Hybrid power assembly parameter optimization method based on wheel edge working condition backward simulation

Abstract

The invention provides a hybrid power assembly parameter optimization method based on wheel edge working condition backward simulation. The hybrid power assembly parameter optimization method comprises the steps that # imgabs0 #: sample data is preprocessed, sample wheel edge power and vehicle speed data are obtained, and wheel edge data are obtained through calculation; # imgabs1 #: extracting a dynamic feature matrix and a static feature matrix of the wheel edge data and performing feature clustering; # imgabs2 #: constructing a backward simulation model of the hybrid power system, and solving simulation oil consumption; # imgabs3 #: establishing a multi-objective optimization data model based on the additional cost and the fuel saving rate; and # imgabs4 #: solving the established multi-objective optimization mathematical model by using an NSGA-II algorithm, and solving power assembly parameters including a motor, an engine, battery capacity, an axle speed ratio, a gearbox model and a tire model so as to realize an optimal power matching decision.

Description

Technical Field

[0001] The present invention discloses a hybrid powertrain parameter optimization method based on backward simulation of wheel-side working conditions, and relates to the technical field of intelligent vehicle networking. Background Art

[0002] With the rapid development of the automotive industry and the continuous advancement of new energy vehicle technology, power matching decisions play a vital role in the vehicle design process. Traditional methods often rely on experimental testing and empirical judgment, which is not only time-consuming and laborious, but also difficult to fully cover the performance under various working conditions. In order to overcome this difficulty, modern automobile design increasingly adopts simulation-based methods for power matching decisions.

[0003] The existing technology uses a cycle road spectrum combined with AVL Cruise software to build a vehicle simulation model for power matching decisions. Usually, the power boundary is derived using dynamic indicators such as maximum vehicle speed, acceleration time, and maximum climbing grade to determine the feasible domain of engine, motor, and battery selection. The vehicle simulation model is built using AVL Cruise software, and the cycle road spectrum is used as input to simulate the economy and power simulation results of different matching parameters. The simulation results are combined with some optimization algorithms to make power matching decisions. There are the following disadvantages: (1) Cyclic operating condition spectra are generally general operating conditions (C-WTVC, etc.) or self-constructed operating conditions. The operating condition synthesis is usually based on the reorganization of motion segments. The selection and reorganization of motion segments will inevitably cause deviations between the constructed operating condition and the original operating condition characteristics. (2) The typical cycle road spectrum is usually only a time-vehicle speed spectrum. The characteristics of altitude and slope are generally reflected in the speed spectrum in the form of acceleration conversion. However, hybrid vehicles need to combine slope and braking cycle information to develop battery energy management strategies, Balanced matching verification: It is difficult to accurately match the motor and battery using a single time-vehicle speed spectrum; (3) To build the AVL Cruise simulation model, it is necessary to input parameters such as the gross weight of the vehicle and cargo and the vehicle resistance coefficient. However, the actual vehicle load and vehicle resistance in the market are affected by the environment and are not fixed parameter values. Therefore, there is a matching deviation when simulating with fixed parameters. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a hybrid powertrain parameter optimization method based on backward simulation of wheel side working conditions, and the specific technical solution is as follows: A hybrid powertrain parameter optimization method based on backward simulation of wheel side working conditions comprises the following steps: : Preprocess the sample data and obtain the sample wheel power and vehicle speed data, and calculate and obtain the wheel data; : Extract the dynamic feature matrix and static feature matrix of wheel edge data and perform feature clustering; : Build a backward simulation model of the hybrid system and calculate the simulated fuel consumption; : Establish a multi-objective optimization data model based on additional cost and fuel saving rate; : The established multi-objective optimization mathematical model is solved using the NSGA-Ⅱ algorithm.

