A new energy vehicle energy-power management verification method and system
By constructing a vehicle control model with coupling operator P and energy flow operator E, the problems of model universality and accuracy in energy management technology for new energy vehicles are solved, and efficient energy management and power system optimization across architectures are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JILIN UNIVERSITY
- Filing Date
- 2026-04-28
- Publication Date
- 2026-05-29
AI Technical Summary
Existing energy management technologies for new energy vehicles are inadequate in terms of model universality, accuracy, and adaptability to complex operating conditions, resulting in high R&D costs, low efficiency, insufficient offline management accuracy, and online management latency and geographical limitations.
A vehicle control model is constructed using coupling operator P and energy flow operator E. By simulating or controlling the energy flow of the response, combined with mathematical and statistical analysis, a general model is built and the control strategy is verified across different hybrid power architectures.
It improves the model's versatility and simulation efficiency, enhances the accuracy of energy management and the optimization efficiency of vehicle control strategies, reduces repetitive work, and supports rapid deployment and updates of online management.
Smart Images
Figure CN122113457A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy vehicle technology, specifically to a method and system for verifying energy-power management in new energy vehicles. Background Technology
[0002] Against the backdrop of a global green and low-carbon transformation, the transportation sector is undergoing unprecedented technological innovation, with new energy vehicles, as a crucial component of this transformation, continuously developing and progressing. To achieve more efficient and lower-carbon transportation, intelligent energy management and control technology for vehicles has become a key breakthrough in the new energy vehicle field. Currently, this technology primarily focuses on the powertrain research of hybrid electric vehicles, typically employing a one-vehicle-one-model approach. While this method allows for optimization for specific models, the models lack versatility, hindering their application in the development of different powertrain architectures. Specifically, each new powertrain architecture requires a completely rebuilt model, which not only increases R&D costs and time but also limits the promotion and application of new technologies.
[0003] Currently, common energy management methods are divided into offline and online management. Offline management typically relies on experience to perform linear interpolation and table lookup for energy estimation, but this method has limited accuracy. Online management, on the other hand, combines vehicle-side data with cloud computing. Although this method allows for real-time adjustments, it is also limited by latency and geographical constraints. These issues not only affect the energy efficiency of new energy vehicles but also limit their performance in complex operating conditions and special environments.
[0004] Overall, while current energy management technologies for new energy vehicles have made some progress, they still fall short in terms of versatility, accuracy, and ability to handle complex operating conditions. These issues urgently need to be addressed through more efficient and flexible methods to drive the development of new energy vehicle technology towards greater efficiency and intelligence. Summary of the Invention
[0005] To overcome the aforementioned problems in the prior art, this application provides a verification method and system for energy-power management of new energy vehicles, so as to realize the construction of a universal model and verification of control strategies across different hybrid power architectures, thereby improving the efficiency and accuracy of energy management.
[0006] According to a first aspect of the present invention, a method for verifying energy-power management of new energy vehicles is provided, the method comprising: Simulation or control is performed in response to the energy-power system architecture of the target new energy vehicle. A vehicle control model is constructed, which includes a coupling operator P and an intrinsic algorithm M; the coupling operator P is used to define the energy flow direction, distribution ratio and conversion loss between nodes; A general simulation verification architecture is determined based on the coupling operator P and the energy flow operator E, and it is defined as several physical field coupling nodes and energy conversion nodes; the energy flow operator E includes the actual energy value and the historical feature number composed of the product of the feature numbers of each coupling operator P it passes through; The vehicle dynamics response is calculated in response to the energy provided by the energy flow operator E; In response to driver commands, the movement of the energy flow operator E and the parameters of the coupling operator P are controlled by the vehicle control model. After operation, the energy flow operator E in each node is subjected to mathematical statistical analysis to quantitatively evaluate the efficiency of different energy flow paths.
[0007] According to a second aspect of the present invention, a new energy vehicle energy-power management verification system is provided, comprising: The simulation control module is used to simulate or control the energy-power system architecture of the target new energy vehicle. The vehicle control module is used to construct a vehicle control model that includes a coupling operator P and an intrinsic algorithm M; the coupling operator P is used to define the energy flow direction, distribution ratio and conversion loss between nodes. The energy flow module is used to determine a general simulation verification architecture based on the coupling operator P and the energy flow operator E, and defines it as several physical field coupling nodes and energy conversion nodes; the energy flow operator E includes the actual energy value and the historical feature number composed of the product of the feature numbers of each coupling operator P it passes through; The vehicle dynamics-road state module is used to calculate the vehicle dynamic response in response to the energy provided by the energy flow operator E; The driver instruction module is used to respond to driver instructions by controlling the movement of the energy flow operator E and adjusting the parameters of the coupling operator P through the vehicle control model. The mathematical statistics module is used to perform mathematical statistics analysis on the energy flow operator E in each node after operation, so as to quantitatively evaluate the efficiency of different energy flow paths.
