An electric drive axle range extender hybrid system driving force distribution optimization method

Abstract

The application relates to the technical field of power optimization, and discloses a driving force distribution optimization method for an electric drive axle range extender hybrid system, which comprises the following steps: acquiring current operation parameters, real-time load information and front road condition information of a cargo commercial vehicle; constructing an efficiency model based on historical torque and rotating speed combination efficiency data of the electric drive axle, and obtaining a target driving force distribution ratio through dynamic load distribution according to the efficiency atlas and the real-time load information; simultaneously, performing delay compensation by using the current operation parameters and the road condition information to generate a dynamic adjustment factor; combining the target distribution ratio with the efficiency characteristics of an engine and an electric motor to determine a torque distribution combination maximizing the total efficiency; correcting the combination through the dynamic adjustment factor to obtain required torques of the engine and the electric motor, and completing driving force optimization distribution; and the application can improve the efficiency of driving force distribution optimization of the electric drive axle range extender hybrid system.

Description

Technical Field

[0001] This invention relates to the field of power optimization technology, and in particular to a method for optimizing the driving force distribution of an electric drive axle range-extended hybrid system. Background Technology

[0002] Current electric drive axle range-extended hybrid systems used in commercial freight vehicles generally suffer from incomplete parameter considerations in their drive force distribution schemes. Most schemes rely solely on the efficiency of the engine or electric motor as the basis for allocation, failing to incorporate real-time vehicle load information and current road conditions into the core decision-making process. In actual operation, commercial freight vehicles experience frequent load transitions and dynamically changing road conditions. This single-parameter-driven allocation method cannot adapt to complex operating conditions, easily leading to insufficient synergy between the electric drive axle and the hybrid system. This not only wastes energy but may also cause power output fluctuations due to a mismatch between drive force and actual demand.

[0003] Meanwhile, existing drive force distribution schemes lack the ability to dynamically model and precisely adjust the efficiency characteristics of the electric drive axle. Most schemes rely on preset fixed distribution ratios, failing to construct a dedicated efficiency model for the electric drive axle using historical torque and speed combination efficiency data. This makes it difficult to optimize the basic distribution ratio based on the real-time operating parameters of the electric drive axle. Furthermore, after determining the torque distribution combination, there is no delay compensation for dynamic fluctuations in vehicle operating parameters, resulting in the distribution scheme's inability to respond promptly to changes in operating conditions. This leads to insufficient overall system efficiency stability. During sudden changes in operating conditions, untimely torque distribution corrections may reduce operational continuity or even trigger system protection mechanisms, impacting the operational efficiency of commercial freight vehicles. Therefore, improving the efficiency of drive force distribution optimization in electric drive axle range-extended hybrid systems has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system, comprising:

[0006] S1. Obtain the current operating parameters, real-time load information, and current road condition information of the commercial freight vehicle;

[0007] S2. Based on the efficiency data of various torque and speed combinations in the electric drive axle of the commercial vehicle under historical conditions, construct an efficiency model for the electric drive axle.

[0008] S3. Based on the efficiency graph in the efficiency model, obtain the basic allocation ratio of the electric drive axle under the current working condition, and dynamically allocate the load according to the real-time load information of the vehicle to obtain the target driving force allocation ratio of the electric drive axle under the current working condition.

[0009] S4. Based on the current operating parameters and the current road condition information, perform delay compensation on the commercial vehicle to obtain the dynamic adjustment factor of the commercial vehicle under the current operating conditions;

[0010] S5. Based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle, determine the torque distribution combination that maximizes the overall efficiency of the commercial vehicle.

[0011] S6. Dynamically correct the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle, and optimize the driving force distribution of the commercial vehicle according to the engine torque requirement and electric motor torque requirement.

[0012] In a preferred embodiment, acquiring the current operating parameters of the commercial vehicle, the real-time load information of the vehicle, and the current road condition information includes:

[0013] The engine speed, motor speed, and battery state of charge of the commercial vehicle are aggregated into the current operating parameters of the commercial vehicle.

[0014] The real-time load information of the commercial vehicle is obtained by measuring the load changes of the commercial vehicle.

[0015] Based on the elevation data and real-time positioning of the road ahead received by the commercial vehicle, the current road condition information of the commercial vehicle is determined.

[0016] In a preferred embodiment, the process of constructing an efficiency model for the electric drive axle in the commercial vehicle based on historical efficiency data of various torque and speed combinations includes:

[0017] During historical operation, the actual efficiency data of the electric drive axle in the commercial vehicle under different torque and speed combinations are collected to obtain the efficiency dataset of the electric drive axle;

[0018] Cluster analysis was performed on the efficiency dataset to obtain the partitioned efficiency map of the electric drive bridge;

[0019] The efficiency map of the partition is matched with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge.

[0020] In a preferred embodiment, matching the partition efficiency map with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge includes:

[0021] The input torque and output speed of the electric drive bridge are collected in real time as real-time operating parameters to obtain the current operating point dataset of the electric drive bridge;

[0022] The current working point dataset is compared with the efficiency region boundaries in the partition efficiency map to determine the specific efficiency region of the current working point;

[0023] Extract typical efficiency values ​​and efficiency change trend characteristics of the specific efficiency region from the partition efficiency map to obtain the efficiency feature set of the current working point;

[0024] Based on the efficiency mapping rules between the efficiency feature set and the current operating point dataset, the real-time efficiency prediction value of the electric drive bridge is determined.

[0025] An efficiency model for the electric drive bridge is constructed based on the real-time efficiency prediction values.

[0026] In a preferred embodiment, the step of dynamically allocating the load according to the basic allocation ratio based on the real-time vehicle load information to obtain the target driving force allocation ratio of the electric drive axle under the current operating conditions includes:

[0027] Based on the real-time load information of the vehicle, the current load status category of the electric drive axle is identified, and the load status identifier of the electric drive axle is obtained.

[0028] Based on the load status identifier, query the preset load-allocation strategy mapping table to obtain the set of allocation adjustment coefficients corresponding to the load status identifier;

[0029] Based on the set of allocation adjustment coefficients, the basic allocation ratio is weighted and corrected to obtain the preliminary optimized allocation ratio of the electric drive bridge;

[0030] Based on the current operating temperature and ambient temperature parameters of the electric drive bridge, thermal management compensation is applied to the preliminary optimized allocation ratio to obtain the temperature-compensated allocation ratio.

[0031] The temperature-compensated distribution ratio, which meets the working constraints of commercial vehicles, is output as the target driving force distribution ratio of the electric drive axle under the current operating conditions.

[0032] In a preferred embodiment, the step of performing delay compensation on the commercial vehicle based on the current operating parameters and the current road condition information to obtain a dynamic adjustment factor for the commercial vehicle under the current operating conditions includes:

[0033] The vehicle speed change rate and battery state of charge change rate are extracted from the current operating parameters to obtain the dynamic change parameter set of the commercial freight vehicle;

[0034] The slope change rate is extracted from the current road condition information to obtain the dynamic road condition characteristics of the commercial freight vehicle;

[0035] Calculate the initial adjustment factor based on the dynamically changing parameter set and the dynamic characteristics of the road conditions;

[0036] By limiting the initial adjustment factor to a range between a predefined minimum and a maximum value, the dynamic adjustment factor of the commercial vehicle is obtained.

[0037] In a preferred embodiment, the initial adjustment factor is calculated using the following formula:

[0038] ;

[0039] In the formula, The initial adjustment factor is... The preset vehicle speed factor, The vehicle speed is one of the current operating parameters. As a time factor, The battery state of charge in the current operating parameters. The preset charge state factor, The gradient is the slope in the current road condition information. The preset slope factor, The rate of change of vehicle speed, The rate of change of the state of charge of the battery. The slope change rate is given.

[0040] In a preferred embodiment, determining the torque distribution combination that maximizes the overall efficiency of the commercial vehicle based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle includes:

[0041] The pre-stored engine efficiency map and electric motor efficiency map are queried separately to obtain the efficiency characteristic curves of the engine and electric motor in the commercial vehicle at the current speed, thus obtaining the engine efficiency feature set and electric motor efficiency feature set.

