Vehicle multi-dimensional management method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle multi-dimensional management method and device, electronic equipment and a storage medium. The method comprises the following steps: determining first energy change information according to first data and second data; determining first control information according to the first energy change rule information and the third data; determining second control information according to the fourth data; and controlling the vehicle according to the first control information and the second control information. According to the method, the control information of the vehicle is generated according to the running condition of the vehicle and the change of the light-weight parts in the vehicle during running of the vehicle, it can be guaranteed that the obtained control information can meet the requirement for safe running of the vehicle, meanwhile, the requirement for the lowest energy consumption of the vehicle can be met, and therefore the cruising ability and the safety performance of the vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption control for new energy vehicles, and in particular to a vehicle multi-dimensional management method, device, electronic equipment and storage medium. Background Art

[0002] Energy management technology for new energy vehicles is rapidly developing toward "global perception, intelligent prediction, and collaborative optimization." With the in-depth application of technologies like artificial intelligence and edge computing, future energy management systems will transition from "vehicle-side optimization" to "vehicle-road-cloud integration," providing more efficient and intelligent energy solutions for new energy vehicles.

[0003] The current technical route is mainly optimized from two independent dimensions: on the one hand, the energy management information of traditional new energy vehicles mainly relies on static rules or short-term optimization algorithms, such as threshold control based on state machines, fuzzy logic or equivalent fuel consumption minimum information to reduce energy consumption; on the other hand, lightweight design is achieved by adopting advanced materials such as high-strength steel, aluminum alloy, carbon fiber composite materials, or through structural topology optimization to achieve vehicle weight reduction.

[0004] However, these two technical paths often operate independently: after the lightweight design is completed, the energy management system still uses the original control parameters and fails to make adaptive adjustments based on the dynamic characteristics of the vehicle after weight reduction, such as changes in mass distribution, reduced moment of inertia, and improved acceleration performance, resulting in a significant reduction in the theoretical energy-saving effect. Summary of the Invention

[0005] The present invention provides a vehicle multi-dimensional management method, device, electronic device and storage medium to solve the problem that the dynamic characteristics of the vehicle after weight reduction are not taken into account, resulting in the generated vehicle control information failing to meet the optimal energy consumption requirements.

[0006] According to one aspect of the present invention, a vehicle multi-dimensional management method is provided, comprising:

[0007] Determining first energy change information based on the first data and the second data; the first data is used to represent the operating status of various components of the vehicle during operation; the second data is used to represent the material properties of lightweight components in the vehicle; the first energy change information is used to represent the energy consumption change of the lightweight components; the lightweight components are components that can still ensure the function and strength of the vehicle after reducing their mass;

[0008] Determining first control information based on the first energy change law information and third data; the third data is used to represent the operating conditions of the vehicle and road conditions; the first control information is used to adjust the operating conditions of components in a cooperative relationship within the vehicle;

[0009] determining second control information based on fourth data; the fourth data is used to describe change information generated by the motor when the vehicle is running; the second control information is used to adjust the operating state of the motor;

[0010] The vehicle is controlled according to the first control information and the second control information.

[0011] According to another aspect of the present invention, there is provided a vehicle multi-dimensional management device, comprising:

[0012] a first energy change information determination module, configured to determine first energy change information based on first data and second data; the first data being used to represent operating status data of various components of the vehicle during operation; the second data being used to represent material properties of lightweight components within the vehicle; the first energy change information being used to represent energy consumption changes of the lightweight components; the lightweight components being components that can still maintain the functionality and strength of the vehicle after reducing their mass;

[0013] a first control information determination module, configured to determine first control information based on the first energy variation law information and third data; the third data being used to characterize the vehicle's operating conditions and road conditions; and the first control information being used to adjust the operating conditions of components in a cooperative relationship within the vehicle;

[0014] a second control information determination module, configured to determine second control information based on fourth data; the fourth data being used to describe information about changes in the motor when the vehicle is running; and the second control information being used to adjust the operating state of the motor;

[0015] A control module is used to control the vehicle according to the first control information and the second control information.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle multi-dimensional management method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle multi-dimensional management method described in any embodiment of the present invention when executed.

