Multi-energy cooperative driving demonstration method and system of new energy electric vehicle teaching aid

Abstract

The invention relates to a multi-energy cooperative driving demonstration method and system of a new energy electric vehicle teaching aid. The method comprises the following steps: acquiring an energy configuration scheme; performing feasibility analysis on the energy configuration scheme to generate a corresponding output scheme set; in response to the obtained priority scheme selection instruction, constructing a new energy electric vehicle model matched with the priority scheme; the new energy electric vehicle model comprises a connection relation formed by energy sources, control parameters and energy management logic; and generating a multi-energy cooperative driving demonstration effect based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type every time the working condition type selection is obtained. By adopting the method, how energy works cooperatively and how the vehicle responds under different working conditions can be seen clearly, and the multi-energy cooperative driving operation mechanism of the new energy electric vehicle can be known comprehensively.

Description

Technical Field

[0001] The present invention belongs to the field of digital control, and particularly relates to a multi - energy collaborative driving demonstration method and system for a new - energy electric vehicle teaching aid. Background Art

[0002] With the development of new - energy vehicle technology, the power system has gradually evolved from a single - energy to a multi - energy collaborative driving mode. The multi - energy collaborative driving technology integrates various energy forms such as batteries, fuel cells, supercapacitors, and solar energy. By intelligently regulating the output of different energies under different working conditions, precise energy matching is achieved.

[0003] In traditional technologies, new - energy electric vehicle teaching aids often use books and deduce energy cooperation formulas to show how the battery drives the vehicle, and how fuel cells, supercapacitors, and solar energy cooperate during vehicle operation, without in - depth and visual demonstration means.

[0004] However, the above - mentioned methods cannot truly and comprehensively simulate the multi - energy collaborative working scenarios of electric vehicles under complex road conditions. It is difficult for students to understand the switching logic and collaborative effects of different energies under different working conditions. Due to the lack of vivid and in - depth demonstrations, students do not master the key points of multi - energy collaborative driving technology well during the teaching process, which is not conducive to cultivating their in - depth understanding and innovative thinking of new - energy vehicle cutting - edge technologies. Summary of the Invention

[0005] Based on this, it is necessary to provide a multi - energy collaborative driving demonstration method and system for a new - energy electric vehicle teaching aid that can meet multi - energy configuration requirements and visualize energy cooperation for the above - mentioned technical problems.

[0006] In a first aspect, the present application provides a multi - energy collaborative driving demonstration method for a new - energy electric vehicle teaching aid, including:

[0007] Obtain an energy configuration plan; the energy configuration plan includes at least one energy component among batteries, fuel cells, supercapacitors, and solar energy;

[0008] Conduct a feasibility analysis on the energy configuration plan to generate a corresponding set of output plans; the set of output plans includes at least one executable priority plan or an infeasibility determination conclusion dynamically generated based on the feasibility analysis result;

[0009] In response to the obtained priority plan selection instruction, construct a new - energy electric vehicle model that matches the priority plan; the new - energy electric vehicle model includes the connection relationship, control parameters, and energy management logic of each energy component;

[0010] Each time the operating condition type selection is obtained, a multi - energy collaborative driving demonstration effect is generated based on the new - energy electric vehicle model and the energy management decision corresponding to the operating condition type.

[0011] In one embodiment, a feasibility analysis is performed on the energy configuration plan to generate a corresponding set of output plans, including:

[0012] The feasibility analysis of the energy configuration plan is carried out based on the feasibility theory basis to obtain the feasibility analysis result; the feasibility analysis result is that the energy configuration plan is feasible or the energy configuration plan is not feasible; the feasibility analysis includes coordination judgment, rationality judgment, and engineering feasibility judgment;

[0013] If the feasibility analysis result is that the energy configuration plan is feasible, an executable priority plan for the energy composition in the corresponding energy configuration plan is generated; the priority plan includes the operating condition type and its corresponding energy priority;

[0014] If the feasibility analysis result is that the energy configuration plan is not feasible, an infeasible determination conclusion is generated; the infeasible determination conclusion includes the violated feasibility theory basis and an infeasible warning.

[0015] In one embodiment, the feasibility analysis of the energy configuration plan is carried out based on the feasibility theory basis to obtain the feasibility analysis result, including:

[0016] A coordination judgment is made on the energy configuration plan, and a coordination judgment result is obtained according to the coordination evaluation parameters generated by the coordination judgment; the coordination evaluation parameters include physical integration indicators and energy coordination coefficients; the coordination judgment result includes meeting the coordination conditions or coordination anomalies;

[0017] A judgment is made on whether the energy configuration plan can cover the operating conditions of the new - energy vehicle to generate a rationality judgment result; the rationality judgment result includes being able to cover the operating conditions of the vehicle or failing to cover the operating conditions of the vehicle;

[0018] A judgment is made on whether the energy configuration plan meets the energy management regulation to generate an engineering feasibility judgment result; the engineering feasibility judgment result includes meeting the energy management regulation or failing to meet the energy management regulation;

[0019] When the feasibility analysis shows that the coordination conditions are met, the operating conditions of the vehicle can be covered, and the energy management regulation is satisfied, the feasibility analysis result is determined to be that the energy configuration plan is feasible;

[0020] When the feasibility analysis shows any one of coordination anomalies, failure to cover the operating conditions of the vehicle, or failure to meet the energy management regulation, the feasibility analysis result is determined to be that the energy configuration plan is not feasible.