[0005] Preferably, the The specific method is: Based on the time of the speed signal, the linear interpolation method is used to obtain the corresponding values ​​of the vehicle speed, torque, load, and slope signals. The speed, speed, torque, load, and slope data of the sample data are aligned to identify the driving and braking conditions, and calculate the corresponding wheel power: ; express The wheel power at the braking condition at this moment meets the vehicle speed , and the brake signal ; is the vehicle mass; is the acceleration due to gravity; is the longitudinal slope, Moment and The altitude at the moment is calculated; is the air density; is the air resistance coefficient; is the windward area; is the rotation mass conversion factor; is the driving acceleration; express The wheel power at the driving condition at the moment meets the vehicle speed And torque ; express The net output power of the engine at the driving condition at the time is express The fan power at the driving condition at the moment is express The pump power at the time of driving operation is is the comprehensive efficiency of the vehicle transmission system at the current moment; Get the wheel data of a single sample through wheel power: .

[0006] Furthermore, the The engine speed at time t and engine torque The calculation results are: .

[0007] Furthermore, the The static characteristic matrix in is a two-dimensional distribution matrix of wheel power-vehicle speed, and the vehicle speed range is 0~120 , with 5 The wheel power range is -600~400 , with 50 For the interval; The dynamic feature matrix in is the state transition probability matrix of the wheel-side features. Specifically, the operation segments are divided according to the continuous driving condition and the continuous braking condition. The average power, duration, and power standard deviation are extracted as features for each operation segment. The average power range is -600~400 , 100 is an interval, the duration range is 0~300 , 60 is the interval, the power standard deviation ranges from 0 to 200, 50 is the interval, and 200 states are obtained to form the state transition probability matrix.

[0008] Furthermore, the In , the method for feature clustering is: Apply the static feature matrix of the sample The method was used to cluster and the silhouette coefficient was used to evaluate the selection value, get Typical working conditions, and then Application of dynamic feature matrix for each class of samples in the class The method is clustered, and after two layers of clustering, we get Typical working conditions of each class; wheel edge data of cluster center samples in each class To represent Characteristics of typical working conditions.

[0009] Furthermore, the The hybrid system backward simulation model includes a wheel-side power allocation function based on an optimal energy management strategy. The wheel-side data is input into the wheel-side power allocation function based on an optimal energy management strategy. As well as the configuration parameters of the motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model to calculate the simulated fuel consumption, the wheel power allocation function based on the optimal energy management strategy is as follows: Under driving conditions, the expression for power distribution is as follows: ; Where: for The wheel drive power of the car at the moment, for The driving power allocated to the engine at all times, Delivering efficiency to the engine power system, for The driving power allocated to the motor at all times, To improve the efficiency of the motor power system transmission, for The maximum power generated by the motor at the moment, for Peak motor power at the moment, for Rated power of motor at all times, for Moment Battery value, The minimum value allowed for normal battery operation value; and They are The engine speed and engine torque corresponding to the lowest operating point of the engine instantaneous fuel consumption rate when the motor and battery constraints and the gearbox gear feasible domain are met at all times; Under braking conditions, the expression for power distribution is as follows: ; Where: for The braking power of the car wheel at the moment, for The braking power allocated to the motor at all times, for The power required by mechanical braking is always exceeded when the motor battery recovery capacity is exceeded. for The maximum power recovered by the motor at that moment, The maximum value of the battery under normal operation value; Battery remaining power calculation function: ; Where: for Motor efficiency at all times, For the efficiency of the battery, Indicates the battery capacity; The motor speed and torque are calculated by the current vehicle speed. With motor power calculate: ; Where: for Motor speed at all times, for Time gearbox gear, for Drive axle ratio at all times, is the tire radius.

[0010] Furthermore, the feasible region condition of the gearbox gear is: ; Where: for Engine speed at the moment, , , and They are the lower and upper limits of the gear shift speed respectively.