[0021] The technical solution of the embodiment of the present invention determines the first energy change information based on the first data and the second data. The determination of the first energy change information can describe the energy change trend of the vehicle in the first time period and provide an energy change basis for the subsequent generation of control information; the first control information is determined based on the first energy change law information and the third data, which can determine the control instructions of the vehicle in the first time period, instruct the vehicle operation and provide the energy change required for the vehicle operation; the second control information is determined based on the fourth data. The determination of the second control information can ensure that the changes in the lightweight components of the vehicle meet the requirements of safe vehicle operation while also ensuring the minimum energy consumption of the vehicle; the vehicle is controlled according to the first control information and the second control information, which can ensure the safe operation of the vehicle while also minimizing the energy consumption of the vehicle. This method generates the control information of the vehicle through the operation of the vehicle and the changes in the lightweight components in the vehicle during the operation of the vehicle, which can ensure that the obtained control information can not only meet the requirements of safe vehicle operation but also meet the requirements of minimum energy consumption of the vehicle, thereby improving the vehicle's endurance and safety performance.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flowchart of a vehicle multi-dimensional management method provided by an embodiment of the present invention;

[0025] Figure 2 A control block diagram of a vehicle multi-dimensional management method provided by an embodiment of the present invention;

[0026] Figure 3 A schematic structural diagram of a vehicle multi-dimensional management device provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of the structure of an electronic device for implementing the multi-dimensional vehicle management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Figure 1 This is a flow chart of a vehicle multi-dimensional management method provided by an embodiment of the present invention. This embodiment is applicable to the control of new energy vehicles. The method can be executed by a vehicle multi-dimensional management device. The vehicle multi-dimensional management device can be implemented in the form of hardware and / or software. The vehicle multi-dimensional management device can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0031] S110. Determine first energy change information based on the first data and the second data; the first data is used to characterize the operating status data of each component of the vehicle during operation; the second data is used to characterize the material properties of the lightweight components in the vehicle; the first energy change information is used to characterize the energy consumption change of the lightweight components; the lightweight components are components that can still ensure the function and strength of the vehicle after reducing the mass.

[0032] The first data is data collected from the operating status of various components within the vehicle while the vehicle is in operation. The components include at least lightweight components. The first data includes at least one of the following: battery pack charge load, motor speed output, motor torque output, vehicle acceleration, suspension displacement, motor temperature, and battery voltage and current.

[0033] Furthermore, the first data is collected via a vehicle bus system configured on the vehicle. Exemplarily, the vehicle bus system may be CAN FD or Ethernet.

[0034] The second data is data obtained by evaluating the material and performance of the lightweight components in the vehicle. The second data includes at least one of the following: elastic modulus, fatigue limit, and creep characteristics of the material.

[0035] The first energy change information, obtained by correlating the first and second data, can characterize the energy consumption of each lightweight component during vehicle operation. This information can reflect the impact of lightweight components on vehicle body energy consumption. For example, for every 10% reduction in the lightweight coefficient, energy consumption can be reduced by approximately 5-8% under the same operating conditions.

[0036] Among them, lightweight components are components whose materials, structures or manufacturing processes are improved while ensuring the structural strength, functionality and safety of the vehicle, so that the quality of the improved components is significantly reduced compared to the quality before the improvement.

[0037] Among them, the lightweight coefficient is used to characterize the balance between the degree of weight reduction of lightweight components and vehicle performance.

[0038] Specifically, the correlation between the first data and the second data is analyzed by multi-source data fusion technology, and the first energy information change is constructed according to the analysis result.

[0039] The multi-source data fusion technology may be: Kalman filtering or Bayesian network.

[0040] Furthermore, the process of obtaining the first energy change information through Kalman filtering is as follows: first, the first data is divided into a state vector, an observation vector, and an input vector; the state vector and the second data are input into a first equation to predict the vehicle's energy consumption to obtain a predicted value; a Kalman gain is determined based on the predicted value and the observation vector; and the energy consumption is corrected based on the Kalman gain and the observation vector to obtain the first energy change information.

[0041] Furthermore, the first equation is generated based on the vehicle's dynamics and energy flow transfer relationships and the second data, wherein the second data is converted into physical properties and added to the first equation.

[0042] Among them, the state vector is the data related to energy consumption in the first data; the observation vector is the data directly measured by the sensor; and the input vector is the instruction that affects energy consumption, that is, the vehicle control instruction.

[0043] Furthermore, the process of obtaining the first energy change information using a Bayesian network includes: performing data screening on the first and second data, specifically, using the sensor data in the first and second data as input variables and the energy consumption data as output variables. A mapping relationship between the input and output variables is established, and a conditional probability table is generated based on the obtained mapping relationship. The probability of energy consumption within a first time period is determined based on the conditional probability table, and the first energy change information is mapped based on the energy consumption probability. The first time period is located after the collection time of the first and second data.