[0021] In one embodiment, the coordination judgment result is obtained according to the coordination evaluation parameter generated by the coordination judgment, including:

[0022] When the physical integration index reaches the preset spatial layout threshold and the energy coordination coefficient meets the dynamic response requirement range, the coordination judgment result is that the coordination condition is satisfied;

[0023] When the physical integration index does not reach the preset spatial layout threshold or the energy coordination coefficient exceeds the dynamic response requirement range, the coordination judgment result is coordination anomaly, and the anomaly marker information corresponding to the coordination anomaly is determined as the violated feasibility theory basis.

[0024] In one embodiment, an executable priority scheme for the energy composition in the corresponding energy configuration scheme is generated, including:

[0025] Generate all priority schemes matching the energy composition in the energy configuration scheme;

[0026] Based on different working condition types, screen all priority schemes according to the principles of energy density and power density to obtain an executable priority scheme.

[0027] In one embodiment, a multi-energy collaborative drive demonstration effect is generated based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type, including:

[0028] Based on the new energy electric vehicle model, the priority scheme and the working condition type, calculate the energy management decision to obtain the power distribution strategy; the energy management decision includes the energy optimal decision, the power optimal decision and the economic optimal decision;

[0029] According to the power distribution strategy, dynamically adjust the energy flow state of the new energy electric vehicle model to generate a multi-energy collaborative drive demonstration effect.

[0030] In one embodiment, according to the power distribution strategy, dynamically adjust the energy flow state of the new energy electric vehicle model to generate a multi-energy collaborative drive demonstration effect, including:

[0031] Determine the timing of switching the energy composition priority according to the power distribution strategy to obtain the energy flow path;

[0032] Adjust the charge and discharge state of the energy composition according to the power distribution strategy to obtain the working state curve of each energy composition;

[0033] Calculate the motor power under different working conditions according to the power distribution strategy to obtain the vehicle response state; the vehicle response state includes power limitation and braking energy recovery;

[0034] Generate a multi - energy collaborative driving demonstration effect by using the energy flow path, the working state curves of each energy source, and the vehicle response state.

[0035] In a second aspect, the present application also provides a multi - energy collaborative driving demonstration system for a new - energy electric vehicle teaching aid, including:

[0036] A data acquisition module, configured to acquire an energy configuration plan;

[0037] A data analysis module, configured to perform a feasibility analysis on the energy configuration plan and generate a corresponding set of output plans;

[0038] A model construction module, configured to construct a new - energy electric vehicle model that matches the priority plan in response to the obtained priority plan selection instruction;

[0039] A visualization demonstration module, configured to generate a multi - energy collaborative driving demonstration effect based on the new - energy electric vehicle model and the energy management decision corresponding to the working condition type each time a working condition type selection is obtained.

[0040] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any of the above - mentioned multi - energy collaborative driving demonstration methods for a new - energy electric vehicle teaching aid are implemented.

[0041] In a fourth aspect, the present application also provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above - mentioned multi - energy collaborative driving demonstration methods for a new - energy electric vehicle teaching aid are implemented.

[0042] For the above - mentioned multi - energy collaborative driving demonstration method and system for a new - energy electric vehicle teaching aid, the energy configuration plan can simulate various different energy combinations, providing users with rich choices. According to different learning needs and research directions, different energy configuration plans can be selected to deeply understand the application and collaborative working principle of various energies in electric vehicles. Performing a feasibility analysis on the energy configuration plan provides users with a scientific decision - making basis, guiding students to select the most suitable plan from numerous energy configuration plans, obtaining different new - energy electric vehicle models according to different plans, improving the applicability and flexibility of the teaching aid, being able to simulate the operation of new - energy electric vehicles under different actual working conditions, visually observing the collaborative working modes and energy management strategies of various energies under different working conditions, and facilitating a profound understanding of the actual operation of new - energy electric vehicles. Description of the Drawings

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or in the related art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the related art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a diagram of the multi - energy collaborative drive demonstration method for the new - energy electric vehicle teaching aid of the present invention;

[0045] Figure 2 It is a schematic diagram of the sub - steps of step S104;

[0046] Figure 3 It is a schematic diagram of the sub - steps of step S202;

[0047] Figure 4 It is a composition structure diagram of the multi - energy collaborative drive demonstration system for the new - energy electric vehicle teaching aid of the present invention. Specific Embodiments

[0048] In order to make the purpose, technical solutions and advantages of the present application more clear, the following further details the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] In one embodiment, as Figure 1 shown, a multi - energy collaborative drive demonstration method for a new - energy electric vehicle teaching aid is provided. In this embodiment, it is exemplified that the method is applied to a terminal. It can be understood that the method can also be applied to a server, or to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0050] S101. Obtain an energy configuration plan; the energy configuration plan includes at least one of battery, fuel cell, supercapacitor and solar energy.

[0051] By obtaining an energy configuration plan to determine the energy composition information of a new energy electric vehicle, a basic framework for multi-energy collaborative driving is constructed. Schematically, the terminal pre-stores the relevant information of four types of energy, namely batteries, fuel cells, supercapacitors, and solar energy, including energy density, power density, charge and discharge efficiency, etc. Optionally, the energy configuration plan can be independently selected by the user through an interactive interface, and the system pre-stores a limited number of energy configuration plans. The energy configuration plan can be one of batteries, fuel cells, supercapacitors, or solar energy, or a combination of two, three, or four of batteries, fuel cells, supercapacitors, and solar energy. Further, the user can configure the energy composition according to their own needs. Exemplarily, if the user is based on the range requirement, the energy composition combination of batteries and fuel cells can be configured; or a combination of batteries, solar energy, and supercapacitors can be adopted based on cost control. Optionally, the user can configure the parameters of the constituent energy, and the parameters include the number of energy components, the capacitance of the supercapacitor, the withstand voltage value, and the conversion efficiency of the solar panel, etc.

