Energy consumption information processing method, device, medium, controller and management module

Abstract

The embodiment of the present invention discloses an energy consumption information processing method, device, medium, controller and management module; its core includes a first information collection preprocessing step and a second energy consumption information processing step; by splicing the first multi-source data in the first local initialization information to a preset timestamp, the synchronous processing of the information can be achieved, providing a basis for subsequent energy consumption information processing; specifically, the method and product of the present invention can solve the problem that the energy management strategy of hybrid electric vehicles (HEV) in related technologies lacks consideration of actual operating conditions; it can also realize vehicle status monitoring, remaining road condition information analysis, vehicle energy management strategy planning, energy management strategy execution and other functions; thereby realizing the optimization of energy indicators for the entire journey based on road traffic conditions, with the goal of optimizing energy consumption throughout the journey, and targeted control of the vehicle's power mode and the operating point settings of components such as the engine and motor, thereby realizing optimization of energy indicators for the entire journey.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart vehicles, and in particular relates to an energy consumption information processing method, device, medium, controller and management module. Background Art

[0002] Hybrid Electric Vehicles (HEVs) are a key technology solution in the development of new energy vehicles. Vehicle performance is closely linked to their energy management and control strategies. Generally speaking, HEVs are hybrid vehicles, using both a traditional internal combustion engine and an electric motor as their power sources. Currently, HEV energy management strategies are often designed based on standard test conditions, failing to optimize energy distribution. This leaves much to be desired in terms of overall vehicle energy efficiency and drivability. Summary of the Invention

[0003] First, an embodiment of the present invention discloses an energy consumption information processing method, the core of which includes a first information collection preprocessing step and a second energy consumption information processing step; wherein, the first information collection preprocessing step obtains first local initialization information, and the first local initialization information includes at least one of first vehicle information, first navigation information, and seventh operating condition information.

[0004] Furthermore, by splicing the first multi-source data in the first local initialization information to a preset timestamp, synchronous processing of the information can be achieved, providing a basis for subsequent energy consumption information processing.

[0005] Furthermore, the second energy consumption information processing step solves the second energy consumption data set, identifies the second energy consumption state, and outputs a second energy consumption instruction set, wherein the second energy consumption instruction set includes at least one of various known values ​​of the target state of charge SOCref.

[0006] Specifically, if the vehicle is currently on a clear road section and the second energy consumption data set corresponds to a second upper limit SOCmax lower than the battery state of charge, the first optimized value SOC3 preset in the second energy consumption data set is used as the target state of charge SOCref.

[0007] Furthermore, if the vehicle is currently on a clear road section and the second energy consumption data set corresponds to a second upper limit SOCmax that is higher than or equal to the battery state of charge, the second upper limit SOCmax is used as the target state of charge SOCref.

[0008] In addition, if the vehicle is currently in a congested road section, the preset first state of charge, ie, SOCmin, is used as the target state of charge SOCref.

[0009] Furthermore, in order to improve the matching capability of energy consumption information processing, a process for verifying the integrity and / or real-time performance of the initialization information can be added to the first information collection preprocessing step; then, the first power drive energy consumption and the second accessory loss energy consumption of the pure electric mode are comprehensively processed, and based on the seventh operating condition information, the first optimized value SOC3 of the charging state is obtained through machine learning and / or a rule-based model.

[0010] Among them, the seventh operating condition information can be at least one of the remaining distance length, travel time, ambient weather, etc.; its first charging state represents the system's default charging state under the power energy balance control mode; its first vehicle information is transmitted via the vehicle communication line, which can be a CAN bus and / or Ethernet network.

[0011] Furthermore, the energy consumption information processing method of this embodiment may also include a third information transmission and control step; a processing process of signing in the cloud by obtaining second cloud initialization information: the second cloud initialization information may include at least one of the first navigation information and the seventh operating condition information transmitted by the communication module.

[0012] Furthermore, information synchronization processing can also be achieved by splicing the fourth multi-source data in the second cloud initialization information to a preset timestamp.

[0013] In addition, the energy consumption information processing method of this embodiment may also include a fourth remote co-processing step; by solving the second energy consumption data set and identifying the second energy consumption state in the cloud, a fourth energy consumption instruction set is obtained and output in the cloud for vehicle energy management; wherein, the cloud-based solution process may include obtaining information of the second energy consumption data set in the local controller, and exchanging data with the controller and / or related control strategy execution units when necessary; similarly, the splicing process can be implemented using a typical ETL process, i.e., an extraction, transformation, and loading process.

[0014] Specifically, the third information transmission and control step can be used to correct or compensate the solution process of the second energy consumption information processing step and / or the fourth remote co-processing step by obtaining real-time and / or offline third intervention data; the real-time and / or offline third intervention data also includes at least one of the second positioning information and the third interactive instruction; and then correct and / or compensate the second energy consumption data set, adjust the second energy consumption state, and output the third energy consumption instruction set.

[0015] Furthermore, the energy consumption information processing method of the embodiment of the present invention also obtains a third real-time state of charge SOC through the first information collection preprocessing step, and realizes the conversion of the vehicle energy control mode by comparing the state with the target state of charge SOCref.

