Adaptive battery charging control method based on risc-v architecture and related device

Abstract

The application provides a RISC-V architecture-based adaptive battery charging control method and device, equipment and a storage medium, and the method comprises the following steps: a sampling period of a timer unit is controlled through an instruction execution unit, battery module temperature, internal resistance and other data are collected and stored in a memory interface unit; data processing instructions in a register group are called by the instruction execution unit, the temperature data and the internal resistance data are processed, and the data are corrected in combination with vehicle speed and torque data; then, the timer unit is triggered by an interrupt control unit to perform clock counting, and the clock deviation is corrected; the charging period and the charging power of the battery module are distributed according to the corrected clock data and characteristic data; further, the execution state of the charging control instruction is subjected to consistency checking, deviation data and balance instructions are obtained; historical data are analyzed and processed, and power consumption adjustment is performed by a power management unit. The application fully utilizes the characteristics of the RISC-V architecture, and realizes accurate charging control of the battery module.

Description

Technical Field

[0001] The present invention relates to the field of cache technology, and in particular to an adaptive battery charging control method, device, equipment and storage medium based on a RISC-V architecture. Background Art

[0002] In the field of new energy vehicles, the vehicle's power system usually uses a multi-module battery pack for energy supply. Each battery module is often equipped with a temperature sensor and an internal resistance monitoring circuit to monitor the thermal and electrical characteristics under different working conditions. In the existing technology, the main control unit is mostly based on a processor with a closed instruction set architecture to manage and control battery charging, collect temperature, current and other data through a fixed sampling period, and charge the battery module under a constant current and constant voltage or variable frequency charging strategy. However, when the vehicle is in a state of long-term high-speed driving or operating in extreme ambient temperatures, the temperature differences and internal resistance differences between different battery modules will be continuously amplified, resulting in obvious inconsistencies in the charging and discharging status of each module. Such inconsistencies may cause individual modules to overheat or have excessive internal resistance, and will also affect the vehicle's range and battery life.

[0003] With the gradual application of the RISC-V architecture, vehicle control units have begun to adopt more open processor instruction sets to meet the parallel monitoring and hierarchical scheduling needs of multiple modules. Although existing technologies have applied RISC-V to some distributed control scenarios, they are still prone to problems such as clock drift, data processing delays, and insufficient integration of external vehicle control system data when it comes to multi-module battery charging control. In particular, when the battery management system enters a deep low-power mode, the periodic deviations between the RISC-V platform's timer unit and the external reference count will gradually accumulate, resulting in a decrease in the accuracy of the perception of the actual operating environment. At the same time, when the temperature and internal resistance state distribution of each battery module vary greatly, if consistency verification and data correction between the modules are not performed in a timely manner, the charging time period and charging power allocated to each module will become unbalanced, causing energy output disturbances of the entire vehicle under actual operating conditions. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem in the prior art that the periodic deviation between the timer unit of the RISC-V platform and the external reference count causes the multi-module temperature difference and internal resistance difference to be difficult to correct in time;

[0005] A first aspect of the present invention provides an adaptive battery charging control method based on a RISC-V architecture, wherein the RISC-V architecture includes an instruction execution unit, a register group, a memory interface unit, an interrupt control unit, a timer unit, and a power management unit. The adaptive battery charging control method based on the RISC-V architecture includes:

[0006] During the charging process of the vehicle's battery module, the instruction execution unit controls the sampling period of the timer unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack, and stores the collected data in the memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set;

[0007] The instruction execution unit calls a data processing instruction in a register group, performs fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, performs trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and corrects the temperature fluctuation characteristic data and the dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set;

[0008] The interrupt control unit triggers the timer unit to perform clock counting, and based on the corrected temperature fluctuation characteristic data and dynamic internal resistance increment data, periodically compares and corrects the deviation between the clock count of the instruction execution unit and the reference count of the entire vehicle to obtain corrected clock reference data;

[0009] The instruction execution unit allocates charging time periods and charging powers to multiple battery modules based on the corrected clock reference data, temperature fluctuation characteristic data, and dynamic internal resistance increment data, thereby obtaining a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for outputting charging control;

[0010] The instruction execution unit performs consistency check on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, stores the check result through the memory interface unit, and obtains module deviation data and charging balance instructions;

[0011] The historical data in the memory interface unit is read by the instruction execution unit, and the temperature curve, discharge depth and charging efficiency of the battery module are analyzed and processed based on the module deviation data, charging balance instructions and the vehicle accumulated operation data in the historical data, and the power management unit performs corresponding power consumption adjustment.

[0012] Optionally, in a first implementation of the first aspect of the present invention, during the charging process of the vehicle's battery module, the instruction execution unit controls the sampling period of the timer unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack, and stores the collected data in a memory interface unit to obtain a battery module temperature dataset, a dynamic internal resistance dataset, a vehicle speed dataset, and a torque demand dataset, including:

[0013] During the charging process of the vehicle's battery modules, the instruction execution unit controls a sampling period of the timer unit, and collects temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack during the sampling period, wherein the external vehicle control system data includes vehicle speed data and torque demand data;

[0014] Performing a multi-threshold level-by-level comparison process on the internal resistance monitoring circuit data to obtain a staged internal resistance mark, and performing correlation analysis on the temperature sensor data and the staged internal resistance mark based on the vehicle speed data and torque demand data to obtain an original operating condition mark applicable to each battery module;

[0015] The original operating condition marks are grouped and arranged to obtain temperature data groups and internal resistance identification groups corresponding to different battery modules. The temperature data groups and internal resistance identification groups are written into a memory interface unit and together with the vehicle speed data set and the torque demand data set, form a basic data segment for time synchronization, thereby obtaining the battery module temperature data set, dynamic internal resistance data set, vehicle speed data set, and torque demand data set.

[0016] Optionally, in a second implementation of the first aspect of the present invention, calling a data processing instruction in a register group by the instruction execution unit, performing fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, performing trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and correcting the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set includes:

[0017] calling, by the instruction execution unit, a data processing instruction in the register group, and performing a comparison operation on temperature differences within different sampling periods according to the battery module temperature data set based on the data processing instruction to obtain a multi-period temperature jump sequence;

[0018] Performing a multi-dimensional correction operation on the temperature jump sequence to obtain temperature fluctuation characteristic data, and performing a layered trend comparison on the internal resistance difference of each battery module in the dynamic internal resistance data set in adjacent sampling periods to obtain dynamic internal resistance increment data;

[0019] performing matching and screening processing on the temperature fluctuation characteristic data and the dynamic internal resistance increment data according to the vehicle speed data set and the torque demand data set to obtain inter-module working condition difference data;

[0020] Numerical smoothing is performed on the inter-module working condition difference data to obtain corrected temperature fluctuation characteristic data and dynamic internal resistance increment data.

[0021] Optionally, in a third implementation of the first aspect of the present invention, the interrupt control unit triggers a timer unit to perform clock counting, and periodically compares and corrects the deviation between the clock count of the instruction execution unit and the reference count of the vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data, to obtain the corrected clock reference data, including:

[0022] The interrupt control unit triggers the timer unit to count the clock, periodically intercepts the clock count of the instruction execution unit and compares it with the vehicle reference count in time sequence to obtain preliminary deviation information;

[0023] Performing a difference threshold comparison on the preliminary deviation information according to the corrected temperature fluctuation characteristic data to obtain a clock interference mark;

[0024] Performing multi-dimensional correlation processing on the clock interference mark and combining it with the vehicle load change level reflected in the dynamic internal resistance increment data to obtain an interference correction coefficient;

[0025] The preliminary deviation information is dynamically corrected according to the interference correction coefficient to obtain a clock error adjustment value, and the clock error adjustment value is written into a register group and the counting reference of the timer unit is synchronously updated to obtain the corrected clock reference data.