[0044] The conditional probability table is used to describe the impact of input variables on output variables. For example, assuming that the input variables include vehicle speed, the conditional probability table represents the impact of changes in vehicle speed on energy consumption.

[0045] The mapping relationship between the energy consumption probability and the first energy change information is obtained using historical data and a Bayesian network. Specifically, the Bayesian network predicts the energy consumption probability based on the historical data and matches the obtained energy consumption probability with the actual energy consumption. The historical data includes the historical first data and the historical second data.

[0046] Furthermore, the first energy change information is obtained through a lightweight state perception layer, wherein the lightweight state perception layer includes: a dynamic parameter adjustment layer and a security protection execution layer.

[0047] Among them, the dynamic parameter adjustment layer is used to establish a characteristic database of lightweight material characteristics for building lightweight components, and adopts multi-source data fusion technology to correlate and analyze the second data collected in real time with the first data to obtain the first energy change information.

[0048] Among them, the safety protection execution layer is used to set dynamic safety thresholds. When it is monitored that the stress of the lightweight component is close to 80% of the material yield strength, the protection mechanism is automatically triggered to reduce the load by limiting the motor output torque or adjusting the energy recovery intensity.

[0049] S120. Determine first control information based on the first energy change law information and third data; the third data is used to characterize the vehicle's operating conditions and road conditions; the first control information is used to adjust the operating conditions of components with a cooperative relationship in the vehicle.

[0050] The third data is the road condition information of the road ahead of the vehicle in the direction of travel obtained when the vehicle is running, and the speed information and position information of the vehicle obtained when the vehicle is running.

[0051] Furthermore, the third data also includes: the changing status of traffic lights and the operation information of vehicles on the road ahead of the vehicle.

[0052] The road condition information in the third data is obtained through high-precision maps, specifically extracting information about road slope, curvature, and elevation changes within a 3-5 km radius ahead of the vehicle. Furthermore, sections of road suitable for energy recovery are marked. These sections can be long downhill sections.

[0053] The vehicle's operating information and traffic light status on the road ahead of the vehicle are obtained through the V2X (vehicle to everything) vehicle network. The vehicle's operating information on the road ahead of the vehicle includes at least vehicle speed and location information.

[0054] Among them, the first control information can instruct components with a cooperative relationship in the vehicle to change their operating status, thereby realizing a change in the vehicle status of the vehicle.

[0055] Furthermore, the first control information includes at least: a vehicle energy demand curve and a structural load distribution within a first time period.

[0056] Components in a collaborative relationship are those that can collaborate to complete a vehicle state change when a vehicle state change is required. A vehicle state change refers to a change from a current operating state to a new operating state. Exemplarily, a vehicle state change can be at least one of the following: vehicle acceleration, vehicle deceleration, sudden stop, and extreme operating conditions. Extreme operating conditions refer to conditions when a vehicle is about to experience an accident.

[0057] Furthermore, the vehicle status change is determined by the road conditions ahead of the vehicle.

[0058] Specifically, the acquired third data, the vehicle pedal change tendency and the first energy change law information are combined with the lightweight characteristics of the lightweight components and input into the first model to predict the change state of the vehicle's cooperative components within the first time period, and generate the first control information based on the acquired change state.

[0059] The vehicle pedal change tendency is the change trend of the vehicle acceleration and brake pedals being lifted or lowered.

[0060] Among them, lightweight features are a series of technical characteristics that achieve weight reduction and comprehensive performance improvement through material optimization, structural design innovation, and manufacturing process improvement, while ensuring the function, strength and reliability of lightweight components.

[0061] The first model can be an improved vehicle dynamics model. This improved vehicle dynamics model is a mathematical model that more accurately describes vehicle dynamic behavior by integrating elements of multi-physics coupling, nonlinear characteristics, the effects of lightweight components, and real-time operating condition adaptability based on traditional vehicle dynamics. Traditional dynamics models characterize the relationship between vehicle motion and forces.

[0062] For example, assuming that the first model predicts that the changing state of the belt is about to enter a long downhill section and the vehicle needs to decelerate, the first control information includes the target value of the battery pack SOC and the motor operating point.

[0063] S130. Determine second control information based on fourth data; the fourth data is used to describe change information generated by the motor when the vehicle is running; the second control information is used to adjust the operating state of the motor.

[0064] The fourth data point is a motor efficiency map generated based on the motor's changing data during vehicle operation. The motor efficiency map is a two-dimensional graph that visually displays the motor's efficiency distribution under all operating conditions, with motor speed and torque as the horizontal and vertical coordinates.

