Method, device and equipment for optimizing energy flow of pure electric light truck and medium

Abstract

The application discloses a pure electric light truck whole vehicle energy flow optimization method, device, equipment and medium. The method comprises the following steps: acquiring random working condition input parameters, performing Monte Carlo simulation according to the random working condition input parameters and a pre-constructed whole vehicle energy flow analysis model to obtain working efficiency statistical data of each energy consumption component of the whole vehicle; identifying working condition associated components based on the working efficiency statistical data, calculating energy consumption influence indexes of the working condition associated components based on preset dot matrix graph rules; and determining a component energy efficiency optimization strategy of the pure electric light truck according to the energy consumption influence indexes. The energy efficiency optimization pertinence is improved, the whole vehicle energy efficiency level of the pure electric light truck is improved, and the overall efficiency of energy flow analysis and optimization is improved.

Description

Technical Field

[0001] This invention relates to the field of energy flow analysis and energy efficiency optimization technology for pure electric light trucks, and particularly to a method, apparatus, equipment and medium for optimizing the energy flow of pure electric light trucks. Background Technology

[0002] Vehicle energy flow analysis is a key technology for clarifying vehicle energy consumption and exploring the potential for energy efficiency improvement in the whole vehicle and its components. Current mainstream energy flow analysis methods fall into three categories: component-level bench testing, whole vehicle bench / road testing, and simulation analysis based on commercial software. Component-level bench testing ignores the energy consumption coupling relationship with other components, making it difficult to simulate the actual operating environment of components in a real vehicle. This leads to discrepancies between the bench test results of the tested components and their energy consumption in real-world vehicle operation. Furthermore, multiple components require multiple test benches, resulting in high costs for bench purchase and testing. Whole vehicle bench / road testing addresses the problems of component bench testing, but is often limited by testing time and conditions, only testing certain operating conditions, resulting in insufficient coverage and deviations from actual user energy consumption. Compared to bench testing and road testing, whole vehicle energy flow simulation analysis based on commercial software significantly shortens the testing cycle, but still suffers from insufficient operating condition coverage, and the accuracy of whole vehicle energy flow analysis is affected by modeling precision.

[0003] Furthermore, the three vehicle energy flow analysis methods mentioned above mainly focus on obtaining the energy consumption results of each component, lacking further detailed and quantitative analysis of the factors affecting the energy consumption of each component. Summary of the Invention

[0004] This invention provides a method, device, equipment, and medium for optimizing the energy flow of a pure electric light truck, so as to achieve accurate energy consumption analysis and component energy efficiency optimization of pure electric light trucks.

[0005] According to one aspect of the present invention, a method for optimizing the energy flow of a pure electric light truck is provided, the method comprising: Obtain random operating condition input parameters, and perform Monte Carlo simulation based on random operating condition input parameters and a pre-built vehicle energy flow analysis model to obtain statistical data on the working efficiency of each energy-consuming component of the vehicle. Based on the work efficiency statistics, identify the working condition-related components, and calculate the energy consumption impact index of the working condition-related components based on the preset dot matrix rules. The component energy efficiency optimization strategy for pure electric light trucks is determined based on the energy consumption impact indicators.

[0006] According to another aspect of the present invention, an energy flow optimization device for a pure electric light truck is provided, the device comprising: The component efficiency statistics module is used to obtain random operating condition input parameters, and perform Monte Carlo simulation based on the random operating condition input parameters and the pre-built vehicle energy flow analysis model to obtain the working efficiency statistics of each energy-consuming component of the vehicle. The energy consumption impact index determination module is used to identify working condition related components based on the working efficiency statistics and to calculate the energy consumption impact index of the working condition related components based on preset dot matrix rules. The optimization strategy determination module is used to determine the component energy efficiency optimization strategy for pure electric light trucks based on the energy consumption impact indicators.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which is then executed by the at least one processor to enable the at least one processor to execute the pure electric light truck energy flow optimization method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the energy flow optimization method for a pure electric light truck according to any embodiment of the present invention.

[0009] The technical solution of this invention obtains random operating condition input parameters, performs Monte Carlo simulation based on the random operating condition input parameters and a pre-built vehicle energy flow analysis model, and obtains statistical data on the working efficiency of each energy-consuming component of the vehicle; identifies operating condition-related components based on the statistical data, calculates the energy consumption impact index of the operating condition-related components based on preset dot matrix rules; and determines the component energy efficiency optimization strategy for the pure electric light truck based on the energy consumption impact index. This solves the technical problems of incomplete operating condition coverage, low modeling accuracy, inability to quantify energy consumption impact, and unclear optimization strategies in existing methods. It improves the coverage and calculation accuracy of vehicle energy flow analysis, enhances the quantitative accuracy of energy consumption influencing factors, improves the targeting of energy efficiency optimization, improves the overall energy efficiency level of the pure electric light truck, and enhances the overall efficiency of energy flow analysis and optimization.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] 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.

