most things should work now
This commit is contained in:
@ -1,79 +0,0 @@
|
||||
function [pltData,laps_strArray] = loadvector(filepath)
|
||||
|
||||
% Load dataset:
|
||||
fprintf("Loading: %s\n",filepath)
|
||||
data = vector2timetable(filepath,"ABX*","Shunt*","AMS_Status*","INV*","XSens*");
|
||||
fprintf("Finished loading\n------------------------\n")
|
||||
|
||||
% get date / time
|
||||
date = dateshift(data.Time(1),'start','day');
|
||||
fprintf("Date: %s\n",date)
|
||||
fprintf("Start: %d:%d:%02.0f\n",hour(data.Time(1)),minute(data.Time(1)),second(data.Time(1)))
|
||||
fprintf("End: %d:%d:%02.0f\n",hour(data.Time(end)),minute(data.Time(end)),second(data.Time(end)))
|
||||
fprintf("------------------------\n")
|
||||
|
||||
% get lapcounter / laptrigger
|
||||
laps_counter = data.ABX_Driver_ABX_Lapcounter;
|
||||
timestamp_hms(1) = data.Time(1) - date;
|
||||
timestamp_index(1) = 1;
|
||||
j = 2;
|
||||
for i = 2:length(laps_counter)
|
||||
if laps_counter(i) ~= laps_counter(i-1)
|
||||
timestamp_hms(j) = data.Time(i) - date;
|
||||
timestamp_index(j) = i;
|
||||
j = j + 1;
|
||||
end
|
||||
end
|
||||
|
||||
% calculate laptimes
|
||||
timestamp_s = seconds(timestamp_hms);
|
||||
for i = 1:length(timestamp_s)-1
|
||||
laps_s(i) = timestamp_s(i+1) - timestamp_s(i);
|
||||
end
|
||||
|
||||
% if laps could be detected find fastest lap and create list of
|
||||
% laptimes, otherwise only "Entire stint" will be selectable
|
||||
laps_strArray(1) = sprintf("Entire stint");
|
||||
if (exist("laps_s","var") == true)
|
||||
% create list of laptimes
|
||||
for i = 1:length(laps_s)
|
||||
if laps_s(i) == min(laps_s)
|
||||
laps_strArray(i+1) = sprintf("Lap %d: %.3fs (best)",i,laps_s(i));
|
||||
else
|
||||
laps_strArray(i+1) = sprintf("Lap %d: %.3fs",i,laps_s(i));
|
||||
end
|
||||
end
|
||||
% display fastest lap
|
||||
[laps_best_time, laps_best] = min(laps_s);
|
||||
fprintf("Fastest lap: %.2fs (%d)\n",laps_best_time,laps_best)
|
||||
end
|
||||
|
||||
% create time arrays in hh:mm:ss and just seconds
|
||||
time_hms = data.Time-date;
|
||||
time_s = seconds(time_hms);
|
||||
time_s = time_s - time_s(1); % set beginning of session to 0
|
||||
|
||||
% calcualte energy consumption and total distance
|
||||
power_kw = data.Shunt_Voltage1_Shunt_Voltage1.*data.Shunt_Current_Shunt_Current/1000;
|
||||
power_regen_kw = power_kw;
|
||||
power_regen_kw(power_regen_kw > 0) = 0;
|
||||
power_used_kw = power_kw;
|
||||
power_used_kw(power_used_kw < 0) = 0;
|
||||
energy_kwh = trapz(time_s, power_kw)/3600;
|
||||
energy_regen_kwh = trapz(time_s, power_regen_kw)/3600;
|
||||
energy_used_kwh = trapz(time_s, power_used_kw)/3600;
|
||||
fprintf("Energy used: %.2fkWh\n",energy_used_kwh)
|
||||
fprintf("Energy regen: %.2fkWh\n",energy_regen_kwh)
|
||||
fprintf("Energy total: %.2fkWh\n",energy_kwh)
|
||||
distance_km = trapz(time_s, movmean(data.ABX_Driver_ABX_Speed,10))/1000;
|
||||
fprintf("Distance driven: %.2fkm\n",distance_km)
|
||||
fprintf("------------------------\n")
|
||||
|
||||
pltData(1) = sortdata(data,1,length(data.Time));
|
||||
% sort data for plotting:
|
||||
for i = 1:length(timestamp_index)-1
|
||||
pltData(i+1) = sortdata(data,timestamp_index(i),timestamp_index(i+1));
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
@ -1,123 +0,0 @@
|
||||
function [pltData] = sortdata(data,start,stop)
|
||||
date = dateshift(data.Time(1),'start','day');
|
||||
time_hms = data.Time-date;
|
||||
time_s = seconds(time_hms);
|
||||
time_s = time_s - time_s(1); % set beginning of session to 0