[0008] The beneficial effects of this invention are as follows: 1. By introducing the coupling operator P and the energy flow operator E, a general verification method for the power-energy management of new energy vehicles is constructed, which can significantly improve the versatility of the model and reduce repetitive work between different hybrid architectures; 2. By transforming the problem of solving complex multiphysics differential-algebraic equations into the tracking and statistical analysis of standardized operators and energy flow paths, the complexity of the whole vehicle control model is simplified, and the computational efficiency of simulation and verification is improved; 3. This application can be used for offline high-precision simulation verification, and its modular concept also provides a basis for the rapid deployment and updating of online energy management, thereby improving the overall efficiency and quality of the research and development of energy-power systems for new energy vehicles. Attached Figure Description
[0009] Figure 1 This is a schematic flowchart of a new energy vehicle energy-power management verification method provided in an embodiment of the present invention;
[0010] Figure 2 This is a schematic diagram of the method flow for simulation and control provided in the embodiments of the present invention;
[0011] Figure 3 A typical example schematic diagram of the vehicle control model provided in the embodiments of the present invention;
[0012] Figure 4 This is a schematic diagram of an energy-power management verification system architecture for new energy vehicles, provided as an embodiment of the present invention. Detailed Implementation
[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.
[0014] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.
[0015] Traditional research on energy-power management technologies for new energy vehicles generally adopts a one-vehicle-one-model approach, resulting in poor model versatility. When developing different power system architectures, it is often necessary to rebuild the model, increasing R&D costs and time. In addition, in terms of energy management, offline management usually relies on linear interpolation and table lookup based on experience, resulting in low accuracy; while online management depends on vehicle-side data and cloud computing, which has latency and significant geographical limitations, making it difficult to achieve efficient and real-time energy scheduling.
[0016] In response, this application proposes a verification method and system for energy-power management of new energy vehicles, see reference. Figure 1The method includes: simulating or controlling the energy-power system architecture of the target new energy vehicle in response; constructing a vehicle control model containing a coupling operator P and an intrinsic algorithm M; the coupling operator P is used to define the energy flow direction, distribution ratio, and conversion loss between nodes; determining a general simulation verification architecture based on the coupling operator P and the energy flow operator E, and defining it as several physical field coupling nodes and energy conversion nodes; the energy flow operator E contains the actual energy value and the historical characteristic number composed of the product of the characteristic numbers of each coupling operator P it passes through; calculating the vehicle dynamic response in response to the energy provided by the energy flow operator E; controlling the movement of the energy flow operator E and adjusting the parameters of the coupling operator P through the vehicle control model in response to driver commands; and performing mathematical statistical analysis on the energy flow operator E in each node after operation to quantitatively evaluate the efficiency of different energy flow paths.
[0017] For ease of understanding, the following explains some key terms in this embodiment: The coupling operator P is used to define the direction of energy flow, the distribution ratio, and the conversion loss between nodes; it can be regarded as the interface between physical field coupling nodes and energy conversion nodes, and is used to describe the behavior of energy in different physical fields or conversion processes.
[0018] The intrinsic algorithm M is used to respond to the inflow and outflow of energy flow operator E, modify the parameters of the corresponding physical field coupling nodes, and dynamically adjust the parameters of coupling operator P. For example, when a large amount of energy flows in or out in a short period of time, intrinsic algorithm M can adjust the parameters of coupling operator P according to the actual situation to reflect the change in energy conversion efficiency.
[0019] The energy flow operator E is used to represent the flow of energy in the system; it includes the actual energy value and a historical characteristic number, which is the product of the characteristic numbers of each coupled operator P it passes through. The actual energy value represents the magnitude of the energy, while the historical characteristic number is used to trace the path of the energy flow.
[0020] Physics coupling nodes and energy conversion nodes are components of a general simulation verification architecture. Physics coupling nodes represent energy interaction points between different physical fields (such as electric fields, mechanical fields, and chemical fields), while energy conversion nodes represent points where energy forms are converted (such as batteries, motors, and internal combustion engines).
[0021] Example 1 This embodiment provides a verification method for energy-power management of new energy vehicles, and the specific implementation method is as follows: S1. Simulate or control the energy-power system architecture of the target new energy vehicle. This process includes analyzing the overall architecture of the new energy vehicle's power system and determining the connection relationships and energy transfer paths between various energy nodes.
[0022] Specifically, see Figure 2 When performing simulations, the initial input data includes the total energy of the high-voltage electrical coupling node, the total energy of the chemical energy coupling node, and the remaining fuel and power battery energy under simulated cold-start conditions. Driver simulation signals such as accelerator pedal depth and regenerative braking gear are also input. Simulated road surface parameters, including slope and road adhesion, are also input. The vehicle control model schedules energy according to the vehicle control algorithm, i.e., the flow of energy flow operators. Source analysis is performed on the energy flow operators in each physical field coupling node to obtain key parameters such as the dependency coefficients between the nodes. These key parameters are then fed back to the vehicle control module, and the vehicle control algorithm is optimized to ensure that the new energy vehicle operates under the energy scheduling strategy with the highest energy utilization rate.
[0023] During control, data read from various sensors, such as DC bus voltage and current, fuel flow meter readings, vehicle speed, and vehicle attitude, are input and converted into energy flow operators E. The vehicle control model schedules energy according to the vehicle control algorithm, i.e., the flow of energy flow operators; source analysis is performed on the energy flow operators in each physical field coupling node to obtain key parameters such as the dependency coefficients between each physical field coupling node; these key parameters, such as the dependency coefficients, are fed back to the vehicle control module, and the vehicle control algorithm is optimized to enable the new energy vehicle to operate under the energy scheduling strategy with the highest energy utilization rate.
[0024] S2, see reference Figure 3 A vehicle control model is constructed that includes a coupling operator P and an intrinsic algorithm M. The coupling operator P is used to define the energy flow direction, distribution ratio and conversion loss between nodes.
