An inverter enable control method and system for a mine car DC-DC power supply

Abstract

The present application relates to a kind of for mine car DC-DC power supply inverter enable control method and system, belong to mine car power control technical field.Method includes: reading external control instruction and parsing, obtains power base signal and pre-processes generation power base dataset;Based on input voltage signal, bus capacitor is pre-charged, triggers main loop switching and adjusts voltage rise slope by adaptive soft-start strategy;Based on power base dataset, voltage current double closed loop regulation is executed to generate inverter control signal, establishes temperature rise trend prediction model and judges temperature rise and starts inverter output;During inverter operation, distinguish between continuous fault and transient disturbance, generate inverter operation whole process monitoring list.The present application solves the problems such as poor stability, fault discrimination is not accurate and the like of mine car DC-DC power supply inverter enable control, improves the reliability and adaptability of inverter control, adapts to the demand of complex working conditions of mine car.

Description

Technical Field

[0001] This invention belongs to the field of mine car power control technology, specifically relating to an inverter enable control method and system for mine car DC-DC power supply. Background Technology

[0002] As a core transportation device in the mining process, the operational stability of mining cars directly affects mining efficiency and operational safety. DC-DC power supplies, as a core component of the mining car's electrical system, play a crucial role in voltage conversion and energy supply. Inverter enable control is the core element for the normal operation of the DC-DC power supply, and its control effect directly determines the power supply stability and equipment lifespan of the mining car's electrical equipment.

[0003] Currently, most existing DC-DC power supply inverter enable control methods for mining trucks adopt fixed parameter control modes, which have several shortcomings: the inverter enable state analysis is not accurate enough, the ability to resist interference from external control commands is weak, and command analysis deviations are prone to occur, leading to malfunctions in inverter start-up, shutdown, or mode switching; the bus capacitor pre-charging process lacks a flexible adjustment mechanism, and the pre-charging current and voltage rise slope are fixed, which can easily cause capacitor damage or excessive start-up impact; voltage and current regulation mostly adopt single closed-loop control, with low adjustment accuracy, making it difficult to adapt to the working conditions of large load fluctuations in mining trucks; the temperature rise prediction and fault identification mechanisms are imperfect, unable to accurately distinguish between continuous faults and transient disturbances, which can easily lead to misjudgment or missed faults, and cannot predict abnormal temperature rises in advance, causing power supply components to be damaged due to overheating; and there is a lack of a complete operation monitoring mechanism, which makes it impossible to effectively trace the entire inverter process, which is not conducive to fault diagnosis and system optimization.

[0004] Meanwhile, existing inverter-enabled control systems are mostly integrated designs with unclear module divisions, poor coordination between components, and inconvenient maintenance. Furthermore, they cannot adaptively adjust to the complex working environment of mining trucks (such as strong electromagnetic interference, large load fluctuations, and drastic temperature changes), resulting in insufficient system stability and reliability, making it difficult to meet the requirements for long-term stable operation of mining trucks. Therefore, developing an inverter-enabled control method and system that can solve the above problems and adapt to the working conditions of mining trucks has become the primary task in the field of mining truck power control. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides an inverter enable control method and system for a DC-DC power supply in a mining truck. The objective of this invention can be achieved through the following technical solutions: An inverter enable control method for a DC-DC power supply in a mining truck includes: S1: Read external control commands in real time, execute the inverter enable status parsing mechanism, synchronously acquire power supply basic signals, and generate power supply basic dataset after preprocessing. S2: Based on the input voltage signal in the power supply basic dataset, perform a pre-charging operation on the bus capacitor. After the bus voltage reaches a preset threshold, trigger the main circuit switching. Based on the adaptive soft-start strategy, dynamically adjust the soft-start voltage rise slope. S3: Based on the power supply basic dataset, execute the voltage and current dual closed-loop regulation mechanism to generate inverter control signals, establish a temperature rise trend prediction model, predict the temperature rise situation, and send the inverter control signals to the inverter control terminal to start inverter output; S4: During inverter operation, a transient fault discrimination mechanism is used to distinguish between continuous faults and transient disturbances. Based on the real-time monitored operating signals, fault discrimination results and temperature rise, a monitoring list for the entire inverter operation is generated.

[0006] Specifically, the specific process of the inverter enable state resolution mechanism includes: Establish a stable connection with the external control command transmission channel, receive control command signals sent from the outside in real time, perform targeted denoising processing on the received command signals to remove electromagnetic interference and transmission noise, decode the denoised command signals to extract the encoded information inside the command, identify the various enable-related command types contained in the command, including inverter enable start, inverter enable stop, and enable mode switching, and simultaneously analyze the enable trigger conditions corresponding to each command, clarify the voltage range, timing requirements and associated signal parameters in the trigger conditions, and comprehensively verify the parsed enable status information, comparing the parsing results with the preset command specifications one by one.

[0007] Specifically, the preprocessing process includes: The various basic signals include input voltage signals, input current signals, bus voltage signals, ambient temperature signals, and inverter control terminal feedback signals. Each basic signal is filtered using a corresponding filtering method. Appropriate filtering strategies are selected based on the noise characteristics of different signals to filter out high-frequency noise and transient interference. Abnormal data generated during signal acquisition due to sensor malfunctions, poor contact, or electromagnetic interference is eliminated using preset abnormal data identification rules. Amplitude calibration is performed on the filtered signals to correct amplitude deviations of different types of sensor output signals based on preset calibration benchmarks. The calibrated signals are then regularized to unify all signals within a preset amplitude range, eliminating parameter magnitude differences between different signals. Finally, the processed signals are integrated according to a preset data format to form a power supply basic dataset.