[0052] New energy electric vehicles usually adopt multiple energy components to optimize energy management performance, and different energy components have different characteristics. Schematically, the battery has a relatively high energy density and is suitable for long-term power supply, but the charge and discharge rate is slow; the fuel cell has a relatively high energy density and is suitable for long-distance driving, but its dynamic response ability is weak; the supercapacitor has a high power density and can be charged and discharged quickly, and is suitable for instantaneous high-power demands, such as vehicle conditions like acceleration or regenerative braking energy recovery; solar energy can be used to supplement electrical energy, but is limited by light conditions and has a small power output.

[0053] S102. Conduct a feasibility analysis on the energy configuration plan to generate a corresponding set of output plans; the set of output plans includes at least one executable priority plan or an infeasibility determination conclusion dynamically generated based on the feasibility analysis result.

[0054] Conduct a feasibility analysis on the obtained energy configuration plan to determine whether the user's energy configuration plan can be used for a new energy electric vehicle and generate a corresponding set of output plans. The executable priority plan is the priority allocation of each energy component under different working conditions. The infeasibility determination conclusion is that when the configuration plan is not feasible, an infeasible conclusion is generated and the reason why the configuration plan is not feasible is output to guide the user to think and modify.

[0055] S103. In response to the obtained priority plan selection instruction, construct a new energy electric vehicle model that matches the priority plan; the new energy electric vehicle model includes the connection relationship, control parameters, and energy management logic of each energy component.

[0056] Schematically, the system sends selectable priority schemes to the user interface for the user to choose. After receiving the priority scheme selection instruction, the new energy electric vehicle model is configured according to the priority scheme and the energy configuration scheme, including the connection relationship of each energy component, the control parameters of each energy component, and the energy management logic such as when to switch energy components and when to perform energy recovery. Exemplarily, it is determined whether the battery and the supercapacitor are connected in series or in parallel, and control parameters such as the charging cut-off voltage and the discharge protection current are set according to the battery capacity, the charge and discharge rate, etc. Further, under the acceleration condition, the energy management logic preferentially calls the supercapacitor to provide instantaneous high power, while the battery supplies power as an auxiliary; during deceleration braking, the vehicle kinetic energy is converted into electrical energy and preferentially stored in the supercapacitor. If the supercapacitor is full, it is stored in the battery.

[0057] S104. Each time a working condition type selection is obtained, a multi-energy collaborative driving demonstration effect is generated based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type.

[0058] Schematically, the working condition types include states such as starting, accelerating, cruising, decelerating, and braking, and also include changes in vehicle speed, acceleration, and ambient light, as well as actual working conditions such as climbing, descending, urban congestion, and long-distance highways. Exemplarily, when driving at a low speed in the city at 0-50 km / h, there is a low acceleration requirement, and at this time, the energy is mainly for power supply, and there can be auxiliary energy for auxiliary charging, etc.; when cruising at a high speed above 80 km / h, there is a stable high-power output requirement, and there can also be auxiliary energy for auxiliary charging, etc.; when in urban congestion, frequent starts and stops are required, there is a short-term high-power requirement, and it is accompanied by driving for a period of time after starting and stopping; when climbing, there is an additional instantaneous high-power requirement; when braking, energy recovery can be performed; extreme weather such as low temperature and high temperature or cloudy days will affect the efficiency of the battery and the stability of auxiliary charging such as solar energy. Different working condition types affect the energy scheduling, that is, the energy management decision, and determine the operation of the new energy electric vehicle under different working condition types with the energy-optimal strategy, the power-optimal strategy, and the economic-optimal strategy, and visually display the energy flow path, the power distribution situation, and the working states of each energy source.

[0059] In the multi-energy collaborative driving demonstration method of the above new energy electric vehicle teaching aid, an interactive and visual teaching tool is formed to help users understand the energy management logic of new energy vehicles, and at the same time, it can be used to verify the feasibility of different energy combinations to optimize the actual application plan.

[0060] In one of the embodiments, a feasibility analysis is performed on the energy configuration scheme, and a corresponding output scheme set is generated, including:

[0061] S21. Conduct a feasibility analysis on the energy allocation plan based on the feasibility theory to obtain the feasibility analysis result. The feasibility analysis result is that the energy allocation plan is feasible or the energy allocation plan is infeasible. The feasibility analysis includes coordination judgment, rationality judgment, and engineering feasibility judgment.

[0062] The energy allocation plan meets all the feasibility criteria, that is, the energy allocation plan is feasible and can be used to build a new energy electric vehicle model; or the energy allocation plan does not meet the feasibility criteria in some aspects, and the user needs to adjust or reselect the energy composition.

[0063] The feasibility analysis is mainly based on three core judgments. Among them, the coordination judgment is to analyze whether each energy can be integrated in the physical structure and whether it meets the system compatibility requirements. Exemplarily, whether the voltage range of the supercapacitor matches the battery. The rationality judgment is to analyze whether the selected energy combination can cover all the operating conditions of the new energy vehicle. Exemplarily, if only solar energy is selected as the power source, it cannot meet the actual driving requirements, and this energy allocation plan is infeasible. The engineering feasibility judgment is to analyze whether the selected plan can meet the control requirements of the energy management system, whether it can dynamically switch the energy supply under different load demands, and whether it conforms to the matching principle of energy density and power density.