[0042] Based on the target driving force distribution ratio, the torque distribution benchmark value of the engine and the electric motor is determined, and the initial torque distribution scheme of the engine and the electric motor is obtained;

[0043] The initial torque allocation scheme that conforms to the constraints of the engine efficiency feature set and the electric motor efficiency feature set shall be used as the candidate torque allocation combination for the commercial cargo vehicle.

[0044] Evaluate the overall efficiency of the candidate torque distribution combinations;

[0045] The candidate torque distribution combination with the highest overall efficiency is output as the torque distribution combination for the commercial cargo vehicle.

[0046] In a preferred embodiment, the overall efficiency is calculated using the following formula:

[0047] ;

[0048] In the formula, The total efficiency is given. This refers to the total torque required by the aforementioned commercial freight vehicle. Let be the angular velocity of the commercial cargo vehicle. The torque value allocated to the engine in the candidate torque allocation combination. This refers to the actual operating speed of the engine. The efficiency value of the engine under specific operating conditions is defined in the engine efficiency feature set. The torque value assigned to the motor in the candidate torque allocation combination. This refers to the actual operating speed of the electric motor. The efficiency value of the motor under specific operating conditions is defined in the set of motor efficiency characteristics.

[0049] In a preferred embodiment, the step of dynamically correcting the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle includes:

[0050] The initial torque distribution combination of the commercial cargo vehicle is generated based on the dynamic adjustment factor and the torque distribution combination.

[0051] Based on the magnitude and direction of the dynamic adjustment factor, the engine torque correction amount and the electric motor torque correction amount are determined, and the torque correction parameter set of the commercial vehicle is obtained.

[0052] Apply the torque correction parameter set to the initial torque distribution combination;

[0053] Verify whether the modified torque distribution combination meets the instantaneous torque response capability of the engine and electric motor and the system protection conditions, and obtain the feasibility verification results of the modified torque distribution combination;

[0054] The modified torque distribution combination, which has passed the feasibility verification, is output as the engine torque requirement and electric motor torque requirement of the commercial cargo vehicle.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This invention significantly improves the drive force distribution efficiency of the electric drive axle range-extended hybrid system through multi-dimensional parameter integration and precise modeling. It first acquires the current operating parameters, real-time load information, and current road condition information of the commercial vehicle. Then, based on the efficiency data of historical torque and speed combinations of the electric drive axle, it constructs a dedicated efficiency model. This model can determine the basic distribution ratio based on the efficiency spectrum and dynamically adjust it in conjunction with real-time load. Subsequently, it further combines the efficiency characteristics of the engine and electric motor to select the torque distribution combination that maximizes overall efficiency. This series of designs allows the drive force distribution scheme to closely adapt to the current operating conditions, fully leveraging the synergistic performance of the electric drive axle and the hybrid system, effectively reducing energy waste, and improving the overall system operating efficiency.

[0057] 2. This invention ensures the accuracy of drive force distribution and the stability of system operation through a dynamic compensation and correction mechanism. Based on vehicle operating parameters and road condition information, it calculates dynamic adjustment factors to dynamically correct the determined torque distribution combination. It also verifies whether the corrected combination meets the instantaneous torque response capability and system protection conditions. This process can respond promptly to dynamic changes in operating conditions, avoid power output deviations caused by parameter fluctuations, ensure that torque distribution always precisely matches actual needs, reduce fluctuations in overall system efficiency, ensure the continuity of commercial vehicle operation, and further support efficient vehicle operation. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating a method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system according to an embodiment of the present invention.

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] This application provides a method for optimizing the drive force distribution of an electric axle range-extended hybrid system. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0062] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing the drive force distribution of a range-extended hybrid system with an electric drive axle, according to an embodiment of the present invention. In this embodiment, the method for optimizing the drive force distribution of a range-extended hybrid system with an electric drive axle includes:

[0063] S1. Obtain the current operating parameters, real-time load information, and current road condition information of the commercial freight vehicle;

[0064] In this embodiment of the invention, obtaining the current operating parameters of the commercial vehicle, the real-time load information of the vehicle, and the current road condition information includes:

[0065] The engine speed, motor speed, and battery state of charge of the commercial vehicle are aggregated into the current operating parameters of the commercial vehicle.

[0066] The real-time load information of the commercial vehicle is obtained by measuring the load changes of the commercial vehicle.

[0067] Based on the elevation data and real-time positioning of the road ahead received by the commercial vehicle, the current road condition information of the commercial vehicle is determined.

[0068] Specifically, a speed sensor is installed on the engine of the commercial vehicle. This sensor monitors the number of revolutions of the engine crankshaft and records the number of revolutions per unit time in real time to obtain the engine speed. A speed sensor is installed on the output shaft of the motor. This sensor monitors the number of revolutions of the motor output shaft and records the number of revolutions per unit time in real time to obtain the motor speed. A power monitoring device is installed on the battery pack. This device continuously monitors the changes in battery voltage and current, and calculates the percentage of the current battery power relative to the rated capacity based on the battery's rated capacity, thus obtaining the battery state of charge. The engine speed, motor speed, and battery state of charge obtained above are sent to the vehicle's central control system via the vehicle data transmission line. The central control system summarizes and stores these three data points to form the current operating parameters of the commercial vehicle.

[0069] Furthermore, pressure sensors are installed at the connection points between the chassis and axle of the commercial vehicle. When the vehicle is loaded with cargo, the weight of the cargo will generate pressure between the chassis and axle, and the pressure sensors will detect the magnitude of this pressure value in real time. As the cargo shakes or is loaded / unloaded during vehicle operation, the pressure value will change, and the pressure sensors will continuously record these changing pressure values. The pressure values ​​detected by the pressure sensors are transmitted to the vehicle control system. The control system converts the real-time detected pressure values ​​into the corresponding load weight based on the correspondence between the pressure values ​​and the vehicle load, thereby obtaining real-time load information of the commercial vehicle through load changes.

[0070] Furthermore, the commercial vehicle is equipped with a GPS positioning module. This module receives satellite signals to determine the vehicle's current geographical coordinates in real time, including longitude and latitude, thus obtaining the vehicle's real-time positioning. The vehicle's navigation system stores an elevation database of the road ahead, which contains altitude information corresponding to different geographical locations. The real-time positioning coordinates obtained by the GPS positioning module are sent to the navigation system, which retrieves the elevation data of the road ahead from the elevation database based on these coordinates. The vehicle control system receives the real-time positioning information and the elevation data of the road ahead, and by comparing the vehicle's current altitude with the altitude of the road at different distances ahead, it determines whether the road ahead is flat, uphill, or downhill. This, combined with the real-time positioning and the elevation data of the road ahead, determines the current road condition information of the commercial vehicle.

[0071] In general, obtaining the current operating parameters of a commercial freight vehicle is achieved by collecting and integrating three data points: engine speed, motor speed, and battery state of charge.

[0072] In general, real-time load information of commercial freight vehicles is obtained by monitoring load changes during vehicle operation.

[0073] In general, determining the current road conditions of a commercial freight vehicle relies on the elevation data of the road ahead received by the vehicle, combined with the vehicle's real-time location information, and is achieved through the combination of the two.

[0074] S2. Based on the efficiency data of various torque and speed combinations in the electric drive axle of the commercial vehicle under historical conditions, construct an efficiency model for the electric drive axle.

[0075] In this embodiment of the invention, the step of constructing an efficiency model for the electric drive axle based on historical efficiency data of various torque and speed combinations in the commercial vehicle includes:

[0076] During historical operation, the actual efficiency data of the electric drive axle in the commercial vehicle under different torque and speed combinations are collected to obtain the efficiency dataset of the electric drive axle;

[0077] Cluster analysis was performed on the efficiency dataset to obtain the partitioned efficiency map of the electric drive bridge;

[0078] The efficiency map of the partition is matched with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge.