[0008] Specifically, the pre-charging operation includes the following steps: The real-time amplitude and fluctuation frequency of the input voltage are extracted from the power supply basic dataset. Combined with the rated parameters of the bus capacitor, the initial pre-charging current is set, and the control element in the pre-charging circuit is turned on to allow the input voltage to be transmitted to the bus capacitor through the pre-charging circuit to charge the bus capacitor. During the charging process, the changes in the pre-charging current are monitored in real time, and the current limiting element in the pre-charging circuit is dynamically adjusted according to the current changes to keep the pre-charging current within the preset range. The real-time value of the bus voltage is continuously collected and compared with the preset charging threshold in real time to track the stable state of the bus voltage. After the bus voltage reaches the preset threshold and remains stable for a preset time, the pre-charging circuit is disconnected to stop the pre-charging operation.

[0009] Specifically, the main circuit switching process includes: Once the bus voltage reaches a preset threshold and remains stable for a preset duration, the system automatically triggers a main circuit switching command. The switching command undergoes comprehensive verification by the logic verification unit. After confirming that the command is error-free and conflict-free, the system controls the control switch in the pre-charging circuit to slowly disconnect according to the preset switching sequence. After the pre-charging circuit is completely disconnected, a preset buffer time is delayed, and the control switch in the main power circuit is slowly turned on. During the switching process, a preset buffer control strategy is adopted to absorb the voltage and current surges generated during circuit switching through buffer elements. The system monitors the voltage and current changes in the main circuit in real time and captures abnormal fluctuations in the circuit. If an abnormal fluctuation is detected, the switching interruption mechanism is triggered, the main power circuit is disconnected, and the system returns to the pre-charging state.

[0010] Specifically, the process of the adaptive soft-start strategy includes: After the soft-start command is triggered, a dedicated soft-start parameter acquisition unit is activated to collect various relevant parameters during the soft-start process in real time, including the instantaneous value of the bus voltage, the rate of change of the bus voltage, the instantaneous value of the input current, the input current ripple coefficient, the load impedance value, and the junction temperature signal of the power devices inside the power supply. The acquired parameters are digitally filtered to remove acquisition noise, and normalized to ensure all parameters are adjusted to the same magnitude, eliminating the impact of differences in parameter magnitudes. Based on the processed parameters, a soft-start slope adjustment decision matrix is ​​constructed. Different load levels are classified according to the magnitude of the load impedance value, and the input... The values ​​of current ripple coefficient and power device junction temperature signal are used to classify different warning levels. A corresponding soft-start voltage rise slope adjustment coefficient is matched for each level. Based on the load level and warning level of the real-time acquired parameters, the corresponding slope adjustment coefficient is called to adjust the soft-start voltage rise slope. The bus voltage feedback signal is acquired in real time and compared with the preset soft-start voltage reference curve. The voltage deviation between the two is calculated. Based on the voltage deviation, the soft-start voltage rise slope is corrected in real time in a closed loop. The adjustment is continued until the bus voltage reaches the rated operating voltage, at which point the soft-start process is terminated and the soft-start parameter acquisition unit is turned off.

[0011] Specifically, the specific process of the voltage and current dual closed-loop regulation mechanism includes: Using the preset output voltage and current in the power supply basic dataset as the core adjustment benchmark, the allowable fluctuation range of voltage and current is clearly defined. The actual voltage and current signals at the inverter output are collected in real time through a dedicated sampling module. The collected actual voltage and current signals are filtered, denoised, and calibrated to remove interference components. The processed actual voltage is compared with the preset output voltage to generate a voltage adjustment deviation signal, and the direction and magnitude of the deviation are analyzed. The processed actual current is compared with the preset output current to generate a current adjustment deviation signal, and the specific situation of the current deviation is analyzed. The voltage adjustment deviation signal and the current adjustment deviation signal are comprehensively calculated through a preset adjustment algorithm. The amplitude and frequency of the inverter control signal are dynamically adjusted according to the calculation results. The voltage closed loop prioritizes the stability adjustment of the output voltage, while the current closed loop assists in the limitation adjustment of the output current.

[0012] Specifically, the process of establishing and predicting the temperature rise trend model includes: Collect various relevant data during the operation of the DC-DC power inverter in the mining truck, including ambient temperature signal, input and output power signal in the power basic dataset, as well as temperature rise data, component loss data, and running time data during historical operation. Filter and clean the collected data, remove abnormal, invalid, and duplicate data, standardize the retained valid data, and adjust all types of data to the preset format and magnitude. A temperature rise trend prediction model is constructed based on the processed effective data. The input and output variables of the model are defined. The input variables include ambient temperature, input and output power, and running time. The output variable is the actual temperature rise. Through data fitting and algorithm training, the correlation between each input and output variable is determined, and the model parameters are optimized. After the model is built, offline verification and parameter calibration are performed. By comparing the model prediction results with the actual temperature rise data, the model parameters are adjusted. During the prediction process, the ambient temperature signal and input and output power signals in the power supply basic dataset are extracted in real time and input into the calibrated temperature rise trend prediction model. The temperature rise trend is obtained in real time through model calculation. When the predicted temperature rise reaches the preset warning value, a temperature rise warning signal is output and fed back to the system control unit.

[0013] Specifically, the transient fault detection mechanism includes the following processes: During inverter operation, a dedicated monitoring module monitors various operating signals in real time, including inverter output voltage, inverter output current, bus voltage, internal component temperature, and input voltage and current. Normal fluctuation ranges for various operating signals are preset, and corresponding disturbance and fault thresholds are set. When an abnormal fluctuation is detected in a certain operating signal, an anomaly recording mechanism is activated to record the duration, amplitude, frequency, and trajectory of the abnormal fluctuation. The status of other related operating signals at the time of the abnormal fluctuation is also captured, forming a complete set of anomaly information. The parameters of the abnormal fluctuation are compared one by one with the preset disturbance and fault thresholds. Based on the comparison results, transient disturbances and persistent faults are distinguished. If the abnormal fluctuation meets the criteria for transient disturbances, disturbance-related information is recorded. If the abnormal fluctuation meets the criteria for persistent faults, a fault flag is triggered, and the fault occurrence time, fault type, and fault severity are recorded.