[0025] Specifically, the coupling operator P can be represented as [T p T t K η p η c In the form of ]
[0026] Where T represents the eigenvalue of the coupling operator P, T p T represents the characteristic number of a coupling operator P itself, which can be understood as the identity identifier of the coupling operator. It is generally represented by a prime number and is used to mark the path of the energy flow operator E. t The target coupling operator characteristic number is used to represent a certain coupling operator P, which can be understood as the identity identifier of the target coupling operator. K represents the allocation coefficient of the coupling operator P, which is used to represent the direction and proportional relationship of the energy flow operator E. If the physical field coupling node is one inlet and two outlets, then K=1 at the inlet and K1+K2=-1 at the outlet.
[0027] η pη represents the energy conversion efficiency coefficient, indicating the energy conversion efficiency of the energy flow operator E when it passes through a certain coupling operator P. c η represents the controllable energy dissipation coefficient. According to the laws of physics, the energy conversion efficiency is less than 1. During energy conversion, some energy will always dissipate, for example, as heat. For this portion of energy, certain devices are designed to guide it to its proper destination, such as radiators or heat recovery devices. However, the energy transfer efficiency for this portion is also less than 1, therefore η... p +η c Less than 1.
[0028] Different intrinsic algorithms M can be used to implement this for different internal combustion engines and power batteries. The specific function of the intrinsic algorithm M is to respond to the inflow and outflow of energy flow operator E and dynamically adjust the various parameters of coupling operator P. Taking power battery as an example, as energy flows in or out in large quantities in a short period of time, its temperature rises and the energy conversion efficiency changes; the vehicle control model should correctly respond to these changes and perform reasonable energy scheduling based on the energy flow relationship.
[0029] S3. Based on the coupling operator P and the energy flow operator E, a general simulation verification architecture is determined and defined as several physical field coupling nodes and energy conversion nodes; the energy flow operator E includes the actual energy value and the historical characteristic number composed of the product of the characteristic numbers of each coupling operator P it passes through.
[0030] The energy flow operator E can be represented in the form of [RW], where R represents the historical feature number of the energy flow operator E, which is equal to the product of the feature numbers of each coupling operator P it passes through, and is used to trace the path of the energy flow operator E; W represents the true value of the energy flow operator E, with the same physical meaning as Joule (J).
[0031] For example, taking a high-voltage power coupling node as an example, the various physical field coupling nodes and energy conversion nodes can be described in matrix form, as shown in the matrix expression below: ;
[0032] Wherein, P0 represents the coupling operator inside the physical field coupling node, which means the loss during energy storage, such as the self-discharge characteristics of a battery. P1, P2, P3, P4, P5, and P6 represent the coupling operators for energy inflow or outflow, corresponding to different destinations and different sources.
[0033] The energy conversion node, taking the internal combustion engine as an example, can be described by the following formula: ;
[0034] Where P1, P2, P3, and P4 represent coupling operators for energy inflow or outflow, and TP This represents the eigenvalues of the coupling operator; since P1~P4 are all used to describe the internal combustion engine, their T... P All values are equal. T P1 T P2 T P3 T P4 These represent the high-voltage electrical energy coupling point, the chemical energy coupling point, the low-voltage electrical energy coupling point, and the mechanical energy coupling point, respectively. K represents the energy distribution coefficient. Since energy coupling nodes do not have the function of storing energy, therefore: .
[0035] For an energy coupling node, it is defined as consisting of n coupling operators, where i represents the number of a certain coupling operator, i.e., the n coupling operators are numbered 1, 2, 3, ..., n.
[0036] S4. Calculate the vehicle's dynamic response in response to the energy provided by the energy flow operator E. In this step, the real-time dynamic response of the vehicle is calculated based on the mechanical energy input represented by the energy flow operator E. This includes a longitudinal dynamics model of the vehicle, used to calculate the vehicle's driving force, drag, acceleration, and velocity. The vehicle dynamics model receives the input from the energy flow operator E and calculates the vehicle's dynamic behavior based on vehicle parameters such as mass, drag coefficient, and rolling resistance coefficient.
[0037] S5. In response to driver commands, the movement of the energy flow operator E and the parameters of the coupling operator P are controlled through the vehicle control model. Driver commands obtained by simulating different styles of real drivers and assisted driving are used to control the movement of the energy flow operator E and the changes of various parameters of the coupling operator P according to the driver commands.
[0038] For example, from the perspective of the energy flow operator E, taking the process of an energy flow operator E0 flowing from a high-voltage energy coupling node to a mechanical energy coupling node as an example, we can illustrate the energy flow mode: Given a primitive energy flow operator E0=[RW];
[0039] Leaving the high-voltage power coupling node ; When passing through the motor and its controller, it will pass through the coupling operator. ; When entering the mechanical energy coupling node, it passes through the coupling operator. ;
[0040] ;
[0041] This represents the energy flow operator E0 after passing through three coupling operators. It indicates that the historical eigenvalues of the energy flow operator E need to be multiplied by the corresponding coupling operator P eigenvalues only when the energy flow operator E enters a physical field coupling node or energy conversion node.
[0042] The flow of energy flow operator E can be viewed from the perspective of a certain energy conversion node, taking an internal combustion engine as an example: Given an energy flow operator E0=[RW] flowing into an internal combustion engine; ;
[0043] That is, E0 is transformed into three energy flow operators: E2, E3, and E4. P1 represents the coupling operator that enters the internal combustion engine from the chemical energy coupling node, P2 represents the energy flow operator that flows out of the internal combustion engine and points to the mechanical energy coupling node, P3 represents the energy flow operator that flows out of the internal combustion engine and points to the low-voltage electrical energy coupling node, and P4 represents the energy flow operator that flows out of the internal combustion engine and points to the high-voltage chemical energy coupling node.