[0079] The step of matching the partition efficiency map with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge includes:

[0080] The input torque and output speed of the electric drive bridge are collected in real time as real-time operating parameters to obtain the current operating point dataset of the electric drive bridge;

[0081] The current working point dataset is compared with the efficiency region boundaries in the partition efficiency map to determine the specific efficiency region of the current working point;

[0082] Extract typical efficiency values ​​and efficiency change trend characteristics of the specific efficiency region from the partition efficiency map to obtain the efficiency feature set of the current working point;

[0083] Based on the efficiency mapping rules between the efficiency feature set and the current operating point dataset, the real-time efficiency prediction value of the electric drive bridge is determined.

[0084] An efficiency model for the electric drive bridge is constructed based on the real-time efficiency prediction values.

[0085] Specifically, a torque sensor is installed on the output shaft of the electric drive axle of the commercial vehicle. This sensor acquires the torque data of the electric drive axle in real time by monitoring the magnitude of the torsional force on the output shaft. A speed sensor is installed at the connection between the motor and the reducer of the electric drive axle to acquire the speed data of the electric drive axle in real time by monitoring the number of rotations at the connection. At the same time, an energy metering device is installed at the power input end of the electric drive axle to record the energy input to the electric drive axle, and another energy metering device is installed at the power output end of the electric drive axle to record the energy output by the electric drive axle. The ratio of output energy to input energy is the actual efficiency of the electric drive axle. Under various operating conditions such as different road conditions and different loads in the vehicle's history, the torque, speed and actual efficiency of each set are continuously recorded. These data are organized into a structured table with multiple rows and columns according to the correspondence of "torque-speed-efficiency" to obtain the efficiency dataset of the electric drive axle.

[0086] Furthermore, each data point in the efficiency dataset is considered as a unit containing three pieces of information: torque, speed, and efficiency. The differences in torque and speed values ​​between different units are compared one by one, and units with similar torque and speed values ​​are grouped together until all units are assigned to their respective groups. Statistical analysis is performed on all units within each group to calculate the average torque, average speed, and average efficiency within that group. These three average values ​​are used as the characteristic values ​​of that group. In a planar coordinate system with torque as the horizontal axis and speed as the vertical axis, the position range of each group is determined based on the average torque and average speed values ​​of each group, dividing the area into multiple non-overlapping regions. The average efficiency value of the group is marked within each region, forming a partitioned efficiency map of the electric drive bridge.

[0087] Furthermore, during real-time operation of the electric drive axle, the installed torque and speed sensors continuously acquire the current torque and speed values, i.e., the real-time operating parameters of the electric drive axle. The torque values ​​in the real-time operating parameters are mapped to the horizontal axis of the partitioned efficiency map, and the speed values ​​are mapped to the vertical axis of the partitioned efficiency map, finding the specific position that these two values ​​point to in the coordinate system. The region to which this position belongs is determined, and the average efficiency marked in this region is the efficiency of the electric drive axle under the current real-time operating parameters. By establishing a direct correspondence between the real-time operating parameters and the efficiency of the corresponding region in the partitioned efficiency map, an efficiency model of the electric drive axle that can directly determine the efficiency of the electric drive axle based on the real-time operating parameters is formed.

[0088] Specifically, a torque sensor is installed at the power input end of the electric drive axle. This sensor monitors the magnitude of the torsional force on the input shaft to obtain the input torque of the electric drive axle in real time. A speed sensor is installed at the power output end of the electric drive axle. This sensor monitors the number of rotations of the output shaft per unit time to obtain the output speed of the electric drive axle in real time. These two sensors are connected to an on-board data acquisition terminal. The acquisition terminal synchronously records the values ​​of input torque and output speed at fixed time intervals. These "input torque - output speed" data pairs arranged in chronological order are organized into a structured table. Each record in the table includes the acquisition time, input torque value, and output speed value, thus obtaining the current operating point dataset of the electric drive axle.

[0089] Furthermore, the partitioned efficiency map is a planar map with input torque as the horizontal axis and output speed as the vertical axis. The map is divided into multiple closed efficiency regions, each with a clear boundary line determined by specific input torque and output speed values. Any operating point is selected from the current operating point dataset, and its input torque and output speed values ​​are extracted. The corresponding coordinate points are then located in the coordinate system of the partitioned efficiency map. It is checked whether these coordinate points are within the boundary line of a certain efficiency region. If they are completely within the boundary of a region, then that region is the specific efficiency region of the current operating point.

[0090] Furthermore, in the regional efficiency map, each specific efficiency region is marked with the average efficiency value of all historical efficiency data within that region. This average efficiency value is the typical efficiency value for that region. At the same time, the map also records the pattern of efficiency variation with input torque and output speed within that region. For example, when the input torque gradually increases within the region while the output speed remains constant, does the efficiency gradually increase or decrease? When the output speed gradually changes within the region while the input torque remains constant, what is the direction of efficiency change? These patterns are the efficiency change trend characteristics of that region. The extracted typical efficiency values ​​and efficiency change trend characteristics are combined to form the efficiency feature set for the current operating point.

[0091] Furthermore, the efficiency mapping rule refers to the correspondence between input torque, output speed, and efficiency within a specific efficiency range. This rule is predetermined based on historical efficiency data for that range. For example, within that range, when the input torque is a certain value and the output speed is a certain value, the corresponding efficiency is a specific value. Substituting the input torque value and output speed value from the current operating point dataset into the efficiency mapping rule for that range, and according to the correspondence explicitly stated in the rule, the efficiency value that matches the current input torque and output speed is found. This value is the real-time efficiency prediction value of the electric drive bridge.

[0092] Furthermore, the current operating point dataset of the electric drive axle under different operating conditions is continuously collected. Following the steps described above, the real-time efficiency prediction value corresponding to each operating point is determined one by one. All the correspondences between "input torque - output speed - real-time efficiency prediction value" are organized into a complete dataset, which covers the possible operating range of the electric drive axle. By establishing this dataset, for any new set of input torque and output speed, the corresponding real-time efficiency prediction value can be directly found in the dataset. This dataset, which enables direct correspondence between input and output, is the efficiency model of the electric drive axle.

[0093] In summary, the efficiency dataset of electric drive axles is obtained by collecting actual efficiency data of electric drive axles of commercial trucks under different torque and speed combinations during historical operation and integrating these data together.

[0094] In summary, obtaining the regional efficiency map of the electric drive bridge involves performing cluster analysis on the collected efficiency dataset. This analysis method divides the data into different regions and forms a map.

[0095] In summary, the efficiency model of the electric drive bridge is constructed by matching the obtained partition efficiency map with the real-time operating parameters of the electric drive bridge.

[0096] In summary, the current operating point dataset of the electric drive axle is obtained by collecting the input torque and output speed of the electric drive axle in real time as real-time operating parameters and integrating these parameters together.

[0097] In general, determining the specific efficiency region of the current working point is achieved by comparing the obtained current working point dataset with the efficiency region boundaries in the partitioned efficiency map.

[0098] In summary, the efficiency feature set of the current working point is obtained by extracting typical efficiency values ​​and efficiency change trend characteristics of specific efficiency regions from the partitioned efficiency map and integrating these contents together.

[0099] In summary, the real-time efficiency prediction of the electric drive bridge is determined based on the efficiency mapping rule between the efficiency feature set and the current operating point dataset, and is implemented in accordance with this rule.

[0100] In summary, the efficiency model of the electric drive bridge is constructed based on the obtained real-time efficiency prediction value.

[0101] S3. Based on the efficiency graph in the efficiency model, obtain the basic allocation ratio of the electric drive axle under the current working condition, and dynamically allocate the load according to the real-time load information of the vehicle to obtain the target driving force allocation ratio of the electric drive axle under the current working condition.

[0102] In this embodiment of the invention, the step of dynamically allocating the load according to the basic allocation ratio based on the real-time load information of the vehicle to obtain the target driving force allocation ratio of the electric drive axle under the current operating conditions includes:

[0103] Based on the real-time load information of the vehicle, the current load status category of the electric drive axle is identified, and the load status identifier of the electric drive axle is obtained.

