# Ts = 0.01
# sim_time = 400
# sim_time_range = np.arange(0,sim_time,0.01)
#
# # ----------------------------- select input set ---------------------------
# veh_speed_set = np.concatenate((0 * np.ones(int(len(sim_time_range)*0.1)), 20 * np.ones(int(len(sim_time_range)*0.4)), 0 * np.ones(int(len(sim_time_range)*0.5))))
u_veh_vel_set = data_driver['var_Desired_Velocity_km_h_']/3.6
Ts = 0.01
sim_time_range = data_driver['var_Time_s_']
#%% 3. Run simulation
for test_case in range(2):
# Set logging data
veh_data = type_DataLog(['Veh_Vel','Pos_X','Pos_Y','Acc_Set','Brk_Set','t_brk'])
motor_data = type_DataLog(['t_mot','t_mot_reg','t_mot_des','w_mot'])
power_data = type_DataLog(['p_mot','p_loss','p_elec','soc','v_mot','i_mot'])
shaft_data = type_DataLog(['w_shaft','w_wheel','w_veh','w_dot_mot','w_dot_veh','w_dot_shaft'])
# Powertrain import and configuration
kona_power = Mod_Power()
# Bodymodel import and configuration
kona_body = Mod_Body()
# Vehicle set
kona_vehicle = Mod_Veh(kona_power, kona_body)
drv_kyunghan = Mod_Driver()
# Behavior model
beh_speed = Mod_Behavior(drv_kyunghan)
# ~~~~
# Driver model
drv_kyunghan = Mod_Driver()
# ~~~~
# Behavior model
beh_cycle = Mod_Behavior(drv_kyunghan)
# ~~~~
#%% 2. Simulation config
# 2. brake torque
u_veh_vel_set = data_driver['var_Desired_Velocity_km_h_'] / 3.6
Ts = 0.01
sim_time_range = data_driver['var_Time_s_']
#%% 3. Simulation
# Set logging data
beh_cycle.conf_filtnum_pedal = 0.5
sim_driver = type_DataLog(['u_acc', 'u_brk'])
sim_vehicle = type_DataLog(['Veh_vel', 't_mot', 't_brk', 'w_mot'])
veh_vel = 0
w_mot = 0
w_shaft = 0
w_wheel = 0
start_time = time.time()
for sim_step in range(len(sim_time_range)):
# Arrange cruise input
u_veh_vel_set_in = u_veh_vel_set[sim_step]
u_acc_in, u_brk_in = beh_cycle.Lon_control(u_veh_vel_set_in, veh_vel)
# Vehicle model sim
# 1. Cockfit system
t_mot = kona_vehicle.Acc_system(u_acc_in)
t_brk, t_reg = kona_vehicle.Brake_system(u_brk_in)
# 1. Drag force
# Behavior model
beh_driving = Mod_Behavior(drv_kyunghan)
#%% 2. Simulation config
# 2. brake torque
data_driver = get_config.load_mat('Data_Kyunghan.mat')
data_result = get_config.load_mat('SimResult_Kyunghan.mat')
u_trq_mot_set = data_driver['Trq_Mot']
u_trq_brk_set = data_driver['Trq_BrkMec']
sim_time_range = data_driver['Veh_Time']
result_veh_vel = data_driver['Veh_Vel']
u_trq_drag = data_result['simresult_trq_drag']
Ts = 0.01
#%% 3. Simulation
# Set logging data
sim_vehicle = type_DataLog(['veh_vel', 'veh_acc', 'wdot_veh'])
sim_states = type_DataLog([
'w_mot', 'w_shaft', 'w_wheel', 'trq_shaft_in', 'trq_wheel_load', 'trq_drag'
])
sim_wheel = type_DataLog(['t_wheel_in', 't_wheel_traction_f'])
veh_vel = result_veh_vel[0]
w_wheel = data_driver['Rot_Wheel'][0]
w_mot = w_wheel * kona_drivetrain.conf_gear
w_shaft = w_wheel
kona_drivetrain.w_vehicle = w_wheel
start_time = time.time()
for sim_step in range(len(sim_time_range)):
# Arrange cruise input
u_t_mot = u_trq_mot_set[sim_step]
u_t_brk = u_trq_brk_set[sim_step]
kona_drive = Mod_Drive()
kona_vehicle = Mod_Veh(kona_power, kona_drive)
#%% 3. Simulation config
# ----------------------------- select input set ---------------------------
# input for drive-train
# 1. motor torque
u_t_mot = data_emachine['var_Torque_Nm_']
# 2. brake torque
u_t_brk = -(data_brake_f['var_Braking_Torque_Nm_'] +
data_brake_r['var_Braking_Torque_Nm_']) * 2
Ts = 0.01
sim_time_range = data_emachine['var_Time_s_']
#%% 3. Run simulation
# Set logging data
data_sim_torque = type_DataLog(
['t_mot_load', 't_wheel_load', 't_traction', 't_drag'])
data_sim_dynamics = type_DataLog(['w_mot', 'w_shaft', 'w_wheel'])