[0012] Figure 1 A flowchart illustrating a method for optimizing the energy flow of a pure electric light truck, as provided in an embodiment of the present invention; Figure 2a This is a schematic diagram of a sample dot matrix for an energy flow optimization method for a pure electric light truck provided in an embodiment of the present invention; Figure 2b A flowchart of another method for optimizing the energy flow of a pure electric light truck provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an energy flow optimization device for a pure electric light truck provided in an embodiment of the present invention; Figure 4 A schematic diagram of the structure of an electronic device for implementing a method for optimizing the energy flow of a pure electric light truck according to an embodiment of the present invention. Detailed Implementation

[0013] 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 should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1This is a flowchart illustrating a method for optimizing the energy flow of a pure electric light truck according to an embodiment of the present invention. This embodiment is applicable to the optimization of the energy flow of a pure electric light truck. The method can be executed by a pure electric light truck energy flow optimization device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method specifically includes the following steps: S110. Obtain random operating condition input parameters, and perform Monte Carlo simulation based on the random operating condition input parameters and the pre-built vehicle energy flow analysis model to obtain the working efficiency statistics of each energy-consuming component of the vehicle.

[0016] Among them, random operating condition input parameters can be understood as the vehicle speed, load, road slope, and ambient temperature that change randomly when the vehicle is driving; the whole vehicle energy flow analysis model can be understood as a whole vehicle calculation model that can simulate the conversion and loss of electrical, thermal, and mechanical energy; Monte Carlo simulation can be understood as a statistical simulation method based on a large number of random samples for iterative calculation; and working efficiency statistics can be understood as the average efficiency and fluctuation data of components obtained through multiple simulations.

[0017] Specifically, random operating condition input parameters that conform to the actual usage probability are obtained and input into a pre-built and calibrated vehicle energy flow analysis model. A large number of random operating condition cyclic calculations are performed through Monte Carlo simulation, and finally the average working efficiency and dispersion data of each energy-consuming component are statistically obtained, which comprehensively covers the actual operation scenarios of the vehicle.

[0018] Optionally, obtaining the random operating condition input parameters includes: The random operating condition input parameters are generated according to the normal distribution rule; wherein, the random operating condition input parameters include vehicle speed, load, road slope and ambient temperature.

[0019] Among them, the normal distribution rule can be understood as the random parameter generation rule that conforms to the actual usage probability of the vehicle; the numerical combination can be understood as a set of parameters such as vehicle speed, load, slope and temperature formed by multiple random combinations.

[0020] Specifically, based on the normal distribution rule corresponding to the actual driving probability of vehicles, multiple sets of random operating condition numerical combinations are generated to make the simulation conditions closer to the actual operating conditions and improve the accuracy and applicability of the analysis results.

[0021] Optionally, before performing Monte Carlo simulation based on random operating condition input parameters and a pre-built multi-physics coupled vehicle energy flow analysis model, the following steps are also included: constructing an initial energy flow analysis model including the battery, motor, electronic control, thermal management system, and mechanical transmission system; using typical operating condition parameters, vehicle structural parameters, and component performance parameters as input data for the initial energy flow analysis model; establishing energy loss calculation relationships for each component based on the conversion and transfer relationships of electrical, thermal, and mechanical energy; collecting energy flow test data from actual vehicle operation and comparing the energy flow test data with the output data of the initial energy flow analysis model; and correcting the initial energy flow analysis model based on the deviation value obtained from the comparison to obtain a multi-physics coupled vehicle energy flow analysis model.

[0022] The initial energy flow analysis model refers to an initial model that has not been calibrated with real vehicle data and only has basic energy flow calculation functions; the battery, motor, electronic control system, thermal management system, and mechanical transmission system are all core components of pure electric light trucks, respectively undertaking the functions of energy storage, power output, control and regulation, temperature control, and power transmission; typical operating condition parameters refer to fixed operating condition data under common vehicle driving scenarios; vehicle structural parameters refer to hardware-related parameters such as body and chassis; component performance parameters refer to the factory standard performance indicators of core components; energy flow test data refer to real data related to energy consumption and efficiency collected in real vehicle road tests; deviation value refers to the difference between the model output results and the real vehicle test data, used for model calibration.

[0023] Specifically, an initial energy flow analysis model is constructed, encompassing the battery, motor, electronic control, thermal management system, and mechanical transmission system. Typical operating condition parameters, vehicle structural parameters, and component performance parameters are used as initial input data to establish a complete energy transfer path. Based on the conversion laws of electricity, heat, and force, the energy loss calculation relationship of each component is established to ensure that the model can accurately reflect the energy flow logic of the entire vehicle. Energy flow data from real vehicle road tests is collected and compared with the output data of the initial model to calculate the deviation value between the two. Based on the deviation value, the parameters of the initial model are adjusted and corrected to optimize the energy transfer path and loss calculation logic, ultimately resulting in a multi-physics coupled energy flow analysis model that meets the accuracy standards and can accurately simulate real vehicle operating conditions.