[0104] Based on the load status identifier, query the preset load-allocation strategy mapping table to obtain the set of allocation adjustment coefficients corresponding to the load status identifier;

[0105] Based on the set of allocation adjustment coefficients, the basic allocation ratio is weighted and corrected to obtain the preliminary optimized allocation ratio of the electric drive bridge;

[0106] Based on the current operating temperature and ambient temperature parameters of the electric drive bridge, thermal management compensation is applied to the preliminary optimized allocation ratio to obtain the temperature-compensated allocation ratio.

[0107] The temperature-compensated distribution ratio, which meets the working constraints of commercial vehicles, is output as the target driving force distribution ratio of the electric drive axle under the current operating conditions.

[0108] Specifically, the real-time vehicle load information includes the weight value currently borne by the electric drive axle. The preset load status categories are divided into three levels: light load, medium load, and heavy load. Each level corresponds to a specific weight range. Light load is the lower part of the vehicle's rated load, medium load is the middle part of the rated load, and heavy load is the higher part of the rated load. The weight value in the vehicle's real-time load information is compared with the weight range of these three levels one by one. If the value falls within the range corresponding to light load, the current load status category is identified as light load. Similarly, medium load or heavy load is determined. A unique symbol or code is assigned to each category. For example, light load corresponds to a specific letter combination, medium load corresponds to another letter combination, and heavy load corresponds to yet another letter combination. This symbol or code is the load status identifier of the electric drive axle.

[0109] Furthermore, the preset load-distribution strategy mapping table is a structured table. The first column of the table records the load status identifier, and the other columns record the distribution adjustment coefficient corresponding to the identifier. Each coefficient corresponds to the driving force adjustment ratio of different electric drive bridges. The obtained load status identifier is used as the query keyword, and the mapping table is searched row by row for an identifier that matches it completely. After finding the matching row, all the coefficients in that row except for the identifier are the set of distribution adjustment coefficients corresponding to the load status identifier.

[0110] Furthermore, the basic allocation ratio is a pre-set proportion of the driving force of each electric drive axle to the total driving force, for example, the front electric drive axle and the rear electric drive axle each account for a certain proportion; the adjustment coefficient corresponding to each electric drive axle is extracted from the allocation adjustment coefficient set, and the basic allocation ratio of each electric drive axle is multiplied by its corresponding adjustment coefficient to obtain the weighted adjusted ratio of that electric drive axle, for example, the basic ratio of the front electric drive axle is multiplied by the corresponding coefficient to obtain the new ratio, and the basic ratio of the rear electric drive axle is multiplied by the corresponding coefficient to obtain the new ratio; the adjusted ratios of all electric drive axles are combined to form the preliminary optimized allocation ratio of the electric drive axles.

[0111] Furthermore, a temperature sensor is installed on the housing of the electric drive axle to detect its operating temperature in real time, and an ambient temperature sensor is installed outside the vehicle to detect the temperature of the environment in which the vehicle is located. A preset temperature compensation rule is established: when the operating temperature of the electric drive axle is higher than the ambient temperature by a certain amount, the allocation ratio of the corresponding electric drive axle needs to be reduced by a certain amount; when the operating temperature is lower than the ambient temperature by a certain amount, the allocation ratio of the corresponding electric drive axle needs to be increased by a certain amount. The detected current operating temperature and ambient temperature are substituted into the temperature compensation rule to calculate the compensation value that each electric drive axle should have. The preliminary optimized allocation ratio is added to the corresponding compensation value to obtain the temperature-compensated allocation ratio.

[0112] Furthermore, the operating constraints of the commercial vehicle include that the driving force ratio of each electric drive axle does not exceed its maximum bearing range, and the sum of the driving force ratios of all electric drive axles is the overall ratio. The temperature-compensated distribution ratio is compared with these constraints one by one to check whether the ratio of each electric drive axle is within its maximum bearing range and whether the sum of the ratios of all electric drive axles meets the overall ratio requirement. If all conditions are met, the temperature-compensated distribution ratio is directly output as the target driving force distribution ratio of the electric drive axles under the current operating conditions.

[0113] In summary, the load status identifier of the electric drive axle is obtained by identifying the current load status category of the electric drive axle through real-time vehicle load information, and the identification result is used to determine the load status identifier.

[0114] In general, obtaining the set of allocation adjustment coefficients corresponding to the load status identifier is a process of querying the preset load-allocation strategy mapping table based on the load status identifier, and finding and extracting the corresponding set of coefficients from the mapping table.

[0115] In summary, the preliminary optimized allocation ratio of the electric drive bridge is obtained by weighting and correcting the basic allocation ratio based on the allocation adjustment coefficient set.

[0116] In summary, the temperature-compensated allocation ratio is obtained by performing thermal management compensation on the initial optimized allocation ratio based on the current operating temperature and ambient temperature parameters of the electric drive bridge.

[0117] In summary, the target driving force distribution ratio of the output electric drive axle under the current operating conditions is the distribution ratio after temperature compensation to meet the working constraints of the commercial vehicle, and the final output is the result.

[0118] S4. Based on the current operating parameters and the current road condition information, perform delay compensation on the commercial vehicle to obtain the dynamic adjustment factor of the commercial vehicle under the current operating conditions;

[0119] In this embodiment of the invention, the step of performing delay compensation on the commercial freight vehicle based on the current operating parameters and the current road condition information to obtain the dynamic adjustment factor of the commercial freight vehicle under the current operating conditions includes:

[0120] The vehicle speed change rate and battery state of charge change rate are extracted from the current operating parameters to obtain the dynamic change parameter set of the commercial freight vehicle;

[0121] The slope change rate is extracted from the current road condition information to obtain the dynamic road condition characteristics of the commercial freight vehicle;

[0122] Calculate the initial adjustment factor based on the dynamically changing parameter set and the dynamic characteristics of the road conditions;

[0123] By limiting the initial adjustment factor to a range between a predefined minimum and a maximum value, the dynamic adjustment factor of the commercial vehicle is obtained.

[0124] In a preferred embodiment, the initial adjustment factor is calculated using the following formula:

[0125] ;

[0126] In the formula, The initial adjustment factor is... The preset vehicle speed factor, The vehicle speed is one of the current operating parameters. As a time factor, The battery state of charge in the current operating parameters. The preset charge state factor, The gradient is the slope in the current road condition information. The preset slope factor, The rate of change of vehicle speed, The rate of change of the state of charge of the battery. The slope change rate is given.

[0127] Specifically, the current operating parameters include the vehicle speed and battery state of charge of the commercial freight vehicle. A vehicle speed sensor is installed on the vehicle to collect speed data in real time, and a battery state of charge monitoring device on the battery pack collects battery state of charge data in real time. Two adjacent sets of vehicle speed and battery state of charge data are recorded synchronously at fixed time intervals. The change in vehicle speed is obtained by subtracting the previous speed data from the subsequent data, and this change is divided by the time interval to obtain the rate of change in vehicle speed. Similarly, the change in battery state of charge is obtained by subtracting the previous battery state of charge data from the subsequent data, and this change is divided by the time interval to obtain the rate of change in battery state of charge. The obtained rates of change in vehicle speed and battery state of charge are then compiled into a set containing these two sets of data to obtain the dynamic parameter set of the commercial freight vehicle.

[0128] Furthermore, the current road condition information includes elevation data of the road ahead and real-time positioning information of the commercial vehicle. Based on the real-time positioning information, the coordinates of the vehicle's current position and two consecutive different positions ahead are determined. The elevation data is used to retrieve the corresponding altitudes of these three positions. The elevation difference is obtained by subtracting the elevation of the current position from the elevation of the first position ahead. The first slope is obtained by dividing the elevation difference by the distance from the current position to the first position ahead. The second elevation difference is obtained by subtracting the elevation of the first position ahead from the elevation of the second position ahead. The second slope is obtained by dividing the elevation difference by the distance from the first position ahead to the second position ahead. The slope change is obtained by subtracting the first slope from the second slope. The slope change rate is obtained by dividing the slope change by the time interval between the two slopes. The slope change rate is the dynamic road condition characteristic of the commercial vehicle.

