Vehicle carbon emission control method, system, equipment and medium

Abstract

The invention relates to a vehicle carbon emission control method, system and device and a medium. The method comprises the steps that operation parameters of a target vehicle are collected; calculating a historical carbon emission sequence based on the operation parameters; performing carbon emission prediction on the target vehicle based on the historical carbon emission sequence to obtain a predicted carbon emission sequence; generating a carbon emission grade and a carbon emission change rate of the target vehicle based on the predicted carbon emission sequence; whether the carbon emission grade or the carbon emission change rate meets a preset interference condition or not is judged, and if yes, a carbon emission control strategy of the target vehicle is generated according to the carbon emission grade, the carbon emission change rate and the operation parameters and applied to the target vehicle; and the steps are executed circularly until the target vehicle reaches the preset carbon emission standard. According to the method, the carbon emission data is calculated in real time based on the vehicle operation parameters, prediction and trend judgment are performed by using the historical sequence, the hierarchical control strategy is generated, and the implementation and maintenance cost of the system is remarkably reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle control, and specifically to a method, system, device, and medium for controlling vehicle carbon emissions. Background Technology

[0002] Vehicle carbon emissions refer to the emissions of greenhouse gases such as carbon dioxide produced during vehicle operation due to the consumption of fossil fuels or the use of high-carbon electricity from the power grid. With the increasing severity of global climate change, controlling vehicle carbon emissions has become a core issue for achieving a green and low-carbon transformation in the transportation sector. Traditional gasoline-powered vehicles directly generate emissions by burning gasoline and diesel, while the carbon emissions of new energy vehicles are indirectly reflected in electricity production or hydrogen production. Precise monitoring and control of vehicle carbon emissions not only helps reduce the carbon footprint of the entire transportation system but also provides data support for vehicle energy efficiency optimization and clean energy dispatch, which has profound significance for promoting technological innovation and policy formulation in the industry.

[0003] Existing technologies employ exhaust gas direct measurement and analysis systems based on high-precision sensors and laboratory-grade emission monitoring equipment. These systems use gas sensor arrays and flow meters installed in the exhaust pipe to collect exhaust gas components in real time and convert them into carbon emissions. While this approach provides relatively accurate data, its implementation cost is extremely high. This is mainly because the system relies on imported high-precision sensing hardware, requires regular calibration and maintenance, needs large computing devices for real-time analysis, and requires customized installation to adapt to different vehicle models. This makes it difficult to deploy economically on large-scale vehicle terminals or ordinary testing platforms. Summary of the Invention

[0004] To address the above problems, the present invention provides a method, system, device, and medium for controlling vehicle carbon emissions.

[0005] The first aspect of this invention discloses a method for controlling vehicle carbon emissions, comprising: The operating parameters of the target vehicle are collected periodically at a preset frequency. Based on the operating parameters, carbon emission data at multiple time points are calculated to obtain a historical carbon emission sequence. Based on historical carbon emission sequences, carbon emissions of target vehicles are predicted to obtain predicted carbon emission sequences. Based on the predicted carbon emission sequence, the carbon emission level and carbon emission change rate of the target vehicle are generated; Determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, generate a carbon emission control strategy for the target vehicle based on the carbon emission level, carbon emission change rate, and operating parameters, and apply it to the target vehicle. Repeat the above steps until the target vehicle meets the preset carbon emission standards.

[0006] Furthermore, the operating parameters include: energy consumption; And the calculation method for the corresponding carbon emission data at each time point: The energy consumption is multiplied by a dynamically updated carbon factor to obtain the carbon emission data corresponding to the time point.

[0007] Furthermore, the dynamic update conditions for the carbon factor are as follows: The proportion of renewable energy used by the target vehicle exceeds a preset conversion threshold, or the time since the last update of the carbon factor has exceeded a preset cycle threshold.

[0008] Furthermore, the operating parameters include: vehicle load; Furthermore, the steps for predicting carbon emissions for target vehicles based on historical carbon emission sequences include: The historical carbon emission data sequence arranged in chronological order is combined with the synchronized vehicle load to form a time-series dataset; Based on pre-trained long short-term memory networks or sliding window linear models, the variation patterns of data in time series datasets are extracted and calculated to predict future carbon emission prediction data sequences.