data_sim_veh = type_DataLog(['veh_acc', 'veh_vel', 'f_lon', 'f_drag'])
veh_vel = 0
w_mot = 0
w_shaft = 0
w_wheel = 0
for sim_step in range(len(sim_time_range)):
# Arrange vehicle input
u_t_mot_in = u_t_mot[sim_step]
u_t_brk_in = u_t_brk[sim_step]
# Vehicle model sim
# 1. Drag force
t_drag, f_drag = kona_vehicle.Drag_system(veh_vel)
# 2. Torque equivalence
ItNumMax = 100
Learning_result = []
Learning_control = []
Learning_qval = []
swt_valid = 1
# Learning results
learn_log_list_veh = [
'veh_vel', 'veh_dis', 'veh_acc', 'veh_acc_set', 'veh_acc_ref',
'mot_trq_set', 'mot_trq_reg', 'mot_trq'
]
learn_log_list_drv = ['drv_acc', 'drv_brk', 'coast_vel', 'coast_dis']
learn_log_list_result = [
'q_list', 'act_val', 'r_drv', 'r_reg', 'r_saf', 'step'
]
lrn_veh = type_DataLog(learn_log_list_veh)
lrn_drv = type_DataLog(learn_log_list_drv)
lrn_result = type_DataLog(learn_log_list_result)
LS = []
for ItNum in range(ItNumMax):
swt_valid = ItNum % 100
egreedy_dis_fac = ItNum // 10
# if swt_valid == 0:
# agent_dqn.conf_egreedy = 0
# print('============================= validation =================================')
# else:
# agent_dqn.conf_egreedy = e_greedy_config - egreedy_dis_fac/50
# 3. Import model - set the initial value
# Powertrain import and configuration
kona_power = Mod_PowerTrain()
len(road_x) * 0.8 * 0.003, 0.003))
road_y = np.concatenate((100 * np.ones(int(len(road_x) * 0.2)), road_sin))
# Environment set
env_sl = Mod_Env(road_x, road_y)
# Set initial vehicle position
env_sl.Vehicle_init_config(kona_vehicle, 2)
#%% 2. Simulation config
Ts = 0.01
sim_time = 150
sim_time_range = np.arange(0, sim_time, 0.01)
u_speed_val = np.concatenate(
(0 * np.ones(int(len(sim_time_range) * 0.1)),
20 * np.ones(int(len(sim_time_range) * 0.9))))
#%% 3. Run simulation
# Set logging data
veh_data_sim = type_DataLog(
['vel_veh', 'the_wheel', 'u_acc', 'u_brk', 'u_str'])
veh_state_sim = type_DataLog(['pos_x', 'pos_y', 'psi_veh'])
# Vehicle initial state
vel_veh = kona_vehicle.vel_veh
pos_x = kona_vehicle.pos_x_veh
pos_y = kona_vehicle.pos_y_veh
psi_veh = kona_vehicle.psi_veh
for sim_step in range(len(sim_time_range)):
# Arrange behavior input
u_vel_veh_set_in = u_speed_val[sim_step]
# Behavior control
u_acc_in, u_brk_in = beh_steer.Lon_control(u_vel_veh_set_in, vel_veh)
u_steer_in = beh_steer.Lat_behavior(pos_x, pos_y, psi_veh, road_x,
road_y)
# Vehicle driven
(0 * np.ones(int(len(sim_time_range) * 0.3)),
0.01 * np.ones(int(len(sim_time_range) * 0.7))))
elif Input_index == 1:
# Sin input
u_acc_val = np.concatenate(
(0 * np.ones(int(len(sim_time_range) * 0.1)),
0.3 * np.ones(int(len(sim_time_range) * 0.9))))
u_brk_val = 0 * np.ones(len(sim_time_range))
u_steer_val = np.concatenate(
(0 * np.ones(int(len(sim_time_range) * 0.3)),
0.01 * np.sin(0.01 * np.arange(int(len(sim_time_range) * 0.7)))))
else:
print('입력을 똑바로 하세요 ~_~')
#%% 3. Run simulation
# Set logging data
sim2 = type_DataLog(['Veh_Vel', 'Pos_X', 'Pos_Y', 'Acc_Set', 'Str_Set'])
for sim_step in range(len(sim_time_range)):
# Arrange vehicle input
u_acc_in = u_acc_val[sim_step]
u_brk_in = u_brk_val[sim_step]
u_str_in = u_steer_val[sim_step]
# Vehicle model sim
[veh_vel, the_wheel] = kona_vehicle.Veh_driven(u_acc=u_acc_in,
u_brake=u_brk_in,
u_steer=u_str_in)
[pos_x, pos_y, pos_s, pos_n,
psi_veh] = kona_vehicle.Veh_position_update(veh_vel, the_wheel)
# Store data
sim2.StoreData([veh_vel, pos_x, pos_y, u_acc_in, u_str_in])
[sim2_veh_vel, sim2_pos_x, sim2_pos_y, sim2_u_acc,
plt.title('Road data')
# Environment set
env_st = Mod_Env(road_x,road_y)