[0024] Preferably, before conducting Monte Carlo simulation, an initial energy flow analysis model of the entire vehicle is first built. This model comprehensively incorporates external conditions such as ambient temperature, operating conditions, load, and slope, as well as internal components such as the motor, battery, multi-in-one controller, thermal management system, braking system, mechanical transmission system, suspension, and tires. It also embeds strategies for energy management, thermal management, and transmission control, forming a multi-field coupled vehicle energy flow analysis model of electro-thermal-mechanical forces. This model adopts a modeling approach that integrates mechanistic and physical principles. It not only describes the energy conversion process but also characterizes the energy loss and conversion efficiency caused by internal friction, heat dissipation, and structural configuration of components. It covers the complete energy transfer path from the environment, operating conditions, and driver to the entire vehicle and wheel drive forces. Furthermore, it employs a modular platform design, facilitating rapid replacement and verification of components and strategies. Then, typical operating condition parameters, vehicle structural parameters, and component performance parameters are used as inputs to establish the energy loss calculation relationship for each component. Real vehicle energy flow test data is then collected and compared with the model output. The model parameters are corrected based on the deviations, ultimately resulting in a more accurate multi-physics coupled vehicle energy flow analysis model that more closely reflects the actual vehicle operating state.

[0025] Specifically, the whole vehicle model is the foundation of energy flow analysis, and the comprehensiveness and accuracy of the model directly affect the effect and accuracy of energy flow analysis at the whole vehicle level and component level. Therefore, considering external factors such as ambient temperature, operating conditions, load, and slope, as well as internal components such as the motor, battery, multi-function control system, thermal management system, braking system, mechanical transmission system, cargo box, suspension, and tires, and embedding strategies such as energy management, thermal management, and transmission control, a whole vehicle energy flow analysis model is built. This model has three main characteristics: Characteristic 1: Multi-field coupling of electricity (motor, battery, electronic control) - heat (thermal management) - force (mechanical transmission, braking). Compared with existing whole vehicle energy flow models (primarily based on electric transmission and "electric-thermal-mechanical" coupling models based on energy conversion processes), this application is based on the principles of electric-force conversion, electric-thermal conversion, and force-electric conversion, and adopts a "mechanism-physics" integrated modeling method in describing... Based on the described energy conversion process, this model further characterizes the multi-energy conversion efficiency and energy loss process dominated by the internal physical characteristics of components (friction, heat dissipation, structural configuration, etc.), resulting in high model accuracy and closer resemblance to real vehicle operation. Feature two: This model covers the complete energy transfer path from "environment → operating condition → driver → vehicle → wheel drive / braking force output." Its advantage lies in facilitating the comprehensive consideration of factors such as ambient temperature, vehicle speed, gradient, and load during vehicle energy flow analysis, enabling coupled analysis of external factors and the energy consumption / operating efficiency of internal vehicle components, thereby broadening the depth and breadth of vehicle energy flow analysis. Feature three: Adopting a platform-based modeling mode, all components, strategies, and external inputs in the model can be modularly and quickly replaced. Its advantage lies in enabling rapid verification of the operating efficiency of similar components from different manufacturers, and rapid development and calibration of the energy-saving effects of different vehicle control strategies and different thermal management strategies.

[0026] For example, through multiple real-vehicle tests, the working efficiency and energy consumption of the whole vehicle and its components are measured and compared with the simulation results (working efficiency and energy consumption of the whole vehicle and its components) output by the model. Components with large errors are identified, and the parameters of such components are corrected and optimized using the least squares method. The specific process is as follows: Let the experimental input and output of a certain component be (xi, yi), and the model output result be... ,in, Indicates the first One experimental sample point, Indicates the first Individual vehicle components, This represents the total number of vehicle parts to be analyzed. For the first The set of model parameters for each component.

[0027] The following least squares method is used to... Model parameter set for each component Perform correction: (1.1); (1.2); in, These are the weighting coefficients. The number of test sample points, This represents the deviation between the experimental results and the model output. The corrected set of model parameters can be obtained by iteratively solving formula (1.2). .

[0028] The model calibration method in this embodiment is based on real vehicle test results and uses the least squares method to automatically calibrate the model parameter set. Compared with traditional model parameter calibration methods based on manual fine-tuning or trial and error, the method in this application has higher model calibration efficiency and can effectively save model calibration time.

[0029] Optionally, the step of performing Monte Carlo simulation based on random operating condition input parameters and a pre-built vehicle energy flow analysis model to obtain statistical data on the working efficiency of each energy-consuming component of the vehicle includes: inputting random operating condition input parameters into the vehicle energy flow analysis model for multiple cyclic simulation calculations; and statistically obtaining the average working efficiency and dispersion data of each energy-consuming component based on the results of multiple simulation calculations.