|
||||
|
||||
% x-Axis:
|
||||
pltData.Time = data.Time(start:stop);
|
||||
pltData.time_s = time_s(start:stop)-time_s(start);
|
||||
pltData.time_hms = time_hms(start:stop);
|
||||
|
||||
% distance calculation
|
||||
speed_mps = movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % TODO: test different speed filters for distance calculation
|
||||
pltData.distance(1) = 0;
|
||||
for i = 2:length(speed_mps)
|
||||
pltData.distance(i) = speed_mps(i)*(pltData.time_s(i)-pltData.time_s(i-1)) + pltData.distance(i-1);
|
||||
end
|
||||
pltData.xAxis = pltData.time_s; % set time as default x-Axis for plotting
|
||||
|
||||
%% Misc
|
||||
pltData.app_percent = data.ABX_Driver_ABX_APPS_percent(start:stop);
|
||||
pltData.speed_kph = 3.6*movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % same filter as for distance calculation ???
|
||||
pltData.steering_deg = data.ABX_Driver_ABX_Steering_Angle(start:stop);
|
||||
|
||||
%% AMS:
|
||||
pltData.ams_soc = data.AMS_Status_SOC(start:stop);
|
||||
pltData.ams_tmax = data.AMS_Status_Max_cell_temp(start:stop);
|
||||
pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1(start:stop);
|
||||
pltData.ams_itot = data.Shunt_Current_Shunt_Current(start:stop);
|
||||
% calculations:
|
||||
pltData.ams_ptot = pltData.ams_utot.*pltData.ams_itot/1000;
|
||||
|
||||
%% Brakes
|
||||
% brake pressure
|
||||
pltData.brakePFront_bar = data.ABX_Driver_ABX_BrakeP_F(start:stop);
|
||||
pltData.brakePRear_bar = data.ABX_Driver_ABX_BrakeP_R(start:stop);
|
||||
% brake disc temperatures
|
||||
pltData.brakeTFrontLeft_degC = data.ABX_BrakeT_ABX_BrakeT_FL(start:stop);
|
||||
pltData.brakeTFrontRight_degC = data.ABX_BrakeT_ABX_BrakeT_FR(start:stop);
|
||||
pltData.brakeTRearLeft_degC = data.ABX_BrakeT_ABX_BrakeT_RL(start:stop);
|
||||
pltData.brakeTRearRight_degC = data.ABX_BrakeT_ABX_BrakeT_RR(start:stop);
|
||||
% calculate brake bias [%]
|
||||
minBrakeP = 5; % minimum brake pressure to avoid artifacts due to sensor noise near 0 bar
|
||||
brakePFront = pltData.brakePFront_bar;
|
||||
brakePFront(brakePFront < minBrakeP) = minBrakeP;
|
||||
brakePRear = pltData.brakePRear_bar;
|
||||
brakePRear(brakePRear < minBrakeP) = minBrakeP;
|
||||
pltData.brakeBias_perc = 100*brakePFront./brakePRear; % check calculation!
|
||||
|
||||
%% Cooling system
|
||||
|
||||
%% Dampers
|
||||
% damper positions
|
||||
pltData.damper_FL_mm = data.ABX_Dampers_ABX_Damper_FL(start:stop); %Heave_F
|
||||
pltData.damper_FR_mm = data.ABX_Dampers_ABX_Damper_FR(start:stop); %Roll_F
|
||||
pltData.damper_RL_mm = data.ABX_Dampers_ABX_Damper_RL(start:stop); %Heave_R
|
||||
pltData.damper_RR_mm = data.ABX_Dampers_ABX_Damper_RR(start:stop); %Roll_R
|
||||
% calculate damper velocities
|
||||
pltData.velocity_FL_mmps(1) = 0;
|
||||
pltData.velocity_FR_mmps(1) = 0;
|
||||
pltData.velocity_RL_mmps(1) = 0;
|
||||
pltData.velocity_RR_mmps(1) = 0;
|
||||
for i = 2:length(pltData.time_s)
|
||||
timestep = pltData.time_s(i)-pltData.time_s(i-1);
|
||||
pltData.velocity_FL_mmps(i) = pltData.damper_FL_mm(i)-pltData.damper_FL_mm(i-1)/timestep;
|
||||
pltData.velocity_FR_mmps(i) = pltData.damper_FR_mm(i)-pltData.damper_FR_mm(i-1)/timestep;
|
||||
pltData.velocity_RL_mmps(i) = pltData.damper_RL_mm(i)-pltData.damper_RL_mm(i-1)/timestep;
|
||||
pltData.velocity_RR_mmps(i) = pltData.damper_RR_mm(i)-pltData.damper_RR_mm(i-1)/timestep;
|
||||
end
|
||||
% filter damper velocities ??? bessere Berechnung über mittelwert aus mehreren werten? Vorfilterung?