[0065] The second control information is used to adjust the voltage, current and load characteristic parameters of the motor to adjust the motor operating point. The motor operating point is the instantaneous corresponding state of the electromagnetic torque and speed of the motor during actual operation.

[0066] Specifically, the fourth data, the third data and the lightweight coefficient are used to generate a motor operating point through the second model, and a control instruction for updating the voltage, current and load characteristics of the motor is generated according to the motor operating point to obtain a second control instruction.

[0067] Furthermore, after determining the motor operating point, the motor's regenerative braking recovery ratio and battery charge-discharge strategy are adjusted based on the motor operating point. The regenerative braking recovery ratio is the percentage of total braking energy converted and stored by the regenerative braking system during braking. The battery charge-discharge strategy dynamically adjusts parameters such as current, voltage, and temperature during the charging and discharging processes of the motor-powered battery to maximize motor energy efficiency, minimize lifespan, and optimize safety.

[0068] Among them, the second model can adopt a deep reinforcement learning algorithm.

[0069] Furthermore, the second model is updated every 100 kilometers of actual driving, ensuring that the second control instructions are always adapted to the current vehicle state and driving habits. Learning mode is automatically triggered under special operating conditions, allowing the system to quickly adapt to new operating environments. Special operating conditions include at least one of the following: extreme temperatures and heavy loads.

[0070] Among them, the lightweight coefficient is used to characterize the balance between the degree of weight reduction of lightweight components and vehicle performance.

[0071] S140: Control the vehicle according to the first control information and the second control information.

[0072] Specifically, the first and second control information are input into the vehicle digital twin model to simulate the vehicle's operating state. If the simulation results meet the preset safety requirements, the vehicle is controlled using the first and second control information. If they do not meet the preset safety requirements, the first and second control information are optimized.

[0073] For example, assuming the vehicle changes state to an extreme operating condition, the first control information and the second control information are input into the vehicle digital twin model to simulate the vehicle's emergency control and obtain simulation results. A safety analysis is performed on the simulation results. If the safety requirements are met, the vehicle is emergency controlled based on the first control information and the second control information. The extreme operating condition is at least one of the following: suspension shock during emergency braking and body torsion under continuous curves. The safety analysis is an analysis of at least one of the following: stress changes in the vehicle's lightweight components, the degree of damage to the lightweight components of the vehicle structure, and the torque of the vehicle body.

[0074] Among them, the vehicle digital twin model consists of at least one of the following: a parameterized vehicle dynamics model, a material mechanical properties database, and an environmental simulation module.

[0075] Furthermore, after controlling the vehicle, it also includes: calculating the error between the simulated stress variation range of the lightweight component and the actually measured stress variation range of the lightweight component. If the obtained error exceeds the preset safety requirement, the calibration process is triggered.

[0076] Furthermore, for each preset time period of the model for generating the first control parameter and the second control parameter, a global parameter optimization is performed to update key data such as material fatigue characteristics.

[0077] In the above steps, controlling the vehicle according to the first control information and the second control information can reduce energy consumption by 18-25%, increase regenerative braking energy recovery efficiency by 22-25%, and optimize the power system operating efficiency by more than 15%.

[0078] Furthermore, before controlling the vehicle according to the first control information and the second control information, it also includes: generating a plan for optimizing the quality of the vehicle's lightweight components. The specific steps are as follows: generating stress constraints of the lightweight components according to the safety requirements of the vehicle, using the stress constraints of the lightweight components as constraints, and using the rolling time domain optimization strategy through the third model to generate a quality optimization plan for the lightweight components, and ensuring that the obtained quality optimization plan meets the target requirements.

[0079] Among them, the third model is the improved model predictive control (MPC).

[0080] Among them, the target requirements are: the power consumption of lightweight components is less than the preset power, the lightweight components can respond quickly to control instructions, and the stress of all lightweight components is within the preset stress range.

[0081] Furthermore, for composite components like carbon fiber, in addition to stress constraints, special metrics must be considered. Flexible constraint processing technology automatically selects a quality optimization solution when multiple objectives conflict. Special metrics include at least strain energy density.

[0082] Furthermore, after the vehicle is controlled, the quality of the vehicle's lightweight components is further optimized according to the actual operating conditions of the vehicle.

[0083] The above steps, which determine the lightweight component quality optimization plan, can simultaneously reduce vehicle mass and improve vehicle range. Furthermore, they can reduce peak stress in lightweight components, extend the fatigue life of key load-bearing structures, and increase structural safety margins under emergency braking conditions.