[0044] To limit the number of energy flow operators, an energy flow operator clearing mechanism and an energy flow operator merging mechanism are introduced to minimize the number of energy flow operators while ensuring computational accuracy.
[0045] The energy flow operator removal mechanism can be understood as removing an energy flow operator when the actual energy magnitude of that operator is negligible for the analysis of energy flow. The judgment criteria are as follows: ;
[0046] W represents the actual energy magnitude of the energy flow operator. This represents the sum of the actual energy of all energy flow operators flowing into or out of a physical field coupling point per unit time. α represents the determination coefficient; the larger the value, the smaller the system error and the more computational resources required; the smaller the value, the larger the system error and the less computational resources required.
[0047] Energy flow operator merging mechanism: When two energy flow operators have the same R value, the two energy flow operators can be merged, and the actual energy value of the new energy flow operator is equal to the sum of the actual energy values of the merged energy flow operators.
[0048] S6. Perform mathematical statistical analysis on the energy flow operator E in each node after operation to quantitatively evaluate the efficiency of different energy flow paths.
[0049] Specifically, after running for a period of time, the energy flow operator E in each physical field coupling node is analyzed. By factoring the historical characteristic numbers of the energy flow operator E, the sequence of coupling operators it flows through is traced, thereby analyzing the distribution of energy flow paths. The frequency of occurrence of the characteristic number of the target coupling operator P1 is statistically analyzed to identify hotspot paths and key energy conversion nodes. For example, for any coupling operator P, the characteristic number T... Pm The number of times N appears can be counted. Tm and proportion H Tm Similarly, the degree of dependence of a given coupled node on other coupled nodes can be statistically determined.
[0050] Taking a mechanical energy coupling node after running for time t as an example, it contains several Since R represents the historical eigenvalue of the energy flow operator E, and its value is equal to the product of the eigenvalues of the coupling operators P it passes through, it can be factored. ;
[0051] According to i n Mathematical statistics can be used to statistically analyze the heatmaps of all energy flow operators arriving at the mechanical energy coupling point. For any coupling operator, the eigenvalues... It can count the number of times it appears. Proportion ,as follows ;
[0052] Among them, i m This represents the total number of occurrences of a certain coupling operator's characteristic number. This represents the proportion of a certain coupling operator's characteristic number. This formula applies to all energy flow operators at a given energy coupling node. For example, after a period of operation, a mechanical energy coupling node has three energy flow operators with historical characteristic numbers of 18, 50, and 75 respectively. Factoring reveals that N... T2 =2;N T3 =3;N T5 ==3, meaning 2 appeared twice, 3 appeared three times, and 5 appeared three times. H Tm The meaning is similar. The subscript m can be understood as any one, where m can represent the number of any coupling operator.
[0053] Similarly, the dependence of a given coupled node on other coupled nodes can be statistically determined, and the total energy W of the coupled nodes can be calculated. total : ;
[0054] The energy flow operator E can then be determined. mActual energy size W m Total energy W total Ratio: ;
[0055] We also need to determine the dependence coefficient of a certain energy flow operator on a certain physical field coupling node. : ;
[0056] Among them, i m This represents the number of times a certain characteristic number appears in the historical characteristic number of an energy flow operator. For example, if the historical characteristic number of a certain energy flow operator is 120, and 120 = 2 * 2 * 2 * 3 * 5, then i2 = 3, and the dependency coefficient is equal to 3 / 5 = 0.6.
[0057] The energy flux dependence coefficient of a certain energy flux operator on a certain physical field coupling node can then be calculated. : ;
[0058] The dependence coefficient U of the coupling node of this physical field on other physical fields can then be calculated. p : ;
[0059] Based on this coefficient, the dependencies between coupling nodes of different physical fields can be analyzed. The higher the coefficient, the stronger the dependency between the two coupling nodes. Therefore, the energy conversion efficiency between the two coupling nodes should be high. This can be achieved by using energy conversion efficiency paths and improving the energy conversion efficiency on a certain path. Specifically, the vehicle control module can be used to rationally schedule different energy conversion paths.
[0060] This embodiment effectively addresses the problems of poor versatility, repetitive modeling, low accuracy in offline management, and latency and geographical limitations in online management of traditional new energy vehicle energy-power management models by constructing a general simulation verification architecture and dynamic control mechanism. Therefore, this method significantly improves the model's versatility, reduces repetitive work between different hybrid architectures, and, through mathematical statistical analysis of energy flow operators, achieves quantitative evaluation of the efficiency of different energy flow paths, providing data support for optimizing vehicle control strategies, thereby improving the efficiency and accuracy of new energy vehicle energy-power management.
[0061] This application further proposes that the coupling operator P includes its own eigenvalues and allocation coefficients, as well as energy conversion efficiency and controllable energy dissipation coefficients.