[0014] Specifically, the real-time monitored operating signals include inverter output voltage, inverter output current, bus voltage, internal component temperature, and input voltage and current. The monitoring process is as follows: high-frequency real-time sampling is used to collect various operating signals. A reasonable sampling frequency is set. During the sampling process, the collected operating signals are filtered and denoised in real time to remove high-frequency noise and transient interference. The change trajectory of various operating signals is recorded in chronological order. The signal amplitude, fluctuation, and other relevant parameters at each sampling time point are recorded. It is monitored whether various signals exceed the preset safety range. If an abnormality is detected, an abnormality prompt mechanism is triggered, and the abnormal signal data is transmitted to the fault judgment unit.

[0015] Specifically, the process of generating the inverter operation monitoring list includes: The system integrates various real-time monitored operating signal data, transient fault identification results, and temperature rise predictions to form a complete raw data set. This raw data set is then categorized and organized according to data type and time sequence, distinguishing between operating parameter data, fault and disturbance-related data, and temperature rise prediction-related data. All data types are comprehensively verified, invalid data is removed, and deviation data is corrected. Operating parameters at each time point are recorded, including input / output voltage, current, bus voltage, and internal component temperature. Transient fault identification results are also recorded, including fault occurrence time, fault type, fault severity, and handling status. If transient disturbances exist, the disturbance occurrence time and disturbance amplitude are recorded. Temperature rise predictions are recorded, including real-time predicted temperature rise values, temperature rise trends, and warning signal triggering. The time, type, and cause of abnormal data are labeled, forming a complete monitoring list for the inverter operation.

[0016] The beneficial effects of this invention are as follows: This invention optimizes the inverter enable state parsing mechanism, performs targeted processing and verification of external control commands, improves the accuracy of command parsing, reduces the impact of electromagnetic interference and transmission noise on command parsing, avoids malfunctions in inverter enable control, and ensures the accuracy and reliability of inverter enable control.

[0017] This invention performs targeted preprocessing on the power supply fundamental signal, filtering out noise, eliminating abnormal data, and calibrating and normalizing the amplitude to generate a standardized power supply fundamental dataset. This provides accurate and reliable data support for subsequent pre-charging, voltage and current regulation, temperature rise prediction, and other processes, thereby improving the stability of the entire control process.

[0018] This invention employs an adaptive soft-start strategy, dynamically adjusting the voltage rise slope based on various parameters during the soft-start process, and ensuring a smooth soft-start process through closed-loop correction. This avoids the impact problems caused by fixed-slope startup, protects the bus capacitors and internal power supply components, and extends the service life of the equipment.

[0019] This invention employs a dual closed-loop regulation mechanism for voltage and current. Based on preset parameters in the power supply basic data set, it dynamically adjusts the inverter control signal through comprehensive calculation of deviation signals, thereby improving the regulation accuracy of inverter output voltage and current. It can adapt to the working conditions of large load fluctuations in mining trucks and ensure power supply stability.

[0020] This invention establishes a temperature rise trend prediction model, which predicts temperature rise changes by using historical data and real-time signals. It can detect abnormal temperature rise in advance and output early warning signals to avoid damage to power components due to overheating. At the same time, it accurately distinguishes between continuous faults and transient disturbances through a transient fault discrimination mechanism, reducing false and missed faults and improving the system's fault response capability. Attached Figure Description

[0021] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0022] Figure 1 This is an overall control flowchart of an inverter enable control method for a DC-DC power supply in a mining truck according to the present invention. Figure 2 This is a timing diagram of the pre-charging and main circuit switching in this invention; Figure 3 This is a diagram of the 40KW power system architecture for the DC-DC mining truck in this invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0024] Please see Figures 1-3 An inverter enable control method for a DC-DC power supply in a mining truck, comprising: S1: Read external control commands in real time, execute the inverter enable status parsing mechanism, synchronously acquire power supply basic signals, and generate power supply basic dataset after preprocessing. S2: Based on the input voltage signal in the power supply basic dataset, perform a pre-charging operation on the bus capacitor. After the bus voltage reaches a preset threshold, trigger the main circuit switching. Based on the adaptive soft-start strategy, dynamically adjust the soft-start voltage rise slope. S3: Based on the power supply basic dataset, execute the voltage and current dual closed-loop regulation mechanism to generate inverter control signals, establish a temperature rise trend prediction model, predict the temperature rise situation, and send the inverter control signals to the inverter control terminal to start inverter output; S4: During inverter operation, a transient fault discrimination mechanism is used to distinguish between continuous faults and transient disturbances. Based on the real-time monitored operating signals, fault discrimination results and temperature rise, a monitoring list for the entire inverter operation is generated.

[0025] Specifically, the specific process of the inverter enable state resolution mechanism includes: It receives external control command signals in real time, performs decoding operations, obtains encoded information, identifies various enable-related command types contained in the external control command signals, including inverter enable start, inverter enable stop, and enable mode switching, synchronously parses the enable trigger conditions corresponding to each command, clarifies the voltage range, timing requirements, and associated signal parameters in the trigger conditions, verifies the parsed enable status information, and compares the parsing results with the preset command specifications one by one.

[0026] Specifically, the preprocessing process includes: The power supply basic signals include input voltage signal, input current signal, bus voltage signal, ambient temperature signal and inverter control terminal feedback signal. Each type of power supply basic signal is filtered using a corresponding filtering method. A matching filtering strategy is selected based on the noise characteristics of different signals to filter out high-frequency noise and transient interference in the signals. By using preset abnormal data identification rules, abnormal data generated during signal acquisition is removed. Amplitude calibration is performed on various filtered signals. The amplitude deviation of output signals from different types of sensors is corrected based on preset calibration benchmarks. The calibrated signals are then regularized to adjust various signals to a preset amplitude range. Finally, the processed signals are integrated according to a preset data format to generate the power supply basic dataset.