[0016] Specifically, if the difference between the third real-time state of charge SOC and the target state of charge SOCref is greater than a first response threshold THD1 , where THD1 is greater than 0, a command is output to operate the vehicle in the pure electric mode.

[0017] Similarly, if the difference between the target state of charge SOCref and the third real-time state of charge SOC is greater than a second response threshold THD2, wherein THD2 is greater than 0, a command is output to operate the vehicle in the active charging mode (332).

[0018] In addition, the vehicle's operating mode remains unchanged; the first optimization value can be processed according to SOC3=(SOC1-SOC2)*X+SOC2+SOCmin; at this time, the value of X can be selected depending on the specific working conditions, or the target SOC calculation process can be adjusted based on similar ideas.

[0019] On the other hand, an embodiment of the present invention also discloses an energy consumption information processing device, the core of which includes a first information acquisition preprocessing unit and a second energy consumption information processing unit; wherein, the first information acquisition preprocessing unit obtains first local initialization information, and the first local initialization information may include at least one of first vehicle information, first navigation information, and seventh operating condition information.

[0020] Furthermore, the first information collection preprocessing unit may splice the first multi-source data in the first local initialization information to a preset timestamp to achieve information synchronization.

[0021] In addition, the second energy consumption information processing unit solves the second energy consumption data set, identifies the second energy consumption state, and outputs a second energy consumption instruction set, which includes at least one of various known values ​​of the target state of charge SOCref.

[0022] Specifically, if the vehicle is currently on a clear road section and the second energy consumption data set corresponds to a second upper limit SOCmax lower than the battery state of charge, the first optimized value SOC3 preset in the second energy consumption data set is used as the target state of charge SOCref.

[0023] Similarly, if the vehicle is currently on a clear road section and the second energy consumption data set corresponds to a second upper limit SOCmax that is higher than or equal to the battery state of charge, the second upper limit SOCmax is used as the target state of charge SOCref.

[0024] In addition, if the vehicle is currently in a congested road section, the preset first state of charge, ie, SOCmin, is used as the target state of charge SOCref.

[0025] Furthermore, the energy consumption information processing device of an embodiment of the present invention, its first information acquisition preprocessing unit can also improve the information processing effect by verifying the integrity and real-time performance of the initialization information; in addition, it can also comprehensively process the first power drive energy consumption and the second accessory loss energy consumption of the pure electric mode, and obtain its first optimization value SOC3 based on the seventh operating condition information through machine learning and / or a rule-based model.

[0026] Among them, the seventh operating condition information can be at least one of the remaining distance length, travel time, and ambient weather; its first charging state represents the system's default charging state under the power energy balance control mode; its first vehicle information is transmitted via the vehicle communication line, and its vehicle communication line includes CAN bus and / or Ethernet.

[0027] Furthermore, the energy consumption information processing device of an embodiment of the present invention may also include a third auxiliary transmission and control unit; by obtaining the second cloud initialization information, the processing process is implemented in the cloud: wherein, the second cloud initialization information can be transmitted by the communication module, and can be at least one of the first navigation information and the seventh operating condition information.

[0028] Similarly, the third auxiliary transmission and control unit can also splice the fourth multi-source data in the second cloud initialization information to the preset timestamp, that is, realize information synchronization processing.

[0029] Furthermore, the energy consumption information processing device of an embodiment of the present invention may also include a fourth remote co-processing unit; the fourth remote co-processing unit can solve the second energy consumption data set, identify the second energy consumption state, and output a fourth energy consumption instruction set in the cloud.

[0030] Among them, the cloud-based solution process may include obtaining information of the second energy consumption data set in the local controller and exchanging data between the controller or related control strategy execution units; its splicing process can also adopt ETL, that is, extraction, transformation and loading method.

[0031] Furthermore, the energy consumption information processing device of an embodiment of the present invention, its third auxiliary transmission and control unit can correct and / or compensate the solution process of the second energy consumption information processing unit and / or the fourth remote co-processing unit by obtaining real-time and / or offline third intervention data.

[0032] Specifically, the real-time and / or offline third intervention data may also include at least one of second positioning information and third interactive instructions; and by correcting and / or compensating the second energy consumption data set, adjusting its second energy consumption state, and outputting its third energy consumption instruction set.

[0033] Furthermore, in the energy consumption information processing device according to the embodiment of the present invention, the first information acquisition and pre-processing unit can also switch the energy control mode by acquiring the third real-time state of charge SOC and comparing it with the target state of charge SOCref.

[0034] Specifically, if the difference between the third real-time state of charge SOC and the target state of charge SOCref is greater than a first response threshold THD1 , where THD1 is greater than 0, a command is output to operate the vehicle in the pure electric mode.

[0035] Similarly, if the difference between the target state of charge SOCref and the third real-time state of charge SOC is greater than the second response threshold THD2, where THD2 is greater than 0, an instruction is output to operate the vehicle in the active charging mode; otherwise, the vehicle's operating mode is maintained unchanged; similarly, its first optimized value can be obtained by SOC3=(SOC1-SOC2)*X+SOC2+SOCmin.

[0036] In addition, the embodiments of the present invention also provide a composition structure and implementation method of a computer storage medium, a controller, and an energy management module, and related products will also fall within the protection scope of the present invention.

