CN112124298A - Hybrid vehicle following cruising energy management method based on rapid solving algorithm - Google Patents

Abstract

The invention discloses a hybrid vehicle following cruising energy management method based on a fast solving algorithm, which comprises the following steps: establishing a nonlinear optimization fast algorithm; modeling an upper-layer speed plan considering following vehicles, and applying a nonlinear optimization fast algorithm to the model; optimizing the driving force and the braking force obtained under the upper-layer speed planning, and controlling and distributing the torque at the lower layer for the purpose of energy conservation; and obtaining the engine torque and the motor torque according to the torque distribution controlled by the lower layer. The method applies a rapid algorithm for solving the optimal solution of the nonlinear system to the upper-layer speed planning, and can reasonably plan the speed, the driving force and the braking force on the premise of ensuring the following.

Description

Hybrid vehicle following cruising energy management method based on rapid solving algorithm

Technical Field

The invention belongs to the technical field of vehicle energy management, and particularly relates to a hybrid vehicle following cruising energy management method based on a fast solving algorithm.

Background

Under the large background of smart cities, intelligent transportation and automobile intellectualization, on the basis of human-vehicle, vehicle-vehicle and vehicle-road communication, the vehicle speed and the like need to be comprehensively controlled to improve the utilization efficiency of the energy of the whole automobile. The introduction of vehicle navigation systems, global positioning systems and geographic information systems has made it possible for vehicles to acquire future road and traffic information, and also has provided better conditions for vehicles to improve energy efficiency, especially energy-saving speed planning has become an important part of automotive energy management. Generally, the influence of road condition information on vehicle driving economy is comprehensively considered based on navigation, a high-precision map and prediction of future road information, so that driving decision behaviors and control output of a power transmission system are improved, and finally the energy utilization efficiency of the whole vehicle is improved.

The existing vehicle energy management algorithm is complex, and a corresponding algorithm is lacked in the aspect of following vehicle cruising.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a hybrid electric vehicle following cruising energy management method based on a fast solving algorithm. The technical scheme of the invention is as follows by combining the attached drawings of the specification:

a hybrid electric vehicle following cruising energy management method based on a fast solving algorithm comprises the following steps:

the method comprises the following steps: establishing a nonlinear optimization fast algorithm;

step two: modeling an upper-layer speed plan considering following vehicles, and applying a nonlinear optimization fast algorithm to the model;

step three: optimizing the driving force and the braking force obtained under the upper-layer speed planning, and controlling and distributing the torque at the lower layer for the purpose of energy conservation;

step four: and obtaining the engine torque and the motor torque according to the torque distribution controlled by the lower layer.

In the first step, the specific process of establishing the nonlinear optimization fast algorithm is as follows:

a nonlinear system is:

y＝Cx

the non-linear part of (a) is converted into a linear equation with time-varying perturbations:

y＝Cx

wherein: x is formed by R^n×1Is a state variable, u ∈ R^m×1For the control variable, d ∈ R^l×1For time-varying perturbations, y ∈ R^s×1For output variables, A ∈ R^n×nIs a state matrix, B_u∈R^n×mTo control the matrix, B_d∈R^n×lFor the perturbation matrix, C ∈ R^s×nIs an output matrix;

the objective function is:

wherein: q ∈ R^s×sAnd R ∈ R^m×mAre all positive definite weighting matrixes;

the control law is expressed as a feedback form of the system state, disturbance and its derivatives:

in a first iteration:

disturbance removal, nonlinear system:

conversion to a linear system:

according to the basic algorithm, the control law is selected as follows:

u₁＝K_xx₁

thus, the control variable u is obtained₁And a state variable x₁The optimal solution of (2);

in the second iteration:

substituting the control law into the original system to obtain the disturbance of the first iteration as follows:

B_dd₁＝f(x₁,u₁)-Ax₁+B_uu₁

the nonlinear system is then converted into:

and (3) updating the system and control law:

combining to obtain the optimal control law u of the second iteration₂And x₂；

According to the iteration process, the system is continuously updated, so that a result of multiple iterations can be obtained, and finally, an optimal control rule of the iteration is obtained and found.