[0030] Among them, multiple cyclic simulation calculations can be understood as the repeated random sampling calculation process of Monte Carlo simulation; the average working efficiency can be understood as the mean of the efficiency results of multiple simulations; and the dispersion data can be understood as the fluctuation range of component efficiency.

[0031] Specifically, multiple sets of random operating condition input parameters are input into the calibrated vehicle energy flow analysis model, and multiple cyclic simulation calculations are performed. Based on all simulation results, the average efficiency and dispersion data of each energy-consuming component are obtained to reflect the efficiency stability of the component under random operating conditions.

[0032] S120. Identify the working condition-related components based on the work efficiency statistics, and calculate the energy consumption impact index of the working condition-related components based on the preset dot matrix rules.

[0033] Among them, operating condition-related components can be understood as components whose energy consumption changes significantly with driving conditions; preset dot matrix rules can be understood as calculation rules that quantify the degree of influence of operating conditions on energy consumption according to quadrants and radii; energy consumption impact indicators can be understood as quantitative values ​​used to represent the magnitude of the effect of a single or multiple operating condition parameters on the energy consumption of a component.

[0034] Specifically, evaluation parameters are established based on work efficiency statistics, and the components associated with the working conditions are automatically identified. Then, according to the dot matrix diagram rules, the degree of influence of single working conditions and multi-working condition coupling on energy consumption is calculated respectively, and finally the energy consumption impact index is obtained, realizing the intuitive quantification of energy consumption impact.

[0035] Optionally, identifying condition-related components based on the work efficiency statistics includes: establishing component evaluation parameters based on the average work efficiency and the dispersion data; and classifying energy-consuming components into condition-related components and non-condition-related components based on the component evaluation parameters.

[0036] Among them, component evaluation parameters can be understood as comprehensive evaluation values ​​used to determine whether a component is affected by operating conditions; non-operating condition related components can be understood as components whose energy consumption does not fluctuate with changes in operating conditions.

[0037] Specifically, the average efficiency and dispersion data of each energy-consuming component are weighted and calculated to obtain component evaluation parameters that comprehensively reflect the component's energy consumption level and fluctuation characteristics. Corresponding classification thresholds are set for these evaluation parameters. Energy-consuming components with evaluation parameter values ​​below the preset thresholds are classified as non-operating-condition-related components, while those with evaluation parameter values ​​above the thresholds are classified as operating-condition-related components. This achieves accurate and automatic classification of operating-condition-related components. The preset classification thresholds can be pre-set based on experience; this embodiment does not impose specific limitations on them.

[0038] Preferably, based on the above embodiments, Monte Carlo simulation analysis is performed based on a normal probability distribution.

[0039] Monte Carlo simulation is a numerical statistical method based on random sampling. When a system has multiple uncertain inputs (such as random slope, random vehicle speed, ambient temperature, random load, etc.), traditional deterministic simulation can only obtain a single result. However, the Monte Carlo method, through repeated sampling and simulation, can obtain descriptive indicators such as the statistical distribution, mean, and variance of the output results. The core idea is to use the statistical results of a large number of random samples to approximately describe the actual behavior of the system or the mathematical expectation of the model. In this application, combining the normal probability distribution, Monte Carlo simulation analysis is used to approximately describe the actual energy consumption distribution characteristics of the entire vehicle and its components. The specific process is as follows: First, the random input variables of the model are determined to be: operating conditions, load, ambient temperature, and slope, and all of the above input variables are defined to follow a normal distribution.

[0040] (1.3); middle, These represent four external inputs: vehicle speed, load, gradient, and ambient temperature. and They represent the inputs respectively. The mean and standard deviation.

[0041] Furthermore, the quantitative indicators output by the model are determined to be the energy consumption and operating efficiency of each component. The specific mathematical expressions are as follows: (1.4); in, Indicates the first This is a combination of input variables obtained by sampling from a normal probability distribution. Indicates the number of test sample points. Let be the normal probability distribution function of the variable shown in formula (1.3). For the first The results of this simulation output. This represents the established energy flow analysis model for the entire vehicle.

[0042] When completed After each simulation, the expected value, variance, and probability distribution of energy consumption or working efficiency of each component are obtained through statistical methods.

[0043] (1.5); in, Indicates the first One test component, This indicates the total number of vehicle components to be tested. Indicates the first The test component is in the first Work efficiency during the simulation. and They represent the first The component in the first Input energy and output energy during the simulation.

[0044] Regarding the working efficiency of the above components Calculate its expected value and variance to achieve quantitative analysis.

[0045] (1.6); (1.7); in, and These represent the expected value and variance, respectively.

[0046] Based on the expected and variance of the working efficiency of each component, the energy-consuming components of the vehicle are divided into operating condition-related components and non-operating condition-related components. First, the expected and variance shown in formulas (1.6) and (1.7) are normalized to obtain the normalized expected value. and variance .