[0011] Furthermore, the The simulation function for calculating the simulated fuel consumption is: ; Where: is the fuel consumption per 100 kilometers of the hybrid vehicle, is the fuel density, is the mileage of the vehicle, for Engine torque at the moment, for Engine speed at the moment With engine torque The fuel consumption rate is as follows.

[0012] Furthermore, the The multi-objective optimization data model includes an additional cost function and a fuel saving rate function; the additional cost function is: ; Where: is the maximum power of the motor, is the maximum power of the engine, is the cost function, is the motor cost, For battery cost, For costs other than motor and battery; The fuel saving rate function is: ; Where: Compile fuel consumption data for the market.

[0013] The present invention uses wheel-side power and vehicle speed timing conditions of big data clustering as inputs to match hybrid powertrain parameters, wherein the wheel-side power includes power requirements under braking and driving conditions, and truly reflects the total mass, slope, resistance, road and traffic status information characteristics of the original conditions; based on the vehicle dynamics balance formula and the principle of optimal energy management strategy, a wheel-side power distribution model and a hybrid system backward simulation model are constructed to solve the hybrid powertrain motion chain and power flow decomposition problems, and simulate to obtain fuel consumption values ​​under different parameter combinations; a multi-objective function with low additional cost and high fuel saving rate is established, and the NSGA-Ⅱ algorithm is used to optimize and solve the powertrain parameters, and the solved powertrain parameters include motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model, so as to achieve the optimal power matching decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of a hybrid powertrain parameter optimization method based on backward simulation of wheel side working conditions. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, a hybrid powertrain parameter optimization method based on backward simulation of wheel side working conditions includes the following steps: : Preprocess the sample data and obtain the sample wheel power and vehicle speed data, and calculate and obtain the wheel data; : Extract the dynamic feature matrix and static feature matrix of wheel edge data and perform feature clustering; : Build a backward simulation model of the hybrid system and calculate the simulated fuel consumption; : Establish a multi-objective optimization data model based on additional cost and fuel saving rate; : The established multi-objective optimization mathematical model is solved using the NSGA-Ⅱ algorithm.

[0017] Said Data preprocessing: Taking a single vehicle as a sample, align the speed, rotation speed, torque, load, slope and other data of the sample data, identify the driving and braking conditions, and calculate the corresponding wheel power: ; express The wheel power at the braking condition at this moment meets the vehicle speed , and the brake signal ; is the vehicle mass ( ); is the gravitational acceleration ( ); is the longitudinal slope ( ),Depend on Moment and The altitude at the moment is calculated; is the air density ( ); is the air resistance coefficient; is the windward area ( ); is the rotation mass conversion factor; is the driving acceleration ( ); express The wheel power at the driving condition at the moment meets the vehicle speed And torque ; express The net output power of the engine at the driving condition at the time is express The fan power at the driving condition at the moment is determined by the current fan speed. By looking up the table, express The pump power at the time of driving operation is is the comprehensive efficiency of the vehicle transmission system at the current moment; Get the wheel data of a single sample through wheel power: .

[0018] Since different signals have different acquisition frequencies, it is necessary to align each signal with the time axis. The acquisition frequency of the speed signal is 1s / time. Based on the time of the speed signal, the linear interpolation method is used to obtain the corresponding values ​​of the vehicle speed, torque, load, and slope signals, so that the time axes of all signals are aligned.

[0019] Said The engine speed at time t and engine torque The calculation results are: .

[0020] Specifically, the The static characteristic matrix in is a two-dimensional distribution matrix of wheel power-vehicle speed, and the vehicle speed range is 0~120 , with 5 The wheel power range is -600~400 , with 50 As interval, we get the following matrix: ; in Indicates the speed range is The power range is The frequency ratio meets ; Said The dynamic feature matrix in is the state transition probability matrix of the wheel-side features. Specifically, the operation segments are divided according to the continuous driving condition and the continuous braking condition. The average power, duration, and power standard deviation are extracted as features for each operation segment. The average power range is -600~400 , 100 is an interval, the duration range is 0~300 , 60 is the interval, the power standard deviation range is 0~200, 50 is the interval, 200 states are obtained, and the state transition probability matrix is ​​formed: ; in Indicates status , Indicated by the state Transfer to The probability of satisfying .