[0026] Optionally, in a fourth implementation of the first aspect of the present invention, the instruction execution unit allocates charging time periods and charging powers to multiple battery modules based on the corrected clock reference data, temperature fluctuation characteristic data, and dynamic internal resistance increment data, to obtain a multi-module charging control instruction set, including:

[0027] The instruction execution unit performs period slicing processing on the corrected clock reference data to obtain multiple timing segments;

[0028] Performing correspondence screening processing on the temperature fluctuation characteristic data and the dynamic internal resistance increment data under the multi-segment time series segments to obtain a module charging demand index;

[0029] Performing multi-dimensional threshold division processing on the module charging demand index, and combining the temperature and internal resistance level of each battery module in different time segments to obtain a segmented power allocation table;

[0030] Priority superposition processing is performed on the segmented power allocation table to generate a multi-module charging control instruction set and write it into a register group for issuing charging time periods and charging powers for different battery modules.

[0031] Optionally, in a fifth implementation of the first aspect of the present invention, the instruction execution unit performs consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, stores the verification result through the memory interface unit, and obtains the module deviation data and the charging balance instruction, including:

[0032] The instruction execution unit performs execution cycle splitting processing on the temperature information and current information of the multi-module charging control instruction set during execution to obtain corresponding execution period marks;

[0033] Performing a hierarchical comparison process on the temperature change and current curve of each battery module according to the execution period mark to obtain a preliminary consistency comparison result;

[0034] Performing difference aggregation processing on the preliminary consistency comparison results to form module deviation data;

[0035] Threshold correction processing is performed on the charge and discharge distribution between different battery modules according to the module deviation data, and a charge balancing instruction is obtained and written into the memory interface unit.

[0036] Optionally, in a sixth implementation of the first aspect of the present invention, reading historical data from the memory interface unit by the instruction execution unit, analyzing and processing the temperature curve, discharge depth, and charging efficiency of the battery module based on the module deviation data, the charge balancing instruction, and the accumulated vehicle operation data in the historical data, and performing corresponding power consumption adjustment by the power management unit includes:

[0037] Reading historical data in the memory interface unit through the instruction execution unit and performing time period aggregation processing on the accumulated vehicle operation data to obtain a long-term operating condition indicator, wherein the historical data includes a temperature curve, a depth of discharge, and a charging efficiency;

[0038] Performing multi-module correlation comparison processing on the long-period operating condition indicator according to the module deviation data and the charging balance instruction to obtain an operating condition correction parameter;

[0039] Cross-calculating the operating condition correction parameter with the temperature curve, discharge depth, and charging efficiency to obtain hierarchical power consumption adjustment instructions for different battery modules;

[0040] The power management unit dynamically adjusts the energy output and power configuration of the vehicle according to the hierarchical power consumption adjustment instruction.

[0041] A second aspect of the present invention provides an adaptive battery charging control device based on a RISC-V architecture, wherein the RISC-V architecture includes an instruction execution unit, a register group, a memory interface unit, an interrupt control unit, a timer unit, and a power management unit. The adaptive battery charging control device based on the RISC-V architecture includes:

[0042] an acquisition module, configured to, during the charging process of the vehicle's battery modules, control the sampling period of the timer unit via the instruction execution unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data from each battery module pack, and store the collected data into a memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set;

[0043] a fusion module, configured to call a data processing instruction in a register group through the instruction execution unit, perform fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, perform trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and correct the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set;

[0044] a correction module, configured to trigger a timer unit to perform clock counting via the interrupt control unit, and periodically compare and correct the deviation between the clock count of the instruction execution unit and the reference count of the vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data, thereby obtaining corrected clock reference data;

[0045] an allocation module, configured to allocate charging time periods and charging powers to multiple battery modules by the instruction execution unit according to the corrected clock reference data, the temperature fluctuation characteristic data, and the dynamic internal resistance increment data, thereby obtaining a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for output of charging control;

[0046] a verification module, configured to perform consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set through the instruction execution unit, store the verification result through the memory interface unit, and obtain module deviation data and charging balance instructions;

[0047] The analysis module is used to read the historical data in the memory interface unit through the instruction execution unit, analyze and process the temperature curve, discharge depth and charging efficiency of the battery module based on the module deviation data, the charge balancing instruction and the accumulated vehicle operation data in the historical data, and perform corresponding power consumption adjustment by the power management unit.

[0048] A third aspect of the present invention provides an adaptive battery charging control device based on a RISC-V architecture, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory so that the adaptive battery charging control device based on the RISC-V architecture performs the steps of the above-mentioned adaptive battery charging control method based on the RISC-V architecture.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium, which stores instructions that, when executed on a computer, enable the computer to execute the steps of the above-mentioned adaptive battery charging control method based on the RISC-V architecture.

[0050] The above-mentioned adaptive battery charging control method, device, equipment and storage medium based on the RISC-V architecture controls the sampling period of the timer unit through the instruction execution unit, collects data such as battery module temperature and internal resistance, and stores them in the memory interface unit; the instruction execution unit calls the data processing instructions in the register group to process the temperature data and internal resistance data, and corrects them in combination with the vehicle speed and torque data; then, the interrupt control unit triggers the timer unit to count the clock and correct the clock deviation; based on the corrected clock data and characteristic data, the charging time period and charging power of the battery module are allocated; then, the execution status of the charging control instruction is checked for consistency to obtain deviation data and balance instructions; combined with historical data, analysis and processing are performed, and the power management unit performs power consumption adjustment. The present invention fully utilizes the characteristics of the RISC-V architecture to achieve precise charging control of the battery module.

[0051] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of a first embodiment of an adaptive battery charging control method based on RISC-V architecture in an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of an embodiment of an adaptive battery charging control device based on RISC-V architecture in an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of an embodiment of an adaptive battery charging control device based on the RISC-V architecture in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0058] To facilitate understanding of this embodiment, first, an adaptive battery charging control method based on RISC-V architecture disclosed in an embodiment of the present invention is described in detail. Figure 1 As shown, this method includes the following steps:

[0059] 101. During the charging process of the vehicle's battery module, the instruction execution unit controls the sampling period of the timer unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module package, and stores the collected data into the memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set;

[0060] In one embodiment of the present invention, during the charging process of the battery module of the vehicle, the sampling period of the timer unit is controlled by the instruction execution unit, the temperature sensor data, internal resistance monitoring circuit data and external vehicle control system data of each battery module pack are collected, and the collected data are stored in the memory interface unit to obtain the battery module temperature data set, dynamic internal resistance data set, vehicle speed data set and torque demand data set. The process includes: during the charging process of the battery module of the vehicle, the sampling period of the timer unit is controlled by the instruction execution unit, and the temperature sensor data, internal resistance monitoring circuit data and external vehicle control system data of each battery module pack are collected during the sampling period, wherein the external vehicle control system data Including vehicle speed data and torque demand data; performing multi-threshold level-by-level comparison processing on the internal resistance monitoring circuit data to obtain a stage-by-stage internal resistance mark, and performing correlation analysis processing on the temperature sensor data and the stage-by-stage internal resistance mark according to the vehicle speed data and torque demand data to obtain an original operating condition mark applicable to each battery module; performing grouping and arrangement processing on the original operating condition mark to obtain temperature data groups and internal resistance identification groups corresponding to different battery modules, and writing the temperature data groups and internal resistance identification groups into a memory interface unit and forming a basic data segment of time synchronization together with the vehicle speed data set and the torque demand data set to obtain the battery module temperature data set, dynamic internal resistance data set, vehicle speed data set and torque demand data set.