[0037] The core of the computer storage medium includes a storage medium body for storing computer programs. When the aforementioned computer program is executed by a microprocessor, any energy consumption information processing method of the present invention can be implemented.

[0038] Similarly, the controller and / or energy management module of the embodiments of the present invention can be implemented by adopting any of the above energy consumption information processing devices and / or storage media; wherein, the energy management module can also be implemented by adopting any of the above controllers.

[0039] Based on the above-mentioned inventive concept, the present invention improves the relevant technical solutions in the field of energy consumption information processing and energy management of HEV and related vehicles. Compared with the relevant technologies, its technical effects are:

[0040] On the one hand, the method or product of the present invention ensures that when the HEV's power reserve is insufficient to complete the entire journey in pure electric operation, power is reserved through active charging or active power preservation mode at high speed, so as to ensure that the relevant power can be used in congested road conditions as much as possible, which is conducive to giving full play to the performance advantages of the engine and motor.

[0041] On the other hand, the method or product of the present invention prioritizes using power near the balance point and can maintain the battery power in a range higher than the balance point throughout the entire journey. Generally, this strategy allows the battery to operate in a more efficient area, thereby obtaining better energy-saving technical indicators.

[0042] Thirdly, after implementing the method or product of the present invention, the average battery state of charge (SOC) throughout the entire journey is improved, thereby ensuring that the battery has a stronger discharge capacity and motor power output capacity, which can reduce the engine running time and the number of starts and stops, and further optimize the vehicle's NVH (Noise, Vibration, Harshness) indicators.

[0043] The methods and products disclosed in the embodiments of the present invention are applicable to parallel and series-parallel HEV and plug-in hybrid electric vehicle (PHEV) models, and can also be applied to extended range electric vehicle (EREV) topologies.

[0044] Compared to Figure 1 The scheme shown can be adjusted as follows in actual engineering applications:

[0045] On the one hand, cloud services can be eliminated, and navigation information can be directly acquired and parsed through the application of APP (APPlication) in the smart car computer, and transmitted to the controller end via communication protocols such as CAN bus or Ethernet; among them, the controller will parse the relevant information to obtain the remaining road condition information, and execute the target SOC planning strategy. The relevant instructions are directly used to optimize the energy management strategy.

[0046] On the other hand, the smart car machine is not responsible for obtaining navigation model information, but directly deploys navigation services in the cloud; the car side uses a large computing power controller such as an onboard computer, such as Figure 1 The related functions of the smart car machine shown are implemented by it; in this case, a modified solution can also be adopted to complete the entire service deployment through the on-board computer.

[0047] Regarding the calculation of the target SOC; where SOC3 = (SOC1-SOC2)*X+ SOC2 + SOCmin, the value of X can be confirmed based on specific operating conditions, or the target SOC calculation process can be adjusted based on similar ideas.

[0048] In addition, it should be noted that the terms "first", "second" and similar terms used in this article are only for describing the various components of the technical solution, and do not constitute a limitation of the technical solution, nor can they be understood as an indication or suggestion of the importance of the corresponding elements; elements with terms such as "first", "second" and similar terms indicate that the corresponding technical solution contains at least one of the elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution of the present invention and facilitate a further understanding of the technical effects, technical features and purposes of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings. The accompanying drawings constitute an essential part of the specification and are used together with the embodiments of the present invention to illustrate the technical solution of the present invention, but do not constitute a limitation to the present invention.

[0050] The same reference numerals in the accompanying drawings represent the same components, specifically:

[0051] Figure 1 This is a schematic diagram of the structure of a product embodiment of the present invention;

[0052] Figure 2 Schematic diagram of the process of the embodiment of the method of the present invention Figure 1 ;

[0053] Figure 3 Schematic diagram of the process of the embodiment of the method of the present invention Figure 2 ;

[0054] Figure 4 Schematic diagram of the process of the embodiment of the method of the present invention Figure 3 ;

[0055] Figure 5 Schematic diagram of the process of the embodiment of the method of the present invention Figure 4 ;

[0056] Figure 6 Schematic diagram of the process of the embodiment of the device of the present invention Figure 1 ;

[0057] Figure 7 Schematic diagram of the structure of each product embodiment of the present invention Figure 1 ;

[0058] Figure 8 Schematic diagram of the structure of each product embodiment of the present invention Figure 2 ;

[0059] Figure 9 Schematic diagram of the structure of each product embodiment of the present invention Figure 3 ;

[0060] Figure 10 Schematic diagram of the structure of each product embodiment of the present invention Figure 4 .