[0021] Furthermore, the In the method of feature clustering, the static feature matrix of the sample is applied The method was used to cluster and the silhouette coefficient was used to evaluate the selection value, get Typical working conditions, and then Application of dynamic feature matrix for each class of samples in the class The method is clustered, and after two layers of clustering, we get Typical working conditions of each class; wheel edge data of cluster center samples in each class To represent Characteristics of typical working conditions.

[0022] Specifically, the The hybrid system backward simulation model includes a wheel-side power allocation function based on an optimal energy management strategy. The wheel-side data is input into the wheel-side power allocation function based on an optimal energy management strategy. As well as the configuration parameters of the motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model to calculate the simulated fuel consumption, the wheel power allocation function based on the optimal energy management strategy is as follows: Under the driving conditions, when the motor and battery constraints are met, the power distribution is carried out based on the principle of minimum instantaneous engine consumption, and the expression is as follows: ; Where: for The vehicle wheel drive power at the moment ( ), for The driving power allocated to the engine at any time ( ), Delivering efficiency to the engine power system, for The driving power allocated to the motor at any time ( ), To improve the efficiency of the motor power system transmission, for The maximum power generated by the motor at the moment ( ), for Peak motor power at time ( ), for Motor rated power at all times ( ), for Moment Battery value, The minimum value allowed for normal battery operation value; and They are The engine speed corresponding to the lowest operating point of the engine instantaneous fuel consumption rate when the motor and battery constraints and the gearbox gear feasible domain are met at all times ( ) and engine torque ( ); The feasible region conditions of the gearbox gear are: ; Where: for Engine speed at the moment, , , and They are the lower and upper limits of the gear shift speed respectively.

[0023] Under braking conditions, when the motor and battery constraints are met, power distribution is based on the principle of recovering as much energy as possible. The expression is as follows: ; Where: for The vehicle wheel braking power at the moment ( ), for The braking power allocated to the motor at any time ( ), for The power required by mechanical braking when the motor battery recovery capacity is exceeded at any time ( ), for The maximum power recovered by the motor at the moment ( ), and the current battery level It is related to the continuous working time of the motor peak power. The maximum value of the battery under normal operation value; Battery remaining power calculation function (assuming the initial battery power is 50%, =50%): ; Where: for The motor efficiency at the moment is obtained by looking up the motor speed and torque table; For the efficiency of the battery, Indicates the battery capacity ( ); The motor speed and torque are calculated by the current vehicle speed. With motor power calculate: ; Where: for Motor speed at all times, for Time gearbox gear, for Drive axle ratio at all times, is the tire radius.

[0024] The simulation function for calculating the simulated fuel consumption in S3 is: ; Where: is the fuel consumption per 100 kilometers of the hybrid vehicle ( ), is the fuel density ( ), The mileage of the vehicle ( ), , for The vehicle speed at the moment, for Engine torque at the moment, for Engine speed at the moment With engine torque Fuel consumption rate under , obtained by looking up the engine speed and torque table.

[0025] Furthermore, the The multi-objective optimization data model includes an additional cost function and a fuel saving rate function; the additional cost function is: ; Where: is the maximum power of the motor, is the maximum power of the engine, is the cost function, is the motor cost, For battery cost, It is the cost excluding the motor and battery. The cost is mainly related to the maximum power of the motor, the maximum power of the engine and the battery capacity. The cost function is mainly obtained through the pricing standards agreed upon by the OEM and component manufacturers. The additional cost (Yuan) is the additional cost of a hybrid vehicle compared to a diesel vehicle, mainly considering the additional costs of the motor, battery and other controllers, as well as the cost saved by matching a small-displacement engine.