[0027] Specifically, the pre-charging operation includes the following steps: The real-time amplitude and fluctuation frequency of the input voltage are extracted from the power supply basic dataset. The initial pre-charge current is set based on the rated parameters of the bus capacitor. The control element in the pre-charge circuit is turned on so that the input voltage is transmitted to the bus capacitor through the pre-charge circuit to charge the bus capacitor. During the charging process, the changes in the pre-charging current are monitored in real time. The current limiting element in the pre-charging circuit is dynamically adjusted according to the current changes to keep the pre-charging current within a preset range. The real-time voltage value is compared with the preset charging threshold to obtain the stable state of the bus voltage.

[0028] Specifically, the main circuit switching process includes: After the bus voltage reaches a preset threshold and remains stable for a preset duration, a main circuit switching command is triggered. According to the preset switching sequence, the control switch in the pre-charge circuit is disconnected. After the pre-charge circuit is completely disconnected, a preset buffer time is delayed, and the control switch in the main power circuit is turned on. During the switching process, based on a preset buffer control strategy, the voltage and current surges generated during circuit switching are absorbed by the buffer element, and the voltage and current changes of the main circuit are monitored in real time to detect abnormal fluctuations in the circuit.

[0029] Specifically, the process of the adaptive soft-start strategy includes: After the soft start command is triggered, a soft start slope adjustment decision matrix is ​​constructed based on various relevant parameters during the soft start process. The load level is divided according to the load impedance value, and the warning level is divided according to the input current ripple coefficient and the junction temperature signal of the power device. A corresponding soft start voltage rise slope adjustment coefficient is matched for each level. Based on the load level and warning level of the real-time parameters, the corresponding slope adjustment coefficient is called to adjust the soft start voltage rise slope. The bus voltage feedback signal is collected in real time and compared with the preset soft start voltage reference curve to calculate the voltage deviation. Based on the voltage deviation, the soft start voltage rise slope is corrected in real time using a closed loop.

[0030] Specifically, the specific process of the voltage and current dual closed-loop regulation mechanism includes: Using the preset output voltage and preset output current in the power supply basic data as the core adjustment benchmark, the allowable fluctuation range of voltage and current is defined, the actual voltage and actual current of the inverter output terminal are obtained in real time, the actual voltage is compared with the preset output voltage to generate a voltage adjustment deviation signal, and the actual current is compared with the preset output current to generate a current adjustment deviation signal. The voltage regulation deviation signal and the current regulation deviation signal are comprehensively calculated by a preset adjustment algorithm, and the amplitude and frequency of the inverter control signal are dynamically adjusted based on the calculation results.

[0031] Specifically, the process of establishing and predicting the temperature rise trend model includes: The input and output variables are clearly defined. The input variables include ambient temperature, input and output power, and running time. The output variable is the actual temperature rise. Through data fitting and training, the correlation between each input and output variable is determined, the model parameters are optimized, and after the model is built, offline validation and parameter calibration are performed. The model parameters are adjusted by comparing the model prediction results with the actual temperature rise data. During the prediction process, the ambient temperature signal and input / output power signal in the power supply basic dataset are extracted in real time and input into the calibrated temperature rise trend prediction model to obtain the temperature rise trend. When the predicted temperature rise reaches the preset warning value, a temperature rise warning signal is output.

[0032] Specifically, the transient fault detection mechanism includes the following processes: During inverter operation, various operating signals are monitored in real time, the normal fluctuation range of various operating signals is preset, and the corresponding disturbance threshold and fault threshold are set. When abnormal fluctuations occur in the operating signal, the abnormal recording process is initiated to record the duration, amplitude, frequency and trajectory of the abnormal fluctuations. The status of other related operating signals at the time of the abnormal fluctuation is obtained, an abnormal information set is generated, and the parameters of the abnormal fluctuations are compared one by one with the preset disturbance thresholds and fault thresholds. Based on the comparison results, the transient disturbances and the continuous faults are distinguished.

[0033] Specifically, the real-time monitoring process includes: Based on the preset sampling frequency, the acquired various operating signals are filtered and denoised in real time to remove high-frequency noise and instantaneous interference from the signals. The change trajectory of various operating signals is recorded in chronological order, and the signal amplitude and fluctuation of each sampling time point are recorded.

[0034] Specifically, the process of generating the inverter operation monitoring list includes: Integrate various real-time monitored operating signal data, transient fault identification results, and temperature rise prediction to generate a raw data set; Organize the data according to data type and time sequence, verify various types of data, correct deviation data, record the operating parameters at each time point, record the transient fault identification results, record the time and amplitude of disturbance occurrence, record the temperature rise prediction, mark the time, type and cause of abnormal data, and generate a full-process monitoring list for inverter operation.

[0035] This embodiment is based on a 40kW DC-DC power supply for a mining truck. The power supply is controlled by a TMS320F2808 DSP processor and provides DC550V power to the water-cooled electronic fan cooling system, air conditioning system, and air pump system of the mining truck. The rated output power is 40kW, the output voltage accuracy is 1%, and it supports CAN2.0 communication. The software runs in the CCS3.3 compilation environment, and the hardware core is the TMS320F2808PZS DSP processor. The overall architecture adopts a high-frequency isolated rectification and inverter control architecture.

[0036] This embodiment, based on the actual hardware parameters and software flow of the 40KW power supply for the DC-DC mining truck, is implemented as follows: S1: Instruction parsing and basic signal preprocessing First, external control commands are read in real time via the CAN communication interface (eCanA / B) of the TMS320F2808 DSP processor. These commands are transmitted through a communication link conforming to the CAN 2.0 standard, and include three types: inverter enable start, inverter enable stop, and enable mode switching. When executing the inverter enable status parsing mechanism, the received command signal is first denoised to filter out electromagnetic interference noise from the mining truck's working environment. Then, the denoised command signal is decoded to extract the internal encoding information, identifying the current command type as inverter enable start. Simultaneously, the corresponding enable trigger conditions are parsed: the trigger voltage range is DC950V±5V, the trigger timing is 100ms delay after command transmission, and the associated signal is the bus voltage ready signal. Subsequently, the parsed enable status information is verified by comparing the parsing results with the preset CAN 2.0 command specifications one by one. After confirming that the parsing is correct, the process proceeds to the next step.