[0129] Furthermore, the influence weights of the preset dynamic parameter set and road condition dynamic features on the initial adjustment factor are defined, with different influence weights for the vehicle speed change rate, battery state of charge change rate, and gradient change rate. The vehicle speed change rate in the dynamic parameter set is multiplied by its corresponding influence weight to obtain the contribution value of the vehicle speed change rate to the adjustment factor; the battery state of charge change rate is multiplied by its corresponding influence weight to obtain the contribution value of the battery state of charge change rate to the adjustment factor; the gradient change rate in the road condition dynamic features is multiplied by its corresponding influence weight to obtain the contribution value of the gradient change rate to the adjustment factor; the sum of these three contribution values ​​is the initial adjustment factor for the commercial freight vehicle.

[0130] Furthermore, a minimum and maximum value for the dynamic adjustment factor are predefined. These minimum and maximum values ​​are preset based on the power system capacity, driving safety requirements, and delay compensation effect of the commercial vehicle. The calculated initial adjustment factor is compared with the predefined maximum value. If the initial adjustment factor is greater than the maximum value, the maximum value is taken as the adjusted value. If the initial adjustment factor is less than the minimum value, the minimum value is taken as the adjusted value. If the initial adjustment factor is between the minimum and maximum values, the initial adjustment factor remains unchanged. The value obtained after the above range limitation is the dynamic adjustment factor of the commercial vehicle under the current operating conditions.

[0131] Specifically, The initial adjustment factor is obtained by multiplying the preset vehicle speed factor by the vehicle speed change rate, the preset state of charge factor by the battery state of charge change rate, and the preset slope factor by the slope change rate. Finally, the results of these three multiplications are added together. The value obtained through this calculation process is the initial adjustment factor.

[0132] Furthermore, the preset vehicle speed factor The origin of this value lies in a fixed value pre-set by technicians before the commercial vehicle leaves the factory. This value is based on the vehicle's power performance parameters, driving safety standards, and the impact of different speed changes on vehicle operation. It is determined through multiple real-vehicle tests and performance verifications. This value remains unchanged during subsequent vehicle operation and is used to measure the impact of the speed change rate on the initial adjustment factor. Speed ​​Change Rate The source is the vehicle speed data extracted from the current operating parameters of the commercial vehicle. Specifically, the vehicle speed sensor installed on the vehicle continuously collects the vehicle speed values ​​at two different time points at fixed time intervals. The vehicle speed value at the second time point is subtracted from the vehicle speed value at the first time point to obtain the change in vehicle speed. Then, this change in vehicle speed is divided by the time interval between the two time points. The result obtained through this calculation is the vehicle speed change rate.

[0133] Furthermore, the preset state of charge factor The origin of this value lies in a fixed value pre-set by technicians before the commercial vehicle leaves the factory. This value is based on the charging and discharging characteristics of the vehicle's battery, battery life protection requirements, and the impact of different battery state-of-charge changes on the vehicle's power supply. It is determined through multiple battery performance tests and vehicle operation simulations. This value remains unchanged during subsequent vehicle operation and is used to measure the degree of influence of the battery state-of-charge rate on the initial adjustment factor. Battery State-of-Charge Rate The data source is the battery state of charge (SOC) data extracted from the current operating parameters of the commercial vehicle. Specifically, the SOC data is continuously collected at two different time points at fixed time intervals by a power monitoring device installed on the battery pack. The change in SOC is obtained by subtracting the change from the change in SOC at the previous time point from the change in SOC at the later time point. This change is then divided by the time interval between the two time points. The result obtained through this calculation is the rate of change of battery SOC.

[0134] Furthermore, the preset slope factor The gradient rate is a fixed value pre-set by technicians before the commercial vehicle leaves the factory. This value is based on the impact of different gradients on vehicle resistance, the vehicle's climbing power requirements, and the impact of gradient changes on vehicle stability. It is determined through multiple real-world driving tests on test roads with varying gradients. This value remains constant during subsequent vehicle operation and is used to measure the degree of influence of the gradient change rate on the initial adjustment factor. Gradient Change Rate The gradient data is extracted from the current road conditions of the commercial vehicle. Specifically, the vehicle's navigation system retrieves the elevation database of the road ahead and combines it with the vehicle's real-time positioning information to determine the elevation values ​​of two different locations ahead. Based on the elevation values ​​and horizontal distance between these two locations, the gradient value of the corresponding location is calculated. The gradient value of the second location is subtracted from the gradient value of the first location to obtain the change in gradient. Then, based on the vehicle's speed and the horizontal distance between the two locations, the time interval between the vehicle's travels through these two locations is calculated. The gradient change is divided by this time interval, and the result obtained through this calculation is the gradient change rate.

[0135] Furthermore, the significance of this formula lies in comprehensively considering three key factors—the current speed change of the commercial vehicle, the battery charge change, and the road gradient change—to calculate the initial adjustment factor. Specifically, the preset speed factor, multiplied by the speed change rate, transforms the change in the vehicle's current speed into a specific contribution value to the initial adjustment factor; the faster the speed change, the more the impact of this contribution value on the initial adjustment factor conforms to the preset trade-off criteria. Similarly, the preset state of charge factor, multiplied by the battery state of charge change rate, transforms the change in battery charge into a specific contribution value to the initial adjustment factor; the faster the battery charge change, the more the impact of this contribution value on the initial adjustment factor conforms to the preset trade-off criteria.

[0136] Furthermore, multiplying the preset slope factor by the slope change rate is to convert the changes in the road slope ahead into a specific contribution value to the initial adjustment factor. The faster the slope changes, the more the impact of this contribution value on the initial adjustment factor conforms to the preset trade-off criteria. The initial adjustment factor obtained by adding these three contribution values ​​can comprehensively reflect the vehicle's current operating status and the road conditions ahead's need for delay compensation. This provides accurate basic data for subsequently limiting the range of the initial adjustment factor and obtaining the dynamic adjustment factor, ensuring that the dynamic adjustment factor can accurately adapt to the vehicle's current operating conditions.

[0137] In summary, the dynamic parameter set of a commercial freight vehicle is obtained by extracting the vehicle speed change rate and the battery state of charge change rate from the current operating parameters and integrating these two rates together.

[0138] In general, the dynamic road condition characteristics of commercial freight vehicles are obtained by extracting the gradient change rate from the current road condition information and using this change rate as the feature content.

[0139] In summary, the initial adjustment factor is calculated by combining the obtained dynamic parameter set and road condition dynamic characteristics.

[0140] In general, the dynamic adjustment factor for a commercial freight vehicle is obtained by limiting the range of the calculated initial adjustment factor to a predefined minimum and maximum value.

[0141] In summary, the initial adjustment factor The calculation formula is obtained by multiplying the preset vehicle speed factor by the vehicle speed change rate, the preset state of charge factor by the battery state of charge change rate, and the preset gradient factor by the gradient change rate, and then summing them. Vehicle speed and battery state of charge are derived from the current operating parameters of the commercial vehicle, gradient is derived from current road condition information, each rate of change reflects the change of the corresponding parameter over time, and the preset factor is a pre-set parameter used to measure the effect of each rate of change on… Parameters indicating the degree of influence.

[0142] S5. Based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle, determine the torque distribution combination that maximizes the overall efficiency of the commercial vehicle.

[0143] In this embodiment of the invention, determining the torque distribution combination that maximizes the overall efficiency of the commercial vehicle based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle includes:

[0144] The pre-stored engine efficiency map and electric motor efficiency map are queried separately to obtain the efficiency characteristic curves of the engine and electric motor in the commercial vehicle at the current speed, thus obtaining the engine efficiency feature set and electric motor efficiency feature set.

[0145] Based on the target driving force distribution ratio, the torque distribution benchmark value of the engine and the electric motor is determined, and the initial torque distribution scheme of the engine and the electric motor is obtained;

[0146] The initial torque allocation scheme that conforms to the constraints of the engine efficiency feature set and the electric motor efficiency feature set shall be used as the candidate torque allocation combination for the commercial cargo vehicle.

[0147] Evaluate the overall efficiency of the candidate torque distribution combinations;

[0148] The candidate torque distribution combination with the highest overall efficiency is output as the torque distribution combination for the commercial cargo vehicle.