[0061] Specifically, during the charging process of the vehicle's battery module, the instruction execution unit writes the sampling frequency parameters to the timer unit and starts the periodic trigger. The acquisition channel of the temperature sensor and the internal resistance monitoring circuit receives the synchronization signal after each trigger and outputs the current measurement value. When the sampling trigger arrives, the instruction execution unit reads the temperature value from the temperature sensor interface and the internal resistance measurement result from the internal resistance monitoring circuit, and at the same time obtains the vehicle speed data and torque demand data provided by the external vehicle control system. The instruction execution unit converts all the collected values ​​into Temporarily stored in the register bank in the form of represents the output of the temperature sensor, Represents the output of the internal resistance monitoring circuit, Indicates vehicle speed data. Represents torque demand data. Subsequently, the instruction execution unit repeats the above process at the defined frequency of the sampling period, and writes the data collected multiple times in succession into the memory interface unit in sequence. In this step, by maintaining strict beat alignment with the timer unit, each data block is ensured to have an accurate timestamp, and the multi-channel measurement results of different modules under the same time slice are centrally recorded. The temperature, internal resistance, vehicle speed and torque data streams thus formed can reflect the actual operating conditions of the vehicle and be connected with the analysis operations in subsequent steps. The internal resistance monitoring circuit data is subjected to multi-threshold level-by-level comparison processing to obtain a stage internal resistance mark, and the temperature sensor data and the stage internal resistance mark are correlated and analyzed according to the vehicle speed data and torque demand data to obtain the original working condition mark applicable to each battery module. The instruction execution unit first performs a process based on a set of preset threshold vectors The sampled internal resistance measurement value Compare with each threshold, if in If the range is within the range, the corresponding stage internal resistance mark will be generated in the register group Then, by matching the mark in the register group with the temperature value output by the temperature sensor , and compare vehicle speeds and torque requirements The timing information of the module is used to establish a mapping relationship between temperature, internal resistance and vehicle load. The instruction execution unit performs multi-dimensional correlation operations during the analysis process, cross-comparing the sudden change sections of the temperature curve with the staged distribution of the internal resistance mark. When it is detected that a specific interval meets the preset load characteristics, the original working condition mark is generated and written to the register group to indicate the temperature and internal resistance performance status of the module in that period and at that load level. Through this multi-threshold step-by-step comparison and the two-way association of vehicle speed and torque data, it is possible to structurally distinguish between low-load conditions, medium-load conditions and high-load conditions, and generate original working condition marks suitable for different modules. Subsequent steps can be grouped or layered according to this mark. The original working condition marks are grouped and processed to obtain temperature data groups and internal resistance identification groups corresponding to different battery modules, and the temperature data groups and internal resistance identification groups are written into the memory interface unit and together with the vehicle speed data set and torque demand data set, form a basic data segment of time synchronization. In the process of obtaining the battery module temperature data set, dynamic internal resistance data set, vehicle speed data set and torque demand data set, the instruction execution unit will retrieve the module ID to which each original working condition mark belongs in the register group, and group and classify the temperature and internal resistance marks according to the ID. At this time, a group index table can be established , the temperature data of the same module is mapped to the internal resistance tag, so that the grouped data between different modules remain independent of each other in the memory interface unit and can be uniformly indexed. Then, the instruction execution unit uses the timestamp of the vehicle speed and torque data as the anchor point and performs a packing operation for each time segment, that is, the temperature group and the internal resistance group are grouped together. Establish a timing coupling relationship to form a basic data segment for subsequent algorithm positioning. After these data segments are written into the memory interface unit, the temperature, internal resistance and vehicle speed of each module at the same timestamp are mapped to the temperature data of the same module and the internal resistance tag, so that the grouped data between different modules remain independent of each other in the memory interface unit and can be uniformly indexed. Then, the instruction execution unit uses the timestamp of the vehicle speed and torque data as an anchor point to perform a packing operation on each time segment, that is, grouping the temperature and internal resistance data together. A time-series coupling relationship is established to form the basic data segments that can be used for subsequent algorithm positioning. Once these data segments are written to the memory interface unit, the temperature, internal resistance, and vehicle operating condition information of each module at the same timestamp are stored in the same index structure, ultimately generating a battery module temperature dataset, a dynamic internal resistance dataset, a vehicle speed dataset, and a torque demand dataset.

[0062] 102. Calling data processing instructions in the register group through the instruction execution unit, fusing the battery module temperature data set to obtain temperature fluctuation characteristic data, performing trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and correcting the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set;

[0063] In one embodiment of the present invention, the instruction execution unit calling the data processing instructions in the register group, fusing the battery module temperature dataset to obtain temperature fluctuation characteristic data, performing trend calculation on the dynamic internal resistance dataset to obtain dynamic internal resistance increment data, and correcting the temperature fluctuation characteristic data and the dynamic internal resistance increment data in combination with the vehicle speed dataset and the torque demand dataset includes: calling the data processing instructions in the register group by the instruction execution unit, performing a comparison operation on temperature differences within different sampling periods according to the battery module temperature dataset based on the data processing instructions to obtain a multi-period temperature jump sequence; performing a multi-dimensional correction operation on the temperature jump sequence to obtain temperature fluctuation characteristic data, performing a hierarchical trend comparison on the internal resistance differences of each battery module in the dynamic internal resistance dataset in adjacent sampling periods to obtain dynamic internal resistance increment data; matching and screening the temperature fluctuation characteristic data and the dynamic internal resistance increment data according to the vehicle speed dataset and the torque demand dataset to obtain inter-module operating condition difference data; and performing numerical smoothing on the inter-module operating condition difference data to obtain corrected temperature fluctuation characteristic data and dynamic internal resistance increment data.

[0064] Specifically, the instruction execution unit calls the data processing instruction in the register group, and based on the data processing instruction, compares the temperature differences in different sampling periods according to the battery module temperature data set to obtain a multi-cycle temperature jump sequence; the process first reads the temperature values ​​of adjacent sampling points from the register group and , then calculate , several By arranging the data in chronological order and determining their positive and negative trends and absolute value ranges, the temperature transition amplitudes and frequencies within several consecutive sampling periods are extracted, generating a multi-period temperature transition sequence. When the transition amplitude reaches the upper limit of the preset interval, the corresponding sampling period can be marked as a high-transition segment, allowing subsequent calibration operations to incorporate these significant transition points for detailed numerical processing. The comparison operation is carried out in a loop within the instruction execution unit, and the register group can be used to quickly access the temperature data and differential threshold value, thereby completing multi-cycle comparison and jump positioning in a shorter time sequence, and writing the results to the memory interface unit for continued use in subsequent steps; a multi-dimensional correction operation is performed on the temperature jump sequence to obtain temperature floating characteristic data, and a layered trend comparison is performed on the internal resistance difference of each battery module in the dynamic internal resistance data set under adjacent sampling cycles to obtain dynamic internal resistance incremental data; wherein the multi-dimensional correction operation adopts a comprehensive algorithm based on weighted filtering and adjacent sample offset judgment to distinguish between extreme values ​​and small fluctuations in the jump sequence. When an extreme jump interval is detected, the local weighting coefficient is increased and a nonlinear correction is performed to generate temperature floating characteristic data that can better reflect the degree of thermal fluctuation. For the dynamic internal resistance data set, the internal resistance measurement values ​​of several adjacent cycles are stored in the register group. ,according to Calculate the internal resistance increment and evaluate the change trend of the increment in different time periods in a hierarchical trend comparison manner. When the trend curve deviates significantly, mark the corresponding interval for key monitoring or correlation analysis in subsequent processing, thereby forming dynamic internal resistance increment data that can distinguish normal changes from abnormal fluctuations; match and filter the temperature floating characteristic data and the dynamic internal resistance increment data according to the vehicle speed data set and the torque demand data set to obtain the working condition difference data between modules; the matching and filtering process performs time series splitting on the vehicle speed and torque data, and summarizes the temperature floating characteristics and internal resistance weight gain at the same time according to various working conditions such as vehicle acceleration, constant speed, and climbing, and compares the upper and lower limits of the temperature and internal resistance curves of each module under the same working condition. When any module presents a significantly high or low floating characteristic or weight gain value in the same load scenario, the instruction execution unit generates a difference mark in the register group and arranges these difference marks into working condition difference data between modules. This process can be achieved through a working condition mapping table Divide the vehicle speed and torque into several intervals, and then mark the interval points where the temperature and internal resistance change. By comparing and screening the difference between different modules, the difference data between modules that can be directly compared are finally generated. The difference data between modules are numerically smoothed to obtain the corrected temperature fluctuation characteristic data and dynamic internal resistance increment data. This process first reads the difference data between modules in the register group, and combines the original sequence of temperature fluctuation characteristics and internal resistance increment to set a set of smoothing coefficient vectors. , multi-order filtering based on convolution operations is performed on the difference data. If the operating condition difference value in a certain interval is localized to the average level, the weighted proportion of this interval in the subsequent correction is reduced to make the overall curve more representative of the normal fluctuations under the actual operating conditions. Finally, the smoothed difference component is superimposed on the temperature fluctuation characteristic data and the internal resistance increment data, so that the abnormal spikes are reduced while maintaining the basic information. The corrected temperature fluctuation characteristic data and dynamic internal resistance increment data are obtained, and the correction results are written to the memory interface unit.