[0062] The characteristic number of the coupling operator P serves as its unique identifier, marking the energy flow path and ensuring that each energy flow path in a complex energy network can be accurately traced and identified. The allocation coefficient of the coupling operator P precisely defines the proportion of input energy distributed across different output paths when energy flows through a physical field coupling node with multiple output paths. The energy conversion efficiency of the coupling operator P quantifies the degree of effective utilization of energy as it is converted from one form to another during the coupling operator's operation. The controllable energy dissipation coefficient of the coupling operator P represents the proportion of energy that dissipates in an unintended form (such as heat) during energy conversion but can be managed or recovered through specific design or control strategies. Through the synergistic effect of these parameters, the energy-power management verification method can perform high-precision modeling and simulation of energy flow under different new energy vehicle architectures, thereby optimizing energy management strategies, improving energy utilization efficiency, and providing more reliable and refined data support for the development and verification of vehicle control algorithms.
[0063] This application further proposes a mathematical statistics method, which includes, but is not limited to, factoring the historical characteristic number of the energy flow operator E to trace the sequence of coupling operators it flows through, thereby analyzing the distribution of energy flow paths; and identifying hotspot paths and key energy conversion nodes of energy flow by statistically analyzing the frequency of occurrence of the characteristic number of the target coupling operator P1.
[0064] In this application, mathematical statistics serves as a core analytical tool. Its role is to extract valuable information from the massive amounts of energy flow operator E data generated during simulation or control operation, thereby quantitatively evaluating the efficiency of different energy flow paths and identifying key energy conversion links in the system. The historical characteristic number of the energy flow operator E is the product of the characteristic numbers of each coupling operator P it passes through in the energy-power system architecture. Factoring this historical characteristic number allows for the reverse reconstruction of the complete path traversed by the energy flow operator E, i.e., identifying which coupling operators P it sequentially passed through.
[0065] After factoring the historical characteristic numbers of the energy flow operator E, the characteristic numbers of all the coupling operators P it traverses can be obtained. Tracing the sequence of coupling operators it passes through helps to further clarify the order of these coupling operators P in the energy flow path, thus accurately reconstructing the complete and ordered path that the energy flow operator E has traversed from its generation to its current position. After tracing a large number of coupling operator sequences of energy flow operators E, analyzing the distribution of energy flow paths helps to comprehensively understand the flow patterns, branching and merging of energy in the entire energy-power system architecture, as well as the frequency and importance of different paths. Statistical analysis of the frequency of occurrence of the characteristic numbers of the target coupling operator P1 quantifies the frequency or importance of this coupling operator P1 in the energy flow process, thereby identifying hotspots in the system.
[0066] This application further proposes that the construction and identification of energy flow paths depend on the characteristic number and allocation coefficient in the coupling operator P; the characteristic number serves as a path marker, and their product constitutes the historical characteristic number of the energy flow operator E; the allocation coefficient responds to the physical field coupling node with multiple outlets, defines the distribution ratio of input energy on different output paths, and determines the branching and merging of energy flow paths.
[0067] Specifically, this step aims to track and understand the energy flow within the system. Its core lies in utilizing specific information contained in the coupling operator P, namely its eigenvalues and distribution coefficients. The eigenvalues are unique identifiers assigned to each coupling operator P, similar to an identity card, and are typically prime numbers or other unique codes. As the energy flow operator E flows through different coupling operators P, its historical eigenvalues are cumulatively recorded as a product of these eigenvalues. Furthermore, the distribution coefficients are an important parameter of the coupling operator P, especially when the physical field coupling node has multiple energy output paths. They determine how the total energy input to the node is proportionally distributed across the various output paths.
[0068] Using the aforementioned characteristic numbers and distribution coefficients, the branching and converging of energy flow paths can be clearly defined. Branching refers to energy flowing from one node to multiple different nodes, while converging refers to energy from multiple different nodes converging at a single node.
[0069] This application further proposes a method to calculate the real-time dynamic response of a vehicle in response to the mechanical energy input characterized by the energy flow operator E. This method may include a longitudinal dynamics model of the vehicle to calculate the driving force, driving resistance, acceleration, and velocity of the vehicle.
[0070] Specifically, the energy flow operator E not only carries the actual energy value W, but also records the energy flow path in the system through its historical characteristic number R. When the energy flow operator E flows through a specific physical field coupling node or energy conversion node and is identified as mechanical energy, the actual energy value W it carries constitutes the mechanical energy input. This mechanical energy input is a direct reflection of the energy conversion and transfer within the system, and its value changes dynamically, reflecting the magnitude of the mechanical energy output by the vehicle's power system in real time.
[0071] Calculating a vehicle's real-time dynamic response refers to dynamically evaluating the vehicle's motion state and performance based on its current mechanical energy input. This typically involves analyzing the various forces acting on the vehicle under specific operating conditions and deriving the vehicle's kinematic parameters based on physical laws. A possible longitudinal dynamics model is a mathematical model describing the vehicle's motion along the direction of travel. This model usually treats the vehicle as a point mass, considering its mass, inertia, and various longitudinal forces acting on it.
[0072] This model is used to calculate a vehicle's driving force, rolling resistance, acceleration, and speed. Driving force refers to the force generated by the vehicle's powertrain and transmitted to the wheels through the transmission system, ultimately propelling the vehicle forward. Its magnitude directly affects the vehicle's acceleration and climbing ability. Rolling resistance refers to all forces that impede the vehicle's motion during operation, primarily including rolling resistance, air resistance, and gradient resistance. Acceleration is the rate of change of vehicle speed over time, reflecting the strength of the vehicle's dynamic performance. Speed refers to the speed of the vehicle relative to the ground and is the most fundamental parameter of the vehicle's motion. By accurately calculating these parameters, the dynamic performance of the vehicle under different energy inputs can be comprehensively and quantitatively evaluated.