[0009] Furthermore, the steps for generating a carbon emission control strategy for the target vehicle based on carbon emission levels, carbon emission change rates, and operating parameters include: Using control operation quantity as optimization variable, a multi-objective optimization problem is constructed. The optimization objectives of the multi-objective optimization problem include at least making the difference between the actual output power of the target vehicle and the preset target power less than a preset difference threshold, and the carbon emission level reaching a preset level. Herein, control operation quantity refers to the physical quantity used to directly adjust the actuator of the target vehicle to change the operating state of the target vehicle. Under the preset system constraints, the multi-objective optimization problem is solved to obtain the optimal sequence of control operations, which serves as the carbon emission control strategy for the target vehicle.

[0010] Furthermore, the system constraints include: power output range constraints determined based on the operating parameters and vehicle hardware performance, carbon emission intensity upper limit constraints determined based on the carbon emission level, and control operation change rate constraints based on system stability requirements.

[0011] Furthermore, the upper limit constraint on carbon emission intensity is dynamically adjusted based on the carbon emission level; The weight allocation of each optimization objective in the multi-objective optimization problem is dynamically adjusted based on the carbon emission change rate.

[0012] A second aspect of the present invention discloses a vehicle carbon emission control system, comprising: The data acquisition module is used to periodically collect the operating parameters of the target vehicle at a preset frequency. The calculation module is used to calculate carbon emission data at multiple time points based on the operating parameters to obtain a historical carbon emission sequence; The prediction module is used to predict the carbon emissions of the target vehicle based on the historical carbon emission sequence, and obtain the predicted carbon emission sequence. The generation module is used to generate the carbon emission level and carbon emission change rate of the target vehicle based on the predicted carbon emission sequence. The judgment module is used to determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, the carbon emission control strategy for the target vehicle is generated based on the carbon emission level, carbon emission change rate and operating parameters, and then applied to the target vehicle. The execution module controls other modules to perform their corresponding functions until the target vehicle meets the preset carbon emission standards.

[0013] A third aspect of the present invention discloses an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of any of the vehicle carbon emission control methods disclosed in the first aspect of the present invention.

[0014] The fourth aspect of the present invention discloses a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of any of the vehicle carbon emission control methods disclosed in the first aspect of the present invention.

[0015] This invention calculates carbon emission data in real time based on vehicle operating parameters and uses historical sequences for prediction and trend judgment to generate a hierarchical control strategy. This effectively avoids the dependence on expensive direct monitoring hardware in traditional methods and significantly reduces the system implementation and maintenance costs. The method relies on software algorithms and prediction models to achieve indirect measurement and forward-looking control of carbon emissions. It makes full use of the existing sensor data resources of the vehicle platform and continuously improves the control accuracy through closed-loop feedback optimization. Ultimately, it achieves the goal of precise carbon emission management while building a carbon control system solution that is both economical and scalable. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This is a schematic flowchart of a vehicle carbon emission control method disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a vehicle carbon emission control system disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device disclosed in the embodiments of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, or product comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, apparatus, or products.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] Please see Figure 1 As shown, Figure 1 This is a schematic flowchart of a vehicle carbon emission control method disclosed in an embodiment of the present invention. Figure 1 As shown, the vehicle carbon emission control method may include the following operations: S101. Periodically collect the operating parameters of the target vehicle according to the preset frequency; In this optional embodiment, operating parameters refer to physical quantity data that can reflect the operating status and energy consumption of the vehicle's powertrain system, collected in real time by built-in or external sensors and on-board network interfaces of the target vehicle. Operating parameters may include: system terminal voltage and current values ​​directly collected by voltage and current sensors; drive motor output power calculated in real time from terminal voltage and current values; energy consumption per unit time obtained by power time integration or energy metering devices; fuel consumption collected by fuel flow metering devices; and temperature and pressure parameters of the battery or fuel cell stack obtained through the vehicle bus.

[0022] S102. Based on the operating parameters, calculate carbon emission data at multiple time points to obtain a historical carbon emission sequence; In an optional embodiment, the operating parameters include: energy consumption; And the calculation method for the corresponding carbon emission data at each time point: The energy consumption is multiplied by a dynamically updated carbon factor to obtain the carbon emission data corresponding to the time point.