[0064] S22. If the feasibility analysis result is that the energy allocation plan is feasible, generate an executable priority plan for the energy composition in the corresponding energy allocation plan. The priority plan includes the type of working conditions and their corresponding energy priorities.

[0065] After the energy allocation plan is determined to be feasible, determine the priority allocation plan of each energy under different vehicle working condition types, that is, how to allocate the output of the energy under different driving states. Schematically, the generation of the priority plan is based on the principles of power density and energy density, dynamic regulation principle, and energy recovery principle. Specifically, based on the principles of power density and energy density, the supercapacitor is preferentially used when high power is required, and the fuel cell or battery is preferentially used when long-term endurance is required; the dynamic regulation principle is that the system needs to be able to perform energy scheduling according to real-time working conditions, such as using the supercapacitor at startup and using the fuel cell or battery during high-speed cruising; the energy recovery principle is that the supercapacitor or battery is preferentially charged during the braking recovery stage.

[0066] S23. If the feasibility analysis result is that the energy allocation plan is infeasible, generate an infeasibility determination conclusion. The infeasibility determination conclusion includes the violated feasibility theory basis and an infeasibility warning.

[0067] When the energy configuration plan is determined to be infeasible, an infeasibility determination conclusion is generated, clearly indicating the theoretical basis of feasibility violated by the plan, and providing optimization suggestions to the user. Schematically, the infeasibility determination conclusion includes the theoretical basis of feasibility violated, that is, it explains in which aspects the energy configuration plan does not meet the energy management requirements of new energy electric vehicles. Exemplarily, it includes the inability to meet power demand, energy mismatch, uncontrollable energy management, etc.; and an infeasibility warning, that is, providing the user with the result of feasibility analysis and prompting them to adjust the energy selection, such as adding energy storage units, modifying the energy composition, etc.

[0068] In one embodiment, a feasibility analysis is performed on the energy configuration plan based on the theoretical basis of feasibility, and the feasibility analysis result is obtained, including:

[0069] S31. Judge the coordination of the energy configuration plan, and obtain the coordination judgment result according to the coordination evaluation parameters generated by the coordination judgment; the coordination evaluation parameters include physical integration indicators and energy coordination coefficients; the coordination judgment result includes meeting the coordination conditions or coordination anomalies.

[0070] Evaluate whether different energy compositions are compatible in the same vehicle system, including the matching relationship of voltage and current, and whether multiple energy compositions are subject to physical size limitations. Evaluate the coordination by calculating the physical integration indicators and energy synergy coefficients. Among them, the physical integration indicators mainly include space occupancy and connection complexity; the energy synergy coefficients include dynamic power response rate and energy complementarity. Exemplarily, the energy synergy coefficient is whether there is a large current impact during energy switching, or it can also be whether each energy composition can efficiently cooperate to complete power compensation. If the coordination judgment result is that the coordination conditions are met, there is no conflict in the physical integration and energy flow of the energy configuration, and the next analysis can be carried out; if the coordination judgment result is a coordination anomaly: there is a mismatch problem in the energy configuration and it cannot operate effectively in coordination, and the energy configuration plan needs to be adjusted.

[0071] S32. Judge whether the energy configuration plan can cover the operating conditions of new energy vehicles, and generate a rationality judgment result; the rationality judgment result includes being able to cover the operating conditions of the vehicle or failing to cover the operating conditions of the vehicle.

[0072] Rationality judgment is to ensure that the energy configuration plan can meet the energy requirements of all vehicle operating conditions, including starting, accelerating, cruising, braking energy recovery, etc. It is necessary to evaluate whether there are single-point failures in the selected energy composition. Schematically, starting requires high-power transient demand; accelerating requires continuous high-power output; cruising requires stable power output; decelerating and braking energy recovery require energy recovery ability. The judgment of whether it can cover the operating conditions of new energy vehicles is whether the existing energy can provide the required power and energy and whether there are key operating conditions that cannot be supported. Exemplarily, solar energy alone cannot drive a vehicle. If the rationality judgment result is that it can cover the vehicle's operating conditions, that is, the energy plan can meet all operating conditions, it can enter the next step of analysis; if the rationality judgment result is that it fails to cover the vehicle's operating conditions, that is, the energy plan cannot meet the requirements under certain conditions, the configuration needs to be adjusted.

[0073] S33. Judge whether the energy configuration plan meets the energy management and control requirements, and generate an engineering feasibility judgment result; the engineering feasibility judgment result includes meeting the energy management and control or failing to meet the energy management and control.

[0074] The engineering feasibility judgment is whether the selected energy composition can be dynamically regulated through the energy management system so that it can perform power distribution, charge and discharge management, energy recovery, etc. according to the demand, and calculate whether the energy management system can effectively execute the power optimal, energy optimal and economic optimal strategies. Schematically, the power distribution ability is whether the energy system can dynamically adjust the power according to the vehicle demand; the charge and discharge control ability is whether the energy storage devices such as batteries and supercapacitors can effectively charge and discharge; the energy switching smoothness is whether the switching between different energies causes a large fluctuation in the system voltage or power. If the engineering feasibility judgment result is that it meets the energy management and control, that is, the energy configuration can be stably controlled by the energy management system; if the engineering feasibility judgment result is that it fails to meet the energy management and control, that is, the energy configuration cannot be stably managed, the plan needs to be adjusted. Exemplarily, configured with solar energy and fuel cells, due to the long start-up time of the fuel cell and its inability to respond to load changes in a timely manner, and the unstable output of solar energy, resulting in large system power fluctuations, the energy management system cannot effectively manage it, so it fails to meet the energy management and control.