[0073] Example 2 In existing technologies, hybrid electric vehicle powertrain systems generally adopt a one-vehicle-one-model approach, which requires repeatedly building models when developing different powertrain architectures, significantly increasing R&D costs and time. At the same time, offline management usually relies on experience for linear interpolation and table lookup, resulting in poor accuracy; while online management is limited by the interaction between vehicle-side data and cloud computing, resulting in communication delays and difficulty in adapting to different regional driving environments, thus failing to achieve efficient and real-time energy scheduling.
[0074] In response, this application proposes a new energy vehicle energy-power management verification system, see reference. Figure 4 It includes a simulation control module, a vehicle control module, an energy flow module, a vehicle dynamics-road condition module, a driver command module, and a mathematical statistics module.
[0075] The simulation control module is used to simulate or control the energy-power system architecture of the target new energy vehicle. The simulation control module can flexibly adjust the simulation environment parameters according to different new energy vehicle architecture configurations, ensuring the consistency between the verification system and the actual vehicle system.
[0076] The vehicle control module constructs a vehicle control model that includes a coupling operator P and an intrinsic algorithm M. The coupling operator P defines the energy flow direction, distribution ratio, and conversion loss between nodes, and is the core algorithm for system energy management. The intrinsic algorithm M is responsible for dynamically correcting the parameters of the coupling operator to adapt to changes in energy conversion efficiency under different operating conditions. For example, when the power battery discharges rapidly in a short period, the intrinsic algorithm M adjusts the parameters of the relevant coupling operators in real time based on the efficiency changes caused by the rise in battery temperature.
[0077] The energy flow module, based on the coupling operator P and the energy flow operator E, establishes a general simulation verification architecture, defining it as several physical field coupling nodes and energy conversion nodes. In the energy flow module, the energy flow operator E contains the actual energy value and a historical characteristic number, which is the product of the characteristic numbers of each coupling operator P it passes through. The historical characteristic number R of the energy flow operator E records the complete path information of the energy flow, facilitating subsequent energy flow path analysis. The physical field coupling nodes in the energy flow module include high-voltage electrical energy coupling points, low-voltage electrical energy coupling points, mechanical energy coupling points, and chemical energy coupling points, while the energy conversion nodes include internal combustion engines, electric motors, and their controllers.
[0078] The Vehicle Dynamics - Road State module calculates the vehicle's dynamic response based on the energy provided by the energy flow operator E. This module receives the energy flow operator E from the energy flow calculation module and calculates the vehicle's real-time dynamic response based on the mechanical energy input represented by the energy flow operator E. The Vehicle Dynamics - Road State module includes a longitudinal dynamics model of the vehicle, used to calculate the vehicle's driving force, drag, acceleration, and velocity. Through these calculations, the system can accurately simulate the vehicle's driving state under various road conditions and driving conditions.
[0079] The driver instruction module, responding to driver commands, controls the movement of the energy flow operator E and adjusts the parameters of the coupling operator P through the vehicle control model. This module is configured to receive and process control signals from a simulated driver or driver assistance system to generate instructions for the vehicle control module. These instructions directly affect the direction and distribution of energy within the system, thereby achieving precise control over the vehicle's dynamic performance. The driver instruction module can simulate different driving styles.
[0080] The mathematical statistics module performs mathematical statistical analysis on the energy flow operator E within each node after operation to quantitatively evaluate the efficiency of different energy flow paths. This module's analysis includes, but is not limited to, factoring the historical characteristic numbers of the energy flow operator E to trace the sequence of coupling operators it passes through, thereby analyzing the distribution of energy flow paths; and identifying hotspot paths and key energy conversion nodes by statistically analyzing the frequency of occurrence of the characteristic numbers of the target coupling operator P1.
[0081] In this verification system, the construction and identification of energy flow paths depend on the eigenvalues and allocation coefficients in the coupling operator P. The eigenvalues serve as path markers, and their products constitute the historical eigenvalues of the energy flow operator. The allocation coefficients respond to the physical field coupling nodes with multiple outlets, defining the distribution ratio of input energy on different output paths and determining the branching and merging of energy flow paths.
[0082] In a preferred embodiment, the system introduces an energy operator clearing mechanism and an energy operator merging mechanism to reduce the number of energy operators and improve system computational efficiency while ensuring computational accuracy. The energy operator clearing mechanism clears energy operators when their actual energy magnitude is negligible, based on the following criteria: ; W represents the actual energy magnitude of the energy operator. This represents the sum of the actual energy values of all energy operators flowing into or out of a physical field coupling point per unit time. Here, α is the determination coefficient; a larger value results in smaller system errors but requires more computational resources. The energy operator merging mechanism merges two energy operators when their historical characteristic numbers R are identical, and the actual energy value of the new energy operator is equal to the sum of the actual energy values of the merged energy operators.
[0083] In a preferred embodiment, the present application further proposes to include a driver instruction module configured to receive and process control signals from a simulated driver or driver assistance system to generate instructions for the vehicle control module.
[0084] Specifically, the driver instruction module, a key component of the simulation verification system, functions as a bridge between external control inputs and the vehicle's core control logic. It is configured to receive and process control signals from the simulated driver or driver assistance systems. This allows the system to acquire diverse control inputs and transform them into a form usable by the vehicle control module. For example, when receiving signals from the simulated driver, the module can acquire real-time operational data from the simulated driver in the simulation environment, such as slope and road surface adhesion, and perform necessary preprocessing on these digital signals. Conversely, when receiving control commands from Advanced Driver Assistance Systems (ADAS) or Autonomous Driving Systems (ADS), such as target vehicle speed, target acceleration, or target steering angle, the driver instruction module parses and verifies these commands to ensure they comply with the vehicle's operating logic and safety regulations.