[0084] For example, Figure 2 As shown, the vehicle's CAN bus acquires first and second data. Based on the vehicle's control information from the first and second data, vehicle simulation control is performed in a vehicle digital twin model equipped with a bench test and a high-precision simulation platform. The quality of the vehicle's lightweight components is optimized based on the simulation results and the vehicle's actual operation. The first and second control information are updated based on the optimization results. After the lightweight components are reduced in weight, the vehicle operates based on the updated first and second control information. Reducing component mass refers to reducing the weight of the vehicle's lightweight components. The hybrid powertrain control system is a vehicle control system connected to the motor.

[0085] Optionally, determining the first energy change information according to the first data and the second data includes steps A1-A3:

[0086] Step A1: Determine the dependency relationship between the first data and the second data; the dependency relationship is used to characterize the impact of changes in the first data and the second data on energy consumption.

[0087] The dependency relationship is used to describe the causal relationship between the first data, the second data, and energy consumption. For example, the dependency relationship can be expressed as follows: vehicle acceleration - lightweight component weight is A - energy consumption increases; vehicle deceleration - lightweight component weight is A - energy consumption decreases.

[0088] Specifically, the first data and the second data are abstracted into network nodes, that is, energy consumption is abstracted into a root node, and the remaining data in the first data and the second data are abstracted into child nodes. Data mining is performed on the root node and the child nodes to obtain the dependency relationship between the root node and the child nodes.

[0089] The data mining is used to extract the correlation between the first data, the second data and the energy consumption from the first data and the second data.

[0090] Step A2: Determine a conditional probability table based on the dependency relationship, the first data, and the second data; the conditional probability table is used for the probability distribution of each data state in the first data under each data state in the second data, and the probability distribution of each data state in the second data under each data state in the first data.

[0091] Specifically, the prior probability of each state of the root node is constructed based on the dependency relationship, the first data, and the second data. For each child node, the conditional probability is determined based on the state combination of the root node, and a conditional probability table is generated based on the obtained conditional probability and the prior probability.

[0092] The prior probability is the initial probability distribution of each state of the root node without considering any child node state, that is, the initial probability distribution of each energy consumption state without considering the first data and the second data.

[0093] The conditional probability is the dependency between the state of the child node and the root node, that is, the dependency between the state of each data in the first data and the second data and the energy consumption.

[0094] Step A3: Determine first energy change information according to the conditional probability table.

[0095] Specifically, the posterior probability of the root node under the first data and the second data is calculated based on the conditional probability table based on Bayes' theorem, the energy consumption probability of the vehicle in the first time period is determined based on the posterior probability, and the first energy change information is determined based on the energy consumption probability.

[0096] Optionally, determining the first control information according to the first energy change law information and the third data includes steps B1-B2:

[0097] Step B1, determining a first driving state based on the third data and the vehicle pedal change tendency; the first driving state is used to characterize the acceleration or deceleration state of the vehicle in the first time period; the vehicle pedal change tendency is used to characterize the change trend of the vehicle acceleration and brake pedal lifting or lowering.

[0098] Specifically, the operation trend of the vehicle in the first time period is predicted based on the acquired third data and the change tendency of the vehicle pedal, and the acquired operation trend is used as the first driving state.

[0099] Step B2: Determine first control information according to the first change information and the first driving state.

[0100] Specifically, the first driving state and the first energy change law information are combined with the lightweight characteristics of the lightweight components and input into the first model to predict the change state of the components with a cooperative relationship in the vehicle within the first time period, and the first control information is generated according to the obtained change state.

[0101] Optionally, determining the second control information according to the fourth data includes steps C1-C3:

[0102] Step C2: determining the motor operating point according to the third data, the fourth data and the lightweight coefficient. The motor operating point is the instantaneous corresponding state of the electromagnetic torque and the rotational speed.

[0103] Specifically, the motor operating point is generated by the second model according to the fourth data, the third data and the lightweight coefficient.

[0104] Among them, the lightweight coefficient is used to characterize the balance between the degree of weight reduction of lightweight components and vehicle performance.

[0105] Furthermore, the lightweight coefficient can be expressed by the following formula:

[0106]

[0107] Among them, the second model can adopt a deep reinforcement learning algorithm.

[0108] Furthermore, the second model is updated every 100 kilometers of actual driving, ensuring that the second control instructions always adapt to the current vehicle state and driving habits. Learning mode is automatically triggered under special operating conditions to quickly adapt to new operating environments.