[0047] (1.8); (1.9); (1.10); in, and Indicates the weighting coefficient. As an evaluation factor, if a certain component If it is not, it is defined as a condition-related component; otherwise, it is a non-condition-related component.

[0048] In this embodiment, compared with traditional energy flow simulation methods, the method of this application has the following features: (1) It integrates normal probability distribution and realizes comprehensive simulation of vehicle energy flow under multi-dimensional random working conditions (vehicle speed, load, slope, ambient temperature) input through Monte Carlo method, with a wide working condition coverage; (2) It has the ability to automatically identify working condition related components and non-working condition related components, providing guidance for formulating component-level energy efficiency optimization schemes.

[0049] Optionally, the step of calculating the energy consumption impact index of the working condition-related components based on preset dot matrix rules includes: fixing some working condition parameters and calculating the single impact value of a single working condition parameter change on the component's energy consumption; and calculating the energy consumption impact index of the working condition-related components based on the single impact value and the coupled impact value generated by the combined effect of multiple working condition parameters.

[0050] Among them, the single impact value can be understood as the change in component energy consumption caused by the change of a single operating parameter; the coupled impact value can be understood as the change in total component energy consumption caused by the simultaneous change of multiple operating parameters.

[0051] Specifically, among multiple operating condition parameters, some parameters are selected and kept constant. Only one operating condition parameter is controlled to gradually change within a reasonable range. The energy consumption data of the component under different values ​​of this parameter are statistically analyzed. By comparing different data, the magnitude of the impact of the individual change of this operating condition parameter on the component's energy consumption is obtained, which is the single impact value. Then, multiple operating condition parameters are changed simultaneously to simulate the scenario of multiple factors acting together in actual driving. The coupled impact value of the combined change of multiple parameters on the component's energy consumption is calculated. Finally, the single impact value and the coupled impact value are weighted and fused to obtain an energy consumption impact index that can comprehensively reflect the degree of influence of operating condition parameters on the component's energy consumption.

[0052] Preferably, based on the above embodiments, Monte Carlo simulation automatically identifies components related to operating conditions and those not related to operating conditions. For non-operating condition-related components, energy efficiency can be improved through selection optimization. Since there are many component selection optimization methods currently available, this application will not repeat them. For operating condition-related components, it is necessary to further quantitatively analyze the impact of external inputs (vehicle speed, load, gradient, ambient temperature) on energy consumption. Specifically, the analysis is carried out using a dot plot method, and the implementation process is as follows: Specifically, extracting arrays during the Monte Carlo simulation process. .in, Indicates the first Each component These represent four external inputs: vehicle speed, load, gradient, and ambient temperature. This represents the input to the model. This represents the energy consumption output of the model.

[0053] Specifically, the system calculates the impact of three of the four input parameters—fixed vehicle speed, load, gradient, and ambient temperature—on the components, and the remaining parameter is a variable. Energy consumption The impact was assessed by selecting the average energy consumption of the component from multiple Monte Carlo simulations as the evaluation index. Maximum energy consumption Median energy consumption and minimum energy consumption . This indicates that the vehicle speed is a variable. This indicates that the load is a variable term. This indicates that the slope is a variable. This indicates that the ambient temperature is a variable.

[0054] Specifically, based on data , , and Calculate the impact of changes in input n on the energy consumption of the k-th component. .

[0055] (1.11); Specifically, for two of the four inputs—fixed vehicle speed, load, gradient, and ambient temperature—an additional variable input is added, based on the variable n. or or ), thereby analyzing the effect of changes in the two inputs on the first The coupling effect of energy consumption of individual components. If ,but , , ;like ,but , , ;like ,but , , ;like ,but , ; .

[0056] Specifically, the calculation is performed when two of the four input parameters—vehicle speed, load, gradient, and ambient temperature—change. Evaluation indicators for each component , and .

[0057] Specifically, in and (or or When two inputs change, calculate its effect on the first input using the following formula. The coupling effect of energy consumption of individual components.

[0058] (1.12); (1.13); (1.14); Formula (1.11) takes into account the input. For the The impact of energy consumption on individual components is considered in formulas (1.12)-(1.14). (or or The coupling effect of the input n and the input n changing simultaneously on the energy consumption of the kth component.

[0059] Furthermore, based on formulas (1.11) to (1.14), the following function is constructed.

[0060] (1.15); (1.16); in, , , and These are the weighting coefficients.

[0061] Optional, for and After normalization, we get and and to Perform the following processing.

[0062] (1.17); based on and Draw a bitmap.

[0063] Figure 2a This is a sample dot matrix diagram of a method for optimizing the energy flow of a pure electric light truck according to an embodiment of the present invention; as shown below. Figure 2a As shown, the first, second, third, and fourth quadrants correspond to... The outer radius is 1, and it passes through Location can be determined The quadrant in the above dot matrix diagram The specific value (with a maximum value of 1 after normalization) is its distance (radius) from the center of the circle in that quadrant. The larger the radius, the more affected it is by the input. The greater the impact, the better. Furthermore, This corresponds to a counter-clockwise rotation angle starting from 0 degrees.