[0023] In this optional embodiment, energy consumption refers to the total amount of primary energy consumed by the target vehicle within a unit operating cycle, which is expressed in different physical forms depending on the type of power system: for electric vehicles, it is expressed as the electrical energy consumption at the input of the drive motor (unit: kWh), which is obtained by collecting and integrating the voltage and current signals of the motor controller; for fuel cell vehicles, it is expressed as the standard volume consumption of hydrogen (unit: Nm³), which is directly measured by the mass flow meter integrated in the hydrogen supply module; for conventional fuel vehicles, it is expressed as the liquid fuel mass consumption (unit: kg or L), which is calculated based on the cumulative conversion of the fuel injection pulse width signal collected by the engine controller.

[0024] As can be seen, this optional embodiment, by introducing dynamically updated carbon factors to calculate carbon emission data, enables the calculation results to reflect changes in the energy structure in real time, significantly improving the accuracy and timeliness of carbon emission monitoring. Traditional methods typically use fixed carbon factors, which cannot adapt to the impact of fluctuations in different energy types and the proportion of renewable energy, resulting in significant deviations between the calculated results and actual emissions. This optional embodiment, by establishing a dynamic correlation between carbon factors and the energy structure, allows vehicles with different energy forms, such as electric drive, fuel cell, or hybrid power, to obtain emission calculation data that matches their energy sources. This provides a more reliable data foundation for subsequent prediction and control, enhancing the adaptability and reliability of the entire control system.

[0025] In an optional embodiment, the dynamic update condition for the carbon factor is: The proportion of renewable energy used by the target vehicle exceeds a preset conversion threshold, or the time since the last update of the carbon factor has exceeded a preset cycle threshold.

[0026] In this optional embodiment, renewable energy refers to onboard energy converted from non-fossil energy, including charging power from clean grids such as solar / wind power, or hydrogen fuel produced through water electrolysis or biomass. The renewable energy proportion refers to the percentage of renewable energy consumption to total energy consumption. The cycle threshold refers to the longest period during which the carbon factor remains unupdated.

[0027] As can be seen, this optional embodiment, by setting scientific carbon factor update conditions, avoids the waste of computational resources caused by frequent updates while ensuring that the carbon factor can respond promptly to significant changes in the energy structure, maintaining the accuracy of carbon emission calculations while ensuring system efficiency. Traditional methods often rely on periodic updates or manual intervention, making it difficult to balance real-time performance and economy. This optional embodiment, based on a dual judgment mechanism of the magnitude and time interval of changes in the proportion of renewable energy, achieves adaptive adjustment of the update strategy, thereby reducing unnecessary computational overhead when the energy structure is stable and triggering updates in a timely manner when the structure changes, ensuring a balance between computational accuracy and energy efficiency in the long-term operation of the system.

[0028] In an optional embodiment, the carbon factor is adaptively corrected by combining rule mapping and regression adjustment, based on the energy type and energy structure changes of the target vehicle.

[0029] Specifically, the system first makes a judgment through a rule base: if the energy type is grid electricity, the carbon factor is dynamically calculated based on the real-time renewable energy ratio of the grid; if it is hydrogen used in hydrogen fuel cells, its carbon factor is determined to be zero; if it is traditional fuels such as diesel, a fixed coefficient is used.

[0030] Building upon this foundation, to adapt to the complex emission characteristics under different energy structures, an online learning mechanism can be integrated. Through parameter fine-tuning models using methods such as recursive least squares, the functional relationships or fixed coefficients in the rule mapping can be dynamically calibrated. This continuously improves the accuracy and environmental adaptability of carbon factors without human intervention, effectively supporting high-precision estimation and closed-loop control of carbon emissions. During the carbon factor update process, a window smoothing mechanism and outlier removal logic can be introduced to prevent instability caused by data fluctuations.

[0031] S103. Based on historical carbon emission sequences, predict carbon emissions of the target vehicle to obtain the predicted carbon emission sequence; In an optional embodiment, the operating parameters include: vehicle load; Furthermore, the steps for predicting carbon emissions for target vehicles based on historical carbon emission sequences include: The historical carbon emission data sequence arranged in chronological order is combined with the synchronized vehicle load to form a time-series dataset; Based on pre-trained long short-term memory networks or sliding window linear models, the variation patterns of data in time series datasets are extracted and calculated to predict future carbon emission prediction data sequences.

[0032] In this optional embodiment, synchronization means that the vehicle load and carbon emission data have a strictly matched timestamp sequence, the carbon emission data and the vehicle load use the same sampling frequency and clock reference source, and the carbon emission data at a certain moment in the historical carbon emission data sequence must correspond to the vehicle load at the same time point.

