New energy automobile state measurement and control system and method

Abstract

The invention provides a new energy automobile state measurement and control system and method, and relates to the technical field of vehicle control, and an acquisition unit in the system can acquire a current state parameter and a target state parameter of a new energy automobile; a model construction unit in the system can construct a performance optimization model of the new energy automobile according to a target state and a current state parameter, and the performance optimization model at least comprises a target function with the target of minimizing total energy consumption in the whole driving process; an execution unit in the system can utilize a preset genetic algorithm to solve the performance optimization model, optimal control parameters of the new energy automobile are obtained, and the optimal control parameters at least comprise the energy recovery rate; a control unit in the system can control the new energy automobile according to the optimal control parameters. By applying the method provided by the embodiment of the invention, the requirement of a user on the vehicle energy consumption aspect can be met on the premise that the vehicle runs efficiently and safely.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a new energy vehicle state measurement and control system and method. Background Art

[0002] As the world pays more and more attention to environmental protection and sustainable energy development, new energy vehicles (such as electric vehicles, hybrid vehicles, etc.) as a clean and efficient means of transportation are gradually becoming the development direction of the future automotive industry.

[0003] However, in actual use, how to make new energy vehicles operate efficiently and safely and meet users' needs in terms of energy consumption is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The technical problem to be solved by this application is to provide a new energy vehicle status measurement and control system and method, which can meet the user's demand for vehicle energy consumption under the premise of efficient and safe operation of the vehicle. The specific solution is as follows:

[0005] A new energy vehicle state measurement and control system, comprising:

[0006] An acquisition unit is used to acquire current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery state of health SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change;

[0007] A model building unit, used to build a performance optimization model of the new energy vehicle according to the target state and the current state parameters, wherein the performance optimization model at least includes an objective function with the goal of minimizing the total energy consumption during the entire driving process, a second objective function with the goal of minimizing the acceleration change, a third objective function with the goal of minimizing the driving time, and a fourth objective function with the goal of maximizing the vehicle stability; the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints;

[0008] An execution unit, configured to solve the performance optimization model using a preset genetic algorithm to obtain optimal control parameters of the new energy vehicle, wherein the optimal control parameters at least include an energy recovery rate;

[0009] A control unit is used to control the new energy vehicle according to the optimal control parameters.

[0010] In the above system, optionally, the execution unit includes:

[0011] A generating subunit, used for generating a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter;

[0012] A first sorting subunit is used to perform a non-dominated sort on the parent population currently generated to obtain a first non-dominated sorting result;

[0013] A first selection subunit, configured to select a plurality of population individuals from the parent population according to the first non-dominated sorting result;

[0014] A first execution subunit performs a crossover operation on the plurality of population individuals selected from the parent population to obtain a crossover population;

[0015] The second execution subunit is used to perform mutation operations on each population individual in the crossover population to generate a descendant population;

[0016] A third execution subunit is used to merge the parent population and the child population to obtain an individual set;

[0017] A second sorting subunit is used to perform a non-dominated sort on the individual set to obtain a second non-dominated sorting result;

[0018] A second selection subunit is used to select a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population;

[0019] The fourth execution subunit is used for returning to trigger the first sorting subunit to execute the step of performing non-dominated sorting on the parent population currently generated when the parent population of the new generation currently generated does not meet the preset stop iteration condition;

[0020] The fifth execution subunit is used to select a target individual from the parent population of the new generation currently generated when the parent population of the new generation currently generated meets a preset stop iteration condition, and use the target individual as the optimal control parameter of the new energy vehicle.

[0021] In the above system, optionally, the acquisition unit includes:

[0022] A detection subunit, configured to display an operation mode configuration interface of the new energy vehicle when detecting that the new energy vehicle enters a preset custom motion mode;

[0023] A receiving subunit, used to receive a configuration operation of a user in the operation mode configuration interface;

[0024] An acquisition subunit is used to obtain the target state parameters of the new energy vehicle according to the configuration operation.

[0025] In the above system, optionally, the control unit comprises:

[0026] The first control subunit is used to generate a first control instruction according to the energy recovery rate, and send the first control instruction to the energy management system of the new energy vehicle.

[0027] The above system may optionally further include:

[0028] A recording unit, used to record the actual energy consumption of the new energy vehicle;

[0029] The output unit is used to output abnormal energy consumption alarm information of the new energy vehicle when the difference between the actual energy consumption of the new energy vehicle and the target energy consumption is greater than a preset difference threshold.