[0026] The fuel saving rate function is: ; Where: Fuel consumption data is collected for the market, and the fuel saving rate is the proportion of the fuel consumption reduction of hybrid vehicles compared to original diesel vehicles.

[0027] For each typical working condition, As input, under a set of parameters configured as motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model, the simulated fuel consumption is obtained through the wheel-side power allocation function based on the optimal energy management strategy, the fuel saving rate is calculated, and the additional cost is obtained. For different configuration parameters, different additional costs and fuel saving rates can be obtained through the backward simulation model. In order to select the best configuration among multiple configurations, from the perspective of economy, a configuration with low cost and high fuel saving rate is selected, so a The multi-objective function. Only pursuing low cost, that is, the motor and battery power are small, and the battery capacity is small, the fuel saving rate is low, low cost and high profit are contradictory, and cannot be solved directly. Therefore, a dual-objective model is established, and the design is improved using the fast non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set. NSGA-2 (non-dominated sorting genetic algorithm) is used to solve the multi-objective problem: NSGA-Ⅱ is a commonly used algorithm for solving multi-objective optimization models. Compared with traditional genetic algorithms, it introduces an elite strategy and combines an immune genetic improvement algorithm, which can better retain excellent individuals in the parent population and suppress individuals with too high concentration to prevent the algorithm from falling into the local optimal solution. In this model, the motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model are variables. For the two objective functions, the NSGA-Ⅱ algorithm parameters are set as follows: crossover probability , mutation probability ,The population size is 100 and the maximum number of iterations is 400.

[0028] The method of the present invention uses wheel-side power and vehicle speed time sequence conditions of big data clustering as inputs to match hybrid powertrain parameters, wherein the wheel-side power includes power requirements under braking and driving conditions, and truly reflects the total mass, slope, resistance, road and traffic status information characteristics of the original conditions; based on the vehicle dynamics balance formula and the principle of optimal energy management strategy, a wheel-side power distribution model and a hybrid system backward simulation model are constructed to solve the hybrid powertrain kinematic chain and power flow decomposition problems, and simulate to obtain fuel consumption values ​​under different parameter combinations; a multi-objective function with low additional cost and high fuel saving rate is established, and the NSGA-Ⅱ algorithm is used to optimize and solve the powertrain parameters, and the solved powertrain parameters include motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model, so as to achieve the optimal power matching decision.

[0029] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A hybrid powertrain parameter optimization method based on backward simulation of wheel side working conditions, characterized in that: The steps include: : Preprocess the sample data and obtain the sample wheel power and vehicle speed data, and calculate and obtain the wheel data; : Extract the dynamic feature matrix and static feature matrix of wheel edge data and perform feature clustering; : Build a backward simulation model of the hybrid system and calculate the simulated fuel consumption; : Establish a multi-objective optimization data model based on additional cost and fuel saving rate; : The established multi-objective optimization mathematical model is solved using the NSGA-Ⅱ algorithm.

2. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 1 is characterized in that: Said The specific method is: Based on the time of the speed signal, the linear interpolation method is used to obtain the corresponding values ​​of the vehicle speed, torque, load, and slope signals. The speed, speed, torque, load, and slope data of the sample data are aligned to identify the driving and braking conditions, and calculate the corresponding wheel power: ； express The wheel power at the braking condition at this moment meets the vehicle speed , and the brake signal ; is the vehicle mass; is the acceleration due to gravity; is the longitudinal slope, Moment and The altitude at the moment is calculated; is the air density; is the air resistance coefficient; is the windward area; is the rotation mass conversion factor; is the driving acceleration; express The wheel power at the driving condition at the moment meets the vehicle speed And torque ; express The net output power of the engine at the driving condition at the time is express The fan power at the driving condition at the moment is express The pump power at the time of driving operation is is the comprehensive efficiency of the vehicle transmission system at the current moment; Get the wheel data of a single sample through wheel power: .

3. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 2 is characterized in that: Said The engine speed at time t and engine torque The calculation results are: 。 4. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 2 is characterized in that: Said The static characteristic matrix in is a two-dimensional distribution matrix of wheel power-vehicle speed, and the vehicle speed range is 0~120 , with 5 The wheel power range is -600~400 , with 50 For the interval; The dynamic feature matrix in is the state transition probability matrix of the wheel-side features. Specifically, the operation segments are divided according to the continuous driving condition and the continuous braking condition. The average power, duration, and power standard deviation are extracted as features for each operation segment. The average power range is -600~400 , 100 is an interval, the duration range is 0~300 , 60 is the interval, the power standard deviation ranges from 0 to 200, 50 is the interval, and 200 states are obtained to form the state transition probability matrix.

5. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 4 is characterized in that: Said In , the method for feature clustering is: Apply the static feature matrix of the sample The method was used to cluster and the silhouette coefficient was used to evaluate the selection value, get Typical working conditions, and then Application of dynamic feature matrix for each class of samples in the class The method is clustered, and after two layers of clustering, we get Typical working conditions of each class; wheel edge data of cluster center samples in each class To represent Characteristics of typical working conditions.

6. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 5 is characterized in that: Said The hybrid system backward simulation model includes a wheel-side power allocation function based on an optimal energy management strategy. The wheel-side data is input into the wheel-side power allocation function based on an optimal energy management strategy. As well as the configuration parameters of the motor, engine, battery capacity, bridge speed ratio, gearbox model, and tire model to calculate the simulated fuel consumption, the wheel power allocation function based on the optimal energy management strategy is as follows: Under driving conditions, the expression for power distribution is as follows: ； Where: for The wheel drive power of the car at the moment, for The driving power allocated to the engine at all times, Delivering efficiency to the engine power system, for The driving power allocated to the motor at all times, To improve the efficiency of the motor power system transmission, for The maximum power generated by the motor at the moment, for Peak motor power at the moment, for Rated power of motor at all times, for Moment Battery value, The minimum value allowed for normal battery operation value; and They are The engine speed and engine torque corresponding to the lowest operating point of the engine instantaneous fuel consumption rate when the motor and battery constraints and the gearbox gear feasible domain are met at all times; Under braking conditions, the expression for power distribution is as follows: ； Where: for The braking power of the car wheel at the moment, for The braking power allocated to the motor at all times, for The power required by mechanical braking is always exceeded when the motor battery recovery capacity is exceeded. for The maximum power recovered by the motor at that moment, The maximum value of the battery under normal operation value; Battery remaining power calculation function: ； Where: for Motor efficiency at all times, For the efficiency of the battery, Indicates the battery capacity; The motor speed and torque are calculated by the current vehicle speed. With motor power calculate: ； Where: for Motor speed at all times, for Time gearbox gear, for Drive axle ratio at all times, is the tire radius.

7. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 6 is characterized in that: The feasible region conditions of the gearbox gear are: ； Where: for Engine speed at the moment, , , and They are the lower and upper limits of the shift speed respectively.

8. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 7 is characterized in that: Said The simulation function for calculating the simulated fuel consumption is: ； Where: is the fuel consumption per 100 kilometers of the hybrid vehicle, is the fuel density, is the mileage of the vehicle, for Engine torque at the moment, for Engine speed at the moment With engine torque The fuel consumption rate is as follows.

9. The hybrid powertrain parameter optimization method based on wheel side working condition backward simulation according to claim 8, characterized in that: Said The multi-objective optimization data model includes an additional cost function and a fuel saving rate function; the additional cost function is: ； Where: is the maximum power of the motor, is the maximum power of the engine, is the cost function, is the motor cost, For battery cost, For costs other than motor and battery; The fuel saving rate function is: ； Where: Compile fuel consumption data for the market.

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