[0061] in:

[0062] 001-First vehicle information,

[0063] 010-Controller Area Network CAN (Controller Area Network) bus,

[0064] 100-First information collection preprocessing step,

[0065] 110-first local initialization information,

[0066] 200-Second energy consumption information processing step,

[0067] 201-Second Energy Consumption Instruction Set,

[0068] 211-Second energy consumption default value,

[0069] 210- The second energy consumption calculation step,

[0070] 220-Second energy consumption planning step,

[0071] 221-Second energy consumption optimization value,

[0072] 231-Second energy consumption upper limit value,

[0073] 270-Second energy consumption dataset,

[0074] 290-Second energy consumption state,

[0075] 300-third information transmission and control step,

[0076] 301-Third Energy Consumption Instruction Set,

[0077] 310-Third real-time charging status,

[0078] 320-third mode determination step,

[0079] 330-third mode conversion step,

[0080] 400-Fourth remote co-processing step;

[0081] 401-Fourth Energy Consumption Instruction Set,

[0082] 442-Fourth remote assistance information (transmission uplink direction),

[0083] 610-First information collection and pre-processing unit,

[0084] 620- Second energy consumption information processing unit,

[0085] 630- Third Information Transmission and Control Unit,

[0086] 631-third auxiliary processing unit,

[0087] 632-the third main control module,

[0088] 640-Fourth remote co-processing unit,

[0089] 641-Fourth Remote Assistance Pre-processing Unit,

[0090] 642-Fourth remote assistance energy consumption information processing unit,

[0091] 643-Fourth remote assistance data input and output and storage unit,

[0092] 644-Fourth Remote Assistance Information Transmission and Control Unit,

[0093] 661-First multi-source data (not marked in the figure),

[0094] 664-Fourth Multi-Source Data,

[0095] 710-First navigation information,

[0096] 720-Second positioning information,

[0097] 730-Third interactive instruction,

[0098] 740-Fourth communication module,

[0099] 750-Fifth upload module,

[0100] 760-sixth delivery module,

[0101] 777-Seventh working condition information

[0102] 900-vehicles,

[0103] 901-Controller,

[0104] 902-Energy consumption information processing device,

[0105] 903-Storage medium,

[0106] 905-Energy management module. DETAILED DESCRIPTION

[0107] The present invention will be further described in detail below with reference to the accompanying drawings and examples. Of course, the specific embodiments described below are only intended to explain the technical solutions of the present invention, rather than to limit the present invention. In addition, the parts described in the embodiments or drawings are merely illustrative of the relevant parts of the present invention, rather than the entire present invention.

[0108] like Figure 1 、 Figure 5As shown, the energy consumption information processing method of this embodiment includes a first information collection preprocessing step 100 and a second energy consumption information processing step 200; wherein, the first information collection preprocessing step 100 obtains the first local initialization information 110, and the first local initialization information 110 includes the first vehicle information 001, the first navigation information 710, and the seventh working condition information 777; the first multi-source data 661 in the first local initialization information 110 is spliced ​​to the preset timestamp, thereby realizing the synchronous processing process of the information.

[0109] Among them, such as Figure 1 As shown, this is the implementation method of the present invention based on the combination of intelligent vehicle machine + controller end + cloud system, and its processing process and related information are as follows.

[0110] It should be noted that the data processing and control strategy implementation of the architecture corresponding to this embodiment is based on a cloud-based implementation. However, for the present invention, the same processing process can also be implemented on the intelligent vehicle machine or controller side. The relevant processing process mainly includes:

[0111] First, communication data processing, by receiving navigation, vehicle condition and other related data packets uploaded by the intelligent vehicle control terminal, and checking the integrity and real-time nature of the data packets; then multi-source data splicing processing is performed according to the timestamp to realize the preprocessing of related data.

[0112] Secondly, in response to the processing conditions of the data packet, the pre-processed data is solved according to the optimization target, and the target charging state, i.e. SOC, and vehicle prediction information are output and sent to the smart car control terminal to realize the switching of the energy management mode.

[0113] Its intelligent vehicle computer can query the remaining road conditions through services such as navigation, and upload data packets to the cloud through the communication module; in addition, it can receive the target SOC and vehicle prediction information sent from the cloud, and send the target SOC and vehicle prediction information to the controller side through the communication module; the controller side can also follow and respond to control instructions such as the target SOC, and control the power mode and the operating points of its core components.

[0114] Based on the processing of the above subsystem units, the relevant functional modules may include the following modules or units:

[0115] like Figure 1 As shown, it can include a data uploading module for realizing the function of uploading data packets to the cloud through the communication module 740; it can also include a data sending module for receiving the target SOC and vehicle prediction information sent from the cloud, and sending the target SOC and vehicle prediction information to the third main control module 632 through the communication module 740; a follow-up processing module, that is, the controller 901, is used to follow the target SOC through the controller end.

[0116] Furthermore, similarly Figure 1 As shown, a data receiving module may be provided for receiving data packets uploaded by the intelligent vehicle control terminal; a data verification module may also be provided for performing integrity and real-time verification on the data packets; a data processing module may also be included for performing multi-source data splicing processing according to the timestamp when the data packets meet the transmission conditions; wherein, as Figure 3 The SOC processing module can be used to compare the target SOC with the vehicle prediction information based on the processed data, and to achieve mode conversion by sending relevant instructions to the intelligent vehicle control terminal.

[0117] Among them, for Figure 3 The target SOC processing process shown in FIG5 is shown in FIG6 , where SOCref represents the target SOC command; SOCmin represents the system default value in the power energy balance control mode; SOCmax represents the upper limit value allowed by the battery target SOC command; and X is a positive real number between 0 and 1 and needs to be set according to the vehicle and navigation information acquisition capabilities.

[0118] In addition, the processing of SOC1 and SOC2 can comprehensively consider the power drive energy consumption and accessory loss energy consumption of the pure electric mode, and based on data such as the remaining distance length, travel time, and ambient weather, obtain relevant energy consumption data through machine learning models or rule-based physical models; specific methods can be found in the implementation methods or products of related technologies; they will not be repeated here.