In the second step, the specific process of applying the nonlinear optimization fast algorithm to the model is as follows:

setting state variables

Controlled variable

Wherein: Δ d_sIs the relative distance error between the vehicle and the preceding vehicle, Δ v is the speed difference between the vehicle and the preceding vehicle, F_tIs the driving force of the host vehicle,

the rate of change of the driving force of the vehicle, F_bBraking force for the vehicle;

order to

Wherein: c_dIs the air resistance coefficient; a. the_fIs the frontal area; ρ is the air density; m is vehicle mass; c. C_rIs a function of f and theta, theta being the slope and f being the rolling resistance coefficient; g is the acceleration of gravity;

further:

c_r＝fcosθ+sinθ；

wherein: t is_hFor following the vehicle, a_pAs acceleration of the front vehicle, a_hFor the acceleration of the vehicle, F_RIs the vehicle running resistance, and v is the vehicle speed;

based on the above settings, the objective function is established as follows:

the function is established for security as follows:

J_T＝w_dΔd_s ²+w_vΔv²

wherein: j. the design is a square_TIs a security goal; w is a_dTracking parameters for the distance; w is a_vTracking a parameter for the speed;

the function is established for the braking force as follows:

J_F＝w_bF_b ²

wherein: j. the design is a square_FIs a braking force target; w is a_bIs a braking force parameter;

the function is established for comfort as follows:

wherein: j. the design is a square_cComfort goals; w is a_tIs a driving force rate of change parameter;

incorporating the Security object J_TBraking force target J_FAnd a comfort goal J_cThe objective function is established as follows:

wherein: beta is a constant;

when the objective function is:

then:

in the third step, the specific process of the lower layer for controlling the distribution torque is as follows:

under each sampling period, the driving force and braking force requirements obtained by upper-layer speed planning are considered, and the lower-layer control mainly carries out energy-saving torque distribution, namely:

wherein: j is an objective function; t is_sTo sampleTime; g_fuelThe fuel consumption rate of the engine; r_torDistributing a ratio for the moment; g_elcIs the motor power;

the fuel consumption rate of the engine is as follows:

G_fuel(R_tor)＝p₀₁T_f+p₁₀w_f+p₁₁T_fw_f+p₀₂T_f ²+p₂₀w_f ²

wherein: p is a radical of₀₁Is a coefficient; p is a radical of₁₀Is a coefficient; p is a radical of₁₁Is a coefficient; p is a radical of₀₂Is a coefficient; p is a radical of₂₀Is a coefficient;

T_fas engine torque:

wherein: k is a vehicle fixed coefficient; t is_demIs the total torque demand; w is a_fIs the engine speed; i.e. i₀Is a main reduction ratio; i.e. i_gIs the transmission ratio of the transmission; v is the speed of the vehicle; r is_wIs the dynamic tire radius;

the power of the motor is as follows:

G_elc(T_m,w_m)＝b₀₁T_m+b₁₀w_m+b₁₁T_mw_m+b₂₀w_m ²+b₀₂T_m ²,

wherein: t is_mIs the motor torque; w is a_mThe motor rotating speed; b₁₀Is a coefficient; b₀₁Is a coefficient; b₁₁Is a coefficient; b₀₂Is a coefficient; b₂₀Is a coefficient;

according to the driving force and braking force requirements under the upper-layer speed plan, predicting [ t, t + Ts [)]Inner vehicle speed, is recorded as

Setting:

therefore, the objective function of the lower layer optimal control is as follows:

the constraints are:

x₁＝(1-ku)T_dem

x₂＝uT_dem

the boundary conditions are as follows:

0≤u≤1

-150≤x₁≤150

-150≤x₂≤150

and (5) solving the optimized control rate in each sampling time Ts through an optimized tool box.

In the fourth step, the specific process of obtaining the engine torque and the motor torque is as follows:

and (3) combining a yalcip optimization platform in matlab with a CPLEX solver, and introducing the speed and the total driving force obtained in the step two to obtain moment distribution, engine moment and motor moment.

Compared with the prior art, the invention has the beneficial effects that:

the hybrid vehicle following cruising energy management method based on the rapid solving algorithm provides an automobile layered energy management strategy considering speed planning, the upper layer of speed planning fully considers the driving information of the front vehicle, and the lower layer of speed planning carries out the optimal calculation of the torque and the gear of the engine and the motor on the basis of the speed planning and provides an optimal solution. Simulation analysis results show that the hybrid vehicle following cruising energy management method based on the rapid solving algorithm has a good effect, and the calculation efficiency is greatly improved compared with the traditional iterative algorithm.