[0065] 103. Trigger the timer unit to count the clock through the interrupt control unit. Periodically compare and correct the deviation between the clock count of the instruction execution unit and the reference count of the vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data to obtain the corrected clock reference data.

[0066] In one embodiment of the present invention, triggering the timer unit to perform clock counting through the interrupt control unit, and periodically comparing and correcting the deviation between the clock count of the instruction execution unit and the reference count of the entire vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data to obtain the corrected clock reference data includes: triggering the timer unit to perform clock counting through the interrupt control unit, periodically intercepting the clock count of the instruction execution unit and performing a time sequence comparison with the reference count of the entire vehicle to obtain preliminary deviation information; performing a difference threshold comparison on the preliminary deviation information based on the corrected temperature fluctuation characteristic data to obtain a clock interference mark; performing multi-dimensional correlation processing on the clock interference mark, and combining the vehicle load change level reflected in the dynamic internal resistance increment data to obtain an interference correction coefficient; dynamically correcting the preliminary deviation information based on the interference correction coefficient to obtain a clock error adjustment amount, and writing the clock error adjustment amount into a register group and synchronously updating the counting reference of the timer unit to obtain the corrected clock reference data.

[0067] Specifically, when the timer unit is triggered to count the clock by the interrupt control unit, the instruction execution unit will set a one-time or cyclic interrupt trigger condition internally and send a start instruction signal to the timer unit. The timer unit then increments the internal clock register according to the pre-set timing parameters. This process transmits the counting completion signal of the timer unit back to the instruction execution unit through the interrupt control unit, so that the latter can read the current clock value in the interrupt service routine and perform a timing comparison with the data of the previous moment. Through this periodic triggering, the timer unit can record the system main frequency, the beat in low power mode, and the changes in the vehicle's external reference frequency in quasi-real time, thereby obtaining a continuous clock sequence that can be accessed by the instruction execution unit. On this basis, the clock register of the timer unit can also be reset or offset adjusted under the instruction of the instruction execution unit to cope with instantaneous timing mutations caused by the external environment. The entire triggering and counting process maintains a predetermined response order through the priority setting of the interrupt control unit to avoid conflicts with other critical tasks, so that stable clock capture can still be performed in a more complex multi-tasking environment; the clock count of the instruction execution unit is periodically intercepted and compared with the timing of the vehicle reference count. When the preliminary deviation information is obtained, the instruction execution unit will read the current clock count in the interrupt service function or the regularly executed task , and obtain the corresponding reference value from the vehicle's external control system or reference count source . Performed within the register bank After operation, Compare with the preset threshold range, if If the situation occurs, the period will be marked as a potential deviation interval and written into the memory interface unit. Periodic interception can capture the clock drift trend in different time segments, provide an accurate basis for subsequent difference analysis, and enable the vehicle control system to grasp the offset trend of the RISC-V internal clock to the external reference count through timing comparison. The preliminary deviation information is compared with the difference threshold based on the corrected temperature floating characteristic data. When the clock interference mark is obtained, the instruction execution unit will search the register group for the high jump or abnormal fluctuation interval marked in the temperature floating characteristic data, and synchronize it with the preliminary deviation information. When the temperature floating characteristic shows a large fluctuation, by calculating ,in It can be determined according to the temperature jump level. With the original Comparison is performed, and if the value exceeds a specific interval, it is marked as a clock interference mark and stored in the corresponding register unit. This difference threshold comparison reflects to a certain extent that when the vehicle is under high thermal load or drastic temperature changes, the deviation between the system's internal clock and the external reference may be additionally amplified, thereby giving a clear interference mark at the calculation level for subsequent correlation operations; multi-dimensional correlation processing is performed on the clock interference mark, combined with the vehicle load change level reflected in the dynamic internal resistance incremental data, to obtain the interference correction coefficient. The system will read the dynamic internal resistance weight gain data in the instruction execution unit, and perform calculations based on the power load level reflected by the internal resistance difference of each module in adjacent sampling cycles, combined with the clock interference mark generated in the previous stage. The instruction execution unit uses a composite function ,in and It can be adjusted according to different load intervals, and the load degree reflected by the dynamic internal resistance and the clock interference degree are added into a single correlation value. The correlation value is compared with the established multi-dimensional reference threshold in the register group. If the result exceeds a certain critical range, an interference correction coefficient is generated to re-weight the correction amplitude of the clock interference on the system timing within the interval. The multi-dimensional correlation processing operation is cyclically performed in the instruction execution unit, and the most appropriate correction magnitude is screened out through a comprehensive judgment of different module loads and clock deviations, making the subsequent correction process more refined. The preliminary deviation information is dynamically corrected according to the interference correction coefficient to obtain the clock error adjustment amount, and the clock error adjustment is rewritten into the register group and the counting base of the timer unit is synchronously updated. In the process of obtaining the corrected clock base data, the instruction execution unit will first convert the preliminary deviation information into the internal operation logic. and interference correction factor Perform integer addition, for example ,in The dynamic proportional constant can be set according to the vehicle operation mode. When the value is obtained, it is written to the register bank as a clock error adjustment for temporary storage, and a synchronization update instruction is issued to the timer unit. After receiving the update instruction, the timer unit appropriately offsets or resets its internal count reference to ensure that the RISC-V clock count tends to be more closely aligned with the vehicle reference count in subsequent timing cycles. With the clock reference data updated, the vehicle's multi-module charging strategy can be further allocated and controlled based on more precise timing.

[0068] 104. The instruction execution unit allocates charging time periods and charging powers to the multiple battery modules according to the corrected clock reference data, the temperature fluctuation characteristic data, and the dynamic internal resistance increment data, and obtains a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for output of charging control;

[0069] In one embodiment of the present invention, the instruction execution unit allocates charging time periods and charging powers to multiple battery modules according to the corrected clock reference data, temperature floating characteristic data and dynamic internal resistance increment data to obtain a multi-module charging control instruction set, including: the instruction execution unit performs period slicing processing on the corrected clock reference data to obtain multi-segment timing segments; performs correspondence screening processing on the temperature floating characteristic data and the dynamic internal resistance increment data under the multi-segment timing segments to obtain a module charging demand index; performs multi-dimensional threshold division processing on the module charging demand index, and combines the temperature and internal resistance level of each battery module under different timing segments to obtain a segmented power allocation table; performs priority superposition processing on the segmented power allocation table to generate a multi-module charging control instruction set and writes it into a register group for issuing charging time periods and charging powers for different battery modules.