|
||||
pltData.velocity_FL_mmps = movmean(pltData.velocity_FL_mmps,100);
|
||||
pltData.velocity_FR_mmps = movmean(pltData.velocity_FR_mmps,100);
|
||||
pltData.velocity_RL_mmps = movmean(pltData.velocity_RL_mmps,100);
|
||||
pltData.velocity_RR_mmps = movmean(pltData.velocity_RR_mmps,100);
|
||||
|
||||
%% IMU
|
||||
% Acceleration
|
||||
% pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX(start:stop),100)/9.81;
|
||||
% pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY(start:stop),100)/9.81;
|
||||
% Rate of turn
|
||||
% pltData.rot_roll_degps = movmean(data.XSens_RateOfTurn_XSens_gyrX(start:stop),100);
|
||||
% pltData.rot_pitch_degps = movmean(data.XSens_RateOfTurn_XSens_gyrY(start:stop),100);
|
||||
% pltData.rot_yaw_degps = movmean(data.XSens_RateOfTurn_XSens_gyrZ(start:stop),100);
|
||||
|
||||
%% Inverters
|
||||
% inverter temperatures
|
||||
pltData.invL_temp = data.INV_L_TxPDO_1_T_Inv_L(start:stop);
|
||||
pltData.invR_temp = data.INV_R_TxPDO_1_T_Inv_R(start:stop);
|
||||
% motor temperatures
|
||||
pltData.motL_temp = data.INV_L_TxPDO_1_T_Mot_L(start:stop);
|
||||
pltData.motR_temp = data.INV_R_TxPDO_1_T_Mot_R(start:stop);
|
||||
% motor velocities
|
||||
pltData.motL_vel_rpm = 60*data.INV_L_TxPDO_4_Velocity_L(start:stop);
|
||||
pltData.motR_vel_rpm = 60*data.INV_R_TxPDO_4_Velocity_R(start:stop);
|
||||
% inverter torque demand
|
||||
pltData.invL_torqueDemand = data.INV_L_TxPDO_3_DemandedTorque_L(start:stop)/10; % /10 to match autobox torque
|
||||
pltData.invR_torqueDemand = data.INV_R_TxPDO_3_DemandedTorque_R(start:stop)/10;
|
||||
% inverter actual torque
|
||||
pltData.invL_torqueActual = data.INV_L_TxPDO_3_ActualTorque_L(start:stop)/10;
|
||||
pltData.invR_torqueActual = data.INV_R_TxPDO_3_ActualTorque_R(start:stop)/10;
|
||||
|
||||
%% Wheelspeed
|
||||
|
||||
|
||||
|
||||
|
||||
%% Statistics:
|
||||
power_regen_kw = pltData.ams_ptot;
|
||||
power_regen_kw(power_regen_kw > 0) = 0;
|
||||
power_used_kw = pltData.ams_ptot;
|
||||
power_used_kw(power_used_kw < 0) = 0;
|
||||
pltData.energy_kwh = trapz(pltData.time_s, pltData.ams_ptot)/3600;
|
||||
pltData.energy_regen_kwh = trapz(pltData.time_s, power_regen_kw)/3600;
|
||||
pltData.energy_used_kwh = trapz(pltData.time_s, power_used_kw)/3600;
|
||||
pltData.distanceTotal_km = trapz(pltData.time_s, pltData.speed_kph./3.6)/1000; % 1/3.6 for km/h to m/s and /1000 for km output
|
||||
pltData.peakPower_kw = max(pltData.ams_ptot);
|
||||
pltData.peakPowerMean_kw = max(movmean(pltData.ams_ptot,500));
|
||||
|
||||
pltData.maxSpeed_kph = max(pltData.speed_kph);
|
||||