[0064] Specifically, in Figure 2a In a dot matrix diagram, coordinate points The main consideration is the input. Impact, while considering , and The coupling effect was analyzed to obtain a quantitative analysis of the energy consumption of the k-th component. In summary, each component k has a coordinate point in each of the four quadrants: At that time, the coordinates of the distribution points in the first quadrant ; At that time, the coordinates of the distribution in the second quadrant ; At that time, the coordinate points are distributed in the third quadrant. ; The coordinates of the distribution in the fourth quadrant .

[0065] For the k-th component Figure 2a The analysis unfolds by considering the distribution of coordinate points in the four quadrants. If a coordinate point in a certain quadrant is located in... Figure 2a Within the inner circle of the dot matrix diagram shown, the influence of the input corresponding to that quadrant on the energy consumption of component k can be ignored (first quadrant). The corresponding input is vehicle speed; second quadrant. The corresponding input is the load; third quadrant. The corresponding input is the slope; fourth quadrant. The corresponding input is ambient temperature.

[0066] exist Figure 2a In the dot matrix diagram shown, if a coordinate point in a quadrant lies between the inner and outer circles, the energy efficiency optimization of component k requires consideration of the influence of the corresponding inputs (vehicle speed, load, slope, ambient temperature). Furthermore, the closer the coordinate point is to the outer circle, the more critical it should be to be considered. Additionally, if the angles of coordinate points in two quadrants are closer, it indicates that the energy consumption of component k should be considered simultaneously with the inputs corresponding to those two quadrants.

[0067] Ultimately, each component generates one Figure 2a The dot matrix diagram shown intuitively and quantitatively represents the degree of coupling influence of external inputs on each component, and focuses on the inputs corresponding to the distribution points within the inner and outer circles of the dot matrix diagram for in-depth analysis and targeted optimization. S130. Determine the component energy efficiency optimization strategy for pure electric light trucks based on the energy consumption impact indicators.

[0068] Among them, the component energy efficiency optimization strategy can be understood as an energy consumption reduction plan formulated for components related to operating conditions.

[0069] Specifically, the key influencing factors of energy consumption are determined based on energy consumption impact indicators, and corresponding component energy efficiency optimization strategies are generated based on these key factors. If the generated strategy cannot meet the energy consumption reduction target, the impact indicators are re-analyzed and the weights of the key factors are adjusted, and an optimization strategy is generated again to form a closed-loop optimization.

[0070] The technical solution of this invention obtains random operating condition input parameters, performs Monte Carlo simulation based on the random operating condition input parameters and a pre-built vehicle energy flow analysis model, and obtains statistical data on the working efficiency of each energy-consuming component of the vehicle; identifies operating condition-related components based on the statistical data, calculates the energy consumption impact index of the operating condition-related components based on preset dot matrix rules; and determines the component energy efficiency optimization strategy for the pure electric light truck based on the energy consumption impact index. This solves the technical problems of incomplete operating condition coverage, low modeling accuracy, inability to quantify energy consumption impact, and unclear optimization strategies in existing methods. It improves the coverage and calculation accuracy of vehicle energy flow analysis, enhances the quantitative accuracy of energy consumption influencing factors, improves the targeting of energy efficiency optimization, improves the overall energy efficiency level of the pure electric light truck, and enhances the overall efficiency of energy flow analysis and optimization.

[0071] Figure 2b This is a flowchart illustrating another method for optimizing the energy flow of a pure electric light truck, provided as an embodiment of the present invention. Based on the above embodiments, this embodiment further refines how to determine component energy efficiency optimization strategies according to energy consumption impact indicators. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 2b As shown, the method specifically includes the following steps: S1301. Identify the key influencing factors that play a leading role in the energy consumption of components based on energy consumption impact indicators.

[0072] Among them, the key influencing factors can be understood as the operating parameters that play a dominant role in the energy consumption of components.

[0073] Specifically, based on the magnitude of energy consumption impact indicators, key influencing factors that play a dominant role in the energy consumption of components related to operating conditions are screened and identified, and the core objectives of energy efficiency optimization are clarified.

[0074] S1302. Based on the type of the key influencing factors and the magnitude of their effect on component energy consumption, determine the component energy efficiency optimization strategy for the operating condition-related components; wherein, the component energy efficiency optimization strategy includes parameter adjustment schemes and control strategy optimization schemes.

[0075] Among them, the parameter adjustment scheme can be understood as a scheme to optimize the performance, structure, and operating parameters of components; the control strategy optimization scheme can be understood as a scheme to optimize the energy management, thermal management, and drive control of the whole vehicle.