[0119] Among them, the target value SOCref calculated based on the predicted energy consumption will change as the distance from the destination changes. When the vehicle reaches the destination, its value will be the same as SOCmin.

[0120] Furthermore, after receiving the target SOC instruction, the controller 901 or other execution components may adopt the following response strategy: Figure 4 The process is shown; among them, the active charging mode is determined according to the characteristics of the power topology. For the parallel / series-parallel mode, the parallel direct drive mode is recommended, and for the extended-range EREV, the high-efficiency series mode can be selected.

[0121] Among them, such as Figure 4 Both THD1 and THD2 are positive values, and the units are the same as SOC. In addition, the setting of relevant values ​​is mainly to avoid frequent switching of power modes in power conservation mode and the impact on the number of engine starts and stops. The values ​​can be determined after comprehensively considering parameters such as battery pack capacity and allowable downtime.

[0122] Furthermore, if Figure 3As shown, the second energy consumption information processing step 200 solves the second energy consumption data set 270, identifies the second energy consumption state 290, and outputs a second energy consumption instruction set 201, wherein the second energy consumption instruction set 201 includes at least one of various known values ​​of the target state of charge SOCref.

[0123] Specifically, if the current road is in a clear section and the second energy consumption data set 270 corresponds to a second upper limit SOCmax below the battery state of charge, that is, Figure 3 As shown in 231, the first optimized value SOC3 preset in the second energy consumption data set 270 is used, that is, Figure 3 221 in is used as the target state of charge SOCref.

[0124] Similarly, if the current road is unblocked and the second energy consumption data set 270 corresponds to a second upper limit SOCmax (231 in the figure) higher than or equal to the battery state of charge, the second upper limit SOCmax (231 in the figure) is used as the target state of charge SOCref.

[0125] In addition, if the vehicle is currently in a congested road section, the preset first state of charge 211 , ie, SOCmin, is used as the value or state of the target state of charge SOCref.

[0126] Further, if Figure 1 and Figure 2 As shown, the first information collection preprocessing step 100 can also verify the integrity and real-time performance of the initialization information 110; comprehensively process the first power drive energy consumption and the second accessory loss energy consumption of the pure electric mode, and obtain the first optimization value SOC3 based on the seventh working condition information 777 through machine learning and / or rule-based model, that is, Figure 3 221 shown.

[0127] Among them, the seventh operating condition information 777 can be the remaining distance, travel time, and ambient weather; its first charging status 211 represents the system's default charging status under the power energy balance control mode; its first vehicle information 001 is transmitted via the vehicle communication line, and its vehicle communication line can be a CAN bus 010 and / or an Ethernet line.

[0128] Furthermore, the energy consumption information processing method of this embodiment further includes a third information transmission and control step 300; Figure 1 As shown, the third information transmission and control step 300 obtains the second cloud initialization information 641, and the second cloud initialization information 641 can be composed of the first navigation information 710 and the seventh operating condition information 777 transmitted by the communication module 740; and then the fourth multi-source data 664 in the second cloud initialization information 641 is spliced ​​to the preset timestamp to realize the synchronous processing of the information.

[0129] like Figure 5 As shown, the energy consumption information processing method of this embodiment may further include a fourth remote co-processing step 400; wherein, Figure 3 As shown, the fourth remote co-processing step 400 solves the second energy consumption data set 270 in the cloud, identifies the second energy consumption state 290 , and outputs a fourth energy consumption instruction set 401 .

[0130] Specifically, its cloud-based solution process can achieve relevant processing by obtaining information of the second energy consumption data set 270 in the local controller and exchanging data between the controller or related control strategy execution units; and then the data can be extracted, converted and loaded through the splicing process ETL.

[0131] Furthermore, the energy consumption information processing method of an embodiment of the present invention can also obtain real-time and / or offline third intervention data 631 through the third information transmission and control step 300, which is used to correct and / or compensate the solution process of the second energy consumption information processing step 200 and / or the fourth remote co-processing step 400.

[0132] like Figure 1 As shown, the real-time and / or offline third intervention data 631 may also include the second positioning information 720 and the third interaction instruction 730; Figure 2 and Figure 3 As shown, the second energy consumption data set 270 may be further corrected and / or compensated, the second energy consumption state 290 may be adjusted, and a third energy consumption instruction set 301 may be output.

[0133] In addition, if Figure 3 As shown, the first information collection preprocessing step 100 can also achieve the switching of the energy management mode by obtaining the third real-time charging state SOC, namely 310 in the figure, and comparing the third real-time charging state SOC with the target charging state SOCref.

[0134] Specifically, if Figure 4 As shown, if the difference between the third real-time state of charge SOC, ie 310 , and the target state of charge SOCref is greater than a first response threshold THD1 , where THD1 is greater than 0, a command is output to operate the vehicle in the pure electric mode 331 .

[0135] Similarly, if Figure 4 , as shown, if the difference between the target state of charge SOCref and the third real-time state of charge SOC is greater than the second response threshold THD2, where THD2 is greater than 0, an instruction is output to operate the vehicle in the active charging mode 332.