[0075] S34. When the feasibility analysis shows that the coordination conditions are met, the vehicle operating conditions can be covered, and the energy management and control are satisfied, the feasibility analysis result is determined to be that the energy configuration plan is feasible.

[0076] If all three of coordination, rationality, and engineering feasibility are met, the plan is considered feasible. Exemplarily, using the battery as the main energy source and the supercapacitor as the auxiliary energy storage unit, their voltages match well; it can provide stable power output under all vehicle conditions; dynamic power scheduling can be achieved through the energy management system. Therefore, this energy configuration plan is determined to be a feasible energy configuration plan.

[0077] S35. When the feasibility analysis shows any one of coordination anomalies, failure to cover the operating conditions of the vehicle, or failure to meet the energy management regulation, the result of the feasibility analysis is determined to be that the energy allocation plan is infeasible.

[0078] If any one of coordination, rationality, and engineering feasibility does not meet the requirements, the energy allocation plan is infeasible.

[0079] In one embodiment, the coordination judgment result is obtained according to the coordination evaluation parameters generated by the coordination judgment, including:

[0080] S41. When the physical integration index reaches the preset spatial layout threshold and the energy coordination coefficient meets the dynamic response requirement range, the coordination judgment result is that the coordination condition is satisfied.

[0081] Confirm whether the multi - energy collaborative configuration of the new energy electric vehicle meets the basic requirements of physical integration and energy coordination, ensure that each energy component can achieve compatibility in physical space and energy flow to ensure the stable operation of the system. The physical integration index refers to the layout rationality of the energy components in the whole vehicle, which can include whether the spatial layout reaches the preset threshold, that is, whether each energy component can be installed inside the vehicle without affecting the normal operation of other systems. Exemplarily, whether there is enough installation space for multiple batteries, and whether the hydrogen storage system of the fuel cell configured with the battery meets the spatial constraints of the vehicle. Further, it can include mass distribution balance. Mass distribution balance means whether the weights of each energy component are evenly distributed to avoid having too much impact on the vehicle's center of gravity. Exemplarily, if the battery pack is too heavy and concentrated on one side, it will affect the handling stability. Further, it can also include connection feasibility. Connection feasibility means whether different energy components can be effectively integrated with the vehicle's power electronics system, cooling system, etc.

[0082] Schematically, the energy coordination coefficient is used to measure the power output and dynamic response ability of the multi - energy system, which can include power matching degree. Power matching degree refers to whether the voltage and current characteristics of different energy components match. Exemplarily, whether the output voltage of the fuel cell is suitable for the motor drive system. Further, it can include dynamic response ability. Dynamic response ability refers to whether the response speed of each energy component under different operating conditions of the vehicle can meet the requirements. Exemplarily, whether the supercapacitor can provide high - power support in a short time to meet the acceleration demand. Further, it can also include energy switching smoothness. Energy switching smoothness refers to whether the energy management system will cause adverse phenomena such as voltage fluctuations and current shocks when switching between different energy components, affecting the system stability.

[0083] Schematically, when the physical integration index meets the spatial layout requirements, the energy coordination coefficient is within the dynamic response range, and the energy composition is compatible in terms of physical space and energy management, the coordination judgment result is determined to meet the coordination conditions.

[0084] Exemplarily, the energy configuration scheme is a fuel cell and a battery. The fuel cell can be installed in the front cabin, and the battery can be arranged on the chassis, meeting the vehicle space requirements; the fuel cell provides stable endurance, and the battery is responsible for short-term power compensation, with good matching between the two; the energy coordination coefficient indicates that the power conversion between the fuel cell and the battery is stable, without voltage impact. That is, this energy configuration scheme meets the coordination conditions.

[0085] S42. When the physical integration index does not reach the preset spatial layout threshold or the energy coordination coefficient exceeds the dynamic response requirement range, the coordination judgment result is coordination anomaly, and the anomaly marker information corresponding to the coordination anomaly is determined as the violated feasibility theoretical basis.

[0086] Coordination anomaly means that there are conflicts in the physical space or energy matching of the energy configuration, which may lead to installation difficulties or unstable operation. Schematically, when the physical integration does not meet the requirements, it violates the "spatial feasibility" theory: the size of the energy module exceeds the available vehicle space; the mass distribution is seriously uneven, which may affect the balance of the whole vehicle; the connection structure is complex or infeasible; the energy coordination does not meet the requirements; the power characteristics between energy sources do not match; the dynamic response characteristics do not match; the energy switching process may cause voltage fluctuations or system shocks; when any of the above situations occurs, the physical integration index does not reach the preset spatial layout threshold or the energy coordination coefficient exceeds the dynamic response requirement range, then the coordination judgment result is determined as coordination anomaly. Exemplarily, the super capacitor occupies a large area and cannot be installed under the vehicle, and the energy configuration scheme configures multiple batteries, resulting in the physical integration index not reaching the preset spatial layout threshold.

[0087] In one of the embodiments, an executable priority scheme for the energy composition in the corresponding energy configuration scheme is generated, including:

[0088] S51. Generate all priority schemes that match the energy composition in the energy configuration scheme.

[0089] The priority scheme refers to the limited order of power supply of each energy composition under different operating condition types. Schematically, the battery has the priority function, and the fuel cell only supplements when the battery power is insufficient; the fuel cell is the priority energy supply, and the battery and the super capacitor are used as supplements in sequence; the super capacitor is used as the priority scheme to quickly respond to high-power demands, and the battery provides basic energy support as the second priority; solar energy is used as the priority energy supply to meet low-power demand operating conditions. Specifically, according to the different combinations in the energy configuration scheme, list all possible priority combinations.

