交通拥堵时混合动力汽车的节能动力系统燃油模型的MATLAB代码

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🔥 内容介绍

There are about 1.2 billion active vehicle on the road whose number is set to increase to about 2 billion by 2035. To meet stringent emission norms, environmental and energy concerns, vehicles have to reduce their CO2 emissions. Hybrid Electric Vehicles contributes to fuel savings and emission reduction aims, which ultimately reduces energy consumption. The aim of this project is to develop efficient powertrain control for hybrid vehicles to reduce energy consumption though heavy traffic. The project involves optimization of two subsystems, namely the IC engine and electric motor. The approach is to reduce fuel consumption by switching between electric drive and internal combustion engine during traffic scenario. The change in acceleration helps to find the velocity which in turn determines the position of the following vehicle which is also dependent on the vehicle ahead of the following vehicle. This calculation considers the efficiency of electric drive and internal combustion engine as well at different velocities.

We have considered parallel configuration of the drivetrain in the hybrid electric vehicle. In this configuration, both electric motor and IC engine are mechanically coupled to drive the wheels

📣 部分代码

clear;clc;close all%% Data
% load('enginedata.mat');% % Data from (TOYOTA PRIUS): https://media.toyota.co.uk/wp-content/files_mf/1329489972120216MTOYOTAPRIUSTECHNICALSPECIFICATIONS.pdf
Vehicle_length = 4.48; % meterVehicle_mass = 1805; % kgGear_ratio = 2.683; % Forward Gear RatioDifferential_drive_ratio = 3.267; % Differential Gear RatioWheel_radius = 0.3175; % m => 12.5 inch tire radius (195/65r15 tire);Max_power = 73000; % watt
% Assumptions:time = 1:100;random_values = 0.2*rand(length(time),1) + 0.6; % limiting range of random variablesRoad_length = 500; % meterVehicle_safe_distance = 3.0 ; % meterVehicle_max_velocity = 29.057; % m/s => 65 mphEngine_efficiency = 0.80;
%% PDE model for Vehicle Density
Road_density_max = Road_length / (Vehicle_length + Vehicle_safe_distance);Road_density_random = random_values * Road_density_max;velocity_vehicle = Vehicle_max_velocity * (1 - (Road_density_random / Road_density_max));
%% Vehicle Modeling
Net_Gear_ratio = Gear_ratio * Differential_drive_ratio;Wheel_rpm = (velocity_vehicle*60)./(2*pi*Wheel_radius);Engine_rpm = Wheel_rpm * Net_Gear_ratio;Engine_torque = Max_power ./ Wheel_rpm;Wheel_torque = Engine_torque .* Net_Gear_ratio * 0.5;
%% Optimization
% Polynomial derived from curve fitting app using given engine data% X axis: eng_consum_spd% Y axis: eng_consum_trq% Z axis: eng_fuel_map_gpkWh% Polynomial order: (5,5)
p00 = 976.7;p10 = -12.14;p01 = -3.574;p20 = 0.1646;p11 = -0.02367;p02 = 0.01739;p30 = -0.0009203;p21 = 0.000117;p12 = 4.708e-06;p03 = -2.651e-05;p40 = 2.344e-06;p31 = -2.78e-07;p22 = -4.89e-09;p13 = 6.132e-10;p04 = 1.604e-08;p50 = -2.234e-09;p41 = 2.812e-10;p32 = -4.378e-11;p23 = 2.251e-11;p14 = -6.75e-12;p05 = -1.881e-12;
% x0 = [Engine_rpm(1) Engine_torque(1)];x0 =[100 100];
bsfc = @(x) (1.5*(p00 + p10*x(1) + p01*x(2) + p20*x(1)^2 + p11*x(1)*x(2) + p02*x(2)^2 + p30*x(1)^3 + p21*x(1)^2*x(2) ... + p12*x(1)*x(2)^2 + p03*x(2)^3 + p40*x(1)^4 + p31*x(1)^3*x(2) + p22*x(1)^2*x(2)^2 ... + p13*x(1)*x(2)^3 + p04*x(2)^4 + p50*x(1)^5 + p41*x(1)^4*x(2) + p32*x(1)^3*x(2)^2 ... + p23*x(1)^2*x(2)^3 + p14*x(1)*x(2)^4 + p05*x(2)^5));dl_0 = 0;df_0 = 0;dt = 1;
for i = time dl(i) = velocity_vehicle(i)*dt + dl_0; % Distance(position) of Leading Vehicle dl_0 = dl(i); end
for j = time
LB = [min(Engine_rpm) min(Engine_torque)];UB = 8*[Engine_rpm(j) Engine_torque(j)];A = [-dl(j) df_0; dl(j) -df_0];B = [-3; 5]; %[Safe_Distance; max_allowed_distance_for_following_purpose]
x = fmincon(bsfc,x0,A,B,[],[],LB,UB);
x1(j) = x(1);x2(j) = x(2);
mf(j) = abs(bsfc(x))/(3.6e9*Engine_efficiency)*0.8;vel(j) = ((x2(j)./Net_Gear_ratio)*(2*pi/60))*(Wheel_radius);
df(j) = vel(j)*dt + df_0; % Distance(position) of following vehicledf_0 = df(j);
x0=x;
end
figure(1);hold on;% subplot(2,1,1);plot(time,mf)xlabel("Time (sec)");ylabel("Mass flow rate (Kg/s)");hold off;
figure(2)hold on;% subplot(2,1,2);plot(time,vel)xlabel("Time (sec)");ylabel("Our Vehicle Optimized Velocity (m/s)");hold off;
cyc_mph(:,1) = time;cyc_mph(:,2) = vel;cd ECMS;save('cyc_mph.mat','cyc_mph')
% ans = fMPG(4);

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