[0136] In addition, the same Figure 4As shown, the vehicle's operating mode 333 may be maintained unchanged; wherein, its first optimized value may be obtained by SOC3=(SOC1-SOC2)*X+SOC2+SOCmin.

[0137] Further, if Figure 6 As shown, the embodiment of the present invention also discloses an energy consumption information processing device, the core of which includes a first information acquisition preprocessing unit 610 and a second energy consumption information processing unit 620; wherein the first information acquisition preprocessing unit 610 obtains the first local initialization information 110; Figure 1 As shown, the first local initialization information 110 may include at least one of the first vehicle information 001, the first navigation information 710, and the seventh operating condition information 777; further, by splicing the first multi-source data 661 in the first local initialization information 110 to a preset timestamp, synchronous processing of the information can be achieved.

[0138] Further, if Figure 3 As shown, the second energy consumption information processing unit 620 realizes energy consumption processing by solving the second energy consumption data set 270, identifying the second energy consumption state 290, and outputting the second energy consumption instruction set 201, wherein the second energy consumption instruction set 201 includes at least one of various known values ​​of the target charging state SOCref.

[0139] Specifically, if the current road is in a clear section and the second energy consumption data set 270 corresponds to a second upper limit SOCmax below the battery state of charge, that is, 231 in the figure, the first optimized value SOC3 preset in the second energy consumption data set 270, that is, Figure 3 221 in is used as the target state of charge SOCref.

[0140] Similarly, if the current road is in a clear section and the second energy consumption data set 270 corresponds to a second upper boundary SOCmax higher than or equal to the battery state of charge, that is, 231 in the figure, the second upper boundary SOCmax, that is, Figure 3 231 is the target state of charge SOCref.

[0141] In addition, if the vehicle is currently in a congested road section, the preset first state of charge 211 , ie, SOCmin, is used as the target state of charge SOCref.

[0142] Further, if Figure 6 In the energy consumption information processing device shown, the first information acquisition preprocessing unit 610 can also verify the integrity and real-time performance of the initialization information 110; comprehensively process the first power drive energy consumption and the second accessory loss energy consumption in the pure electric mode, and obtain the first optimization value SOC3 based on the seventh working condition information 777 through machine learning and / or a rule-based model, that is, Figure 3 221 shown.

[0143] Among them, such as Figure 1 As shown, the seventh operating condition information 777 can be the remaining distance length, travel time, and ambient weather; its first charging state 211 represents the system's default charging state under the power energy balance control mode; its first vehicle information 001 is transmitted via the vehicle communication line, wherein the vehicle communication line can be a CAN bus 010 and / or an Ethernet network line.

[0144] Furthermore, if Figure 6 The energy consumption information processing device shown may further include a third auxiliary transmission and control unit 630; Figure 1 As shown, by obtaining the second cloud initialization information 641, the relevant energy consumption processing process is implemented in the cloud; wherein, the second cloud initialization information 641 can provide information reference by at least one of the first navigation information 710 and the seventh operating condition information 777 transmitted by the communication module 740; and then by splicing the fourth multi-source data 664 in the second cloud initialization information 641 to the preset timestamp, the synchronous processing of the information is realized.

[0145] Specifically, if Figure 1 、 Figure 6 As shown, the embodiment of the present invention may further include a fourth remote co-processing unit 640; and Figure 3 As shown, the second energy consumption data set 270 is solved in the cloud, the second energy consumption state 290 is identified, and the fourth energy consumption instruction set 401 is output.

[0146] The cloud-based solution process obtains information of the second energy consumption data set 270 in the local controller and exchanges data between the controller or related control strategy execution units; the splicing process can adopt ETL, that is, extraction, transformation and loading method.

[0147] Furthermore, if Figure 1 The energy consumption information processing device shown, its third auxiliary transmission and control unit 630 obtains real-time and / or offline third intervention data 631 for correcting or compensating the solution process of the second energy consumption information processing unit 620 and / or the fourth remote co-processing unit 640; its real-time and / or offline third intervention data 631 may also include second positioning information 720 and third interaction instructions 730.

[0148] Furthermore, Figure 3 As shown, by correcting and / or compensating the second energy consumption data set 270 , adjusting the second energy consumption state 290 , and outputting a third energy consumption instruction set 301 .

[0149] Specifically, if Figure 4 、 Figure 6 As shown, the first information acquisition preprocessing unit 610 obtains the third real-time charging state SOC, that is, Figure 4 As shown in 310 , the energy management mode is switched by comparing the state of charge SOC with the target state of charge SOCref.

[0150] Among them, such as Figure 4 As shown, if the difference between the third real-time state of charge 310 and the target state of charge SOCref is greater than a first response threshold THD1 , where THD1 is greater than 0, a command is output to operate the vehicle in the pure electric mode 331 .

[0151] Similarly, if Figure 4 As shown, if the difference between the target state of charge SOCref and the third real-time state of charge 310 is greater than a second response threshold THD2 , where THD2 is greater than 0, a command is output to operate the vehicle in the active charging mode 332 .

[0152] In addition, if Figure 4 , as shown, the vehicle's operating mode 333 remains unchanged; wherein, the first optimized value can be obtained by SOC3=(SOC1-SOC2)*X+SOC2+SOCmin.

