuously lower than the preset volatility threshold and the duration of this state exceeds the set minimum diagnostic time, the current period is determined to be the stable load period. Generate a limited diagnostic window signal, which serves as the sole trigger condition for initiating subsequent diagnostic processes.

3. The intelligent self-diagnosis assessment and risk warning method for storage batteries according to claim 1, characterized in that: The precise reduction of the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage includes: After receiving the limited diagnostic window signal, the current terminal voltage of the battery pack is locked and recorded by a voltmeter connected in parallel with the battery pack. Send a digital voltage regulation command to the charging module to smoothly reduce its DC output voltage from the current float charge voltage to a target voltage, which is equal to the locked current terminal voltage minus a preset small voltage difference; When the battery discharge current is detected to change from zero to a stable positive value, the system is confirmed to have entered the transient discharge state.

4. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 1, characterized in that: The step of calculating the real-time current compensation command that reflects the difference between the actual discharge current and the target value of the standard discharge current includes: A time-series data stream of the actual discharge current is obtained by continuously sampling the current through a current sensor placed in the battery output circuit. Read the preset standard discharge current target value from the system configuration memory; The mathematical difference between the actual discharge current and the target value of the standard discharge current is calculated in real time, and the difference is encapsulated into a signal with a positive or negative sign and a specific value to form the real-time current compensation command.

5. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 4, characterized in that: The closed-loop regulation of the charging module's output current to form a dynamically stable compensation current includes: When the real-time current compensation command is a positive value, the command charging module enters the constant current output mode, and the magnitude of its output current is equal to the value of the real-time current compensation command, forming a dynamic and stable compensation current. By using this dynamic and stable compensation current to handle the portion of the load current that exceeds the standard discharge current target value, the net discharge current flowing out of the battery is precisely reduced and stabilized at the standard discharge current target value.

6. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 1, characterized in that: The generated gain-adjustable load control command includes: Start the timer to record the cumulative duration for which the difference between the real-time current compensation command and the actual current compensation command is negative; When the cumulative duration exceeds the preset duration, query the pre-configured list of devices containing device identifiers, rated current values, and control addresses; Select a suitable combination of secondary non-critical load devices based on the magnitude of the negative value difference, and issue a gain-adjusting load command to drive the intelligent relays of the corresponding devices to close, connecting these devices to the DC system, thereby enabling the compensation mechanism of dynamic stable compensation current to take effect again.

7. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 1, characterized in that: The specific process for generating the multidimensional dynamic performance data package includes: A multi-channel voltage acquisition module is activated, with each channel independently connected to each individual cell in the battery pack, to monitor and record the voltage data stream with timestamps with high precision, and to aggregate the data to form a dynamic curve of the individual cell voltage. When the constant current discharge state is established, a built-in timer is triggered to record the cumulative discharge duration; Obtain the initial temperature at the beginning of a preset time interval and the final temperature at the end of the time interval; The temperature change rate is obtained by dividing the difference between the final temperature and the initial temperature by the preset time interval. The discharge duration, individual cell voltage dynamic curves, and temperature change rate are encapsulated in real time to generate a continuously updated multi-dimensional dynamic performance data package.

8. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 7, characterized in that: The specific components of the generated diagnostic stop and resume instructions include: The latest voltage reading of each individual cell in the dynamic voltage curve of the individual cell is compared with the preset discharge cutoff voltage threshold. Compare the latest battery surface temperature reading with the preset maximum safe temperature threshold; The system bus voltage is compared with the preset minimum operating voltage threshold. When any of the above comparison operations shows that the parameters touch the safety boundary, the diagnostic abort and recovery instructions are given the highest execution priority and generated immediately.

9. The intelligent self-diagnosis assessment and risk warning method for a storage battery according to claim 1, characterized in that: The published comprehensive battery health assessment report, which includes capacity conclusions and predictive risk warnings, specifically includes: The final discharge duration is extracted from the multidimensional dynamic performance data package and multiplied by the standard discharge current target value to calculate the actual discharge capacity. The actual discharge capacity percentage is then obtained by comparing it with the rated capacity of the battery. The actual discharge capacity percentage is compared with the actual discharge capacity percentage range corresponding to each capacity conclusion stored in the database. If the actual discharge capacity percentage is within the actual discharge capacity percentage range corresponding to a certain capacity conclusion, then that capacity conclusion is taken as the capacity conclusion of the battery. Process the dynamic curve of individual cell voltage and calculate the standard deviation of all individual cell voltage values ​​at multiple key time points to quantify the dispersion of individual cell voltage. The predicted risk index of the battery is obtained by weighting and summing the individual cell voltage dispersion, voltage drop rate, and temperature rise amplitude. The predicted risk index of the battery is compared with the predicted risk index range corresponding to each risk warning level stored in the database. If the predicted risk index of the battery is within the predicted risk index range corresponding to a certain risk warning level, then that risk warning level is taken as the risk warning level of the battery.

10. A battery intelligent self-diagnosis assessment and risk warning system, characterized in that, include: The diagnostic window signal generation module acquires the real-time load current of the DC system and calculates the fluctuation rate of the real-time load current within a preset time period. Based on the fluctuation rate and the preset fluctuation threshold and duration, it identifies the stable load period and generates a limited diagnostic window signal. The transient discharge state activation module responds to the limited diagnostic window signal and precisely lowers the output voltage of the charging module in the DC system to a preset small voltage difference below the current terminal voltage, so that the system enters the transient discharge state. The current compensation command generation module samples the actual discharge current of the battery at high frequency during transient discharge and calculates a real-time current compensation command that reflects the difference between the actual discharge current and the target value of the standard discharge current. The stable compensation current generating module adjusts the output current of the charging module in a closed loop according to the real-time current compensation command to form a dynamic stable compensation current. The gain-adjustable load adjustment module activates and links secondary non-critical load devices in the system to generate gain-adjustable load adjustments when the difference between the real-time current compensation command and the current compensation command is negative and continues for more than a preset time. The performance data package generation module synchronously collects and integrates the discharge duration of the battery pack, the dynamic curve of the individual cell voltage, and the temperature change rate calculated from the battery temperature difference during the maintenance of the dynamic stable compensation current, in order to generate a multi-dimensional dynamic performance data package. The real-time security monitoring module compares the dynamic parameters in the multi-dimensional dynamic performance data package with the preset multi-level security thresholds in real time. Once any dynamic parameter touches its corresponding security boundary, it immediately generates a diagnostic stop and recovery command. The battery comprehensive health assessment module, after the diagnosis is completed or the diagnosis is stopped or resumed, publishes a comprehensive battery health assessment report containing capacity conclusions and predictive risk warnings by correlating the actual discharge capacity with the individual cell voltage dispersion, voltage drop rate and temperature rise amplitude in the multi-dimensional dynamic performance data package.

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