[0037] Simultaneously, various basic power supply signals are acquired through the internal voltage and current detection circuit (16CH 12Bit sampling), including input voltage signal (DC950V bus voltage), input current signal, bus capacitor voltage signal, ambient temperature signal, and inverter control feedback signal. These basic signals are preprocessed: a low-pass filtering strategy is used for the input voltage and current signals to filter high-frequency noise and transient interference; and abnormal sampling values ​​caused by poor sensor contact are eliminated through preset abnormal data identification rules.

[0038] Based on the calibration benchmark built into the DSP processor, the amplitude of the filtered signal is calibrated to correct the sensor output deviation. The specific calibration calculation process is as follows: Assume the original amplitude of the sensor output signal is Vraw, and the calibration coefficient is K (obtained from sensor calibration; in this embodiment, K=1.02). The calibrated signal amplitude Vcal=Vraw×K. For example, if the original sampling value is 3.2V, the calibrated value is 3.2×1.02=3.264V. All types of signals are uniformly normalized to the 0-3.3V amplitude range. The normalization calculation process is as follows: Assume the original signal amplitude is Vin, the maximum signal value is Vmax, and the minimum signal value is Vmin. The normalized signal Vreg=(Vin-Vmin) / (Vmax-Vmin)×3.3V. For example, if the original range of the input voltage signal is 0-1000V, and the sampling value at a certain moment is 950V, the normalized value is (950-0) / (1000-0)×3.3=3.135V. The data is then integrated according to a preset data format to generate a power supply basic dataset, which is stored in the DSP processor's HO. In SARAM (8KX16), for subsequent steps to call.

[0039] S2: Pre-charge, main circuit switching and adaptive soft start Pre-charge operation: Extract the real-time amplitude (DC950V) and fluctuation frequency (50Hz) of the input voltage from the power supply basic dataset. Combined with the rated parameters of the bus capacitor (adapted to 40KW power, capacitor capacity 1000μF), set the initial pre-charge current to 5A. The initial current calculation is based on: Pre-charge current I pre ≤C×ΔU / Δt, where C is the bus capacitance (1000μF=1×10 - ³F), ΔU is the pre-charge voltage difference (500V-0V=500V), Δt is the pre-charge target time (set to 100ms=0.1s), then the maximum allowable pre-charge current I pre_max =1×10 - ³F×500V / 0.1s=5A, therefore the initial current is set to 5A.

[0040] The DSP processor's GPIO interface controls the control elements in the pre-charge circuit to conduct, allowing the DC 950V input voltage to be transmitted to the bus capacitor for charging. During charging, a current detection circuit monitors the changes in the pre-charge current in real time. When the current exceeds 5.5A, the current-limiting element in the pre-charge circuit is dynamically adjusted to maintain the current within a preset range of 5±0.2A. Simultaneously, the real-time value of the bus voltage is continuously acquired and compared with a preset charging threshold (DC 500V) to track the stable state of the bus voltage. When the bus voltage reaches 500V and remains stable for 300ms, the pre-charge circuit is disconnected, stopping the pre-charge operation.

[0041] Main circuit switching: After the bus voltage reaches the preset threshold and stabilizes, the DSP processor automatically triggers the main circuit switching command. After the command is confirmed by the logic verification unit to be error-free and conflict-free, the control switch in the pre-charge circuit is slowly disconnected according to the preset switching sequence, with a disconnection time of 50ms. After the pre-charge circuit is completely disconnected, a 20ms buffer time is delayed before the control switch in the main power circuit is slowly turned on. During the switching process, a preset buffer control strategy is adopted. The buffer element absorbs the voltage and current surges generated during the switching of the circuit. The voltage and current changes of the main circuit are monitored in real time, and abnormal fluctuations in the circuit are captured. If the voltage fluctuation exceeds ±10V or the current fluctuation exceeds 1A, the switching interruption mechanism is triggered, the main power circuit is disconnected, and the circuit is restored to the pre-charging state. The voltage fluctuation threshold is calculated based on the following: output voltage accuracy of 1%, rated output voltage of 550V, allowable fluctuation of ±5.5V, and ±10V considering the switching surge margin. The current fluctuation threshold is calculated based on the following: rated output current of 72.7A, 5% fluctuation range of ±3.635A, and ±1A considering the switching surge margin.

[0042] Adaptive soft start: After the soft start command is triggered, the soft start parameter acquisition unit built into the DSP processor is started to collect relevant parameters in real time during the soft start process, including the instantaneous value of the bus voltage, the rate of change of the bus voltage, the instantaneous value of the input current, the input current ripple coefficient (the preset ripple threshold is 5%), the load impedance value, and the junction temperature signal of the power device (MOSFET) inside the power supply (the preset junction temperature threshold is 85℃).

[0043] The collected parameters are digitally filtered to remove noise, and then normalized to eliminate differences in parameter magnitudes. The normalization calculation process is as follows: V norm =(V real -V min ) / (V max -V min ), where V real V is a real-time parameter value. min V is the minimum value of the parameter. max This represents the maximum value of the parameter. For example, if the load impedance range is 0-200Ω and the real-time value at a certain moment is 100Ω, then after normalization, it becomes (100-0) / (200-0)=0.5.