[0030] A method for measuring and controlling the state of a new energy vehicle, comprising:

[0031] Obtaining current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery state of health SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change;

[0032] A performance optimization model of the new energy vehicle is constructed according to the target state and the current state parameters, wherein the performance optimization model includes an objective function for minimizing the total energy consumption during the entire driving process, a second objective function for minimizing the acceleration change, a third objective function for minimizing the driving time, and a fourth objective function for minimizing the suspension displacement; the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints;

[0033] Solving the performance optimization model using a preset genetic algorithm to obtain optimal control parameters of the new energy vehicle, wherein the optimal control parameters include at least an energy recovery rate;

[0034] The new energy vehicle is controlled according to the optimal control parameters so that the new energy vehicle reaches the target state.

[0035] In the above method, optionally, the using of a preset genetic algorithm to solve the performance optimization model to obtain the optimal control parameters of the new energy vehicle includes:

[0036] Generate a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter;

[0037] Performing a non-dominated sort on the currently generated parent population to obtain a first non-dominated sort result;

[0038] Selecting a plurality of population individuals from the parent population according to the first non-dominated sorting result;

[0039] Performing a crossover operation on the plurality of population individuals selected from the parent population to obtain a crossover population;

[0040] Perform mutation operations on individuals in the crossover population to generate offspring populations;

[0041] Merging the parent population and the offspring population to obtain an individual set;

[0042] Performing a non-dominated sorting on the individual set to obtain a second non-dominated sorting result;

[0043] Selecting a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population;

[0044] When the parent population of the new generation currently generated does not meet the preset stop iteration condition, returning to the step of performing non-dominated sorting on the parent population currently generated;

[0045] When the parent population of the new generation currently generated meets the preset stop iteration condition, a target individual is selected from the parent population of the new generation currently generated, and the target individual is used as the optimal control parameter of the new energy vehicle.

[0046] The above method, optionally, the process of obtaining the target state parameters of the new energy vehicle includes:

[0047] When it is detected that the new energy vehicle enters a preset custom sports mode, displaying an operation mode configuration interface of the new energy vehicle;

[0048] Receiving configuration operations by the user in the operation mode configuration interface;

[0049] The target state parameters of the new energy vehicle are obtained according to the configuration operation.

[0050] The above method, optionally, controlling the new energy vehicle according to the optimal control parameter so that the new energy vehicle reaches the target state includes:

[0051] A first control instruction is generated according to the energy recovery rate, and the first control instruction is sent to the energy management system of the new energy vehicle.

[0052] The above method, optionally, after controlling the new energy vehicle according to the optimal control parameters, further comprises:

[0053] Recording the actual energy consumption of the new energy vehicle;

[0054] When the difference between the actual energy consumption of the new energy vehicle and the target energy consumption is greater than a preset threshold, abnormal energy consumption warning information of the new energy vehicle is output.

[0055] Based on the above-mentioned implementation of the present application, a new energy vehicle state measurement and control system and method are provided, the system includes: an acquisition unit, used to acquire current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery health state SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change; a model construction unit, used to construct a performance optimization model of the new energy vehicle according to the target state and the current state parameters, wherein the performance optimization model at least includes an objective function with the goal of minimizing total energy consumption during the entire driving process, a second objective function with the goal of minimizing acceleration change, a third objective function with the goal of minimizing driving time and a fourth objective function with the goal of maximizing vehicle stability; the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints and end state constraints; an execution unit, used to solve the performance optimization model using a preset genetic algorithm to obtain the optimal control parameters of the new energy vehicle, the optimal control parameters at least include energy recovery rate; a control unit, used to control the new energy vehicle according to the optimal control parameters. The method provided in the embodiment of the present application can meet the user's demand for vehicle energy consumption under the premise of efficient and safe operation of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0057] Figure 1 A schematic diagram of the structure of a new energy vehicle status measurement and control system provided in this application;

[0058] Figure 2 A schematic diagram of the structure of an acquisition unit provided in this application;

[0059] Figure 3 A method flow chart of a new energy vehicle state measurement and control method provided in this application;

[0060] Figure 4 A flowchart of a process for obtaining optimal control parameters of a new energy vehicle provided in this application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0063] See also Figure 1 , is a schematic diagram of the structure of a new energy vehicle state measurement and control system provided in an embodiment of the present application, the system comprising:

[0064] The acquisition unit 101 is used to acquire the current state parameters and target state parameters of the new energy vehicle; the current state parameters include the battery state of charge SOC, the battery state of health SOH, the motor temperature, the vehicle speed and the vehicle load; the target state parameters include the target energy consumption, the target arrival time, the maximum allowable suspension position and the maximum allowable acceleration change;

[0065] In this embodiment, the SOC and SOH of the new energy vehicle can be calculated based on the voltage, current and temperature of the battery monitored in real time by sensors installed in the battery pack; the motor temperature can be collected based on a temperature sensor installed inside or near the motor; the vehicle speed can be collected based on a vehicle speed sensor installed on the wheel or drive shaft; and the vehicle load can be collected based on a weight sensor installed on the vehicle chassis or suspension.

[0066] The model building unit 102 is used to build a performance optimization model of the new energy vehicle according to the target state and the current state parameters, wherein the performance optimization model at least includes an objective function with the goal of minimizing the total energy consumption during the entire driving process. The constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints.

[0067] Optionally, the objective function of the performance optimization model can be expressed as:

[0068]

[0069] in, Represents the minimization of the objective function value , u represents the control parameters of new energy vehicles, It represents the total energy consumption of new energy vehicles during the entire driving process, which can be determined by factors such as energy recovery rate, motor power output, braking intensity and driving mode. The optimization goal of this item is to minimize the total energy consumption to improve the vehicle's energy efficiency and cruising range. It represents the time required for a new energy vehicle to reach the destination from the starting point, which can be determined by factors such as motor power output and driving mode. The optimization goal of this item is to minimize the arrival time to improve the vehicle's driving efficiency and response speed. It indicates the degree of change of acceleration of new energy vehicles during driving, reflecting the stability of the vehicle and the comfort of passengers. It can be determined by factors such as braking intensity and driving mode. The optimization goal of this item is to minimize the acceleration change to reduce the impact and vibration felt by passengers and improve comfort. It means minimizing suspension displacement to reduce vehicle bumps on uneven roads and improve passenger comfort and vehicle stability. , , and are the weight coefficients of each item, which are used for the importance of each sub-goal; each weight coefficient can be set according to the application scenario and user needs.

[0070] In some embodiments, the control parameter u of the new energy vehicle is [u1, u2, u3, u4], where u1 is the energy recovery rate , u2 is the motor power output , u3 is the braking intensity B, and u4 is the driving mode selection M. The driving mode parameter M can take different values, each value corresponding to a driving mode. The driving modes may include economic mode, standard mode, sports mode, snow mode, etc.

[0071] Optional, the total energy consumption of new energy vehicles during the entire driving process ,in, is the kinetic energy consumed during driving. is the motor power consumption, It is brake energy recovery.

[0072] Optional, , where m is the mass of the new energy vehicle, v(t) is the vehicle speed, t is the time variable, and T is the total time from the starting point to the end point.

[0073] Optional, ,in, is the motor power output.

[0074] Optional, B(t) is the braking intensity.

[0075] Optional, ,in, is the influence coefficient of driving mode M on suspension displacement, is the road height, is the vehicle height.

[0076] Optional, ,in, It is the acceleration of new energy vehicles. is the rate of change of acceleration.

[0077] Optional, , where T is the total time from the start point to the end point, It's the car speed.

[0078] In some embodiments, the objective function of the performance optimization model may include at least one of a first objective function item aimed at minimizing the total energy consumption during the entire driving process, a second objective function item aimed at minimizing the acceleration change, a third objective function item aimed at minimizing the driving time, and a fourth objective function item aimed at minimizing the suspension displacement.

[0079] Optionally, the vehicle performance constraints can be expressed as:

[0080]

[0081] The maximum driving speed of new energy vehicles cannot exceed the maximum speed setting value , cannot be lower than the minimum speed setting value ;The maximum acceleration of new energy vehicles cannot exceed the acceleration setting value , cannot be lower than the minimum acceleration setting value .

[0082] Optionally, the battery safety constraint can be expressed as:

[0083]

[0084] The state of charge of the battery of the new energy vehicle is not greater than the maximum state of charge setting value , not less than the minimum state of charge setting value ; The battery health status of new energy vehicles is not greater than the maximum health status , not less than the minimum health status .

[0085] Optionally, the road constraints can be expressed as:

[0086]

[0087] The height of new energy vehicles is always greater than or equal to the height of the road surface. is the height of the vehicle at time t and the control parameter u(t).