Drawings

FIG. 1 is a schematic flow chart of a hybrid electric vehicle following cruise energy management method based on a fast solving algorithm;

FIG. 2 is a schematic diagram of a simulation result of a distance between a front vehicle and a rear vehicle obtained through a fast algorithm in the hybrid electric vehicle following cruising energy management method;

FIG. 3 is a schematic diagram of a simulation result of a distance error between a front vehicle and a rear vehicle obtained through a fast algorithm in the following cruising energy management method of the hybrid electric vehicle;

FIG. 4 is a schematic diagram of a speed difference simulation result of a front vehicle and a rear vehicle obtained through a fast algorithm in the following cruising energy management method of the hybrid electric vehicle;

FIG. 5 is a schematic diagram of a vehicle speed simulation result obtained by a fast algorithm in the hybrid vehicle following cruise energy management method according to the present invention;

FIG. 6 is a schematic diagram of a preceding vehicle speed simulation result obtained through a fast algorithm in the hybrid vehicle following cruising energy management method of the present invention;

FIG. 7 is a schematic diagram of a simulation result of the driving force and the braking force of the vehicle obtained through a fast algorithm in the following cruising energy management method of the hybrid vehicle;

FIG. 8 is a schematic diagram of a power simulation result of the hybrid electric vehicle obtained through a fast algorithm in the following cruising energy management method of the hybrid electric vehicle of the present invention;

FIG. 9 is a schematic diagram of a simulation result of a driving force variation rate of the hybrid vehicle obtained through a fast algorithm in the following cruising energy management method of the hybrid vehicle according to the present invention;

FIG. 10 is a schematic diagram of a torque distribution ratio of a vehicle obtained by the following cruising energy management method for a hybrid vehicle according to the present invention;

fig. 11 is a schematic diagram of the engine torque and the motor torque of the hybrid vehicle obtained by the method for managing the following cruising energy of the hybrid vehicle.

Detailed Description

For clearly and completely describing the technical scheme and the specific working process thereof, the specific implementation mode of the invention is as follows by combining the attached drawings of the specification:

as shown in fig. 1, the invention discloses a hybrid electric vehicle following cruise energy management method based on a fast solution algorithm, which comprises the following specific processes:

the method comprises the following steps: establishing a nonlinear optimization fast algorithm;

a nonlinear system is:

y＝Cx

the non-linear part of (1) is regarded as a linear system with time-varying disturbance, and is converted into a linear equation with time-varying disturbance:

y＝Cx

wherein: x is formed by R^n×1Is a state variable, u ∈ R^m×1For the control variable, d ∈ R^l×1For time-varying perturbations, y ∈ R^s×1For output variables, A ∈ R^n×nIs a state matrix, B_u∈R^n×mTo control the matrix, B_d∈R^n×lFor the perturbation matrix, C ∈ R^s×nIs an output matrix;

the objective function is:

wherein: q ∈ R^s×sAnd R ∈ R^m×mAre all positive definite weighting matrices.

The control law is expressed as a feedback form of the system state, disturbance and its derivatives:

in a first iteration:

disturbance removal, nonlinear system:

conversion to a linear system:

according to the basic algorithm, the control law is selected as follows:

u₁＝K_xx₁

thus, the control variable u is obtained₁And a state variable x₁The optimal solution of (2);

in the second iteration:

substituting the control law into the original system to obtain the disturbance of the first iteration as follows:

B_dd₁＝f(x₁,u₁)-Ax₁+B_uu₁

the nonlinear system is then converted into:

and (3) updating the system and control law:

combining to obtain the optimal control law u of the second iteration₂And x₂；

According to the iteration process, the system is continuously updated, so that a result of multiple iterations can be obtained, and finally, an optimal control rule of the iteration is obtained and found.