[0090] S52. Based on different working condition types, screen all priority schemes in accordance with the principles of energy density and power density to obtain executable priority schemes.

[0091] From all possible priority schemes, screen out executable schemes that conform to the principles of energy density and power density to ensure that under different working conditions, each energy component can meet the operation requirements of new energy, and improve operation efficiency and stability.

[0092] Schematically, the principle of energy density ensures that the energy component can provide sufficient endurance energy, and the principle of power density ensures that the energy component can meet the instantaneous high-power demand. Different priority schemes must match the working condition types. Schematically, in the starting working condition, there is an instantaneous high-power demand, requiring the energy component to quickly provide a large amount of power; in the accelerating working condition, there is a continuous high-power demand, requiring the energy component to stably supply sufficient power; in the constant-speed working condition, there is a low-power demand, suitable for an energy source with high energy density but low power output; in the decelerating working condition, there is a kinetic energy recovery demand, requiring the energy system to be able to efficiently recover kinetic energy; in the idle working condition, there is a low-power maintenance demand, requiring a small-power energy source.

[0093] Exemplarily, the energy configuration scheme is a battery, a fuel cell, and a supercapacitor. In the starting working condition, it can be battery priority, fuel cell priority, or supercapacitor priority; based on the principles of energy density and power density, the feasible schemes screened out are that in the starting working condition, it can be battery priority or supercapacitor priority, and these two schemes are used as the executable priority schemes in the starting working condition.

[0094] In one of the embodiments, as Figure 2 shown, generate a multi-energy collaborative drive demonstration effect based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type, including:

[0095] S201. Based on the new energy electric vehicle model, the priority scheme, and the working condition type, calculate the energy management decision to obtain the power distribution strategy; the energy management decision includes the energy optimal decision, the power optimal decision, and the economic optimal decision.

[0096] Based on the new energy electric vehicle model, the selected priority scheme, and the current working condition type, calculate the optimal energy management decision and determine the power distribution strategy to optimize the collaborative working mode of different energy components, and generate a multi-energy collaborative drive with optimal efficiency, endurance, and energy consumption. Specifically, the energy optimal decision aims to maximize the energy utilization efficiency, reduce energy damage, and improve the endurance efficiency. Schematically, according to the characteristics of the energy density, charge-discharge efficiency, etc. of each energy source, combined with the energy demand under the current working condition, calculate how to reasonably distribute the energy to minimize the energy loss. The power optimal decision aims to provide the optimal power output to meet the power demand under different working conditions, and the economic optimal decision aims to reduce the energy cost and improve the operation economy. The power distribution strategy determines the final power ratio and time sequence that each energy source should provide. It can be set that the supercapacitor discharges only when there is a high-power demand, reducing the high-power discharge of the battery and the frequent start-stop of the fuel cell, etc. Exemplarily, for a new energy electric vehicle model with a combination of battery and supercapacitor, under the acceleration condition, calculate the energy strategy management to meet the power optimal strategy, with the supercapacitor providing instantaneous power and the battery maintaining the basic power supply, and the obtained power distribution strategy is that the supercapacitor provides 70% power and the battery provides 30% power.

[0097] S202. According to the power distribution strategy, dynamically adjust the energy flow state of the new energy electric vehicle model to generate the demonstration effect of multi-energy collaborative drive.

[0098] Schematically, through ways such as controlling the switches of energy components and adjusting the parameters of energy conversion devices, realize the reasonable distribution and transmission of energy. Exemplarily, when the power distribution strategy requires the battery to provide the main power for the motor, the system will control the output circuit of the battery to output electrical energy according to the set power; at the same time, if it is necessary to supplement part of the power from the supercapacitor, the connection circuit between the supercapacitor and the motor will be opened, and the discharge parameters of the supercapacitor will be adjusted.

[0099] Optionally, during the process of adjusting the energy flow state, present the dynamic changes in an intuitive way to generate the demonstration effect of multi-energy collaborative drive. The energy state, power output curve, energy flow direction, etc. of each energy source can be displayed through a graphical interface, allowing users to clearly observe the collaborative working conditions of multiple energies under different working conditions. Exemplarily, use lines of different colors to represent the energy flow of different energies, and the thickness of the lines represents the power magnitude; use dynamic charts to show the change of the energy state of each energy source over time.

[0100] In one embodiment, as Figure 3 shown, according to the power distribution strategy, dynamically adjust the energy flow state of the new energy electric vehicle model to generate the demonstration effect of multi-energy collaborative drive, including:

[0101] S301. Determine the timing of switching the priority of the energy composition according to the power distribution strategy to obtain the energy flow path.

[0102] Schematically, according to the power distribution strategy, analyze the sequence and specific flow direction of each energy composition providing or receiving energy under different working conditions. Exemplarily, when the vehicle starts, if the power distribution strategy sets that the supercapacitor provides instant high power first and then the battery supplements continuous energy, then the energy flow path is that the supercapacitor supplies power to the motor first, and then the battery intervenes and supplies power stably. When the vehicle enters the acceleration stage from startup and the supercapacitor's charge drops to a certain extent, switch in a timely manner according to the strategy to let the battery undertake the main power supply task, thereby determining a complete and dynamic energy flow path. Optionally, the energy transmission situation between each energy composition is displayed through energy flow lines of different colors. Specifically, when the fuel cell is the main energy source, start the chemical reaction of the fuel cell, set the output power of the fuel cell, and adjust the voltage converter to increase the voltage to match the motor's requirements. If the battery is the main energy source, set the battery discharge power and adjust the battery output parameters. If the supercapacitor participates in power supply, control the supercapacitor discharge threshold and adjust the short-term discharge strategy. If solar energy is available, dynamically adjust the maximum power point tracking to optimize the solar charging power and decide whether to charge the battery. If there is an energy recovery opportunity, set the supercapacitor charging current to ensure priority storage of regenerative electric energy.