[0149] The formula for calculating the overall efficiency is as follows:

[0150] ;

[0151] In the formula, The total efficiency is given. This refers to the total torque required by the aforementioned commercial freight vehicle. Let be the angular velocity of the commercial cargo vehicle. The torque value allocated to the engine in the candidate torque allocation combination. This refers to the actual operating speed of the engine. The efficiency value of the engine under specific operating conditions is defined in the engine efficiency feature set. The torque value assigned to the motor in the candidate torque allocation combination. This refers to the actual operating speed of the electric motor. The efficiency value of the motor under specific operating conditions is defined in the set of motor efficiency characteristics.

[0152] Specifically, the pre-stored engine efficiency map is a set of curves recording the relationship between torque and efficiency at different engine speeds. This map is stored in the database of the vehicle control unit in the form of structured data. The pre-stored electric motor efficiency map is a set of curves recording the relationship between torque and efficiency at different electric motor speeds, also stored in the database of the vehicle control unit. The current engine speed and electric motor speed are extracted from the current operating parameters of the commercial vehicle. The curve that perfectly matches the current engine speed is found in the engine efficiency map. This curve is the efficiency characteristic curve of the engine at the current speed, including the curve and its corresponding efficiency range, torque range, etc., and is organized to form an engine efficiency feature set. Similarly, the curve that perfectly matches the current electric motor speed is found in the electric motor efficiency map. This curve is the efficiency characteristic curve of the electric motor at the current speed, including the curve and its corresponding efficiency range, torque range, etc., and is organized to form an electric motor efficiency feature set.

[0153] Furthermore, the target driving force allocation ratio clarifies the proportion of driving force that each engine and electric motor need to provide in the total driving force. The total driving force demand is predetermined based on the current load, road conditions, and driving requirements of the commercial vehicle. The total driving force demand is then broken down according to the target driving force allocation ratio to obtain the driving force share that the engine needs to provide and the driving force share that the electric motor needs to provide. Based on the correspondence between driving force and torque at the current engine speed, the driving force share that the engine needs to provide is converted into a corresponding torque value, which serves as the torque allocation benchmark value for the engine. Similarly, based on the correspondence between driving force and torque at the current electric motor speed, the driving force share that the electric motor needs to provide is converted into a corresponding torque value, which serves as the torque allocation benchmark value for the electric motor. Finally, the torque allocation benchmark values ​​for the engine and the electric motor are integrated to form the initial torque allocation scheme for the engine and the electric motor.

[0154] Furthermore, the constraint range of the engine efficiency feature set refers to the torque range corresponding to the effective operating range of the engine efficiency characteristic curve at the current speed; the constraint range of the electric motor efficiency feature set refers to the torque range corresponding to the effective operating range of the electric motor efficiency characteristic curve at the current speed. The system checks whether the engine torque allocation benchmark value in the initial torque allocation scheme falls within the constraint range of the engine efficiency feature set, and simultaneously checks whether the electric motor torque allocation benchmark value falls within the constraint range of the electric motor efficiency feature set. If both benchmark values ​​are within their respective constraint ranges, the initial torque allocation scheme is directly used as a candidate torque allocation combination for the commercial freight vehicle.

[0155] Furthermore, the engine efficiency characteristic curve at the current speed is extracted from the engine efficiency feature set. The efficiency value corresponding to the engine torque value in the candidate torque distribution combination is found on this curve; this value is the engine efficiency under the candidate combination. Similarly, the electric motor efficiency characteristic curve at the current speed is extracted from the electric motor efficiency feature set. The efficiency value corresponding to the electric motor torque value in the candidate torque distribution combination is found on this curve; this value is the electric motor efficiency under the candidate combination. Based on the driving force ratio of the engine and electric motor in the target driving force distribution ratio, the engine efficiency is multiplied by the engine driving force ratio to obtain the contribution value of the engine efficiency to the total efficiency. The electric motor efficiency is multiplied by the electric motor driving force ratio to obtain the contribution value of the electric motor efficiency to the total efficiency. The two contribution values ​​are added together to obtain the total efficiency of the candidate torque distribution combination. The candidate torque distribution combination with the highest total efficiency is sent to the engine control module and the electric motor control module through the output interface of the vehicle control unit. This combination is the torque distribution combination for the commercial freight vehicle.

[0156] Specifically, overall efficiency The total efficiency is calculated using a formula. Specifically, the numerator is the product of the total required torque and angular velocity of the commercial vehicle. Then, the denominator is calculated by multiplying the engine's allocated torque in the candidate torque distribution combination by its actual operating speed, divided by the engine's efficiency value under specific operating conditions within the engine efficiency feature set. Finally, the denominator is the product of the electric motor's allocated torque in the candidate torque distribution combination and its actual operating speed, divided by the electric motor's efficiency value under specific operating conditions within the electric motor efficiency feature set. The numerator is then divided by the denominator. This calculation process yields the total efficiency. Total torque required by the vehicle The data is derived from the current operating conditions of the commercial vehicle, including real-time vehicle load, current road conditions, and speed requirements. After collecting this information through onboard sensors, the onboard control unit analyzes and determines the total torque required to meet the vehicle's current driving needs. This total torque is the vehicle's total torque demand. .

[0157] Furthermore, the torque value allocated by the engine in the candidate torque distribution combination. The torque value is extracted directly from the previously determined candidate torque distribution combinations for commercial freight vehicles. These candidate torque distribution combinations explicitly include the specific torque values ​​allocated to the engine. Extracting these values ​​yields the torque values ​​allocated to the engine within the candidate torque distribution combinations. The actual operating speed of the engine The source of this speed is a speed sensor installed on the engine crankshaft, which monitors the crankshaft's rotation in real time, records the number of crankshaft rotations per unit time, and converts this number of rotations into the engine's actual operating speed. The resulting value is the engine's actual operating speed. .

[0158] Furthermore, the engine efficiency characteristic set represents the engine efficiency value under specific operating conditions. The data is obtained from the engine efficiency feature set, which contains the engine's efficiency data under different torque and speed combinations. The torque value allocated to the engine in the current candidate torque allocation combination is then used as the basis for this data. and the actual operating speed of the engine The engine efficiency feature set is used to find the operating conditions corresponding to these two parameters, and the engine efficiency data under these operating conditions is extracted. The extracted values ​​are the engine efficiency values ​​under specific operating conditions in the engine efficiency feature set. The torque value allocated to the motor in the candidate torque distribution combination. The value is directly extracted from the previously determined candidate torque distribution combinations for commercial freight vehicles. These candidate torque distribution combinations explicitly include the specific torque values ​​allocated to the electric motor. Extracting these values ​​yields the torque values ​​allocated to the electric motor within the candidate torque distribution combinations. .

[0159] Furthermore, the actual operating speed of the electric motor The speed is determined by a speed sensor installed on the motor rotor, which monitors the rotor's rotation in real time, records the number of rotations per unit time, and converts this number of rotations into the motor's actual operating speed. The resulting value is the motor's actual operating speed. The efficiency characteristics of electric motors are the efficiency values ​​of electric motors under specific operating conditions. The data is obtained from the electric motor efficiency feature set, which contains efficiency data of the electric motor under different torque and speed combinations. The torque value allocated to the electric motor in the current candidate torque allocation combination is then used as the basis for this data. and the actual operating speed of the motor The process involves finding the operating conditions corresponding to these two parameters in the motor efficiency feature set, extracting the motor efficiency data for that operating condition, and obtaining the extracted values ​​as the motor efficiency values ​​for that specific operating condition within the motor efficiency feature set. .

[0160] Furthermore, the significance of this formula lies in calculating the overall efficiency of a commercial vehicle under the current candidate torque distribution combination. The numerator is obtained by multiplying the total required torque of the vehicle by its angular velocity, yielding the total output power required to meet the current driving demand. This power reflects the effective power that the vehicle's power system needs to output. The denominator is obtained by calculating and adding the input power of the engine and the electric motor separately, yielding the total input power of the power system. The engine's input power is obtained by multiplying the engine's allocated torque value by its actual operating speed, then dividing by the engine's efficiency value under specific operating conditions. Similarly, the electric motor's input power is obtained by multiplying the electric motor's allocated torque value by its actual operating speed, then dividing by the electric motor's efficiency value under specific operating conditions. The overall efficiency is the ratio of total output power to total input power. This ratio directly reflects the commercial vehicle's power system's ability to convert input energy into effective output energy under the current candidate torque distribution combination. This provides an accurate energy efficiency assessment basis for subsequently selecting the candidate torque distribution combination with the highest overall efficiency, ensuring that the final determined torque distribution combination maximizes the overall efficiency of the commercial vehicle.