[0153] Furthermore, if Figures 7 to 10 As shown, the embodiment of the present invention further discloses a computer storage medium 903, a controller 901 and an energy management module 905, the implementation process of which is the same as the inventive concept of the above-mentioned device 902; it will not be repeated here.

[0154] The present invention discloses an energy consumption information processing method, device, medium, controller and management module through embodiments; it can solve the problem in related technologies that the energy management strategy of hybrid electric vehicles (HEV) lacks consideration of actual operating conditions; it can also realize functions such as vehicle status monitoring, remaining road condition information analysis, vehicle energy management strategy planning, and energy management strategy execution; and thus realizes the optimization of energy indicators for the entire journey based on road traffic conditions, with the goal of optimizing energy consumption throughout the journey, and targeted control of the vehicle's power mode and the operating point settings of components such as the engine and motor, thereby realizing optimization of energy indicators for the entire journey.

[0155] It should be noted that the above embodiments are only for the purpose of more clearly illustrating the technical solutions of the present invention. Those skilled in the art will understand that the implementation methods of the present invention are not limited to the above contents, and obvious changes, replacements or substitutions based on the above contents do not exceed the scope covered by the technical solutions of the present invention; other implementation methods will also fall within the scope of the present invention without departing from the concept of the present invention.

Claims

1. A method for processing energy consumption information, characterized in that: include: A first information collection pre-processing step (100), a second energy consumption information processing step (200); wherein, The first information acquisition preprocessing step (100) acquires first local initialization information (110), wherein the first local initialization information (110) includes first vehicle information (001), first navigation information (710), and seventh operating condition information (777); splicing first multi-source data (661) in the first local initialization information (110) to a preset timestamp to achieve information synchronization processing; The second energy consumption information processing step (200) solves a second energy consumption data set (270), identifies a second energy consumption state (290), and outputs a second energy consumption instruction set (201), wherein the second energy consumption instruction set (201) includes at least one of various known values ​​of the target state of charge SOCref: If the current road is in a clear section and the second energy consumption data set (270) corresponds to a second upper limit SOCmax (231) lower than the battery state of charge, a first optimized value SOC3 (221) preset in the second energy consumption data set (270) is used as a target state of charge SOCref; If the current road is in a clear section and the second energy consumption data set (270) corresponds to a second upper limit SOCmax (231) of the battery state of charge that is higher than or equal to the second upper limit SOCmax (231), the second upper limit SOCmax (231) is used as the target state of charge SOCref; If the vehicle is currently in a congested road section, a preset first state of charge (211), namely SOCmin, is used as the target state of charge SOCref.

2. The energy consumption information processing method according to claim 1, wherein: The first information collection preprocessing step (100) further verifies the integrity and real-time performance of the first local initialization information (110); comprehensively processes the first power drive energy consumption and the second accessory loss energy consumption in the pure electric mode, and obtains the first optimized value SOC3 (221) based on the seventh operating condition information (777) through machine learning and / or a rule-based model; The seventh operating condition information (777) includes the remaining distance, travel time, and ambient weather; the first charging state (211) represents the system's default charging state in the power energy balance control mode; the first vehicle information (001) is transmitted via a vehicle communication line, and the vehicle communication line includes a CAN bus (010) and / or Ethernet.

3. The energy consumption information processing method according to claim 1 or 2, further comprising a third information transmission and control step (300); The third information transmission and control step (300) obtains second cloud initialization information (641), wherein the second cloud initialization information (641) includes the first navigation information (710) and the seventh operating condition information (777) transmitted by the communication module (740); and splices the fourth multi-source data (664) in the second cloud initialization information (641) to a preset timestamp, thereby also realizing information synchronization processing.

4. The energy consumption information processing method according to claim 3, further comprising a fourth remote co-processing step (400); The fourth remote co-processing step (400) solves the second energy consumption data set (270) in the cloud, identifies the second energy consumption state (290), and outputs a fourth energy consumption instruction set (401); wherein, The cloud-based solution process includes obtaining information of the second energy consumption data set (270) in a local controller and exchanging data between controllers or control strategy execution units; the splicing process adopts ETL, i.e., extraction, transformation and loading method.

5. The energy consumption information processing method according to claim 4, wherein: The third information transmission and control step (300) obtains real-time and / or offline third intervention data (631) for correcting or compensating the solution process of the second energy consumption information processing step (200) and / or the fourth remote co-processing step (400); the real-time and / or offline third intervention data (631) further includes second positioning information (720) and third interactive instructions (730); corrects and / or compensates the second energy consumption data set (270), adjusts the second energy consumption state (290), and outputs a third energy consumption instruction set (301).

6. The energy consumption information processing method according to claim 1, 2, 4 or 5, wherein: The first information collection and preprocessing step (100) acquires a third real-time state of charge SOC (310) and compares it with the target state of charge SOCref; If the difference between the third real-time state of charge SOC (310) and the target state of charge SOCref is greater than a first response threshold THD1, wherein THD1 is greater than 0, outputting a command to operate the vehicle in a pure electric mode (331); If the difference between the target state of charge SOCref and the third real-time state of charge SOC (310) is greater than a second response threshold THD2, wherein THD2 is greater than 0, an instruction is output to operate the vehicle in an active charging mode (332); in addition, the vehicle operation mode (333) is maintained unchanged; wherein the first optimized value SOC3 = (SOC1-SOC2)*X+SOC2+SOCmin; wherein SOC1 is the cumulative power consumption demand for the entire journey, SOC2 is the cumulative power consumption demand for the congested road section, and X is a positive real number between 0 and 1.