[0103] S302. Adjust the charge and discharge states of the energy composition according to the power distribution strategy to obtain the working state curves of each energy composition.

[0104] Schematically, based on the regulations of the charging and discharging power and time for each energy component in the power distribution strategy, the system records the charging and discharging states of each energy at different times in real time. For the battery, according to parameters such as the charging or discharging current and voltage in the strategy, calculate the change of its power with time; for the fuel cell, based on the relationship between the fuel consumption rate and the power generation power, determine the time change of its output power. Each energy component will generate a working state curve that changes with time, intuitively showing whether it is in a charging, discharging or idle state at different stages, and the specific parameter changes in the corresponding state. Optionally, display the current power output of the battery, capacitor, and fuel cell, and display the vehicle power demand, remaining energy, energy consumption, and charging status in numerical or graphical form. Further, balance the battery power to prevent over-discharge or over-charging. If the minimum battery power is lower than the set threshold, increase the fuel cell energy supply and reduce the battery discharge power. If the solar charging capacity is sufficient, preferentially use solar energy to supplement the battery. If the battery is close to full charge and there is still solar input, reduce the fuel cell power generation; adjust the charging and discharging strategy of the super capacitor. If it is in an acceleration condition, the super capacitor releases energy and the voltage drops rapidly. If it is in a braking condition, the super capacitor is quickly charged and the voltage rises; control the working state of the fuel cell. If the power demand is stable, maintain a constant output of the fuel cell. If the power demand fluctuates, appropriately reduce the dynamic response of the fuel cell to avoid frequent changes affecting its lifespan.

[0105] S303. Calculate the motor power under different working conditions according to the power distribution strategy to obtain the vehicle response state; the vehicle response state includes power limitation and braking energy recovery.

[0106] Schematically, the power distribution strategy calculates the power that the motor should output under this working condition according to the current working condition. When the energy supply cannot meet the maximum power required by the motor under a specific working condition, the vehicle response state is power limitation. Exemplarily, when the vehicle is fully loaded and climbing a slope, if the output power of the energy system is insufficient, the motor cannot reach the ideal speed and the vehicle speed is limited. Under the vehicle deceleration braking condition, if the power distribution strategy includes a braking energy recovery scheme, the system will calculate the size of the recoverable energy and adjust the charging state of the energy component accordingly, converting the braking energy into electrical energy and storing it, thus obtaining the vehicle response state of braking energy recovery.

[0107] S304. Generate a multi-energy collaborative driving demonstration effect by using the energy flow path, the working state curves of each energy component, and the vehicle response state.

[0108] Schematically, the energy flow path, the working state curves of each energy component, and the vehicle response state information are integrated to present the multi - energy collaborative driving demonstration effect in an intuitive way. Through animation simulation, on a virtual new - energy electric vehicle model, different - colored lines are used to represent the energy flow paths of different energies, and the thickness of the lines reflects the power magnitude; the working state curves of each energy component are displayed in the form of a chart on one side of the interface, and the data is updated in real - time; for the vehicle response state, it is presented through the driving performance of the vehicle model and text prompts such as "power limited" and "braking energy recovery in progress". Users can clearly see how the energies work together and how the vehicle responds under different working conditions, and comprehensively understand the operation mechanism of the multi - energy collaborative driving system.

[0109] It should be understood that although the steps in the flowcharts involved in the above - described embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above - described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0110] Based on the same inventive concept, the embodiments of the present application also provide a multi - energy collaborative driving demonstration system for a new - energy electric vehicle teaching aid for implementing the above - mentioned multi - energy collaborative driving demonstration method for a new - energy electric vehicle teaching aid. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above - mentioned method. Therefore, the specific limitations in one or more embodiments of the multi - energy collaborative driving demonstration system for a new - energy electric vehicle teaching aid provided below can refer to the limitations on the multi - energy collaborative driving demonstration method for a new - energy electric vehicle teaching aid in the above text, and will not be repeated here.

[0111] In an exemplary embodiment, as Figure 4 shown, a multi - energy collaborative driving demonstration system for a new - energy electric vehicle teaching aid is provided, including:

[0112] A data acquisition module, configured to acquire an energy configuration plan.

[0113] An energy analysis module, configured to perform a feasibility analysis on the energy configuration plan and generate a corresponding set of output plans.

[0114] A model construction module, configured to construct a new energy electric vehicle model that matches the priority scheme in response to the obtained priority scheme selection instruction.

[0115] A visualization demonstration module, configured to generate a multi - energy collaborative driving demonstration effect based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type each time a working condition type selection is obtained.

[0116] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the above - mentioned method embodiments are implemented.

[0117] In one embodiment, a computer - readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above - mentioned method embodiments are implemented.