[0070] Specifically, when the instruction execution unit performs periodic slicing on the modified clock reference data to obtain multiple timing segments, the instruction execution unit will establish a fixed-length or variable-length timing partitioning table in the register group, and divide the modified clock reference data into several discontinuous sub-intervals based on the table. In order to keep the clock reference in different sub-intervals relatively stable, a slicing function can be defined. ,in is the timestamp in the clock reference data, is the pre-selected slice precision constant. The instruction execution unit will The clock sequence is intercepted and arranged based on the output of the clock generator, with each subinterval assigned a unique index ID. This index ID is also written to the register bank for subsequent retrieval. Since the corrected clock reference data has been corrected for interference in the previous step, it can now be considered locally aligned with the vehicle reference count within a relatively short period of time. Cycle slicing segments sequences with longer time spans to avoid overloading the control strategy within a single time segment due to frequent changes in vehicle driving conditions. The instruction execution unit records the start and end times of each slice at the end of each slice, enabling precise matching of the operating periods of different battery modules in subsequent steps. After slicing is completed, each subinterval is assigned a specific duration attribute and stored in the memory interface unit, enabling analysis and calculation logic to quickly retrieve the start and end times of these subintervals and correlate them with factors such as temperature and internal resistance. Furthermore, cycle slicing leverages the clock reference and coordinates with the multi-core instruction pipeline to complete slice calibration and index updates within microsecond intervals, leaving an appropriate time window for subsequent multi-module parallel computation and allocation. The temperature fluctuation characteristic data and the dynamic internal resistance increment data are screened for correspondence under the multi-segment timing segments to obtain the module charging demand index. The instruction execution unit matches the sampling timestamps of the temperature fluctuation characteristic data and the dynamic internal resistance increment data according to the timing segment index ID generated in the first step, thereby screening out the temperature and internal resistance information that matches the current segment. To achieve correspondence screening, a matching function can be defined in the register group. ,in Indicates the temperature fluctuation characteristics or internal resistance increment in the first The record of sampling time, Indicates the start or end time of a time series segment. time When the instruction execution unit attributes the temperature or internal resistance weight gain value to the index ID After the screening is completed, the temperature and internal resistance fluctuation amplitude of each module in each segment will be counted and distinguished in the register group. When the temperature fluctuation characteristic or internal resistance increment value exceeds a certain internal threshold, it will be marked as high demand or low demand to indicate the energy state of the module in this timing segment. Subsequently, the instruction execution unit aggregates these marking information according to the module ID to form a module charging demand index. This index can be expressed as a two-dimensional table. Record the demand status of different modules in different time segments in the form of . After writing to the memory interface unit, the matching screening results of each module and each fragment can be quickly queried. Through this correspondence screening, it is possible to avoid the data of asynchronous sampling points from being mistakenly mixed into the same timing range, thereby ensuring that the identification of temperature and internal resistance fluctuations is based on a consistent time reference. In the process of performing multi-dimensional threshold division processing on the module charging demand index and combining the temperature and internal resistance levels of each battery module in different timing fragments to obtain a segmented power allocation table, the instruction execution unit will set several temperature and internal resistance reference intervals in the register group, and compare the modules marked as local demand or low demand in each fragment with these reference intervals based on the module charging demand index. If the temperature fluctuation characteristics of the local demand module in the corresponding fragment are too local and the internal resistance increment is small, it can be inferred that the module has the potential for fast charging, and the instruction execution unit will execute internally Operation, that is, the temperature level and internal resistance level Perform combined mapping to calculate a comprehensive power allocation coefficient On the contrary, if a significant internal resistance increase is detected under the local demand label, the power allocation coefficient will be reduced, reducing the additional charging load of the module in this time segment. Through this multi-dimensional threshold division, combinational logic can be formulated for temperature ranges, internal resistance ranges, and demand labels. After the calculation is completed, the instruction execution unit will calculate the value of the internal resistance increase. The power allocation between modules and time segments is written into a temporary power table based on the value of the time segment ID, module ID and allocation coefficient. Classify and form a segmented power allocation table. Since different segments have different demand labels and temperature and internal resistance states, the generated allocation table will be able to accurately define the maximum power or minimum power that can be allocated to each module in different time windows, avoiding the problem that a single charging power curve is difficult to cover actual needs under multiple working conditions. Priority superposition processing is performed on the segmented power allocation table to generate a multi-module charging control instruction set and write it into the register group. In the process of issuing the charging time period and charging power of different battery modules, the instruction execution unit will register each module according to its priority level, the segment it is in, and the previously obtained instruction set. To achieve priority superposition, an integer addition function can be set ,in It is the priority factor of the module, which is used to give some modules a higher charging weight in emergency timing segments or high reliability requirement segments. After extracting the superposition result, the instruction execution unit will organize the superposition result into a multi-module charging control instruction set according to the timing segment ID. In this instruction set, each module will receive clear instructions on the charging period and charging power in each segment, and the instruction execution unit will eventually write these commands into the register group. The vehicle control system or the next-level execution component will allocate the actual charging voltage, current and control timing by reading the multi-module charging control instruction set generated in the register group, and send a switching signal to each module separately at the appropriate time. In subsequent operation, this instruction set can automatically switch to a new power allocation strategy as the segment ID changes, so that each battery module can maintain a more flexible power supply in the continuously changing vehicle operating conditions.

[0071] 105. The instruction execution unit performs consistency check on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, stores the check result through the memory interface unit, and obtains the module deviation data and the charging balance instruction;

[0072] In one embodiment of the present invention, the instruction execution unit performs consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, and stores the verification result through the memory interface unit to obtain module deviation data and charging balancing instructions, including: performing execution cycle splitting processing on the temperature information and current information of the multi-module charging control instruction set during execution by the instruction execution unit to obtain corresponding execution time period marks; performing layered comparison processing on the temperature change and current curve of each battery module according to the execution time period mark to obtain preliminary consistency comparison results; performing difference aggregation processing on the preliminary consistency comparison results to form module deviation data; performing threshold correction processing on the charge and discharge distribution between different battery modules according to the module deviation data to obtain charging balancing instructions and write them into the memory interface unit.