[0109] Step C2: Generate second control information according to the motor operating point.

[0110] Specifically, a control instruction for updating the voltage, current and load characteristics of the motor is generated according to the motor operating point to obtain a second control instruction.

[0111] Furthermore, after obtaining the motor operating point information, the motor's regenerative braking recovery ratio and the battery charging and discharging strategy are adjusted according to the motor operating point.

[0112] Optionally, determining the motor operating point according to the third data, the fourth data, and the lightweight coefficient includes steps D1-D2:

[0113] Step D1: Determine vehicle power change information based on the lightweight coefficient; the vehicle power change is used to represent the change in power required by the motor when the vehicle is running.

[0114] The vehicle power change information includes the vehicle acceleration when the vehicle is running and the load coefficient when climbing a slope.

[0115] Specifically, the weight reduction of the vehicle mass is determined according to the lightweight coefficient of the vehicle, and the vehicle power change information is determined according to the relationship between the vehicle acceleration, the load coefficient and the vehicle mass based on the obtained weight reduction.

[0116] Among them, the relationship between vehicle acceleration and vehicle can be expressed as:

[0117] F = ma;

[0118] Among them, F is the driving force provided by the motor; m is the mass of the vehicle; and a is the vehicle acceleration.

[0119] The relationship between the load factor and vehicle mass can be expressed as:

[0120]

[0121] Step D2: Generate a motor operating point based on the vehicle power change information, the third data, and the fourth data.

[0122] Specifically, the vehicle power change information, the third data and the fourth data are input into the second model, and the second model predicts the operating point of the vehicle motor based on the vehicle power change information, the third data and the fourth data to obtain the motor operating point.

[0123] Optionally, controlling the vehicle according to the first control information and the second control information includes steps E1-E3:

[0124] Step E1: Simulate the operation process of the vehicle according to the first control information and the second control information to obtain a simulated operation process.

[0125] Specifically, the first control information and the second control information are input into the vehicle digital twin model to simulate the vehicle operation state to obtain a simulated operation process.

[0126] Step E2: If the simulation operation process meets the preset safety requirements, the vehicle is controlled through the first control information and the second control information.

[0127] Specifically, the simulation operation process is analyzed to obtain first stress change data of each lightweight component in the vehicle. If the first stress change data meets the preset safety requirements, the vehicle is controlled through the first control information and the second control information.

[0128] Furthermore, when the vehicle is controlled by using the first control information and the second control information, the stress variation range of the lightweight components in the vehicle is obtained in real time as the second stress variation range.

[0129] Step E3: If the simulation operation process does not meet the preset safety requirements, the first control information and the second control information are corrected.

[0130] Specifically, the simulation operation process is analyzed to obtain the first stress change data of each lightweight component in the vehicle. If the first stress change data does not meet the preset safety requirements, the first control information and the second control information are corrected according to the simulation operation process.

[0131] Among them, the preset safety requirement is a pre-set stress difference range, which is determined based on actual needs and experience.

[0132] Optionally, modifying the first control information and the second control information includes steps F1-F2:

[0133] Step F1, obtaining first stress change data; the first stress change data is stress change data of a lightweight component when simulating vehicle operation.

[0134] Specifically, when the operation simulation of the vehicle is performed according to the first control information and the second control information, stress change data of the lightweight components in the simulated vehicle are acquired in real time to obtain first stress change data.

[0135] Step F2: Update the first control information and the second control information according to the first stress change data and the second stress change data.

[0136] The second stress change data is stress change data of the lightweight component obtained when the vehicle is directly controlled according to the first control information and the second control information.

[0137] Specifically, if the first stress change data is compared with the second stress change data, if the comparison result meets the preset safety requirements, it is considered that the generated first control information and second control information can meet the requirements; if the comparison result does not meet the preset safety requirements, it indicates that the generated first control information and second control information cannot meet the requirements, triggering the calibration system to update the first control information and the second control information, and calibrate the model parameters for generating the first control information and the second control information.