[0088] Optional, initial state constraints:

[0089] ,

[0090] Among them, the battery charge state of the new energy vehicle at the beginning of the optimization process is The vehicle speed at the start time is , the acceleration at the start time is .

[0091] Optional, end state constraints:

[0092] ,

[0093] Among them, the nuclear power state of new energy vehicles at the end of the optimization process is The vehicle speed at the end time is , the acceleration at the end is .

[0094] The execution unit 103 is used to solve the performance optimization model using a preset genetic algorithm to obtain the optimal control parameters of the new energy vehicle, where the optimal control parameters at least include the energy recovery rate.

[0095] In this embodiment, the optimal control parameters may also include one or more of motor power output, braking intensity B, and behavioral driving mode parameters.

[0096] The control unit 104 is used to control the new energy vehicle according to the optimal control parameters.

[0097] By applying the system provided in the embodiment of the present application, a multi-objective performance optimization model is solved through a genetic algorithm, and the optimal control parameters are obtained. The vehicle operation is controlled according to the optimal control parameters, so that the new energy vehicle can operate efficiently and safely, and at the same time meet the user's needs for energy consumption, driving time, riding comfort and other aspects.

[0098] In an embodiment provided by the present application, based on the above solution, optionally, the execution unit includes:

[0099] A generating subunit is used to generate a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter;

[0100] A first sorting subunit is used to perform non-dominated sorting on the currently generated parent population to obtain a first non-dominated sorting result;

[0101] A first selection subunit, used for selecting a plurality of population individuals from the parent population according to the first non-dominated sorting result;

[0102] The first execution subunit performs a crossover operation on a plurality of population individuals selected from the parent population to obtain a crossover population;

[0103] The second execution subunit is used to perform mutation operations on each population individual in the crossover population to generate a descendant population;

[0104] The third execution subunit is used to merge the parent population and the child population to obtain an individual set;

[0105] The second sorting subunit is used to perform non-dominated sorting on the individual set to obtain a second non-dominated sorting result;

[0106] A second selection subunit is used to select a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population;

[0107] The fourth execution subunit is used to return to trigger the first sorting subunit to execute the step of performing non-dominated sorting on the parent population of the new generation being generated when the parent population of the new generation being generated does not meet the preset stop iteration condition;

[0108] The fifth execution subunit is used to select a target individual from the parent population of the new generation currently generated when the parent population of the new generation currently generated meets a preset stop iteration condition, and use the target individual as the optimal control parameter of the new energy vehicle.

[0109] In this embodiment, the genetic algorithm may be a non-dominated sorting genetic algorithm NSGA-II.

[0110] Optionally, the condition for stopping the iteration may be that the number of iterations is greater than a preset iteration number threshold.

[0111] In an embodiment provided by the present application, based on the above solution, optionally, a structural diagram of the acquisition unit 101 is as follows: Figure 2 As shown, including:

[0112] The detection subunit 1011 is used to display the operation mode configuration interface of the new energy vehicle when detecting that the new energy vehicle enters the preset custom motion mode;

[0113] The receiving subunit 1012 is used to receive the configuration operation of the user in the operation mode configuration interface;

[0114] The acquisition subunit 1013 is used to obtain the target state parameters of the new energy vehicle according to the configuration operation.

[0115] In this embodiment, the motion model configuration interface configures target energy consumption, target arrival time, maximum allowable suspension position, and maximum allowable acceleration change, etc. The target energy consumption can be selected by the user within a preset energy consumption range; the target arrival time can be selected by the user within an arrival time range; the maximum allowable suspension position can be selected by the user within a suspension position range; and the maximum allowable acceleration change can be selected by the user within an acceleration change range.

[0116] Optionally, a feasible way to detect that the new energy vehicle has entered a preset custom sports mode may be button or switch detection. Specifically, the new energy vehicle is equipped with buttons or switches for switching different driving modes. These buttons or switches are marked with "custom mode" and other logos. When the driver presses or toggles these buttons / switches, the internal control system of the new energy vehicle will receive the corresponding signal and recognize that the new energy vehicle has entered the custom sports mode.

[0117] In some embodiments, new energy vehicles are often equipped with a touch screen or voice control system. When the driver selects a custom sports mode on the touch screen or issues a corresponding command through voice commands, the control system will recognize and confirm that the new energy vehicle has entered this mode.