[0076] Specifically, based on whether the key influencing factors are vehicle speed, load, gradient, or ambient temperature, their corresponding types are determined. Then, considering the strength of the factor's impact on component energy consumption, targeted component energy efficiency optimization strategies are formulated. The parameter adjustment scheme mainly optimizes and adapts the operating, structural, or performance parameters of components such as the battery, motor, and transmission. The control strategy optimization scheme adjusts the vehicle's drive control, energy management, and thermal management strategies. Through the synergistic effect of these two types of schemes, component energy consumption can be effectively reduced and overall vehicle energy efficiency improved under different operating conditions.

[0077] The technical solution of this invention improves the targeting and execution efficiency of component energy efficiency optimization by accurately identifying key factors affecting energy consumption, thereby increasing the overall energy utilization rate of pure electric light trucks, reducing energy consumption fluctuations under random operating conditions, and enhancing the practicality and reliability of the optimization strategy.

[0078] Figure 3 This is a schematic diagram of a pure electric light truck energy flow optimization device provided in an embodiment of the present invention. Figure 3 As shown, the device includes: a component efficiency statistics module 310, an energy consumption impact index determination module 320, and an optimization strategy determination module 330.

[0079] The component efficiency statistics module 310 is used to acquire random operating condition input parameters, perform Monte Carlo simulation based on the random operating condition input parameters and a pre-built vehicle energy flow analysis model, and obtain statistical data on the working efficiency of each energy-consuming component of the vehicle; the energy consumption impact index determination module 320 is used to identify operating condition-related components based on the working efficiency statistical data, and calculate the energy consumption impact index of the operating condition-related components based on preset dot matrix rules; the optimization strategy determination module 330 is used to determine the component energy efficiency optimization strategy of the pure electric light truck based on the energy consumption impact index.

[0080] The technical solution of this invention obtains random operating condition input parameters, performs Monte Carlo simulation based on the random operating condition input parameters and a pre-built vehicle energy flow analysis model, and obtains statistical data on the working efficiency of each energy-consuming component of the vehicle; identifies operating condition-related components based on the statistical data, calculates the energy consumption impact index of the operating condition-related components based on preset dot matrix rules; and determines the component energy efficiency optimization strategy for the pure electric light truck based on the energy consumption impact index. This solves the technical problems of incomplete operating condition coverage, low modeling accuracy, inability to quantify energy consumption impact, and unclear optimization strategies in existing methods. It improves the coverage and calculation accuracy of vehicle energy flow analysis, enhances the quantitative accuracy of energy consumption influencing factors, improves the targeting of energy efficiency optimization, improves the overall energy efficiency level of the pure electric light truck, and enhances the overall efficiency of energy flow analysis and optimization.

[0081] Optionally, the component efficiency statistics module is specifically used for: The random operating condition input parameters are generated according to the normal distribution rule; wherein, the random operating condition input parameters include vehicle speed, load, road slope and ambient temperature.

[0082] Optionally, the device further includes: The model building module is used to build an initial energy flow analysis model that includes the battery, motor, electronic control, thermal management system and mechanical transmission system before performing Monte Carlo simulation based on random operating condition input parameters and a pre-built multi-physics coupled vehicle energy flow analysis model. The input data determination module is used to take typical operating condition parameters, vehicle structural parameters, and component performance parameters as input data for the initial energy flow analysis model; The calculation relationship establishment module is used to establish the energy loss calculation relationship for each component based on the conversion and transfer relationship of the three types of energy: electricity, heat, and force. The data comparison module is used to collect energy flow test data from actual vehicle operation and compare the energy flow test data with the output data of the initial energy flow analysis model. The model correction module is used to correct the initial energy flow analysis model based on the deviation value obtained from the comparison, so as to obtain a multi-physics coupled vehicle energy flow analysis model.

[0083] Optionally, the component efficiency statistics module includes: The simulation calculation unit is used to input random operating condition input parameters into the vehicle energy flow analysis model for multiple cyclic simulation calculations. The results statistics unit is used to statistically obtain the average working efficiency and dispersion data of each energy-consuming component based on the results of multiple simulation calculations.

[0084] Optionally, the energy consumption impact index determination module includes: An evaluation parameter establishment unit is used to establish component evaluation parameters based on the average work efficiency and the dispersion data. The component division unit is used to divide energy-consuming components into operating condition-related components and non-operating condition-related components according to the component evaluation parameters.

[0085] Optionally, the energy consumption impact index determination module includes: The single impact value determination unit is used to fix some operating parameters and calculate the single impact value of a change in a single operating parameter on the energy consumption of the component. The impact index calculation unit is used to calculate the energy consumption impact index of the operating condition related components based on the single impact value and the coupled impact value generated by the combined effect of multiple operating condition parameters.