[0033] Long Short-Term Memory (LSTM) networks, as a recurrent neural network structure, selectively memorize long-term dependency features of historical carbon emission sequences through a gating mechanism. Their cell state transfer mechanism can effectively capture the nonlinear trends and periodic fluctuations of carbon emission data. In carbon emission prediction applications, historical carbon emission sequences and synchronous vehicle load data are combined to form a time-series input vector. After the hidden layers of the network extract time-related features hierarchically, the final output layer generates a future carbon emission prediction data sequence with a preset step size.

[0034] The sliding window linear model uses a fixed-length time window to extract segments of historical carbon emission sequences. It fits a linear regression equation to the data points within the window using the least squares method, and the slope parameter directly characterizes the short-term trend of carbon emissions. During prediction, the latest window data is substituted into the regression equation to extrapolate the estimated carbon emissions for a single future control cycle, and continuous prediction is achieved through rolling updates of the sliding window. This model relies on the strong linear correlation between vehicle load and carbon emissions, making it suitable for lightweight prediction needs under steady-state operating conditions, such as carbon emission trend estimation in constant-power road spectrum testing of pure electric vehicles. This modeling approach is particularly suitable for vehicle testing scenarios with complex operating condition transitions, such as the transient prediction of fuel cell vehicles from idle to peak power.

[0035] As can be seen, this optional embodiment significantly improves the accuracy and reliability of predictions by fusing vehicle load data with historical carbon emission sequences to form a time-series dataset and using a sequence prediction model for carbon emission trend analysis. Vehicle load directly reflects driving conditions and power demand, and is a key factor affecting carbon emissions; ignoring its changes often leads to prediction models deviating from reality. This optional embodiment, through collaborative modeling of load and carbon emission data, enables the prediction process to capture the coupling relationship between the external driving environment and internal power output, thereby more accurately predicting future emission trends. This provides forward-looking information support for the formulation of control strategies, enhancing the system's responsiveness and control effectiveness.

[0036] S104. Based on the predicted carbon emission sequence, generate the carbon emission level and carbon emission change rate of the target vehicle. In this optional embodiment, carbon emission level refers to the vehicle emission status level classified based on the comparison between real-time carbon emission data and preset target values, used to qualitatively characterize the severity of carbon emissions. Carbon emission change rate refers to the magnitude of change in carbon emissions per unit time, used to quantitatively characterize the rate of increase or decrease in carbon emission trends and their risk.

[0037] S105. Determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, generate a carbon emission control strategy for the target vehicle based on the carbon emission level, carbon emission change rate and operating parameters, and apply it to the target vehicle. In this optional embodiment, the interference condition refers to the judgment rule used to trigger the generation of the control strategy. When the carbon emission level or carbon emission change rate meets the critical condition set by the rule, the calculation and execution of the control strategy are initiated. For example, the carbon emission level is divided into three levels: normal, warning, and exceeding the standard. The interference condition is that the carbon emission level reaches the warning or exceeding the standard state, or the carbon emission change rate exceeds the upper limit of the system's preset allowable change rate.

[0038] In an optional embodiment, the step of generating a carbon emission control strategy for a target vehicle based on carbon emission levels, carbon emission change rates, and operating parameters includes: Using control operation quantity as optimization variable, a multi-objective optimization problem is constructed. The optimization objectives of the multi-objective optimization problem include at least making the difference between the actual output power of the target vehicle and the preset target power less than a preset difference threshold, and the carbon emission level reaching a preset level. Herein, control operation quantity refers to the physical quantity used to directly adjust the actuator of the target vehicle to change the operating state of the target vehicle. Under the preset system constraints, the multi-objective optimization problem is solved to obtain the optimal sequence of control operations, which serves as the carbon emission control strategy for the target vehicle.

[0039] In this optional embodiment, the multi-objective optimization problem optimizes the control operation variables by constructing an objective function containing at least two conflicting objectives to achieve collaborative optimization: the first objective requires minimizing the absolute value of the deviation between the actual output power of the vehicle and the preset target power, and the second objective forces the carbon emission level to approach the preset level. At the same time, the objective of minimizing the control response delay can be extended to include the objective. During the solution process, the system combines the above objectives into a single objective function according to preset weights. Combined with the system constraints, the system uses a gradient descent-based sequential quadratic programming algorithm to solve the problem online. During each optimization, the system uses the current vehicle operating state as the initial condition to perform finite-domain prediction in a rolling manner. The system outputs a sequence of control operation variables that satisfies all constraints and is closest to the Pareto optimal frontier. This sequence serves as the core parameter instruction of the carbon emission control strategy to the actuator to adjust the power output or switch the energy distribution mode.