[0044] A soft-start slope adjustment decision matrix is ​​constructed based on the processed parameters. Load levels are divided into high, medium, and low (high impedance ≥100Ω, medium impedance 50-100Ω, low impedance <50Ω) according to load impedance values. Normal and warning levels are also defined based on input current ripple coefficient and power device junction temperature signal. A corresponding soft-start voltage rise slope adjustment coefficient is matched to each level (0.5V / ms for high load level, 1V / ms for medium load level, and 1.5V / ms for low load level; a 0.3V / ms coefficient is applied to the warning level).

[0045] Based on the load level and warning level of the real-time acquired parameters, the corresponding slope adjustment coefficient is called to adjust the soft-start voltage rise slope. Simultaneously, the bus voltage feedback signal is acquired in real-time and compared with the preset soft-start voltage reference curve (linearly rising from 500V to 550V), and the voltage deviation ΔU=U is calculated. ref -U fb U ref U is the reference curve voltage value. fb To provide feedback voltage values, the slope of the soft-start voltage rise is corrected in real time based on the voltage deviation. The correction coefficient is Kp=0.05, and the corrected slope is K=K0+ΔU×Kp (K0 is the initial slope). For example, if the reference voltage is 520V, the feedback voltage is 518V, ΔU=2V, the initial slope is 1V / ms, and the corrected slope is 1+2×0.05=1.1V / ms. The adjustment continues until the bus voltage reaches the rated operating voltage of DC550V, at which point the soft-start process terminates.

[0046] S3: Voltage and current dual closed-loop regulation and temperature rise prediction Dual closed-loop regulation of voltage and current: The core regulation benchmark is the preset output voltage (DC550V) and preset output current (approximately 72.7A, suitable for 40KW rated power) in the power supply basic data. The output current calculation process is as follows: According to the power formula P=U×I, we can transform it to I=P / U. Substituting the rated power P=40KW=40000W and the rated output voltage U=550V, the preset output current I=40000W / 550V≈72.7A. The allowable voltage fluctuation range is specified as 550V±1%, calculated as follows: 550V×1%=5.5V, therefore the allowable voltage fluctuation range is from 550V-5.5V=544.5V to 550V+5.5V=555.5V; the allowable current fluctuation range is specified as 72.7A±5%, calculated as follows: 72.7A×5%≈3.635A, therefore the allowable current fluctuation range is from 72.7A-3.635A≈69.065A to 72.7A+3.635A≈76.335A.

[0047] The sampling module is controlled via the PWM interface of the DSP processor to acquire the actual voltage and current at the inverter output in real time. The acquired signals are filtered, denoised, and calibrated to remove interference components. The processed actual voltage is then compared with the preset output voltage to generate a voltage regulation deviation signal ΔU. v =U set -U act (U) set U is the preset output voltage. act (The actual output voltage) is analyzed to determine the direction and magnitude of the deviation; simultaneously, the processed actual current is compared with the preset output current to generate a current adjustment deviation signal ΔI. i =I set -I act (I) set To preset the output current, I act (This refers to the actual output current), and the current deviation is analyzed. The two types of deviation signals are comprehensively calculated using a preset PI control algorithm. The PI control calculation process is as follows: U ctrl =K p ×(ΔU v +ΔI i )+K i ×∫(ΔU v +ΔI i )dt, where K p The scaling factor (K in this embodiment) p =0.8), K i The integral coefficient (K in this embodiment) i=0.02), dynamically adjust the amplitude and frequency of the inverter control signal according to the calculation results. The voltage closed loop prioritizes the adjustment of the output voltage stability, while the current closed loop assists in limiting the output current, ensuring that the inverter output voltage is stable within the range of DC550V±1% and the output current is stable within the range of 72.7A±5%.

[0048] Establishment and prediction of temperature rise trend: First, various relevant data were collected during the operation of the 40KW power inverter in the DC-DC mining truck, including ambient temperature signals (-20℃-60℃), input and output power signals (0-40KW) from the power supply basic dataset, as well as temperature rise data, component loss data, and runtime data from historical operation. The collected data was screened and cleaned, removing abnormal, invalid, and duplicate data. The retained valid data was standardized and uniformly adjusted to a preset format and magnitude. The standardization calculation process was the same as the normalization calculation in S1.

[0049] A temperature rise trend prediction model was constructed based on the processed effective data. The input variables were set as ambient temperature T, input / output power P, and runtime t, and the output variable was the actual temperature rise ΔT. Through data fitting and PI algorithm training, the correlation between each input and output variable was determined. The fitting formula was ΔT = a × T + b × P + c × t + d, where a, b, c, and d were fitting coefficients (determined during training: a = 0.12, b = 0.08, c = 0.05, d = 2.3). The model parameters were then optimized. After the model was built, offline validation and parameter calibration were performed. By comparing the model's prediction results with the actual temperature rise data, the model parameters were adjusted to improve prediction accuracy. The calibration calculation process was as follows: Let the model predict the temperature rise as ΔT. pre The actual temperature rise is ΔT act Calibration coefficient K cal =ΔT act / ΔT pre After calibration, the predicted temperature rise ΔT pre_cal =ΔT pre ×K cal During the prediction process, the ambient temperature signal and input / output power signal are extracted from the power supply basic dataset in real time and input into the calibrated temperature rise trend prediction model. For example, if the ambient temperature T=25℃, the input / output power P=30KW, and the running time t=600s, substituting into the fitting formula, we get ΔT=0.12×25+0.08×30+0.05×600+2.3=3+2.4+30+2.3=37.7℃. The temperature rise trend is obtained in real time through model calculation. When the predicted temperature rise reaches the preset warning value (80℃), a temperature rise warning signal is output and fed back to the detection indication module of the DSP processor to trigger the warning prompt.

[0050] Finally, the generated inverter control signal is sent to the inverter control circuit through the PWM interface of the DSP processor to start the inverter output and provide a stable DC550V power supply for the mine truck's water-cooled electronic fan cooling system, air conditioning system, and air pump system.