pltData.startTime = time_hms(start);
|
||||
pltData.stopTime = time_hms(stop);
|
||||
end
|
||||
|
||||
@ -1,122 +0,0 @@
|
||||
function [pltData] = sortdata(data,start,stop)
|
||||
date = dateshift(data.Time(1),'start','day');
|
||||
time_hms = data.Time-date;
|
||||
time_s = seconds(time_hms);
|
||||
time_s = time_s - time_s(1); % set beginning of session to 0
|
||||
|
||||
% x-Axis:
|
||||
pltData.time_s = time_s(start:stop)-time_s(start);
|
||||
pltData.time_hms = time_hms(start:stop);
|
||||
|
||||
% distance calculation
|
||||
speed_mps = movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % TODO: test different speed filters for distance calculation
|
||||
pltData.distance(1) = 0;
|
||||
for i = 2:length(speed_mps)
|
||||
pltData.distance(i) = speed_mps(i)*(pltData.time_s(i)-pltData.time_s(i-1)) + pltData.distance(i-1);
|
||||
end
|
||||
pltData.xAxis = pltData.time_s; % set time as default x-Axis for plotting
|
||||
|
||||
%% Misc
|
||||
pltData.app_percent = data.ABX_Driver_ABX_APPS_percent(start:stop);
|
||||
pltData.speed_kph = 3.6*movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % same filter as for distance calculation ???
|
||||
pltData.steering_deg = data.ABX_Driver_ABX_Steering_Angle(start:stop);
|
||||
|
||||
%% AMS:
|
||||
pltData.ams_soc = data.AMS_Status_SOC(start:stop);
|
||||
pltData.ams_tmax = data.AMS_Status_Max_cell_temp(start:stop);
|
||||
pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1(start:stop);
|
||||
pltData.ams_itot = data.Shunt_Current_Shunt_Current(start:stop);
|
||||
% calculations:
|
||||
pltData.ams_ptot = pltData.ams_utot.*pltData.ams_itot/1000;
|
||||
|
||||
%% Brakes
|
||||
% brake pressure
|
||||
pltData.brakePFront_bar = data.ABX_Driver_ABX_BrakeP_F(start:stop);
|
||||
pltData.brakePRear_bar = data.ABX_Driver_ABX_BrakeP_R(start:stop);
|
||||
% brake disc temperatures
|
||||
pltData.brakeTFrontLeft_degC = data.ABX_BrakeT_ABX_BrakeT_FL(start:stop);
|
||||
pltData.brakeTFrontRight_degC = data.ABX_BrakeT_ABX_BrakeT_FR(start:stop);
|
||||
pltData.brakeTRearLeft_degC = data.ABX_BrakeT_ABX_BrakeT_RL(start:stop);
|
||||
pltData.brakeTRearRight_degC = data.ABX_BrakeT_ABX_BrakeT_RR(start:stop);
|
||||
% calculate brake bias [%]
|
||||
minBrakeP = 5; % minimum brake pressure to avoid artifacts due to sensor noise near 0 bar
|
||||
brakePFront = pltData.brakePFront_bar;
|
||||
brakePFront(brakePFront < minBrakeP) = minBrakeP;
|
||||
brakePRear = pltData.brakePRear_bar;
|
||||
brakePRear(brakePRear < minBrakeP) = minBrakeP;
|
||||
pltData.brakeBias_perc = 100*brakePFront./brakePRear; % check calculation!