[0118] In some embodiments, the vehicle control system can monitor energy consumption data in real time. When it is detected that the energy consumption data has undergone specific changes and meets the characteristics of the custom motion mode, it can be determined that the new energy vehicle has entered this mode.

[0119] In some embodiments, when the driver uses a smart key or a mobile phone application APP to issue an instruction to switch to a custom sports mode, the vehicle control system will receive a corresponding signal and confirm that the vehicle has entered this mode.

[0120] In some embodiments, the vehicle may be installed with various sensors, such as acceleration sensors, steering angle sensors, etc. These sensors can monitor the movement status of the vehicle in real time.

[0121] When the vehicle enters the custom sport mode, the driver's driving behavior (such as acceleration, braking, steering, etc.) may change. These changes can be detected by sensors and identify that the vehicle has entered the custom sport mode.

[0122] In an embodiment provided by the present application, based on the above solution, optionally, the control unit includes:

[0123] The first control subunit is used to generate a first control instruction according to the energy recovery rate, and send the first control instruction to the energy management system of the new energy vehicle.

[0124] Optionally, the control unit also includes a second control subunit, which is used to generate a second control instruction according to the motor power output parameter and send the second control instruction to the motor control system of the new energy vehicle.

[0125] The control unit also includes a third control subunit, which is used to generate a third control instruction according to the braking intensity and send the third control instruction to the braking control system of the new energy vehicle.

[0126] The control unit also includes a fourth control subunit, which is used to generate a fourth control instruction according to the driving mode parameters and send the fourth control instruction to the driving mode control system of the new energy vehicle.

[0127] In an embodiment provided in the present application, based on the above solution, optionally, further comprising:

[0128] A recording unit for recording the actual energy consumption of the vehicle;

[0129] The output unit is used to output abnormal energy consumption warning information of the new energy vehicle when the difference between the actual energy consumption of the vehicle and the target energy consumption is greater than a preset difference threshold.

[0130] In this embodiment, the difference value between the actual energy consumption and the target energy consumption represents the degree of deviation between the actual energy consumption and the target energy consumption.

[0131] Optionally, the difference value may be an absolute value of the difference between the actual energy consumption and the target energy consumption, or a percentage difference = (actual energy consumption - target energy consumption) / target energy consumption × 100%, or an energy consumption deviation rate = |actual energy consumption - target energy consumption| / target energy consumption × 100%, and so on.

[0132] Based on this, the embodiment of the present application provides a method for measuring and controlling the state of a new energy vehicle. The method can be applied to an electronic device, which can be a central controller VCU or other controller in a new energy vehicle. The electronic device can also be a server, computer, tablet, smart phone or smart wearable device that establishes a communication connection with the new energy vehicle. The method flow chart of the method is as follows Figure 3 As shown, specifically including:

[0133] S301: Obtain current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery state of health SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change.

[0134] S302: Constructing a performance optimization model for a new energy vehicle according to the target state and the current state parameters, wherein the performance optimization model includes an objective function for minimizing the total energy consumption during the entire driving process, a second objective function for minimizing the acceleration change, a third objective function for minimizing the driving time, and a fourth objective function for minimizing the suspension displacement; the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints.

[0135] S303: Solving the performance optimization model using a preset genetic algorithm to obtain optimal control parameters of the new energy vehicle, where the optimal control parameters at least include an energy recovery rate.

[0136] S304: Control the new energy vehicle according to the optimal control parameters so that the new energy vehicle reaches the target state.

[0137] In an embodiment provided by the present invention, based on the above solution, optionally, a preset genetic algorithm is used to solve the performance optimization model to obtain the optimal control parameters of the new energy vehicle, such as Figure 4 As shown, including:

[0138] S401: Generate a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter.

[0139] S402: Perform a non-dominated sort on the currently generated parent population to obtain a first non-dominated sort result.

[0140] In this embodiment, the first non-dominated sorting result may include the Pareto level of each population individual in the currently generated parent population.

[0141] S403: Select multiple population individuals from the parent population according to the first non-dominated sorting result.

[0142] In this embodiment, a plurality of population individuals may be selected from the parent population according to the Pareto level of each population individual in the first non-dominated sorting result.

[0143] In some embodiments, a plurality of population individuals may be selected from the parent population based on the Pareto rank of each population individual in the parent population and the crowding degree of each population individual in the parent population.

[0144] Alternatively, the greater the crowding degree of a population individual, the lower the similarity between the population individual and other individuals.