[0161] In summary, obtaining the engine efficiency feature set and the electric motor efficiency feature set involves querying the pre-stored engine efficiency map and electric motor efficiency map respectively, obtaining the efficiency characteristic curves of the engine and electric motor of the commercial vehicle at the current speed, and then organizing the curves and related information.

[0162] In summary, the initial torque distribution scheme for the engine and electric motor is obtained based on the target driving force distribution ratio. First, the total driving force demand is broken down to obtain the driving force share of the engine and electric motor. Then, the corresponding relationship between driving force and torque at the current speed is combined to convert the share into their respective torque distribution benchmark values. Finally, the benchmark values ​​are integrated to form the initial torque distribution scheme.

[0163] In general, obtaining candidate torque distribution combinations for commercial vehicles involves checking whether the torque distribution benchmark values ​​of the engine and electric motor in the initial torque distribution scheme fall within the constraints of the engine efficiency feature set and the electric motor efficiency feature set, respectively. If both are met, the initial scheme is considered as a candidate combination.

[0164] In general, evaluating the overall efficiency of candidate torque distribution combinations involves finding the efficiency values ​​of the engine and electric motor under the corresponding efficiency feature set, calculating their contributions to the overall efficiency according to the target driving force distribution ratio, and finally adding the contribution values ​​to obtain the overall efficiency.

[0165] In summary, the output torque distribution combination for commercial freight vehicles is determined by comparing the overall efficiency of all candidate torque distribution combinations, identifying the candidate combination with the highest overall efficiency, and outputting that combination as the final result.

[0166] Overall efficiency The calculation formula uses the product of the total demand torque of a commercial vehicle and its angular velocity as the numerator, and the sum of the correlation calculation results of the engine's allocated torque value, actual engine speed, and engine efficiency value under specific operating conditions in the candidate torque allocation combination and the correlation calculation results of the electric motor's allocated torque value, actual electric motor speed, and electric motor efficiency value under specific operating conditions in the candidate torque allocation combination as the denominator. The total demand torque of the vehicle comes from the current operating condition analysis, the angular velocity comes from the conversion between wheel speed and wheel radius, the allocated torque of the engine and electric motor comes from the candidate torque allocation combination, their actual speed comes from their respective sensors, and the efficiency value comes from the corresponding efficiency feature set. This formula is used to reflect the power system's ability to convert input energy into effective output energy, providing a basis for selecting the candidate torque allocation combination with the highest overall efficiency.

[0167] S6. Dynamically correct the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle, and optimize the driving force distribution of the commercial vehicle according to the engine torque requirement and electric motor torque requirement.

[0168] In this embodiment of the invention, the step of dynamically correcting the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle includes:

[0169] The initial torque distribution combination of the commercial cargo vehicle is generated based on the dynamic adjustment factor and the torque distribution combination.

[0170] Based on the magnitude and direction of the dynamic adjustment factor, the engine torque correction amount and the electric motor torque correction amount are determined, and the torque correction parameter set of the commercial vehicle is obtained.

[0171] Apply the torque correction parameter set to the initial torque distribution combination;

[0172] Verify whether the modified torque distribution combination meets the instantaneous torque response capability of the engine and electric motor and the system protection conditions, and obtain the feasibility verification results of the modified torque distribution combination;

[0173] The modified torque distribution combination, which has passed the feasibility verification, is output as the engine torque requirement and electric motor torque requirement of the commercial cargo vehicle.

[0174] Specifically, the torque distribution combination includes the pre-set engine base torque and electric motor base torque of the commercial vehicle under the current operating conditions. The dynamic adjustment factor is used as the adjustment basis. According to the overall distribution ratio tendency indicated by the dynamic adjustment factor, the engine base torque and electric motor base torque are respectively associated and integrated with the dynamic adjustment factor. For example, if the dynamic adjustment factor indicates that the proportion of electric motor torque needs to be increased, then while retaining the core proportion of engine base torque, the proportion of electric motor base torque in the total torque is initially increased, forming the initial torque distribution combination of the commercial vehicle that includes the initial engine torque and the initial electric motor torque.

[0175] Furthermore, the magnitude of the dynamic adjustment factor corresponds to the torque correction range. The stronger the adjustment demand reflected by the dynamic adjustment factor value, the larger the corresponding torque correction range. The direction of the dynamic adjustment factor is divided into positive and negative. A positive direction indicates that the corresponding torque needs to be increased, and a negative direction indicates that the corresponding torque needs to be decreased. The correction range of engine torque and electric motor torque is determined according to the magnitude of the dynamic adjustment factor. The direction determines whether to increase or decrease the corresponding torque. For example, when the dynamic adjustment factor is positive and the adjustment demand is strong, the engine torque correction is determined to be a large increase. The electric motor torque correction is determined to be an increase or decrease based on the overall distribution tendency. The determined engine torque correction and electric motor torque correction are combined to obtain the torque correction parameter set of the commercial truck.

[0176] Furthermore, the initial engine torque and initial electric motor torque are extracted from the initial torque distribution combination. The engine torque correction amount from the torque correction parameter set is calculated with the initial engine torque. If the correction amount is positive, the initial engine torque is added to the correction amount; if it is negative, the initial engine torque is subtracted from the correction amount to obtain the corrected engine torque. Similarly, the electric motor torque correction amount is calculated with the initial electric motor torque. If the correction amount is positive, it is added; if it is negative, it is subtracted to obtain the corrected electric motor torque. The corrected engine torque and the corrected electric motor torque are integrated to complete the operation of applying the torque correction parameter set to the initial torque distribution combination, resulting in the corrected torque distribution combination.

[0177] Furthermore, the instantaneous torque response capability of the engine is obtained in advance through engine performance testing, that is, the maximum torque value that the engine can output instantly per unit time. The instantaneous torque response capability of the electric motor is obtained through electric motor performance testing, that is, the maximum torque value that the electric motor can output instantly per unit time. At the same time, the system protection conditions are defined, including the torque limit corresponding to the highest operating temperature of the engine and electric motor, and the torque limit corresponding to the maximum load that the transmission system components can withstand.

[0178] Furthermore, the engine torque in the modified torque distribution combination is compared with the engine's instantaneous torque response capability to check whether the engine can reach the torque value within the required time. The modified electric motor torque is also compared with the electric motor's instantaneous torque response capability to check whether the electric motor can reach the torque value within the required time. At the same time, it is checked whether the modified engine torque is lower than the upper limit of torque corresponding to engine temperature and load, and whether the modified electric motor torque is lower than the upper limit of torque corresponding to electric motor temperature and load. If all the checks are met, the feasibility verification result is passed; if any one of them is not met, the feasibility verification result is failed. The feasibility verification result of the modified torque distribution combination is obtained.

[0179] Furthermore, the feasibility verification results are judged. If the result is passed, it means that the modified torque distribution combination can enable the engine and electric motor to respond and output the corresponding torque in an instant without exceeding the protection limits of each component of the system. The modified engine torque in the verified modified torque distribution combination is directly determined as the engine torque required by the commercial vehicle, and the modified electric motor torque is directly determined as the electric motor torque required by the commercial vehicle. Through the signal transmission channel of the vehicle control system, these two torque requirements are sent to the engine control unit and the electric motor control unit respectively to complete the output of the engine torque required by the commercial vehicle and the electric motor torque required by the commercial vehicle.

[0180] In summary, the initial torque distribution combination for a commercial freight vehicle is generated by combining the dynamic adjustment factor and the torque distribution combination.