[0051] S4: Transient Fault Identification and Monitoring Inventory Generation Transient fault detection: During inverter operation, the voltage and current detection circuit and temperature detection module of the DSP processor monitor various operating signals in real time, including inverter output voltage (DC550V), inverter output current, bus voltage (DC950V), internal component temperature, and input voltage and current. Normal fluctuation ranges for various operating signals are preset, and corresponding disturbance and fault thresholds are set. The threshold calculation process is as follows: Inverter output voltage disturbance threshold = 550V × 0.9% ≈ 4.95V, rounded to ±5V; Fault threshold = 550V × 1.8% ≈ 9.9V, rounded to ±10V; Internal component temperature disturbance threshold = 85℃ × 3.5% ≈ 2.975℃, rounded to ±3℃; Fault threshold = 85℃ × 5.9% ≈ 5.015℃, rounded to ±5℃.

[0052] When an abnormal fluctuation is detected in a certain operating signal (such as a sudden increase in input current to 80A), the abnormal recording process is initiated to record the duration, amplitude, frequency, and trajectory of the abnormal fluctuation. The amplitude is calculated as follows: ΔI = I act -I set =80A-72.7A=7.3A. Simultaneously, the status of other related operating signals (such as whether the bus voltage and component temperature are synchronously abnormal) is captured when abnormal fluctuations occur, generating an abnormal information set. Each parameter of the abnormal fluctuation is compared with the preset disturbance threshold and fault threshold. If the duration of the abnormal fluctuation is <100ms, the fluctuation amplitude does not exceed the disturbance threshold, and there is no obvious abnormality in the related operating signals, it is judged as an instantaneous disturbance, and only the disturbance-related information is recorded. If the duration of the abnormal fluctuation is ≥100ms, the fluctuation amplitude exceeds the fault threshold, or the related operating signals are synchronously abnormal, it is judged as a continuous fault, triggering a fault flag, and recording the fault occurrence time, fault type (such as overcurrent fault), and fault severity.

[0053] Real-time monitoring: A high-frequency real-time sampling method is adopted, with the sampling frequency set to 1kHz. The sampling period is calculated as follows: T=1 / f=1 / 1000Hz=1ms, that is, the running signal is collected once every 1ms. The acquired running signals are filtered and denoised in real time to remove high-frequency noise and instantaneous interference. The change trajectory of various running signals is recorded in chronological order, and the signal amplitude and fluctuation of each sampling time point are recorded and stored in the Flash (64KX16) of the DSP processor for easy subsequent traceability.

[0054] Inverter operation monitoring checklist generation: This involves integrating various real-time monitored operating signal data, transient fault identification results, and temperature rise predictions to generate a raw data set. The data is then organized according to data type (operating parameter data, fault and disturbance data, and temperature rise prediction data) and time sequence. All data types are verified, and deviations are corrected. The deviation correction calculation process is as follows: Let the deviation data be V. err Corrected data V cor =V err ×K cor (K) cor The correction factor, K, is derived from calibration in this embodiment. cor =0.998). Detailed records of operating parameters at each time point are maintained, including input and output voltage, current, bus voltage, and internal component temperature; transient fault identification results are recorded, including fault occurrence time, fault type, fault severity, and handling status. If instantaneous disturbances exist, the disturbance occurrence time and disturbance amplitude are recorded simultaneously; temperature rise prediction is recorded, including real-time predicted temperature rise value, temperature rise trend, and early warning signal triggering; the time, type, and cause of abnormal data are labeled (e.g., overcurrent faults caused by sudden load changes), ultimately forming a complete monitoring list for the entire inverter operation process.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An inverter enable control method for a DC-DC power supply in a mining truck, characterized in that, include: S1: Read external control commands in real time, execute the inverter enable status parsing mechanism, synchronously acquire power supply basic signals, and generate power supply basic dataset after preprocessing. S2: Based on the input voltage signal in the power supply basic dataset, perform a pre-charging operation on the bus capacitor. After the bus voltage reaches a preset threshold, trigger the main circuit switching. Based on an adaptive soft-start strategy, the rise rate of the soft-start voltage is dynamically adjusted. S3: Based on the power supply basic dataset, execute the voltage and current dual closed-loop regulation mechanism to generate inverter control signals, establish a temperature rise trend prediction model, predict the temperature rise situation, and send the inverter control signals to the inverter control terminal to start inverter output; S4: During inverter operation, a transient fault discrimination mechanism is used to distinguish between continuous faults and transient disturbances. Based on the real-time monitored operating signals, fault discrimination results and temperature rise, a monitoring list for the entire inverter operation is generated.

2. The method according to claim 1, characterized in that, In S1, the specific process of the inverter enable state resolution mechanism includes: It receives external control command signals in real time, performs decoding operations, obtains encoded information, identifies various enable-related command types contained in the external control command signals, including inverter enable start, inverter enable stop, and enable mode switching, synchronously parses the enable trigger conditions corresponding to each command, clarifies the voltage range, timing requirements, and associated signal parameters in the trigger conditions, verifies the parsed enable status information, and compares the parsing results with the preset command specifications one by one.

3. The method according to claim 1, characterized in that, In S1, the preprocessing process specifically includes: The power supply basic signals include input voltage signal, input current signal, bus voltage signal, ambient temperature signal and inverter control terminal feedback signal. Each type of power supply basic signal is filtered using a corresponding filtering method. A matching filtering strategy is selected based on the noise characteristics of different signals to filter out high-frequency noise and transient interference in the signals. By using preset abnormal data identification rules, abnormal data generated during signal acquisition is removed. Amplitude calibration is performed on various filtered signals. The amplitude deviation of output signals from different types of sensors is corrected based on preset calibration benchmarks. The calibrated signals are then regularized to adjust various signals to a preset amplitude range. Finally, the processed signals are integrated according to a preset data format to generate the power supply basic dataset.