[0073] Specifically, the instruction execution unit performs execution cycle splitting processing on the temperature information and current information of the multi-module charging control instruction set during execution, and obtains the corresponding execution period mark. The instruction execution unit first reads the start and end time of the current execution cycle from the register group, and segments the temperature and current data according to the configuration signal of the execution cycle. At this time, a set of cycle identifiers can be established in the register group. , let each Points to the corresponding time period boundary. When the instruction execution unit reads the temperature and current data flow, it uses or other discretization methods to determine which one the data falls into Range, and define the range as an execution period mark and write it into the memory interface unit. Through this splitting, it is possible to generate corresponding execution period indexes for rapidly changing or smoothly changing working conditions, and aggregate temperature and current records in each period, so that differentiated operations can be performed based on different cycle segments in the subsequent steps. After the splitting is completed, the instruction execution unit will bind the temperature curve and current change curve within each execution cycle to the same label to facilitate the synchronous comparison of multi-module information, and ensure that each data fragment is tracked and managed during continuous operation. In the process of performing a hierarchical comparison of the temperature change and current curves of each battery module according to the execution period mark, and obtaining a preliminary consistency comparison result, the instruction execution unit retrieves the execution period mark The temperature and current data covered are layered and mapped to the curves of different battery modules in the same period. To achieve layered comparison, a double-layer index table can be maintained in the register group: the first-layer index distinguishes the time period ,The second level index distinguishes the module ID and records the module in the time period The instruction execution unit traverses the double-layer index table in turn to and The temperature and current differences of each module in the period are calculated in the form of, and these differences are compared with a global threshold. If the difference deviation exceeds the preset range, it will be marked as an abnormal fluctuation. When all modules maintain similar temperature and current trajectories in the same period, "high consistency" or similar marks will be written in the preliminary consistency comparison results. The hierarchical comparison processing is cyclically unfolded in the register group to screen out consistent or inconsistent performances between multiple modules in the same period, and these results are written to the memory interface unit to guide the next stage of difference aggregation operation to perform difference aggregation processing on the preliminary consistency comparison results. In the process of forming module deviation data, the instruction execution unit reads the comparison mark of each time period in the register group, and accumulates or counts the temperature and current differences of the same module in different time periods to determine its comprehensive deviation level in the global range. A deviation function can be introduced ,in Indicates the module ID. Indicates the time period when the module is detected When the deviation mark is reached within a certain period of time, the instruction execution unit passes The differences in these time periods are weighted and synthesized to generate module deviation data. The weighting factor can be set according to the priority of the time period or the operating mode of the vehicle. If some time periods are in high-power conditions, the corresponding deviation ratio will be amplified, thereby more accurately reflecting the overall status of the module under critical operating conditions. This difference image combination merges the abnormal distribution of the same module across time periods into a quantifiable numerical sequence. After the aggregation is completed, the instruction execution unit will write the deviation result of each module in the register group and map the result to a location in the memory interface unit for quick retrieval, so that the subsequent steps can be further scheduled or corrected according to the deviation of different modules. According to the module deviation data, the charge and discharge distribution between different battery modules is threshold corrected. In the process of obtaining the charge balancing instruction and writing it into the memory interface unit, the instruction execution unit will follow the pre-defined threshold interval. Perform interval comparison on module deviation data. If the deviation exceeds A correction flag is written into the register group, indicating that the module needs to increase or decrease the charge and discharge weight in the next execution cycle. A set of correction coefficients can be calculated in the instruction execution unit Used to modify the power distribution or charging period length of the module, where The formula definition, is the deviation value of the corresponding module, To control the sensitivity constant. The instruction execution unit then synthesizes the power allocation or time period adjustment information after threshold correction into a charge balancing instruction and writes it to the memory interface unit, waiting for the charge management module or vehicle controller to read and execute. Charge balancing instructions typically include arrangements to limit charging power or extend rest intervals for modules with high deviation, while also relaxing charging parameter settings for modules with low deviation, thereby aligning the temperature and current responses of multiple modules under more complex operating conditions.

[0074] 106. The instruction execution unit reads the historical data in the memory interface unit, analyzes and processes the temperature curve, discharge depth and charging efficiency of the battery module based on the module deviation data, the charge balance instruction and the vehicle cumulative operation data in the historical data, and the power management unit performs corresponding power consumption adjustment.

[0075] In one embodiment of the present invention, the instruction execution unit reads historical data from the memory interface unit, analyzes and processes the temperature curve, discharge depth, and charging efficiency of the battery module based on the module deviation data, the charging balance instruction, and the vehicle cumulative operation data in the historical data, and the power management unit performs corresponding power consumption adjustment, including: reading historical data from the memory interface unit through the instruction execution unit, and performing time period aggregation processing on the vehicle cumulative operation data to obtain long-cycle operating condition indicators, wherein the historical data includes temperature curve, discharge depth, and charging efficiency; performing multi-module correlation comparison processing on the long-cycle operating condition indicators based on the module deviation data and the charging balance instruction to obtain operating condition correction parameters; cross-operating the operating condition correction parameters with the temperature curve, discharge depth, and charging efficiency to obtain tiered power consumption adjustment instructions for different battery modules; and the power management unit dynamically adjusts the energy output and power configuration of the vehicle according to the tiered power consumption adjustment instructions.

[0076] Specifically, the instruction execution unit reads the historical data in the memory interface unit and performs time period aggregation processing on the accumulated vehicle operating data to obtain long-cycle operating condition indicators. In the process where the historical data includes temperature curves, discharge depth and charging efficiency, the instruction execution unit first locates a set of index information for time segmentation in the register group, and based on the index information, sequentially retrieves the temperature records, discharge depth curves and charging efficiency data of previous driving or parking stages from the memory interface unit. The instruction execution unit aggregates these discretized historical data in segments according to the timestamps of their respective records, and uses a set of aggregation functions to perform integer addition or average operations on the temperature, discharge depth and efficiency fluctuations in adjacent time periods, thereby obtaining comprehensive indicators on a larger time scale at the cycle division level. The aggregation function uses a double buffering mechanism in the register group to read and write data blocks. One buffer is responsible for real-time calculations, and the other buffer is synchronously updated to the memory interface unit to ensure that the system's parallelism can still be maintained in long-cycle statistical scenarios. Accumulated vehicle operating data typically includes driving records at various altitudes, temperatures, and load conditions. Therefore, when aggregating data over time periods, a condition mapping table can be introduced to categorize the environmental and load patterns for each time period. This allows the aggregated results to include not only simple numerical means but also multiple quantile or interval density distributions. After reading and aggregating all historical data, the instruction execution unit writes the results as long-term operating condition indicators to a dedicated section of the memory interface unit, along with period index information. This facilitates deeper correlation analysis of statistical results across different time scales in subsequent steps. Based on the module deviation data and the charge balancing instructions, a multi-module correlation comparison of the long-term operating condition indicators is performed. To obtain corrected operating condition parameters, the instruction execution unit first retrieves the deviation data for each module from the register bank and loads the long-term operating condition indicators obtained in the previous step from the memory interface unit. The module deviation data retained in the register bank typically covers various aspects such as temperature anomalies, internal resistance fluctuations, and current offsets, while the charge balancing instructions reflect the power corrections or period adjustments allocated to different modules in the previous stage. The instruction execution unit maps this information onto the time dimension of the long-term operating condition indicator and counts the deviation of each module in the same time slice or the same operating condition type. To implement multi-module correlation and comparison operations, a set of functions can be executed within the register group, where represents the module and represents a certain operating condition type. The function will perform a weighted comparison of the module deviation with data such as temperature and discharge depth in the operating condition indicator, and combine it with the actual charging strategy recorded in the charge balancing instruction to generate a temporary matrix containing positive or negative correction weights. When certain modules frequently show high deviations under the same operating condition, the instruction execution unit will mark them as groups that urgently need correction and calculate the corresponding correction amount.Subsequently, all temporary matrix elements are accumulated or subtracted to obtain the operating condition correction parameter. The instruction execution unit then writes this parameter into the register bank and marks it as the output of the correlation comparison in this stage, preparing for the next cross-calculation operation. During the cross-calculation operation between the operating condition correction parameter and the temperature curve, depth of discharge, and charging efficiency to obtain tiered power consumption adjustment instructions for different battery modules, the instruction execution unit establishes a cross-calculation mapping table within the register bank, combining the operating condition correction parameter with the temperature curve, depth of discharge, and charging efficiency of the aforementioned long-term operating condition indicators to form a multidimensional input. A composite function can be defined, where represents the operating condition correction parameter corresponding to a particular module, represents the temperature or depth of discharge interval division result, and represents the dynamic sampling value of the charging efficiency. This is the key coefficient in the power consumption adjustment instruction. The instruction execution unit iterates through all modules and operating conditions in the register bank, performing a multidimensional convolution or dot product operation on and to obtain a guidance value that reflects how the module should adjust power output or energy recovery under the current operating conditions. The system then manages these guidance values ​​in a hierarchical manner. For highly heat-sensitive modules, strategies such as power reduction and extended cooling intervals can be adopted, while for modules with low internal resistance and good health, a moderate increase in discharge load can be allowed. After completing the calculation, the instruction execution unit stores the hierarchical power adjustment instructions in the register group and internally generates a set of indexes that identify the priority and power limit of each module. This index is also written to the memory interface unit, providing the power management unit with an intuitive and detailed adjustment plan.