[0181] In summary, the torque correction parameter set for commercial freight vehicles is obtained by determining the engine torque correction amount and the electric motor torque correction amount based on the magnitude and direction of the dynamic adjustment factor, and then integrating these two correction amounts.

[0182] In summary, applying the torque correction parameter set to the initial torque distribution combination means applying the determined torque correction parameter set to the previously generated initial torque distribution combination, thus completing the parameter application operation.

[0183] In summary, obtaining the feasibility verification results of the modified torque distribution combination is achieved by verifying whether the modified torque distribution combination meets the instantaneous torque response capabilities of the engine and electric motor, and whether it complies with the system protection conditions.

[0184] In summary, the output of the engine torque demand and electric motor torque demand of the commercial vehicle is the final output of the modified torque distribution combination with the feasibility verification result passed.

[0185] In the several embodiments provided by this invention, it should be understood that the disclosed method can be implemented in other ways.

[0186] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0187] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the driving force distribution of an electric drive axle range-extended hybrid system, characterized in that, The method includes: S1. Obtain the current operating parameters, real-time load information, and current road condition information of the commercial freight vehicle; S2. Based on the efficiency data of various torque and speed combinations in the electric drive axle of the commercial vehicle under historical conditions, construct an efficiency model for the electric drive axle. S3. Based on the efficiency graph in the efficiency model, obtain the basic allocation ratio of the electric drive axle under the current working condition, and dynamically allocate the load according to the real-time load information of the vehicle to obtain the target driving force allocation ratio of the electric drive axle under the current working condition. S4. Based on the current operating parameters and the current road condition information, perform delay compensation on the commercial vehicle to obtain the dynamic adjustment factor of the commercial vehicle under the current operating conditions; S5. Based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle, determine the torque distribution combination that maximizes the overall efficiency of the commercial vehicle. S6. Dynamically correct the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle, and optimize the driving force distribution of the commercial vehicle according to the engine torque requirement and electric motor torque requirement.

2. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The acquisition of the current operating parameters, real-time load information, and current road condition information of the commercial vehicle includes: The engine speed, motor speed, and battery state of charge of the commercial vehicle are aggregated into the current operating parameters of the commercial vehicle. The real-time load information of the commercial vehicle is obtained by measuring the load changes of the commercial vehicle. Based on the elevation data and real-time positioning of the road ahead received by the commercial vehicle, the current road condition information of the commercial vehicle is determined.

3. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The efficiency model of the electric drive axle in the commercial vehicle is constructed based on historical efficiency data of various torque and speed combinations, including: During historical operation, the actual efficiency data of the electric drive axle in the commercial vehicle under different torque and speed combinations are collected to obtain the efficiency dataset of the electric drive axle; Cluster analysis was performed on the efficiency dataset to obtain the partitioned efficiency map of the electric drive bridge; The efficiency map of the partition is matched with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge.

4. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 3, characterized in that, The step of matching the partition efficiency map with the real-time operating parameters of the electric drive bridge to obtain the efficiency model of the electric drive bridge includes: The input torque and output speed of the electric drive bridge are collected in real time as real-time operating parameters to obtain the current operating point dataset of the electric drive bridge; The current working point dataset is compared with the efficiency region boundaries in the partition efficiency map to determine the specific efficiency region of the current working point; Extract typical efficiency values ​​and efficiency change trend characteristics of the specific efficiency region from the partition efficiency map to obtain the efficiency feature set of the current working point; Based on the efficiency mapping rules between the efficiency feature set and the current operating point dataset, the real-time efficiency prediction value of the electric drive bridge is determined. An efficiency model for the electric drive bridge is constructed based on the real-time efficiency prediction values.

5. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The step of dynamically allocating load based on the real-time vehicle load information to obtain the target driving force allocation ratio of the electric drive axle under the current operating conditions includes: Based on the real-time load information of the vehicle, the current load status category of the electric drive axle is identified, and the load status identifier of the electric drive axle is obtained. Based on the load status identifier, query the preset load-allocation strategy mapping table to obtain the set of allocation adjustment coefficients corresponding to the load status identifier; Based on the set of allocation adjustment coefficients, the basic allocation ratio is weighted and corrected to obtain the preliminary optimized allocation ratio of the electric drive bridge; Based on the current operating temperature and ambient temperature parameters of the electric drive bridge, thermal management compensation is applied to the preliminary optimized allocation ratio to obtain the temperature-compensated allocation ratio. The temperature-compensated distribution ratio, which meets the working constraints of commercial vehicles, is output as the target driving force distribution ratio of the electric drive axle under the current operating conditions.

6. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The process of performing delay compensation on the commercial vehicle based on the current operating parameters and the current road condition information to obtain the dynamic adjustment factor of the commercial vehicle under the current operating conditions includes: The vehicle speed change rate and battery state of charge change rate are extracted from the current operating parameters to obtain the dynamic change parameter set of the commercial freight vehicle; The slope change rate is extracted from the current road condition information to obtain the dynamic road condition characteristics of the commercial freight vehicle; Calculate the initial adjustment factor based on the dynamically changing parameter set and the dynamic characteristics of the road conditions; By limiting the initial adjustment factor to a range between a predefined minimum and a maximum value, the dynamic adjustment factor of the commercial vehicle is obtained.

7. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 6, characterized in that, The initial adjustment factor is calculated using the following formula: ； In the formula, The initial adjustment factor is... The preset vehicle speed factor, The vehicle speed is one of the current operating parameters. As a time factor, The battery state of charge in the current operating parameters. The preset state of charge factor, The gradient is the slope in the current road condition information. The preset slope factor, The rate of change of vehicle speed, The rate of change of the state of charge of the battery. The slope change rate is given.

8. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The step of determining the torque distribution combination that maximizes the overall efficiency of the commercial vehicle based on the target driving force distribution ratio and the efficiency characteristics of the engine and electric motor in the commercial vehicle includes: The pre-stored engine efficiency map and electric motor efficiency map are queried separately to obtain the efficiency characteristic curves of the engine and electric motor in the commercial vehicle at the current speed, thus obtaining the engine efficiency feature set and electric motor efficiency feature set. Based on the target driving force distribution ratio, the torque distribution benchmark value of the engine and the electric motor is determined, and the initial torque distribution scheme of the engine and the electric motor is obtained; The initial torque allocation scheme that conforms to the constraints of the engine efficiency feature set and the electric motor efficiency feature set shall be used as the candidate torque allocation combination for the commercial cargo vehicle. Evaluate the overall efficiency of the candidate torque distribution combinations; The candidate torque distribution combination with the highest overall efficiency is output as the torque distribution combination for the commercial cargo vehicle.

9. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 8, characterized in that, The formula for calculating the overall efficiency is as follows: ； In the formula, The total efficiency is given. This refers to the total torque required by the aforementioned commercial freight vehicle. Let be the angular velocity of the commercial cargo vehicle. The torque value allocated to the engine in the candidate torque allocation combination. This refers to the actual operating speed of the engine. The efficiency value of the engine under specific operating conditions is defined in the engine efficiency feature set. The torque value assigned to the motor in the candidate torque allocation combination. This refers to the actual operating speed of the electric motor. The efficiency value of the motor under specific operating conditions is defined in the set of motor efficiency characteristics.

10. The method for optimizing the drive force distribution of an electric drive axle range-extended hybrid system as described in claim 1, characterized in that, The step of dynamically correcting the torque distribution combination according to the dynamic adjustment factor to obtain the engine torque requirement and electric motor torque requirement of the commercial vehicle includes: The initial torque distribution combination of the commercial cargo vehicle is generated based on the dynamic adjustment factor and the torque distribution combination. Based on the magnitude and direction of the dynamic adjustment factor, the engine torque correction amount and the electric motor torque correction amount are determined, and the torque correction parameter set of the commercial vehicle is obtained. Apply the torque correction parameter set to the initial torque distribution combination; Verify whether the modified torque distribution combination meets the instantaneous torque response capability of the engine and electric motor and the system protection conditions, and obtain the feasibility verification results of the modified torque distribution combination; The modified torque distribution combination, which has passed the feasibility verification, is output as the engine torque requirement and electric motor torque requirement of the commercial cargo vehicle.

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