# Set initial vehicle state at environment
env_st.Vehicle_init_config(kona_vehicle, 2)
# env_st.Obj_add('Tl','red',495)
road_env_obj = env_st.object_list
road_env_road_len = env_st.road_len
beh_driving.conf_filtnum_spdset = 3
#%% 2. Simulation config
Ts = 0.01
sim_time = 668
sim_time_range = np.arange(0,sim_time,0.01)
# Set logging data
simdata_veh = type_DataLog(['veh_vel','vel_set','acc_in','brk_in','str_in','pos_x','pos_y','pos_s'])
simdata_body = type_DataLog(['t_mot','t_wheel_load','t_drag','t_brk'])
envdata = type_DataLog(['stc_objec','lat_off','head_off'])
# initial vehicle position
pos_x = kona_vehicle.pos_x_veh
pos_y = kona_vehicle.pos_y_veh
pos_s = kona_vehicle.pos_s_veh
psi_veh = kona_vehicle.psi_veh
veh_vel = kona_vehicle.vel_veh
start_time = time.time()
for sim_step in range(len(sim_time_range)):
# Arrange vehicle position
# Behavior control
[u_acc_in, u_brk_in] = beh_driving.Lon_behavior(road_env_obj, pos_s, road_env_road_len, veh_vel)
u_acc_val = np.concatenate((0 * np.ones(int(len(sim_time_range)*0.1)), 0.3 * np.ones(int(len(sim_time_range)*0.9))))
u_brk_val = 0 * np.ones(len(sim_time_range))
elif Input_index == 1:
# Sin wave : Input_index = 1
u_acc_val = np.concatenate((0 * np.ones(int(len(sim_time_range)*0.1)), 0.3 + 0.1*np.sin(0.01*np.arange(int(len(sim_time_range)*0.9)))))
u_brk_val = 0 * np.ones(len(sim_time_range))
# Brake : Input_index = 2
elif Input_index == 2:
u_acc_val = np.concatenate((0 * np.ones(int(len(sim_time_range)*0.1)), 0.3 * np.ones(int(len(sim_time_range)*0.4)), 0 * np.ones(int(len(sim_time_range)*0.5))))
u_brk_val = np.concatenate((0 * np.ones(int(len(sim_time_range)*0.55)), 0.3 * np.ones(int(len(sim_time_range)*0.45))))
else:
print('입력을 똑바로 하세요 ~_~')
#%% 3. Run simulation
# Set logging data
sim1 = type_DataLog(['Veh_Vel','Pos_X','Pos_Y','Acc_Set','Brk_Set'])
w_veh_deb = []
for sim_step in range(len(sim_time_range)):
# Arrange vehicle input
u_acc_in = u_acc_val[sim_step]
u_brk_in = u_brk_val[sim_step]
# Vehicle model sim
[veh_vel, the_wheel] = kona_vehicle.Veh_driven(u_acc = u_acc_in, u_brake = u_brk_in)
[pos_x, pos_y, pos_s, pos_n, psi_veh] = kona_vehicle.Veh_position_update(veh_vel, the_wheel)
# Store data
sim1.StoreData([veh_vel, pos_x, pos_y, u_acc_in, u_brk_in])
w_veh_deb.append(kona_vehicle.ModDrive.w_vehicle)
[sim1_veh_vel, sim1_pos_x, sim1_pos_y, sim1_u_acc, sim1_u_brk] = sim1.get_profile_value(['Veh_Vel','Pos_X','Pos_Y','Acc_Set','Brk_Set'])
#%% 4. Result plot
fig = plt.figure(figsize=(8,4))
Ts = 0.01
sim_time = 100
sim_time_range = np.arange(0, sim_time, 0.01)
# Initial state
w_mot = kona_power.ModMotor.w_mot
t_mot_load = kona_power.ModMotor.t_load
# ----------------------------- select input set ---------------------------
u_acc_val = Get_SqrVal([0.3, 0.1, 0], [0.1, 0.3, 0.5], sim_time / Ts)
u_brk_val = Get_SqrVal([0.3, 0], [0.55, 0.8], sim_time / Ts)
#%% 3. Run simulation
# Set logging data
data_log_list = [
'veh_vel', 'w_wheel', 'w_mot', 'u_acc', 'u_brk', 'SOC', 't_mot_set',
't_mot', 'v_mot', 't_brk', 't_drag', 't_load'
]
sim = type_DataLog(data_log_list)
sim_reg = type_DataLog(data_log_list)
for sim_step in range(len(sim_time_range)):
# Arrange vehicle input
u_acc_in = u_acc_val[sim_step]
u_brk_in = u_brk_val[sim_step]
# Vehicle model sim
kona_vehicle.Veh_driven(u_acc_in, u_brk_in)
kona_vehicle_reg.Veh_driven(u_acc_in, u_brk_in)
# Module driven sim
# [t_mot_des, t_brake] = kona_body.Lon_driven_in(u_acc_in, u_brk_in)
# [w_mot, t_mot] = kona_power.ModMotor.Motor_control(t_mot_des, t_mot_load)
# [w_wheel, t_mot_load, vel_veh] = kona_body.Lon_driven_out(t_brake, w_mot)
# Store data
mot = kona_power.ModMotor