[0145] S404: Perform a crossover operation on multiple population individuals selected from the parent population to obtain a crossover population.

[0146] In this embodiment, by performing a crossover operation on each population individual, the virtual machines in two physical machines can be exchanged, so that the population individuals in the crossover population obtained by the crossover are more diverse.

[0147] S405: Perform mutation operations on individuals in each population in the crossover population to generate a descendant population.

[0148] In this embodiment, new control parameters can be searched by performing mutation operations on each population individual in the crossover population.

[0149] S406: Merge the parent population and the offspring population to obtain an individual set.

[0150] In this embodiment, the parent population and the child population may be merged, and duplicate population individuals after the merger may be removed to obtain an individual set.

[0151] S407: Perform a non-dominated sort on the individual set to obtain a second non-dominated sort result.

[0152] In this embodiment, the second non-dominated sorting result includes the Pareto level of each population individual in the individual set.

[0153] S408: Selecting a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population.

[0154] S409: Determine whether the parent population of the new generation currently generated meets the preset stop iteration condition; if not, execute S402; if so, execute S410.

[0155] In this embodiment, when the number of generations of the parent population of the new generation reaches a preset iteration threshold, it is determined that the parent population of the new generation meets the preset stop iteration condition; otherwise, it is determined that the parent population of the new generation does not meet the stop iteration condition.

[0156] S410: Select a target individual from the parent population of the new generation currently generated, and use the target individual as the optimal control parameter of the new energy vehicle.

[0157] In an embodiment provided by the present invention, based on the above solution, optionally, the process of obtaining the target state parameter of the new energy vehicle includes:

[0158] When it is detected that the new energy vehicle has entered the preset custom sports mode, the operation mode configuration interface of the new energy vehicle is displayed;

[0159] Receive configuration operations from the user in the operation mode configuration interface;

[0160] The target state parameters of the new energy vehicle are obtained according to the configuration operation.

[0161] In an embodiment provided by the present invention, based on the above solution, optionally, controlling the new energy vehicle according to the optimal control parameters so that the new energy vehicle reaches the target state includes:

[0162] generating a first control instruction according to the energy recovery rate, and sending the first control instruction to an energy management system of the new energy vehicle;

[0163] A second control instruction is generated according to the driving mode parameter, and the second control instruction is sent to the new energy vehicle.

[0164] In an embodiment provided by the present invention, based on the above solution, optionally, after controlling the new energy vehicle according to the optimal control parameters, the method further includes:

[0165] Record the actual energy consumption of the vehicle;

[0166] When the difference between the actual energy consumption of the vehicle and the target energy consumption is greater than a preset threshold, an abnormal energy consumption alarm message of the new energy vehicle is output.

[0167] The specific principles and execution process of a new energy vehicle state measurement and control method disclosed in the above-mentioned embodiment of the present application are the same as the various units and modules in a new energy vehicle state measurement and control system disclosed in the above-mentioned embodiment of the present application. Please refer to the corresponding parts of a new energy vehicle state measurement and control system provided in the above-mentioned embodiment of the present application, and no further details will be given here.

[0168] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0169] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0170] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0171] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0172] The above is a detailed introduction to a new energy vehicle status measurement and control system and method provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A new energy vehicle status measurement and control system, characterized in that: include: An acquisition unit is used to acquire current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery state of health SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change; A model building unit, configured to build a performance optimization model of the new energy vehicle according to the target state and the current state parameters, wherein the performance optimization model at least includes an objective function with the goal of minimizing the total energy consumption during the entire driving process; and the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints; An execution unit, configured to solve the performance optimization model using a preset genetic algorithm to obtain optimal control parameters of the new energy vehicle, wherein the optimal control parameters at least include an energy recovery rate; A control unit is used to control the new energy vehicle according to the optimal control parameters.

2. The system according to claim 1, characterized in that The execution unit comprises: A generating subunit, used for generating a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter; A first sorting subunit is used to perform a non-dominated sort on the parent population currently generated to obtain a first non-dominated sorting result; A first selection subunit, configured to select a plurality of population individuals from the parent population according to the first non-dominated sorting result; A first execution subunit performs a crossover operation on the plurality of population individuals selected from the parent population to obtain a crossover population; The second execution subunit is used to perform mutation operations on each population individual in the crossover population to generate a descendant population; A third execution subunit is used to merge the parent population and the child population to obtain an individual set; A second sorting subunit is used to perform a non-dominated sort on the individual set to obtain a second non-dominated sorting result; A second selection subunit is used to select a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population; The fourth execution subunit is used for returning to trigger the first sorting subunit to execute the step of performing non-dominated sorting on the parent population currently generated when the parent population of the new generation currently generated does not meet the preset stop iteration condition; The fifth execution subunit is used to select a target individual from the parent population of the new generation currently generated when the parent population of the new generation currently generated meets a preset stop iteration condition, and use the target individual as the optimal control parameter of the new energy vehicle.