[0086] Optionally, the optimization strategy determination module includes: The key factor identification unit is used to identify the key influencing factors that play a dominant role in the energy consumption of components based on energy consumption impact indicators. The optimization strategy determination unit is used to determine the component energy efficiency optimization strategy for the operating condition-related components based on the type of the key influencing factors and the magnitude of their impact on component energy consumption; wherein, the component energy efficiency optimization strategy includes parameter adjustment schemes and control strategy optimization schemes. The pure electric light truck energy flow optimization device provided in this embodiment of the invention can execute the pure electric light truck energy flow optimization method provided in any embodiment of the invention, possessing the corresponding functional modules and beneficial effects of the method.

[0087] Figure 4 This is a schematic diagram of the electronic device used to implement the energy flow optimization method for pure electric light trucks according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0088] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0089] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method of optimizing the energy flow of a pure electric light truck.

[0091] In some embodiments, the method for optimizing the energy flow of a pure electric light truck can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for optimizing the energy flow of a pure electric light truck described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for optimizing the energy flow of a pure electric light truck by any other suitable means (e.g., by means of firmware).

[0092] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0097] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0098] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0099] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing the energy flow of a pure electric light truck, characterized in that, include: Obtain random operating condition input parameters, and perform Monte Carlo simulation based on random operating condition input parameters and a pre-built vehicle energy flow analysis model to obtain statistical data on the working efficiency of each energy-consuming component of the vehicle. Based on the work efficiency statistics, identify the working condition-related components, and calculate the energy consumption impact index of the working condition-related components based on the preset dot matrix rules. The component energy efficiency optimization strategy for pure electric light trucks is determined based on the energy consumption impact indicators.

2. The method according to claim 1, characterized in that, The acquisition of random operating condition input parameters includes: The random operating condition input parameters are generated according to the normal distribution rule; wherein, the random operating condition input parameters include vehicle speed, load, road slope and ambient temperature.

3. The method according to claim 1, characterized in that, Before performing Monte Carlo simulation based on random operating condition input parameters and a pre-built multiphysics coupled vehicle energy flow analysis model, the following steps are also included: Construct an initial energy flow analysis model that includes the battery, motor, electronic control, thermal management system, and mechanical transmission system; Typical operating condition parameters, vehicle structural parameters, and component performance parameters are used as input data for the initial energy flow analysis model; Based on the conversion and transfer relationships of electrical, thermal, and mechanical energy, the energy loss calculation relationships for each component are established. Collect energy flow test data from actual vehicle operation and compare the energy flow test data with the output data of the initial energy flow analysis model; The initial energy flow analysis model is corrected based on the deviation value obtained from the comparison, resulting in a multi-physics coupled vehicle energy flow analysis model.

4. The method according to claim 1, characterized in that, The Monte Carlo simulation is performed based on random operating condition input parameters and a pre-built vehicle energy flow analysis model to obtain statistical data on the working efficiency of each energy-consuming component of the vehicle, including: The random operating condition input parameters are input into the vehicle energy flow analysis model for multiple cyclic simulation calculations. Based on multiple simulation calculations, the average efficiency and dispersion data of each energy-consuming component were statistically obtained.

5. The method according to claim 4, characterized in that, The component for identifying working condition associations based on the work efficiency statistics includes: Component evaluation parameters are established based on the average work efficiency and the dispersion data; Based on the component evaluation parameters, energy-consuming components are divided into operating condition-related components and non-operating condition-related components.

6. The method according to claim 1, characterized in that, The calculation of the energy consumption impact index of the operating condition-related components based on preset dot matrix rules includes: With certain operating parameters fixed, calculate the single impact of a change in a single operating parameter on the energy consumption of the component. Based on the single influence value and the coupled influence value generated by the combined effect of multiple operating condition parameters, the energy consumption influence index of the operating condition-related components is calculated.

7. The method according to claim 1, characterized in that, The method for determining component energy efficiency optimization strategies based on energy consumption impact indicators includes: Identify the key influencing factors that play a dominant role in the energy consumption of components based on energy consumption impact indicators; Based on the type of the key influencing factors and the magnitude of their impact on component energy consumption, a component energy efficiency optimization strategy for the operating condition-related components is determined; wherein, the component energy efficiency optimization strategy includes parameter adjustment schemes and control strategy optimization schemes.

8. A device for optimizing the energy flow of a pure electric light truck, characterized in that, include: The component efficiency statistics module is used to obtain random operating condition input parameters, and perform Monte Carlo simulation based on the random operating condition input parameters and the pre-built vehicle energy flow analysis model to obtain the working efficiency statistics of each energy-consuming component of the vehicle. The energy consumption impact index determination module is used to identify working condition related components based on the working efficiency statistics and to calculate the energy consumption impact index of the working condition related components based on preset dot matrix rules. The optimization strategy determination module is used to determine the component energy efficiency optimization strategy for pure electric light trucks based on the energy consumption impact indicators.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the energy flow optimization method for pure electric light trucks as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the energy flow optimization method for a pure electric light truck as described in any one of claims 1-7.

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