[0138] The technical solution of this embodiment is to determine the first energy change information based on the first data and the second data. The determination of the first energy change information can describe the energy change trend of the vehicle in the first time period and provide the energy change basis for the subsequent generation of control information; determine the first control information based on the first energy change law information and the third data, and determine the control instructions of the vehicle in the first time period, instruct the vehicle operation and provide the energy change required for the vehicle operation; determine the second control information based on the fourth data, and determine the second control information to ensure that the changes in the lightweight components of the vehicle meet the requirements of safe vehicle operation while also ensuring the minimum energy consumption of the vehicle; control the vehicle based on the first control information and the second control information, and ensure the safe operation of the vehicle while also minimizing the energy consumption of the vehicle. This method generates the control information of the vehicle through the operation of the vehicle and the changes in the lightweight components in the vehicle during the operation of the vehicle, and can ensure that the obtained control information can not only meet the requirements of safe vehicle operation but also meet the requirements of minimum energy consumption of the vehicle, thereby improving the endurance and safety performance of the vehicle.

[0139] Figure 3 This is a schematic diagram of the structure of a multi-dimensional vehicle management device provided by an embodiment of the present invention. This embodiment is applicable to the control of new energy vehicles. The new energy vehicle control device can be implemented in the form of hardware and / or software. The new energy vehicle control device can be configured in any electronic device with network communication function. Figure 3 As shown, the device includes: a first energy change information determination module 210, a first control information determination module 220, a second control information determination module 230 and a control module 240, wherein:

[0140] First energy change information determining module 210: configured to determine first energy change information based on first data and second data; the first data is used to represent the operating state data of various components of the vehicle during operation; the second data is used to represent the material properties of lightweight components in the vehicle; the first energy change information is used to represent the energy consumption change of the lightweight components; lightweight components are components that can still ensure the function and strength of the vehicle after reducing their mass;

[0141] First control information determination module 220: used to determine first control information based on the first energy change law information and third data; the third data is used to represent the operating status of the vehicle and road conditions; the first control information is used to adjust the operating status of components in a cooperative relationship within the vehicle;

[0142] Second control information determination module 230: used to determine second control information based on fourth data; the fourth data is used to describe the change information generated by the motor when the vehicle is running; the second control information is used to adjust the operating state of the motor;

[0143] Control module 240: used to control the vehicle according to the first control information and the second control information.

[0144] Optionally, the first energy change information determining module 210 includes:

[0145] A dependency determination unit is configured to determine a dependency between the first data and the second data; the dependency is configured to characterize the impact of changes in the first data and the second data on energy consumption;

[0146] A conditional probability table determining unit is configured to determine a conditional probability table based on the dependency relationship, the first data, and the second data; the conditional probability table is configured to represent the probability distribution of each data state in the first data under each data state in the second data, and the probability distribution of each data state in the second data under each data state in the first data;

[0147] A first energy change information determining unit is configured to determine the first energy change information according to a conditional probability table.

[0148] Optionally, the first control information determining module 220 includes:

[0149] A first driving state determining unit is configured to determine a first driving state based on the third data and the vehicle pedal change tendency; the first driving state is configured to represent an acceleration or deceleration state of the vehicle in a first time period; the vehicle pedal change tendency is configured to represent a change trend of the vehicle acceleration and brake pedal being lifted or lowered;

[0150] The first control information determining unit is configured to determine the first control information according to the first change information and the first driving state.

[0151] Optionally, the second control information determining module 230 includes:

[0152] A motor operating point determination unit is configured to determine the motor operating point according to the third data, the fourth data, and the lightweight coefficient. The motor operating point is an instantaneous corresponding state between the electromagnetic torque and the speed.

[0153] The second control information determining unit is configured to generate the second control information according to the motor operating point.

[0154] Optionally, the second control information determining unit includes:

[0155] Vehicle power change information determination subunit: used to determine vehicle power change information based on the lightweight coefficient; vehicle power change is used to represent the change in power required by the motor when the vehicle is running;

[0156] The motor operating point determination subunit is used to generate the motor operating point according to the vehicle power change information, the third data and the fourth data.

[0157] Optionally, the control module 240 includes:

[0158] A simulated operation process determining unit is configured to simulate the operation process of the vehicle according to the first control information and the second control information to obtain a simulated operation process;

[0159] Control unit: used to control the vehicle through the first control information and the second control information if the simulation operation process meets the preset safety requirements;

[0160] Correction unit: used to correct the first control information and the second control information if the simulation operation process does not meet the preset safety requirements.

[0161] Optional, correction unit, including:

[0162] A first stress change data determining subunit is used to obtain first stress change data; the first stress change data is stress change data of lightweight components when simulating vehicle operation;

[0163] Update subunit: used to update the first control information and the second control information according to the first stress change data and the second stress change data.

[0164] The vehicle multi-dimensional management device provided in the embodiment of the present invention can execute the vehicle multi-dimensional management method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the new energy vehicle control method. For detailed process, please refer to the relevant operations of the new energy vehicle control method in the above embodiment.