3. The system according to claim 1, characterized in that The acquisition unit comprises: A detection subunit, configured to display an operation mode configuration interface of the new energy vehicle when detecting that the new energy vehicle enters a preset custom motion mode; A receiving subunit, used to receive a configuration operation of a user in the operation mode configuration interface; An acquisition subunit is used to obtain the target state parameters of the new energy vehicle according to the configuration operation.

4. The system according to claim 1, characterized in that The control unit comprises: The first control subunit is used to generate a first control instruction according to the energy recovery rate, and send the first control instruction to the energy management system of the new energy vehicle.

5. The system according to claim 1, characterized in that Also includes: A recording unit, used to record the actual energy consumption of the new energy vehicle; The output unit is used to output abnormal energy consumption alarm information of the new energy vehicle when the difference between the actual energy consumption of the new energy vehicle and the target energy consumption is greater than a preset difference threshold.

6. A method for measuring and controlling the state of a new energy vehicle, characterized in that: include: Obtaining current state parameters and target state parameters of the new energy vehicle; the current state parameters include battery state of charge SOC, battery state of health SOH, motor temperature, vehicle speed and vehicle load; the target state parameters include target energy consumption, target arrival time, maximum allowable suspension position and maximum allowable acceleration change; A performance optimization model of the new energy vehicle is constructed according to the target state and the current state parameters, wherein the performance optimization model includes an objective function with the goal of minimizing the total energy consumption during the entire driving process; the constraints of the performance optimization model include vehicle performance constraints, battery safety constraints, road constraints, initial state constraints, and end state constraints; Solving the performance optimization model using a preset genetic algorithm to obtain optimal control parameters of the new energy vehicle, wherein the optimal control parameters include at least an energy recovery rate; The new energy vehicle is controlled according to the optimal control parameters.

7. The method according to claim 6, characterized in that The method of solving the performance optimization model by using a preset genetic algorithm to obtain the optimal control parameters of the new energy vehicle includes: Generate a parent population corresponding to the performance optimization model; the parent population includes a preset number of population individuals, each of which represents an initial control parameter; Performing a non-dominated sort on the currently generated parent population to obtain a first non-dominated sort result; Selecting a plurality of population individuals from the parent population according to the first non-dominated sorting result; Performing a crossover operation on the plurality of population individuals selected from the parent population to obtain a crossover population; Perform mutation operations on individuals in the crossover population to generate offspring populations; Merging the parent population and the offspring population to obtain an individual set; Performing a non-dominated sorting on the individual set to obtain a second non-dominated sorting result; Selecting a preset number of population individuals from the individual set according to the second non-dominated sorting result to generate a new generation of parent population; When the parent population of the new generation currently generated does not meet the preset stop iteration condition, returning to the step of performing non-dominated sorting on the parent population currently generated; When the parent population of the new generation currently generated meets the preset stop iteration condition, a target individual is selected from the parent population of the new generation currently generated, and the target individual is used as the optimal control parameter of the new energy vehicle.

8. The method according to claim 6, characterized in that The process of obtaining the target state parameters of new energy vehicles includes: When it is detected that the new energy vehicle enters a preset custom sports mode, displaying an operation mode configuration interface of the new energy vehicle; Receiving configuration operations by the user in the operation mode configuration interface; The target state parameters of the new energy vehicle are obtained according to the configuration operation.

9. The method according to claim 6, characterized in that The controlling the new energy vehicle according to the optimal control parameter includes: A first control instruction is generated according to the energy recovery rate, and the first control instruction is sent to the energy management system of the new energy vehicle.

10. The method according to claim 6, characterized in that After the new energy vehicle is controlled according to the optimal control parameters, the method further includes: Recording the actual energy consumption of the new energy vehicle; When the difference between the actual energy consumption of the new energy vehicle and the target energy consumption is greater than a preset threshold, abnormal energy consumption warning information of the new energy vehicle is output.

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