[0085] After the above processing, the driver instruction module further generates instructions for the vehicle control module. These instructions are standardized and refined, enabling the vehicle control module to directly recognize and execute them to manage energy flow and adjust coupling operator parameters.
[0086] The system provided in this embodiment can comprehensively evaluate the energy flow efficiency of new energy vehicles, identify key nodes and hotspot paths of energy conversion, and provide data support and theoretical basis for optimizing vehicle energy management strategies.
[0087] Example 3 This example aims to illustrate how a new energy vehicle energy-power management verification method can be applied to the energy management strategy optimization of a plug-in hybrid electric vehicle (PHEV). This PHEV has a series-parallel hybrid system architecture, including key components such as an internal combustion engine, generator, drive motor, power battery, transmission, and wheels.
[0088] S1. Perform simulation or control of the energy-power system architecture of this PHEV. Simulation is typically used for verification during the R&D phase.
[0089] S2. Construct the vehicle control model. This model includes a series of coupling operators P and intrinsic algorithms M. For example, each key component or energy conversion link, such as the internal combustion engine, generator, drive motor, power battery, transmission, and wheels, is abstracted as one or more coupling operators P. Each coupling operator P is used to define the flow direction, distribution ratio, and conversion loss of energy between nodes. For example, the coupling operator P of the internal combustion engine defines the efficiency of converting fuel chemical energy into mechanical energy, the proportion of mechanical energy allocated to the generator or directly driving the wheels, and the heat loss during the conversion process. The intrinsic algorithm M is used to dynamically adjust the parameters of the corresponding coupling operator P according to real-time operating conditions (such as battery temperature, engine speed, and load), such as correcting the battery charging and discharging efficiency or the fuel consumption rate of the internal combustion engine. This construction method allows for different hybrid architectures without having to build the entire model from scratch; only the corresponding coupling operators P and their intrinsic algorithms M need to be adjusted or replaced, greatly improving the model's versatility and thus solving the limitations of the traditional one-vehicle-one-model approach.
[0090] S3. Based on the constructed coupling operator P and energy flow operator E, determine the general simulation verification architecture. This architecture is defined as several physical field coupling nodes and energy conversion nodes. Physical field coupling nodes can include high-voltage power buses, mechanical transmission chains, fuel tanks, etc., while energy conversion nodes correspond to internal combustion engines, electric motors, power batteries, etc. The energy flow operator E is an abstract representation of energy, containing the actual energy value W (e.g., in joules) and a historical characteristic number R, which is the product of the characteristic numbers of each coupling operator P it passes through. For example, if an energy flow operator E flows out of the fuel tank, through the internal combustion engine, and then through the generator, its historical characteristic number R will be the product of the characteristic numbers T of the coupling operators P corresponding to the fuel tank, internal combustion engine, and generator. The coupling operator P itself contains its characteristic number T (as a unique identifier, usually a prime number), allocation coefficient K, and energy conversion efficiency η. p and controllable energy dissipation coefficient η c The distribution coefficient K, at a physical field coupling node with multiple outlets, defines the proportion of input energy distributed along different output paths, thus determining the branching and merging of energy flow paths. For example, at a power splitter, K determines how much of the mechanical energy output from the internal combustion engine flows to the generator and how much flows to the wheels.
[0091] S4. Calculate the vehicle's dynamic response in response to the energy supplied by the energy flow operator E. When the mechanical energy input represented by the energy flow operator E reaches the wheels, the vehicle dynamics-road state module calculates the vehicle's real-time dynamic response based on this, including the vehicle's driving force, drag, acceleration, and velocity. This is typically achieved through a longitudinal dynamics model of the vehicle.
[0092] S5. During simulation, in response to driver commands, the vehicle control model controls the movement of the energy flow operator E and adjusts the parameters of the coupling operator P. For example, when the simulated driver issues an acceleration command, the vehicle control model adjusts the parameters of the coupling operator P between the internal combustion engine and the drive motor according to the current operating conditions using the intrinsic algorithm M, increasing output power and guiding more energy flow operators E from the power battery to the drive motor, or from the internal combustion engine to the wheels, to meet acceleration requirements. The driver command module can receive and process control signals from the simulated driver or the driver assistance system to generate commands for the vehicle control module.
[0093] S6. After the simulation runs, perform mathematical statistical analysis on the energy flow operators E within each node to quantitatively evaluate the efficiency of different energy flow paths. The mathematical statistical analysis includes factoring the historical characteristic number R of the energy flow operator E to trace the sequence of coupling operators it flows through, thereby analyzing the distribution of energy flow paths. For example, by factoring the historical characteristic number R, it can be identified whether energy flows from the fuel-internal combustion engine-generator-battery path or the fuel-internal combustion engine-wheel path. By statistically analyzing the frequency of the characteristic number T of the target coupling operator P1 (e.g., the drive motor), hotspot paths and key energy conversion nodes can be identified. For example, if a large number of energy flow operators E frequently flow through the drive motor, it indicates that the drive motor is a key node in energy flow, and its efficiency has a significant impact on the overall vehicle performance.