[0165] Figure 4A schematic structural diagram of an electronic device for implementing a control method for a new energy vehicle according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present invention described and / or required herein.

[0166] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0167] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0168] The processor 11 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the control method for a new energy vehicle.

[0169] In some embodiments, the new energy vehicle control method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the new energy vehicle control method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the new energy vehicle control method in any other appropriate manner (for example, by means of firmware).

[0170] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0172] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0173] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0174] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0175] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0176] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0177] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A multi-dimensional vehicle management method, characterized in that: include: determining first energy change information according to the first data and the second data; The first data is used to represent the operating status data of each component of the vehicle during operation; The second data is used to characterize the material properties of the lightweight components in the vehicle; the first energy change information is used to characterize the energy consumption change of the lightweight components; the lightweight components are components that can still ensure the function and strength of the vehicle after reducing their mass; Determining first control information based on the first energy change law information and third data; the third data is used to represent the operating conditions of the vehicle and road conditions; the first control information is used to adjust the operating conditions of components in a cooperative relationship within the vehicle; determining second control information based on fourth data; wherein the fourth data is used to describe change information generated by the motor when the vehicle is running; The second control information is used to adjust the operating state of the motor; The vehicle is controlled according to the first control information and the second control information.

2. The method according to claim 1, characterized in that The determining the first energy change information according to the first data and the second data includes: Determining a dependency relationship between first data and second data; wherein the dependency relationship is used to characterize the impact of changes in the first data and the second data on energy consumption; Determine a conditional probability table based on the dependency relationship, the first data, and the second data; the conditional probability table is used for the probability distribution of each data state in the first data under each data state in the second data, and the probability distribution of each data state in the second data under each data state in the first data; The first energy change information is determined according to the conditional probability table.

3. The method according to claim 1, characterized in that The determining the first control information according to the first energy change law information and the third data includes: determining a first driving state based on the third data and the vehicle pedal change tendency; the first driving state is used to represent the acceleration or deceleration state of the vehicle in the first time period; the vehicle pedal change tendency is used to represent the change trend of the vehicle acceleration and brake pedal being lifted or lowered; First control information is determined according to the first change information and the first driving state.

4. The method according to claim 1, wherein The determining the second control information according to the fourth data includes: Determining a motor operating point based on the third data, the fourth data, and the lightweight coefficient, wherein the motor operating point is an instantaneous corresponding state between the electromagnetic torque and the rotational speed; Second control information is generated according to the motor operating point.

5. The method according to claim 4, characterized in that Determining the motor operating point according to the third data, the fourth data, and the lightweight coefficient includes: Determining vehicle power change information based on the lightweight coefficient; the vehicle power change is used to represent the change in power required by the motor when the vehicle is running; The motor operating point is generated according to the vehicle power change information, the third data, and the fourth data.

6. The method according to claim 1, characterized in that The controlling the vehicle according to the first control information and the second control information includes: Simulating the operation process of the vehicle according to the first control information and the second control information to obtain a simulated operation process; If the simulation operation process meets the preset safety requirements, the vehicle is controlled by using the first control information and the second control information; If the simulation operation process does not meet the preset safety requirements, the first control information and the second control information are corrected.

7. The method according to claim 6, characterized in that The modifying the first control information and the second control information includes: Acquire first stress change data; the first stress change data is stress change data of a lightweight component when simulating vehicle operation; The first control information and the second control information are updated according to the first stress change data and the second stress change data.

8. A multi-dimensional vehicle management device, characterized in that: include: A first energy change information determining module, configured to determine first energy change information based on the first data and the second data; The first data is used to represent the operating status data of each component of the vehicle during operation; The second data is used to characterize the material properties of the lightweight components in the vehicle; the first energy change information is used to characterize the energy consumption change of the lightweight components; the lightweight components are components that can still ensure the function and strength of the vehicle after reducing their mass; a first control information determination module, configured to determine first control information based on the first energy variation law information and third data; the third data being used to characterize the vehicle's operating conditions and road conditions; and the first control information being used to adjust the operating conditions of components in a cooperative relationship within the vehicle; A second control information determination module is configured to determine second control information based on fourth data; the fourth data is configured to describe change information generated by the motor when the vehicle is running; The second control information is used to adjust the operating state of the motor; A control module is used to control the vehicle according to the first control information and the second control information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle multi-dimensional management method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle multi-dimensional management method according to any one of claims 1 to 7 when executed.

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