[0040] As can be seen, this optional embodiment achieves synergistic optimization of vehicle power tracking and carbon emission control by constructing a multi-objective optimization problem with control operation variables as optimization variables, effectively reducing carbon emission levels while ensuring power performance. This optional embodiment incorporates both power deviation and carbon emission levels into the optimization objectives and solves them on a rolling basis using a model predictive control framework. This allows for the dynamic generation of control strategies that balance power performance and environmental friendliness within each control cycle, thereby improving the overall performance of the system and meeting the complex and ever-changing operational requirements in practical applications.

[0041] In an optional embodiment, the system constraints include: a power output range constraint determined based on the operating parameters and vehicle hardware performance, a carbon emission intensity upper limit constraint determined based on the carbon emission level, and a control operation change rate constraint based on system stability requirements.

[0042] In this optional embodiment, the power output range constraint is determined based on hardware performance parameters such as the demagnetization temperature threshold of the permanent magnet of the vehicle drive motor and the maximum discharge rate of the battery. This means that the actual output power must be within a closed range formed by the preset minimum power and maximum power. The purpose of this setting is to prevent irreversible hardware damage caused by overload operation of the power system. The emission intensity upper limit constraint is dynamically set in real time. By establishing a negative feedback mechanism between the emission level and the constraint intensity, the control system is forced to intervene in advance to avoid actual emissions exceeding regulatory limits. The control operation quantity change rate constraint limits the jump amplitude of the adjustment command within adjacent control cycles. Its setting is based on the mechanical response delay characteristics of the actuator. It is used to smooth the control trajectory, eliminate the risk of high-frequency oscillation, and ensure the dynamic stability of the system.

[0043] As can be seen, this optional embodiment, through the comprehensive design of system constraints, ensures a balance between the control strategy and the physical limits of the vehicle, environmental requirements, and system stability, avoiding system safety or performance problems caused by pursuing a single objective. Power output range constraints ensure that the vehicle's power components operate within safe limits, carbon emission intensity upper limit constraints reflect emission control policies or technical objectives, and control operation rate of change constraints suppress abrupt command changes, improving the smoothness and stability of control. This multi-constraint collaborative mechanism enhances the system's robustness and practicality, making it more suitable for engineering applications.

[0044] In an optional embodiment, the upper limit constraint on carbon emission intensity is dynamically adjusted based on the carbon emission level; The weight allocation of each optimization objective in the multi-objective optimization problem is dynamically adjusted based on the carbon emission change rate.

[0045] In this optional embodiment, the upper limit constraint of carbon emission intensity is dynamically adjusted according to the carbon emission level. Specifically, when the system determines that the current carbon emission level is high, it automatically reduces the upper limit of carbon emission intensity, thereby forcing the control strategy to generate a more stringent emission reduction direction by constraining the feasible solution range of the optimization problem. At the same time, the weight allocation of each objective in the multi-objective optimization problem is dynamically adjusted according to the carbon emission change rate. If the carbon emission change rate indicates that the emission trend is deteriorating rapidly, the optimization algorithm will significantly increase the weight coefficient of the objective of "reducing carbon emissions" and correspondingly reduce the weight of objectives such as "power tracking accuracy". This allows the control strategy obtained from the solution to prioritize suppressing the rapid growth of carbon emissions, thereby adaptively balancing multiple control objectives such as environmental protection, power performance, and stability under different operating conditions.

[0046] As can be seen, this optional embodiment dynamically adjusts carbon emission intensity constraints based on carbon emission levels and allocates the weight of optimization targets in real time according to the rate of change of carbon emissions. This enables the control system to have online adaptability and adopt differentiated control strategies for different emission conditions and trends of urgency. When the carbon emission level is high or the rate of change is large, the system automatically tightens emission constraints and increases the weight of carbon emission targets, prompting the control strategy to tilt towards low carbon emissions; conversely, it appropriately relaxes constraints to prioritize power performance. This adaptive mechanism significantly improves the control intelligence and strategy flexibility of the system in complex operating environments, achieving continuous optimization under dynamic conditions.

[0047] S106. Repeat the above steps until the target vehicle meets the preset carbon emission standard.