7. An energy consumption information processing device, comprising: A first information collection pre-processing unit (610) and a second energy consumption information processing unit (620); wherein, The first information acquisition preprocessing unit (610) acquires first local initialization information (110), the first local initialization information (110) including first vehicle information (001), first navigation information (710), and seventh operating condition information (777); splices first multi-source data (661) in the first local initialization information (110) to a preset timestamp to achieve information synchronization processing; The second energy consumption information processing unit (620) solves the second energy consumption data set (270), identifies the second energy consumption state (290), and outputs a second energy consumption instruction set (201), wherein the second energy consumption instruction set (201) includes at least one of various known values ​​of the target state of charge SOCref: If the current road is in a clear section and the second energy consumption data set (270) corresponds to a second upper limit SOCmax (231) lower than the battery state of charge, a first optimized value SOC3 (221) preset in the second energy consumption data set (270) is used as a target state of charge SOCref; If the current road is in a clear section and the second energy consumption data set (270) corresponds to a second upper limit SOCmax (231) of the battery state of charge that is higher than or equal to the second upper limit SOCmax (231), the second upper limit SOCmax (231) is used as the target state of charge SOCref; If the vehicle is currently in a congested road section, a preset first state of charge (211), namely SOCmin, is used as the target state of charge SOCref.

8. The energy consumption information processing device according to claim 7, wherein: The first information acquisition preprocessing unit (610) further verifies the integrity and real-time performance of the first local initialization information (110); comprehensively processes the first power drive energy consumption and the second accessory loss energy consumption in the pure electric mode, and obtains the first optimized value SOC3 (221) based on the seventh operating condition information (777) through machine learning and / or a rule-based model; The seventh operating condition information (777) includes the remaining distance, travel time, and ambient weather; the first charging state (211) represents the system's default charging state in the power energy balance control mode; the first vehicle information (001) is transmitted via a vehicle communication line, and the vehicle communication line includes a CAN bus (010) and / or Ethernet.

9. The energy consumption information processing device according to claim 7 or 8, further comprising a third auxiliary transmission and control unit (630); The third auxiliary transmission and control unit (630) obtains second cloud initialization information (641), wherein the second cloud initialization information (641) includes the first navigation information (710) and the seventh operating condition information (777) transmitted by the communication module (740); and splices the fourth multi-source data (664) in the second cloud initialization information (641) to a preset timestamp, thereby also realizing information synchronization processing.

10. The energy consumption information processing device according to claim 9, further comprising a fourth remote co-processing unit (640); The fourth remote co-processing unit (640) solves the second energy consumption data set (270) in the cloud, identifies the second energy consumption state (290), and outputs a fourth energy consumption instruction set (401); wherein, The cloud-based solution process includes obtaining information of the second energy consumption data set (270) in a local controller and exchanging data between controllers or control strategy execution units; the splicing process adopts ETL, i.e., extraction, transformation and loading method.

11. The energy consumption information processing device according to claim 10, wherein: The third auxiliary transmission and control unit (630) obtains real-time and / or offline third intervention data (631) for correcting or compensating the solution process of the second energy consumption information processing unit (620) and / or the fourth remote co-processing unit (640); the real-time and / or offline third intervention data (631) further includes second positioning information (720) and third interaction instructions (730); corrects and / or compensates the second energy consumption data set (270), adjusts the second energy consumption state (290), and outputs a third energy consumption instruction set (301).

12. The energy consumption information processing device according to claim 7, 8, 10 or 11, wherein: The first information acquisition preprocessing unit (610) acquires a third real-time state of charge SOC (310) and compares it with the target state of charge SOCref; If the difference between the third real-time state of charge SOC (310) and the target state of charge SOCref is greater than a first response threshold THD1, wherein THD1 is greater than 0, outputting a command to operate the vehicle in a pure electric mode (331); If the difference between the target state of charge SOCref and the third real-time state of charge SOC (310) is greater than a second response threshold THD2, wherein THD2 is greater than 0, an instruction is output to operate the vehicle in an active charging mode (332); in addition, the vehicle operation mode (333) is maintained unchanged; wherein the first optimized value SOC3 = (SOC1-SOC2)*X+SOC2+SOCmin; wherein SOC1 is the cumulative power consumption demand for the entire journey, SOC2 is the cumulative power consumption demand for the congested road section, and X is a positive real number between 0 and 1.

13. A computer storage medium comprising: A storage medium for storing a computer program; when the computer program is executed by a microprocessor, the energy consumption information processing method according to any one of claims 1 to 6 is implemented.

14. A controller comprising: The energy consumption information processing device according to any one of claims 7 to 12; And / or the storage medium as claimed in claim 13.

15. An energy management module, comprising: The energy consumption information processing device according to any one of claims 7 to 12; and / or the storage medium as claimed in claim 13; And / or the controller as claimed in claim 14.

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