Step two: modeling an upper-layer speed plan considering following vehicles, and applying a nonlinear optimization fast algorithm to the model;

setting state variables

Controlled variable

Wherein: Δ d_sIs the relative distance error between the vehicle and the preceding vehicle, Δ v is the speed difference between the vehicle and the preceding vehicle, F_tIs the driving force of the host vehicle,

the rate of change of the driving force of the vehicle, F_bBraking force for the vehicle;

order to

Wherein: c_dTaking the coefficient of air resistance as 0.373; a. the_fFor windward area, take 2.58m²(ii) a Rho is air density, and is 1.29; taking 1658kg as the mass of the vehicle; c. C_rIs a function of f and theta, theta is the gradient and is taken as 0, f is the rolling resistance coefficient and is taken as 0.02; g is gravity acceleration, and is 9.8m/s²；

After further dumping:

c_r＝fcosθ+sinθ；

wherein: t is_hFor following the vehicle, a_pAs acceleration of the front vehicle，a_hFor the acceleration of the vehicle, F_RIs the vehicle running resistance, and v is the vehicle speed;

based on the above-mentioned reversal, the system is controllable and considerable;

the objective function is established as follows:

1. to ensure security performance, the function is established for security as follows:

J_T＝w_dΔd_s ²+w_vΔv²

wherein: j. the design is a square_TIs a security goal; w is a_dTaking 0.6 as a distance tracking parameter; w is a_vTaking 0.01 as a speed tracking parameter;

2. to avoid excessive braking force, the function is established for the braking force as follows:

J_F＝w_bF_b ²

wherein: j. the design is a square_FIs a braking force target; w is a_bTaking 0.00005 as a braking force parameter;

3. to ensure comfort performance, the function is established for comfort as follows:

wherein: j. the design is a square_cComfort goals; w is a_tTaking 0.00005 as a driving force change rate parameter;

in combination with the above safety objective J_TBraking force target J_FAnd a comfort goal J_cThe objective function is established as follows:

wherein: beta is a constant, 0.001 is taken;

when the objective function is:

then:

simulating and simulating the vehicle running state based on the upper-layer speed planning model of the nonlinear optimization fast algorithm, wherein the front vehicle working condition selects a part of the classic working condition UDDS, and matlab simulation time T_fTaking for 25 seconds; sampling time T_sTaking for 0.1 second; the number N of sampling points is 250; as shown in fig. 2 to 9, it can be seen from the obtained simulation results of the distance between the front and rear vehicles, the simulation results of the error between the front and rear vehicles, the simulation results of the speed difference between the front and rear vehicles, the simulation results of the speed of the vehicle, the simulation results of the speed of the front vehicle, the simulation results of the driving force and braking force of the vehicle, the simulation results of the power of the vehicle, and the simulation results of the rate of change of the driving force of the vehicle: the nonlinear optimization fast algorithm can enable the vehicle to keep a proper distance with the front vehicle, and information such as the optimal speed, the optimal total driving force and the optimal braking force of the vehicle can be obtained in a short time.

Step three: optimizing the driving force and the braking force obtained under the upper-layer speed planning, and controlling and distributing the torque at the lower layer for the purpose of energy conservation;

under each sampling period, the driving force and braking force requirements obtained by upper-layer speed planning are considered, and the lower-layer control mainly carries out energy-saving torque distribution, namely:

wherein: j is an objective function; t is_sTaking 0.1s as sampling time; g_fuelThe fuel consumption rate of the engine; r_torDistributing a ratio for the moment; g_elcIs the motor power;

the fuel consumption rate of the engine is as follows:

G_fuel(R_tor)＝p₀₁T_f+p₁₀w_f+p₁₁T_fw_f+p₀₂T_f ²+p₂₀w_f ²

wherein: p is a radical of₀₁Taking the coefficient as-0.0024; p is a radical of₁₀Taking the coefficient as 0.00019; p is a radical of₁₁Taking the coefficient as 5.25 × 10^-6；p₀₂Taking 2.635X 10 as coefficients^-5；p₂₀As a coefficient, take 6.7 × 10^-8；

T_fAs engine torque:

T_f＝(1-kR_tor)T_dem,

wherein: k is a vehicle fixed coefficient and is taken as 0.5; t is_demIs the total torque demand; w is a_fIs the engine speed; i.e. i₀Taking the main reduction ratio as 3.94; i.e. i_gThe following were selected for the transmission ratios: when v ∈ [0,5 ]]When i is_g4.16, when v ∈ (5, 8)]When i is_g2.45; when v ∈ (8, 12)]When i is_g1.61; when v ∈ (12, 16)]When i is_g1.20; when v ∈ (16, 20)]When i is_g0.92, when v ∈ (20, 33)]When i is_g0.70; v is the speed of the vehicle; r is_wTaking the radius of the dynamic tire to be 0.3 m;

the power of the motor is as follows:

G_elc(T_m,w_m)＝b₀₁T_m+b₁₀w_m+b₁₁T_mw_m+b₂₀w_m ²+b₀₂T_m ²,

wherein: t is_mIs the motor torque; w is a_mThe motor rotating speed; b₁₀Taking the coefficient as 0.00019; b₀₁Taking the coefficient as-0.0024; b₁₁Taking the coefficient as 5.25 × 10^-6；b₀₂Taking 2.635X 10 as coefficients^-5；b₂₀As a coefficient, take 6.7 × 10^-8；

According to the driving force and braking force requirements under the upper-layer speed plan, predicting [ t, t + Ts [)]Inner vehicle speed, is recorded as

Setting:

therefore, the objective function of the lower layer optimal control is as follows:

the constraints are:

x₁＝(1-ku)T_dem

x₂＝uT_dem

the boundary conditions are as follows:

0≤u≤1

-150≤x₁≤150

-150≤x₂≤150

and (3) solving the optimized control rate in each sampling time Ts through an optimized tool box, wherein the sampling time Ts of the lower-layer control is set to be 10ms in order to meet the requirement of fast control on the engine and the motor.

Step four: obtaining the torque of an engine and the torque of a motor according to the torque distribution controlled by the lower layer;

and (3) combining a yalcip optimization platform in matlab with a CPLEX solver, introducing the speed of the vehicle and the total driving force in the whole process obtained in the step two, and obtaining moment distribution as shown in fig. 10 and engine moment and motor moment as shown in fig. 11.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.

Claims (5)

1. A hybrid vehicle following cruising energy management method based on a fast solving algorithm is characterized in that:

the method comprises the following steps:

the method comprises the following steps: establishing a nonlinear optimization fast algorithm;

step two: modeling an upper-layer speed plan considering following vehicles, and applying a nonlinear optimization fast algorithm to the model;

step three: optimizing the driving force and the braking force obtained under the upper-layer speed planning, and controlling and distributing the torque at the lower layer for the purpose of energy conservation;

step four: and obtaining the engine torque and the motor torque according to the torque distribution controlled by the lower layer.

2. The hybrid vehicle following cruise energy management method based on the fast solution algorithm as claimed in claim 1, characterized in that:

in the first step, the specific process of establishing the nonlinear optimization fast algorithm is as follows:

a nonlinear system is:

y＝Cx

the non-linear part of (a) is converted into a linear equation with time-varying perturbations:

y＝Cx

wherein: x is formed by R^n×1Is a state variable, u ∈ R^m×1For the control variable, d ∈ R^l×1For time-varying perturbations, y ∈ R^s×1For output variables, A ∈ R^n×nIs a state matrix, B_u∈R^n×mTo control the matrix, B_d∈R^n×lFor the perturbation matrix, C ∈ R^s×nIs an output matrix;

the objective function is:

wherein: q ∈ R^s×sAnd R ∈ R^m×mAre all positive definite weighting matrixes;

the control law is expressed as a feedback form of the system state, disturbance and its derivatives:

in a first iteration:

disturbance removal, nonlinear system:

conversion to a linear system:

according to the basic algorithm, the control law is selected as follows:

u₁＝K_xx₁

thus, the control variable u is obtained₁And a state variable x₁The optimal solution of (2);

in the second iteration:

substituting the control law into the original system to obtain the disturbance of the first iteration as follows:

B_dd₁＝f(x₁,u₁)-Ax₁+B_uu₁

the nonlinear system is then converted into:

and (3) updating the system and control law:

combining to obtain the optimal control law u of the second iteration₂And x₂；

According to the iteration process, the system is continuously updated, so that a result of multiple iterations can be obtained, and finally, an optimal control rule of the iteration is obtained and found.