[0048] In an optional embodiment, after the carbon emission control strategy is applied to the target vehicle, vehicle carbon emission control further includes: Collect actual response data of the target vehicle after executing the carbon emission control strategy. The actual response data includes actual output power, control response delay, and control deviation. The actual response data is compared with the expected target to obtain the performance evaluation results; Based on the performance evaluation results, the parameters of the multi-objective optimization problem are dynamically adjusted, and the parameters include at least the prediction step size and the control gain.

[0049] In this optional embodiment, the actual output power is used to calculate the tracking deviation from the target power. If the deviation remains too large, the control gain parameter in the multi-objective optimization problem is adjusted to enhance the control response strength or the weight allocation is adjusted to optimize convergence. The control response delay directly reflects the agility of the system execution. If the delay is too long, the prediction step size of the multi-objective optimization problem may be appropriately reduced to reduce computational complexity and obtain a faster response speed, or the weight of the optimization function with delay as the objective in the multi-objective optimization problem may be adjusted. The control deviation comprehensively reflects the overall control error, including the mismatch in the multi-objective optimization problem. Based on the historical deviation trend, the system can use the parameter identification algorithm to fine-tune the state space parameters inside the prediction model, so that the output of the multi-objective optimization problem model is closer to the real dynamic characteristics of the system, thereby improving the control accuracy from the root.

[0050] As can be seen, this optional embodiment, by introducing a performance evaluation and model parameter dynamic adjustment mechanism based on actual response data, constructs a closed-loop control system with self-learning and adaptive capabilities, thereby significantly improving the accuracy and robustness of carbon emission control. This optional embodiment continuously evaluates the controller's performance by collecting actual response data after each control strategy execution and comparing it with the expected target. Based on this, it adjusts the key parameters of the predictive model online, enabling the multi-objective optimization problem model to continuously self-correct and better fit the actual dynamic characteristics of the vehicle. This ensures that the control system maintains efficient and accurate control throughout its entire lifecycle, effectively overcoming the problem of control performance degradation caused by equipment aging, environmental changes, or external disturbances.

[0051] Please see Figure 2 As shown, Figure 2 This is a schematic diagram of a vehicle carbon emission control system disclosed in an embodiment of the present invention, comprising: The acquisition module 201 is used to periodically acquire the operating parameters of the target vehicle at a preset frequency; Calculation module 202 is used to calculate carbon emission data at multiple time points based on the operating parameters to obtain a historical carbon emission sequence; Prediction module 203 is used to predict the carbon emissions of a target vehicle based on historical carbon emission sequences, and obtain the predicted carbon emission sequence. The generation module 204 is used to generate the carbon emission level and carbon emission change rate of the target vehicle based on the predicted carbon emission sequence. The judgment module 205 is used to determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, a carbon emission control strategy for the target vehicle is generated based on the carbon emission level, carbon emission change rate and operating parameters, and applied to the target vehicle. The execution module 206 is used to control other modules to perform their corresponding functions until the target vehicle reaches the preset carbon emission standard.

[0052] Specific limitations regarding vehicle carbon emission control systems can be found in the above section on vehicle carbon emission control methods, and will not be repeated here. The various modules in the aforementioned vehicle carbon emission control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware format within or independently of the processor in the electronic device, or stored in software format in the memory of the electronic device, allowing the processor to call the corresponding operations of each module.

[0053] It should be noted that, in order to highlight the innovative aspects of this invention, this embodiment does not include modules that are not closely related to solving the technical problems proposed by this invention, but this does not mean that there are no other modules in this embodiment.

[0054] like Figure 3 As shown, the electronic device 1 provided by the present invention may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a vehicle carbon emission control program.

[0055] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as vehicle carbon emission control code, but also to temporarily store data that has been output or will be output.

[0056] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 through various interfaces and lines. It executes programs or modules (such as vehicle carbon emission control programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0057] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described vehicle carbon emission control method.

[0058] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a data acquisition module 201, a calculation module 202, a prediction module 203, a generation module 204, a judgment module 205, and an execution module 206.

[0059] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module stored in the storage medium includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the vehicle carbon emission control method described in the various embodiments of this application.