4. The method according to claim 1, characterized in that, In S2, the specific process of the pre-charging operation includes: The real-time amplitude and fluctuation frequency of the input voltage are extracted from the power supply basic dataset. The initial pre-charge current is set based on the rated parameters of the bus capacitor. The control element in the pre-charge circuit is turned on so that the input voltage is transmitted to the bus capacitor through the pre-charge circuit to charge the bus capacitor. During the charging process, the changes in the pre-charging current are monitored in real time. The current limiting element in the pre-charging circuit is dynamically adjusted according to the current changes to keep the pre-charging current within a preset range. The real-time voltage value is compared with the preset charging threshold to obtain the stable state of the bus voltage.

5. The method according to claim 1, characterized in that, In S2, the specific process of the main circuit switching includes: After the bus voltage reaches a preset threshold and remains stable for a preset duration, a main circuit switching command is triggered. According to the preset switching sequence, the control switch in the pre-charge circuit is disconnected. After the pre-charge circuit is completely disconnected, a preset buffer time is delayed, and the control switch in the main power circuit is turned on. During the switching process, based on a preset buffer control strategy, the voltage and current surges generated during circuit switching are absorbed by the buffer element, and the voltage and current changes of the main circuit are monitored in real time to detect abnormal fluctuations in the circuit.

6. The method according to claim 1, characterized in that, In S2, the specific process of the adaptive soft-start strategy includes: After the soft start command is triggered, a soft start slope adjustment decision matrix is ​​constructed based on various relevant parameters during the soft start process. The load level is divided according to the load impedance value, and the warning level is divided according to the input current ripple coefficient and the junction temperature signal of the power device. A corresponding soft start voltage rise slope adjustment coefficient is matched for each level. Based on the load level and warning level of the real-time parameters, the corresponding slope adjustment coefficient is called to adjust the soft start voltage rise slope. The bus voltage feedback signal is collected in real time and compared with the preset soft start voltage reference curve to calculate the voltage deviation. Based on the voltage deviation, the soft start voltage rise slope is corrected in real time using a closed loop.

7. The method according to claim 1, characterized in that, In S3, the specific process of the voltage and current dual closed-loop regulation mechanism includes: Using the preset output voltage and preset output current in the power supply basic data as the core adjustment benchmark, the allowable fluctuation range of voltage and current is defined, the actual voltage and actual current of the inverter output terminal are obtained in real time, the actual voltage is compared with the preset output voltage to generate a voltage adjustment deviation signal, and the actual current is compared with the preset output current to generate a current adjustment deviation signal. The voltage regulation deviation signal and the current regulation deviation signal are comprehensively calculated by a preset adjustment algorithm, and the amplitude and frequency of the inverter control signal are dynamically adjusted based on the calculation results.

8. The method according to claim 1, characterized in that, In S3, the establishment and prediction process of the temperature rise trend prediction model specifically includes: The input and output variables are clearly defined. The input variables include ambient temperature, input and output power, and running time. The output variable is the actual temperature rise. Through data fitting and training, the correlation between each input and output variable is determined, the model parameters are optimized, and after the model is built, offline validation and parameter calibration are performed. The model parameters are adjusted by comparing the model prediction results with the actual temperature rise data. During the prediction process, the ambient temperature signal and input / output power signal in the power supply basic dataset are extracted in real time and input into the calibrated temperature rise trend prediction model to obtain the temperature rise trend. When the predicted temperature rise reaches the preset warning value, a temperature rise warning signal is output.

9. The method according to claim 1, characterized in that, In S4, the specific process of the transient fault detection mechanism includes: During inverter operation, various operating signals are monitored in real time, the normal fluctuation range of various operating signals is preset, and the corresponding disturbance threshold and fault threshold are set. When abnormal fluctuations occur in the operating signal, the abnormal recording process is initiated to record the duration, amplitude, frequency and trajectory of the abnormal fluctuations. The status of other related operating signals at the time of the abnormal fluctuation is obtained, an abnormal information set is generated, and the parameters of the abnormal fluctuations are compared one by one with the preset disturbance thresholds and fault thresholds. Based on the comparison results, the transient disturbances and the continuous faults are distinguished.

10. The method according to claim 1, characterized in that, In S4, the real-time monitoring process specifically includes: Based on the preset sampling frequency, the acquired various operating signals are filtered and denoised in real time to remove high-frequency noise and instantaneous interference from the signals. The change trajectory of various operating signals is recorded in chronological order, and the signal amplitude and fluctuation of each sampling time point are recorded.

11. The method according to claim 1, characterized in that, In S4, the process of generating the inverter operation monitoring list specifically includes: Integrate various real-time monitored operating signal data, transient fault identification results, and temperature rise prediction to generate a raw data set; Organize the data according to data type and time sequence, verify various types of data, correct deviation data, record the operating parameters at each time point, record the transient fault identification results, record the time and amplitude of disturbance occurrence, record the temperature rise prediction, mark the time, type and cause of abnormal data, and generate a full-process monitoring list for inverter operation.

12. An inverter enable control system for a DC-DC power supply in a mining truck, characterized in that, For implementing the method as described in any one of claims 1 to 11, comprising: The signal processing module reads external control commands in real time, executes the inverter enable state parsing mechanism, synchronously acquires various basic signals, and generates a power supply basic dataset after preprocessing the various basic signals. The pre-charge start control module performs a pre-charge operation on the bus capacitor based on the input voltage signal in the power supply basic data set. After the bus voltage reaches a preset threshold, it triggers the main circuit switching and dynamically adjusts the soft start voltage rise slope based on an adaptive soft start strategy. Based on the power supply basic dataset, the inverter regulation and monitoring module executes a voltage and current dual closed-loop regulation mechanism to generate an inverter control signal, establishes a temperature rise trend prediction model and predicts the temperature rise situation, and sends the inverter control signal to the inverter control terminal to start the inverter output. During inverter operation, the fault identification module distinguishes between continuous faults and transient disturbances through a transient fault identification mechanism, and generates a full-process monitoring list of inverter operation based on real-time monitored operating signals, fault identification results, and temperature rise.

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