3. The hybrid vehicle following cruise energy management method based on the fast solution algorithm as claimed in claim 2, characterized in that:

in the second step, the specific process of applying the nonlinear optimization fast algorithm to the model is as follows:

setting state variables

Controlled variable

Wherein: Δ d_sIs the relative distance error between the vehicle and the preceding vehicle, Δ v is the speed difference between the vehicle and the preceding vehicle, F_tIs the driving force of the host vehicle,

the rate of change of the driving force of the vehicle, F_bFor braking the vehicleForce;

order to

b＝c_rg

Wherein: c_dIs the air resistance coefficient; a. the_fIs the frontal area; ρ is the air density; m is vehicle mass; c. C_rIs a function of f and theta, theta being the slope and f being the rolling resistance coefficient; g is the acceleration of gravity;

further:

c_r＝f cosθ+sinθ；

wherein: t is_hFor following the vehicle, a_pAs acceleration of the front vehicle, a_hFor the acceleration of the vehicle, F_RIs the vehicle running resistance, and v is the vehicle speed;

based on the above settings, the objective function is established as follows:

the function is established for security as follows:

J_T＝w_dΔd_s ²+w_vΔv²

wherein: j. the design is a square_TIs a security goal; w is a_dTracking parameters for the distance; w is a_vTracking a parameter for the speed;

the function is established for the braking force as follows:

J_F＝w_bF_b ²

wherein: j. the design is a square_FIs a braking force target; w is a_bIs a braking force parameter;

the function is established for comfort as follows:

wherein: j. the design is a square_cComfort goals; w is a_tIs a driving force rate of change parameter;

incorporating the Security object J_TBraking force target J_FAnd a comfort goal J_cThe objective function is established as follows:

wherein: beta is a constant;

when the objective function is:

then:

4. the hybrid vehicle following cruise energy management method based on the fast solution algorithm as claimed in claim 3, characterized in that:

in the third step, the specific process of the lower layer for controlling the distribution torque is as follows:

under each sampling period, the driving force and braking force requirements obtained by upper-layer speed planning are considered, and the lower-layer control mainly carries out energy-saving torque distribution, namely:

wherein: j is an objective function; t is_sIs the sampling time; g_fuelThe fuel consumption rate of the engine; r_torDistributing a ratio for the moment; g_elcIs the motor power;

the fuel consumption rate of the engine is as follows:

G_fuel(R_tor)＝p₀₁T_f+p₁₀w_f+p₁₁T_fw_f+p₀₂T_f ²+p₂₀w_f ²

wherein: p is a radical of₀₁Is a coefficient; p is a radical of₁₀Is a coefficient; p is a radical of₁₁Is a coefficient; p is a radical of₀₂Is a coefficient; p is a radical of₂₀Is a coefficient;

T_fas engine torque:

T_f＝(1-kR_tor)T_dem,

wherein: k is a vehicle fixed coefficient; t is_demIs the total torque demand; w is a_fIs the engine speed; i.e. i₀Is a main reduction ratio; i.e. i_gIs the transmission ratio of the transmission; v is the speed of the vehicle; r is_wIs the dynamic tire radius;

the power of the motor is as follows:

G_elc(T_m,w_m)＝b₀₁T_m+b₁₀w_m+b₁₁T_mw_m+b₂₀w_m ²+b₀₂T_m ²,

wherein: t is_mIs the motor torque; w is a_mThe motor rotating speed; b₁₀Is a coefficient; b₀₁Is a coefficient; b₁₁Is a coefficient; b₀₂Is a coefficient; b₂₀Is a coefficient;

according to the driving force and braking force requirements under the upper-layer speed plan, predicting [ t, t + Ts [)]Inner vehicle speed, is recorded as

Setting:

therefore, the objective function of the lower layer optimal control is as follows:

the constraints are:

x₁＝(1-ku)T_dem

x₂＝uT_dem

the boundary conditions are as follows:

0≤u≤1

-150≤x₁≤150

-150≤x₂≤150

and (5) solving the optimized control rate in each sampling time Ts through an optimized tool box.

5. The hybrid vehicle following cruise energy management method based on the fast solution algorithm as claimed in claim 4, characterized in that:

in the fourth step, the specific process of obtaining the engine torque and the motor torque is as follows:

and (3) combining a yalcip optimization platform in matlab with a CPLEX solver, and introducing the speed and the total driving force obtained in the step two to obtain moment distribution, engine moment and motor moment.

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