[0060] In summary, the present invention discloses a vehicle carbon emission control method, system, device, and medium. This invention calculates carbon emission data in real time based on vehicle operating parameters and uses historical sequences for prediction and trend judgment, thereby generating a tiered control strategy. This effectively avoids the reliance on expensive direct monitoring hardware in traditional methods, significantly reducing system implementation and maintenance costs. The method relies on software algorithms and predictive models to achieve indirect measurement and forward-looking control of carbon emissions. It fully utilizes existing sensor data resources on the vehicle platform and continuously improves control accuracy through closed-loop feedback optimization. Ultimately, it achieves the goal of precise carbon emission management while constructing a carbon control system solution that is both economical and scalable. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0061] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for controlling vehicle carbon emissions, characterized in that, The method comprises: The operating parameters of the target vehicle are collected periodically at a preset frequency. Based on the operating parameters, carbon emission data at multiple time points are calculated to obtain a historical carbon emission sequence. Based on historical carbon emission sequences, carbon emissions of target vehicles are predicted to obtain predicted carbon emission sequences. Based on the predicted carbon emission sequence, the carbon emission level and carbon emission change rate of the target vehicle are generated; Determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, generate a carbon emission control strategy for the target vehicle based on the carbon emission level, carbon emission change rate, and operating parameters, and apply it to the target vehicle. Repeat the above steps until the target vehicle meets the preset carbon emission standards.

2. The vehicle carbon emission control method according to claim 1, characterized in that, The operating parameters include: energy consumption; And the calculation method for the corresponding carbon emission data at each time point: The energy consumption is multiplied by a dynamically updated carbon factor to obtain the carbon emission data corresponding to the time point.

3. The vehicle carbon emission control method according to claim 2, characterized in that, The dynamic update conditions for the carbon factor are as follows: The proportion of renewable energy used by the target vehicle exceeds a preset conversion threshold, or the time since the last update of the carbon factor has exceeded a preset cycle threshold.

4. The vehicle carbon emission control method according to claim 1, characterized in that, The operating parameters include: vehicle load; Furthermore, the steps for predicting carbon emissions for target vehicles based on historical carbon emission sequences include: The historical carbon emission data sequence arranged in chronological order is combined with the synchronized vehicle load to form a time-series dataset; Based on pre-trained long short-term memory networks or sliding window linear models, the variation patterns of data in time series datasets are extracted and calculated to predict future carbon emission prediction data sequences.

5. The vehicle carbon emission control method according to claim 1, characterized in that, The steps for generating a carbon emission control strategy for a target vehicle based on carbon emission levels, carbon emission change rates, and operating parameters include: Using control operation quantity as optimization variable, a multi-objective optimization problem is constructed. The optimization objectives of the multi-objective optimization problem include at least making the difference between the actual output power of the target vehicle and the preset target power less than a preset difference threshold, and the carbon emission level reaching a preset level. Herein, control operation quantity refers to the physical quantity used to directly adjust the actuator of the target vehicle to change the operating state of the target vehicle. Under the preset system constraints, the multi-objective optimization problem is solved to obtain the optimal sequence of control operations, which serves as the carbon emission control strategy for the target vehicle.

6. A vehicle carbon emission control method according to claim 5, characterized in that, The system constraints include: power output range constraints determined based on the operating parameters and vehicle hardware performance; carbon emission intensity upper limit constraints determined based on the carbon emission level; and control operation change rate constraints based on system stability requirements.

7. A vehicle carbon emission control method according to claim 6, characterized in that, The upper limit constraint on carbon emission intensity is dynamically adjusted based on the carbon emission level; The weight allocation of each optimization objective in the multi-objective optimization problem is dynamically adjusted based on the carbon emission change rate.

8. A vehicle carbon emission control system, characterized in that, include: The data acquisition module is used to periodically collect the operating parameters of the target vehicle at a preset frequency. The calculation module is used to calculate carbon emission data at multiple time points based on the operating parameters to obtain a historical carbon emission sequence; The prediction module is used to predict the carbon emissions of the target vehicle based on the historical carbon emission sequence, and obtain the predicted carbon emission sequence. The generation module is used to generate the carbon emission level and carbon emission change rate of the target vehicle based on the predicted carbon emission sequence. The judgment module is used to determine whether the carbon emission level or carbon emission change rate meets the preset interference conditions. When the conditions are met, the carbon emission control strategy for the target vehicle is generated based on the carbon emission level, carbon emission change rate and operating parameters, and then applied to the target vehicle. The execution module controls other modules to perform their corresponding functions until the target vehicle meets the preset carbon emission standards.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle carbon emission control method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle carbon emission control method as described in any one of claims 1 to 7.

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