[0077] The power management unit dynamically adjusts the vehicle's energy output and power configuration based on the hierarchical power adjustment instructions. The unit periodically retrieves the latest hierarchical power adjustment instructions from the memory interface unit and, within its internal control logic, interprets the power caps, charge and discharge priorities, and temperature safety thresholds for each module. This unit, typically connected to the vehicle's main inverter or DC-DC converter module, implements real-time power redistribution by jointly controlling the PWM duty cycle, bus voltage, and charging channel switching. If a module is identified as exhibiting a high deviation in the hierarchical instructions and a significant temperature rise, the power management unit adjusts its power output to a lower level or allocates charging time segments, allowing it to operate synchronously with other modules while reducing the risk of overheating and overload. For modules with high health, the power management unit reserves a larger output window or a more tolerant charging sequence, improving overall vehicle energy utilization while meeting vehicle driving requirements. The power configuration table built into the power management unit maps the hierarchical instructions into voltage and current setting values ​​at the hardware level, and sends these setting values ​​to the actuator through the vehicle bus.

[0078] In this embodiment, the instruction execution unit controls the sampling period of the timer unit, collects data such as battery module temperature and internal resistance, and stores them in the memory interface unit. The instruction execution unit calls the data processing instructions in the register group to process the temperature data and internal resistance data, and corrects them in combination with the vehicle speed and torque data. The interrupt control unit then triggers the timer unit to count the clock and correct the clock deviation. Based on the corrected clock data and characteristic data, the charging period and charging power of the battery module are allocated. The execution status of the charging control instruction is then checked for consistency to obtain deviation data and balancing instructions. Combined with historical data, the data is analyzed and processed, and the power management unit performs power consumption adjustment. This invention fully utilizes the characteristics of the RISC-V architecture to achieve precise charging control of the battery module.

[0079] The above describes the adaptive battery charging control method based on the RISC-V architecture in the embodiment of the present invention. The following describes the adaptive battery charging control device based on the RISC-V architecture in the embodiment of the present invention. The RISC-V architecture includes an instruction execution unit, a register group, a memory interface unit, an interrupt control unit, a timer unit and a power management unit. The adaptive battery charging control device based on the RISC-V architecture can be found in Figure 2 In one embodiment of the present invention, an adaptive battery charging control device based on the RISC-V architecture includes:

[0080] an acquisition module, configured to, during the charging process of the vehicle's battery modules, control the sampling period of the timer unit via the instruction execution unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data from each battery module pack, and store the collected data into a memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set;

[0081] a fusion module, configured to call a data processing instruction in a register group through the instruction execution unit, perform fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, perform trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and correct the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set;

[0082] a correction module, configured to trigger a timer unit to perform clock counting via the interrupt control unit, and periodically compare and correct the deviation between the clock count of the instruction execution unit and the reference count of the vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data, thereby obtaining corrected clock reference data;

[0083] an allocation module, configured to allocate charging time periods and charging powers to multiple battery modules by the instruction execution unit according to the corrected clock reference data, the temperature fluctuation characteristic data, and the dynamic internal resistance increment data, thereby obtaining a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for output of charging control;

[0084] a verification module, configured to perform consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set through the instruction execution unit, store the verification result through the memory interface unit, and obtain module deviation data and charging balance instructions;

[0085] The analysis module is used to read the historical data in the memory interface unit through the instruction execution unit, analyze and process the temperature curve, discharge depth and charging efficiency of the battery module based on the module deviation data, the charge balancing instruction and the accumulated vehicle operation data in the historical data, and perform corresponding power consumption adjustment by the power management unit.

[0086] In an embodiment of the present invention, the RISC-V architecture-based adaptive battery charging control device runs the above-mentioned RISC-V architecture-based adaptive battery charging control method. The RISC-V architecture-based adaptive battery charging control device controls the sampling period of the timer unit through the instruction execution unit, collects data such as battery module temperature and internal resistance, and stores them in the memory interface unit; the instruction execution unit calls the data processing instructions in the register group to process the temperature data and internal resistance data, and corrects them in combination with the vehicle speed and torque data; then, the interrupt control unit triggers the timer unit to count the clock and correct the clock deviation; according to the corrected clock data and characteristic data, the charging time period and charging power of the battery module are allocated; then, the execution status of the charging control instruction is checked for consistency to obtain deviation data and balance instructions; combined with historical data, analysis and processing are performed, and the power management unit performs power consumption adjustment. The present invention fully utilizes the characteristics of the RISC-V architecture to achieve precise charging control of the battery module.

[0087] above Figure 2 The adaptive battery charging control device based on the RISC-V architecture in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The adaptive battery charging control device based on the RISC-V architecture in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0088] Figure 3This is a schematic diagram of the structure of an adaptive battery charging control device based on the RISC-V architecture, provided by an embodiment of the present invention. The adaptive battery charging control device 300 based on the RISC-V architecture may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors), memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage medium 330 may be either ephemeral or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions for operating on the adaptive battery charging control device 300 based on the RISC-V architecture. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, executing the series of instructions stored in the storage medium 330 on the adaptive battery charging control device 300 based on the RISC-V architecture to implement the steps of the adaptive battery charging control method based on the RISC-V architecture described above.

[0089] The adaptive battery charging control device 300 based on the RISC-V architecture may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the adaptive battery charging control device based on the RISC-V architecture shown does not constitute a limitation on the adaptive battery charging control device based on the RISC-V architecture provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0090] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the adaptive battery charging control method based on the RISC-V architecture.

[0091] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive battery charging control method based on RISC-V architecture, characterized in that: The RISC-V architecture includes an instruction execution unit, a register group, a memory interface unit, an interrupt control unit, a timer unit, and a power management unit. The adaptive battery charging control method based on the RISC-V architecture includes: During the charging process of the vehicle's battery module, the instruction execution unit controls the sampling period of the timer unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack, and stores the collected data in the memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set; The instruction execution unit calls a data processing instruction in a register group, performs fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, performs trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and corrects the temperature fluctuation characteristic data and the dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set; The interrupt control unit triggers the timer unit to perform clock counting, and based on the corrected temperature fluctuation characteristic data and dynamic internal resistance increment data, periodically compares and corrects the deviation between the clock count of the instruction execution unit and the reference count of the entire vehicle to obtain corrected clock reference data; The instruction execution unit allocates charging time periods and charging powers to multiple battery modules based on the corrected clock reference data, temperature fluctuation characteristic data, and dynamic internal resistance increment data, thereby obtaining a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for outputting charging control; The instruction execution unit performs consistency check on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, stores the check result through the memory interface unit, and obtains module deviation data and charging balance instructions; The historical data in the memory interface unit is read by the instruction execution unit, and the temperature curve, discharge depth and charging efficiency of the battery module are analyzed and processed based on the module deviation data, charging balance instructions and the vehicle accumulated operation data in the historical data, and the power management unit performs corresponding power consumption adjustment.

2. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein during the charging process of the vehicle's battery module, the instruction execution unit controls the sampling period of the timer unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack, and stores the collected data in the memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set, including: During the charging process of the vehicle's battery modules, the instruction execution unit controls a sampling period of the timer unit, and collects temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data of each battery module pack during the sampling period, wherein the external vehicle control system data includes vehicle speed data and torque demand data; Performing a multi-threshold level-by-level comparison process on the internal resistance monitoring circuit data to obtain a staged internal resistance mark, and performing correlation analysis on the temperature sensor data and the staged internal resistance mark based on the vehicle speed data and torque demand data to obtain an original operating condition mark applicable to each battery module; The original operating condition marks are grouped and arranged to obtain temperature data groups and internal resistance identification groups corresponding to different battery modules. The temperature data groups and internal resistance identification groups are written into a memory interface unit and together with the vehicle speed data set and the torque demand data set, form a basic data segment for time synchronization, thereby obtaining the battery module temperature data set, dynamic internal resistance data set, vehicle speed data set, and torque demand data set.

3. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein the instruction execution unit calls the data processing instructions in the register group, performs fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, performs trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and corrects the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set, including: calling, by the instruction execution unit, a data processing instruction in the register group, and performing a comparison operation on temperature differences within different sampling periods according to the battery module temperature data set based on the data processing instruction to obtain a multi-period temperature jump sequence; Performing a multi-dimensional correction operation on the temperature jump sequence to obtain temperature fluctuation characteristic data, and performing a layered trend comparison on the internal resistance difference of each battery module in the dynamic internal resistance data set in adjacent sampling periods to obtain dynamic internal resistance increment data; performing matching and screening processing on the temperature fluctuation characteristic data and the dynamic internal resistance increment data according to the vehicle speed data set and the torque demand data set to obtain inter-module working condition difference data; Numerical smoothing is performed on the inter-module working condition difference data to obtain corrected temperature fluctuation characteristic data and dynamic internal resistance increment data.

4. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein the interrupt control unit triggers the timer unit to perform clock counting, and the deviation between the clock count of the instruction execution unit and the vehicle reference count is periodically compared and corrected based on the corrected temperature fluctuation characteristic data and dynamic internal resistance increment data, to obtain the corrected clock reference data, including: The interrupt control unit triggers the timer unit to count the clock, periodically intercepts the clock count of the instruction execution unit and compares it with the vehicle reference count in time sequence to obtain preliminary deviation information; Performing a difference threshold comparison on the preliminary deviation information according to the corrected temperature fluctuation characteristic data to obtain a clock interference mark; Performing multi-dimensional correlation processing on the clock interference mark and combining it with the vehicle load change level reflected in the dynamic internal resistance increment data to obtain an interference correction coefficient; The preliminary deviation information is dynamically corrected according to the interference correction coefficient to obtain a clock error adjustment value, and the clock error adjustment value is written into a register group and the counting reference of the timer unit is synchronously updated to obtain the corrected clock reference data.

5. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein the instruction execution unit allocates charging time periods and charging powers to multiple battery modules according to the corrected clock reference data, temperature fluctuation characteristic data, and dynamic internal resistance increment data to obtain a multi-module charging control instruction set, including: The instruction execution unit performs period slicing processing on the corrected clock reference data to obtain multiple timing segments; Performing correspondence screening processing on the temperature fluctuation characteristic data and the dynamic internal resistance increment data under the multi-segment time series segments to obtain a module charging demand index; Performing multi-dimensional threshold division processing on the module charging demand index, and combining the temperature and internal resistance level of each battery module in different time segments to obtain a segmented power allocation table; Priority superposition processing is performed on the segmented power allocation table to generate a multi-module charging control instruction set and write it into a register group for issuing charging time periods and charging powers for different battery modules.

6. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein the instruction execution unit performs consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set, stores the verification result through the memory interface unit, and obtains the module deviation data and the charging balance instruction, including: The instruction execution unit performs execution cycle splitting processing on the temperature information and current information of the multi-module charging control instruction set during execution to obtain corresponding execution period marks; Performing a hierarchical comparison process on the temperature change and current curve of each battery module according to the execution period mark to obtain a preliminary consistency comparison result; Performing difference aggregation processing on the preliminary consistency comparison results to form module deviation data; Threshold correction processing is performed on the charge and discharge distribution between different battery modules according to the module deviation data, and a charge balancing instruction is obtained and written into the memory interface unit.

7. The adaptive battery charging control method based on the RISC-V architecture according to claim 1, wherein the instruction execution unit reads historical data from the memory interface unit, analyzes and processes the temperature curve, depth of discharge, and charging efficiency of the battery module based on the module deviation data, the charge balancing instruction, and the accumulated vehicle operation data in the historical data, and the power management unit performs corresponding power consumption adjustment, comprising: Reading historical data in the memory interface unit through the instruction execution unit and performing time period aggregation processing on the accumulated vehicle operation data to obtain a long-term operating condition indicator, wherein the historical data includes a temperature curve, a depth of discharge, and a charging efficiency; Performing multi-module correlation comparison processing on the long-period operating condition indicator according to the module deviation data and the charging balance instruction to obtain an operating condition correction parameter; Cross-calculating the operating condition correction parameter with the temperature curve, discharge depth, and charging efficiency to obtain hierarchical power consumption adjustment instructions for different battery modules; The power management unit dynamically adjusts the energy output and power configuration of the vehicle according to the hierarchical power consumption adjustment instruction.

8. An adaptive battery charging control device based on RISC-V architecture, characterized in that: The RISC-V architecture includes an instruction execution unit, a register group, a memory interface unit, an interrupt control unit, a timer unit and a power management unit. The adaptive battery charging control device based on the RISC-V architecture includes: an acquisition module, configured to, during the charging process of the vehicle's battery modules, control the sampling period of the timer unit via the instruction execution unit to collect temperature sensor data, internal resistance monitoring circuit data, and external vehicle control system data from each battery module pack, and store the collected data into a memory interface unit to obtain a battery module temperature data set, a dynamic internal resistance data set, a vehicle speed data set, and a torque demand data set; a fusion module, configured to call a data processing instruction in a register group through the instruction execution unit, perform fusion processing on the battery module temperature data set to obtain temperature fluctuation characteristic data, perform trend calculation on the dynamic internal resistance data set to obtain dynamic internal resistance increment data, and correct the temperature fluctuation characteristic data and dynamic internal resistance increment data in combination with the vehicle speed data set and the torque demand data set; a correction module, configured to trigger a timer unit to perform clock counting via the interrupt control unit, and periodically compare and correct the deviation between the clock count of the instruction execution unit and the reference count of the vehicle based on the corrected temperature fluctuation characteristic data and the dynamic internal resistance increment data, thereby obtaining corrected clock reference data; an allocation module, configured to allocate charging time periods and charging powers to multiple battery modules by the instruction execution unit according to the corrected clock reference data, the temperature fluctuation characteristic data, and the dynamic internal resistance increment data, thereby obtaining a multi-module charging control instruction set, wherein the multi-module charging control instruction set is used for output of charging control; a verification module, configured to perform consistency verification on the temperature information and current information of each battery module according to the execution status of the multi-module charging control instruction set through the instruction execution unit, store the verification result through the memory interface unit, and obtain module deviation data and charging balance instructions; The analysis module is used to read the historical data in the memory interface unit through the instruction execution unit, analyze and process the temperature curve, discharge depth and charging efficiency of the battery module based on the module deviation data, the charge balancing instruction and the accumulated vehicle operation data in the historical data, and perform corresponding power consumption adjustment by the power management unit.

9. An adaptive battery charging control device based on RISC-V architecture, characterized in that: The adaptive battery charging control device based on the RISC-V architecture includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the adaptive battery charging control device based on the RISC-V architecture performs the steps of the adaptive battery charging control method based on the RISC-V architecture as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of the adaptive battery charging control method based on the RISC-V architecture as described in any one of claims 1 to 7 are implemented.

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