[0118] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0119] The above - described embodiments only represent several implementation manners of the embodiments of the present application. Their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the application embodiments. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A multi-energy collaborative driving demonstration method for a new energy electric vehicle teaching aid, characterized in that: The method comprises: Obtaining an energy configuration scheme; the energy configuration scheme includes at least one energy source selected from batteries, fuel cells, supercapacitors and solar energy; Performing a feasibility analysis on the energy configuration scheme to generate a corresponding output scheme set; the output scheme set includes at least one executable priority scheme or infeasibility determination conclusion dynamically generated based on the feasibility analysis result; In response to the acquired priority scheme selection instruction, a new energy electric vehicle model matching the priority scheme is constructed; the new energy electric vehicle model includes a connection relationship, control parameters and energy management logic of each energy component; Each time a working condition type selection is obtained, a multi-energy collaborative driving demonstration effect is generated based on the new energy electric vehicle model and the energy management decision corresponding to the working condition type.

2. The method according to claim 1, characterized in that: The feasibility analysis of the energy configuration scheme is performed to generate a corresponding output scheme set, including: Conducting a feasibility analysis on the energy configuration scheme based on the feasibility theory to obtain a feasibility analysis result; the feasibility analysis result is that the energy configuration scheme is feasible or the energy configuration scheme is not feasible; the feasibility analysis includes coordination judgment, rationality judgment and engineering feasibility judgment; If the feasibility analysis result shows that the energy configuration scheme is feasible, then generating the executable priority scheme corresponding to the energy composition in the energy configuration scheme; the priority scheme includes the operating condition type and its corresponding energy priority; If the feasibility analysis result is that the energy configuration plan is not feasible, an infeasibility determination conclusion is generated; the infeasibility determination conclusion includes the feasibility theoretical basis that has been violated and an infeasibility warning.

3. The method according to claim 2, characterized in that The feasibility analysis of the energy configuration scheme based on the feasibility theory is performed to obtain the feasibility analysis results, including: Performing coordination judgment on the energy configuration scheme, and obtaining a coordination judgment result according to coordination evaluation parameters generated by the coordination judgment; the coordination evaluation parameters include a physical integration index and an energy coordination coefficient; the coordination judgment result includes satisfying coordination conditions or coordination anomalies; Judging whether the energy configuration scheme can cover the operating conditions of the new energy vehicle, and generating a rationality judgment result; the rationality judgment result includes whether the operating conditions of the vehicle can be covered or whether the operating conditions of the vehicle cannot be covered; Determine whether the energy configuration scheme meets the energy management regulation and generates a project feasibility judgment result; the project feasibility judgment result includes whether the energy management regulation is met or not; When the feasibility analysis shows that the coordination conditions are met, the operating conditions of the vehicle can be covered, and the energy management regulation is met, the feasibility analysis result is determined as the energy configuration scheme is feasible; When the feasibility analysis results in any one of the coordination anomaly, the failure to cover the vehicle's operating conditions, or the failure to meet energy management regulation, the feasibility analysis result is determined as the energy configuration plan is not feasible.

4. The method according to claim 3, characterized in that: The coordination judgment result is obtained according to the coordination evaluation parameters generated by the coordination judgment, including: When the physical integration index reaches the preset spatial layout threshold and the energy coordination coefficient meets the dynamic response requirement range, the coordination judgment result is that the coordination condition is met; When the physical integration index does not reach the preset spatial layout threshold or the energy coordination coefficient exceeds the dynamic response requirement range, the coordination judgment result is a coordination exception, and the abnormal marking information corresponding to the coordination exception is determined as the feasibility theoretical basis of the violation.

5. The method according to claim 2, characterized in that: The generating of the executable priority scheme corresponding to the energy composition in the energy configuration scheme includes: Generate all the priority schemes matching the energy composition in the energy configuration scheme; Based on the different types of working conditions, all the priority schemes are screened in accordance with the principles of energy density and power density to obtain the executable priority schemes.

6. The method according to claim 1, characterized in that The energy management decision based on the new energy electric vehicle model and the operating condition type generates a multi-energy collaborative driving demonstration effect, including: Based on the new energy electric vehicle model, the priority scheme and the operating condition type, an energy management decision is calculated to obtain a power allocation strategy; the energy management decision includes an energy optimal decision, a power optimal decision and an economic optimal decision; According to the power allocation strategy, the energy flow state of the new energy electric vehicle model is dynamically adjusted to generate a multi-energy coordinated driving demonstration effect.

7. The method according to claim 6, characterized in that The method of dynamically adjusting the energy flow state of the new energy electric vehicle model according to the power allocation strategy to generate a multi-energy coordinated driving demonstration effect includes: Determine the timing of switching the energy composition priority according to the power allocation strategy to obtain the energy flow path; Adjusting the charging and discharging state of the energy composition according to the power allocation strategy to obtain a working state curve of each energy composition; Calculate the motor power under different working conditions according to the power allocation strategy to obtain the vehicle response state; the vehicle response state includes power limitation and braking energy recovery; The multi-energy collaborative driving demonstration effect is generated by utilizing the energy flow path, the working state curves of the various energy sources and the vehicle response state.

8. A multi-energy coordinated driving demonstration system for a new energy electric vehicle teaching aid, characterized in that: The system comprises: A data acquisition module, used to obtain energy configuration solutions; A data analysis module, used to perform feasibility analysis on the energy configuration scheme and generate a corresponding output scheme set; A model building module, configured to build a new energy electric vehicle model matching the priority scheme in response to the acquired priority scheme selection instruction; The visualization demonstration module is used to generate a multi-energy collaborative driving demonstration effect based on the new energy electric vehicle model and the energy management decision corresponding to the operating condition type each time the operating condition type is obtained.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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