[0094] Compared to existing technologies that only mention energy flow analysis without providing specific methods, this embodiment offers more accurate and traceable analytical capabilities. By identifying inefficient energy flow paths or key loss nodes, it can guide the vehicle control algorithm module to adjust its energy scheduling strategy. For example, under specific operating conditions, it can prioritize more efficient energy paths or optimize the operating strategies of key nodes. This directly searches for the most efficient energy flow path to adjust the energy scheduling strategy, rather than simply comparing it with a preset energy flow. This method not only improves the optimization accuracy of energy management strategies but also provides a universal and efficient tool for the development of different hybrid architectures and the support of lifecycle control strategy algorithms.
[0095] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A verification method for energy-power management of new energy vehicles, characterized in that, The method includes: Simulation or control is performed in response to the energy-power system architecture of the target new energy vehicle. A vehicle control model is constructed, which includes a coupling operator P and an intrinsic algorithm M; the coupling operator P is used to define the energy flow direction, distribution ratio and conversion loss between nodes; A general simulation verification architecture is determined based on the coupling operator P and the energy flow operator E, and it is defined as several physical field coupling nodes and energy conversion nodes; the energy flow operator E includes the actual energy value and the historical feature number composed of the product of the feature numbers of each coupling operator P it passes through; The vehicle dynamics response is calculated in response to the energy provided by the energy flow operator E; In response to driver commands, the movement of the energy flow operator E and the parameters of the coupling operator P are controlled by the vehicle control model. After operation, the energy flow operator E in each node is subjected to mathematical statistical analysis to quantitatively evaluate the efficiency of different energy flow paths.
2. The verification method for energy-power management of new energy vehicles according to claim 1, characterized in that, The coupling operator P includes the characteristic number of the coupling operator, the allocation coefficient of the coupling operator, the energy conversion efficiency, and the controllable energy dissipation coefficient.
3. The verification method for energy-power management of new energy vehicles according to claim 1, characterized in that, The mathematical statistics mentioned include, but are not limited to, factoring the historical characteristic numbers of the energy flow operator E to trace the sequence of coupling operators it passes through and analyze the distribution of energy flow paths; and identifying hotspot paths and key energy conversion nodes of energy flow by statistically analyzing the frequency of occurrence of the characteristic numbers of the target coupling operator P1.
4. The verification method for energy-power management of new energy vehicles according to claim 1, characterized in that, The construction and identification of the energy flow path depends on the feature number and allocation coefficient in the coupling operator P; the feature number serves as a path marker, and their product constitutes the historical feature number of the energy flow operator E; the allocation coefficient responds to the physical field coupling node with multiple outlets, defines the distribution ratio of input energy on different output paths, and determines the branching and merging of the energy flow path.
5. The verification method for energy-power management of new energy vehicles according to claim 1, characterized in that, In response to the mechanical energy input represented by the energy flow operator E, the real-time dynamic response of the vehicle is calculated, which may include a longitudinal dynamic model of the vehicle to calculate the driving force, driving resistance, acceleration and velocity of the vehicle.
6. A new energy vehicle energy-power management verification system, characterized in that, include: The simulation control module is used to simulate or control the energy-power system architecture of the target new energy vehicle. The vehicle control module is used to construct a vehicle control model that includes a coupling operator P and an intrinsic algorithm M; the coupling operator P is used to define the energy flow direction, distribution ratio and conversion loss between nodes. The energy flow module is used to determine a general simulation verification architecture based on the coupling operator P and the energy flow operator E, and defines it as several physical field coupling nodes and energy conversion nodes; the energy flow operator E includes the actual energy value and the historical feature number composed of the product of the feature numbers of each coupling operator P it passes through; The vehicle dynamics-road state module is used to calculate the vehicle dynamic response in response to the energy provided by the energy flow operator E; The driver instruction module is used to respond to driver instructions by controlling the movement of the energy flow operator E and adjusting the parameters of the coupling operator P through the vehicle control model. The mathematical statistics module is used to perform mathematical statistics analysis on the energy flow operator E in each node after operation, so as to quantitatively evaluate the efficiency of different energy flow paths.
7. The new energy vehicle energy-power management verification system according to claim 6, characterized in that, The driver instruction module is configured to receive and process control signals from a simulated driver or driver assistance system to generate instructions for the vehicle control module.
8. The new energy vehicle energy-power management verification system according to claim 6, characterized in that, The mathematical statistics mentioned include, but are not limited to, factoring the historical characteristic numbers of the energy flow operator E to trace the sequence of coupling operators it passes through and analyze the distribution of energy flow paths; and identifying hotspot paths and key energy conversion nodes of energy flow by statistically analyzing the frequency of occurrence of the characteristic numbers of the target coupling operator P1.
9. The new energy vehicle energy-power management verification system according to claim 6, characterized in that, The construction and identification of the energy flow path depends on the feature number and allocation coefficient in the coupling operator P; the feature number serves as a path marker, and their product constitutes the historical feature number of the energy flow operator E; the allocation coefficient responds to the physical field coupling node with multiple outlets, defines the distribution ratio of input energy on different output paths, and determines the branching and merging of the energy flow path.
10. The new energy vehicle energy-power management verification system according to claim 6, characterized in that, The vehicle dynamics-road state module is configured to receive an energy flow operator E from the energy flow calculation module and calculate the real-time dynamic response of the vehicle based on the mechanical energy input represented by the energy flow operator E. The vehicle dynamics-road state module may include a longitudinal dynamics model of the vehicle for calculating the driving force, driving resistance, acceleration and velocity of the vehicle.