|
||||
|
||||
%% Cooling system
|
||||
|
||||
%% Dampers
|
||||
% damper positions
|
||||
pltData.damperHeaveFront_mm = data.ABX_Dampers_ABX_DamperHeave_F(start:stop);
|
||||
pltData.damperRollFront_mm = data.ABX_Dampers_ABX_DamperRoll_F(start:stop);
|
||||
pltData.damperHeaveRear_mm = data.ABX_Dampers_ABX_DamperHeave_R(start:stop);
|
||||
pltData.damperRollRear_mm = data.ABX_Dampers_ABX_DamperRoll_R(start:stop);
|
||||
% calculate damper velocities
|
||||
pltData.velocityHeaveFront_mmps(1) = 0;
|
||||
pltData.velocityRollFront_mmps(1) = 0;
|
||||
pltData.velocityHeaveRear_mmps(1) = 0;
|
||||
pltData.velocityRollRear_mmps(1) = 0;
|
||||
for i = 2:length(pltData.time_s)
|
||||
timestep = pltData.time_s(i)-pltData.time_s(i-1);
|
||||
pltData.velocityHeaveFront_mmps(i) = pltData.damperHeaveFront_mm(i)-pltData.damperHeaveFront_mm(i-1)/timestep;
|
||||
pltData.velocityRollFront_mmps(i) = pltData.damperRollFront_mm(i)-pltData.damperRollFront_mm(i-1)/timestep;
|
||||
pltData.velocityHeaveRear_mmps(i) = pltData.damperHeaveRear_mm(i)-pltData.damperHeaveRear_mm(i-1)/timestep;
|
||||
pltData.velocityRollRear_mmps(i) = pltData.damperRollRear_mm(i)-pltData.damperRollRear_mm(i-1)/timestep;
|
||||
end
|
||||
% filter damper velocities ??? bessere Berechnung über mittelwert aus mehreren werten? Vorfilterung?
|
||||
pltData.velocityHeaveFront_mmps = movmean(pltData.velocityHeaveFront_mmps,100);
|
||||
pltData.velocityRollFront_mmps = movmean(pltData.velocityRollFront_mmps,100);
|
||||
pltData.velocityHeaveRear_mmps = movmean(pltData.velocityHeaveRear_mmps,100);
|
||||
pltData.velocityRollRear_mmps = movmean(pltData.velocityRollRear_mmps,100);
|
||||
|
||||
%% IMU
|
||||
% Acceleration
|
||||
pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX(start:stop),100)/9.81;
|
||||
pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY(start:stop),100)/9.81;
|
||||
% Rate of turn
|
||||
pltData.rot_roll_degps = movmean(data.XSens_RateOfTurn_XSens_gyrX(start:stop),100);
|
||||
pltData.rot_pitch_degps = movmean(data.XSens_RateOfTurn_XSens_gyrY(start:stop),100);
|
||||
pltData.rot_yaw_degps = movmean(data.XSens_RateOfTurn_XSens_gyrZ(start:stop),100);
|
||||
|
||||
%% Inverters
|
||||
% inverter temperatures
|
||||
pltData.invL_temp = data.INV_L_TxPDO_1_T_Inv_L(start:stop);
|
||||
pltData.invR_temp = data.INV_R_TxPDO_1_T_Inv_R(start:stop);
|
||||
% motor temperatures
|
||||
pltData.motL_temp = data.INV_L_TxPDO_1_T_Mot_L(start:stop);
|
||||
pltData.motR_temp = data.INV_R_TxPDO_1_T_Mot_R(start:stop);
|
||||
% motor velocities
|
||||
pltData.motL_vel_rpm = 60*data.INV_L_TxPDO_4_Velocity_L(start:stop);
|
||||
pltData.motR_vel_rpm = 60*data.INV_R_TxPDO_4_Velocity_R(start:stop);
|
||||
% inverter torque demand
|
||||
pltData.invL_torqueDemand = data.INV_L_TxPDO_3_DemandedTorque_L(start:stop)/10; % /10 to match autobox torque
|
||||
pltData.invR_torqueDemand = data.INV_R_TxPDO_3_DemandedTorque_R(start:stop)/10;
|
||||
% inverter actual torque
|
||||
pltData.invL_torqueActual = data.INV_L_TxPDO_3_ActualTorque_L(start:stop)/10;
|
||||
pltData.invR_torqueActual = data.INV_R_TxPDO_3_ActualTorque_R(start:stop)/10;
|
||||
|
||||
%% Wheelspeed
|
||||
|
||||
|
||||
|
||||
|
||||
%% Statistics:
|
||||
power_regen_kw = pltData.ams_ptot;
|
||||
power_regen_kw(power_regen_kw > 0) = 0;
|
||||
power_used_kw = pltData.ams_ptot;
|
||||
power_used_kw(power_used_kw < 0) = 0;
|
||||
pltData.energy_kwh = trapz(pltData.time_s, pltData.ams_ptot)/3600;
|
||||
pltData.energy_regen_kwh = trapz(pltData.time_s, power_regen_kw)/3600;
|
||||
pltData.energy_used_kwh = trapz(pltData.time_s, power_used_kw)/3600;
|
||||
pltData.distanceTotal_km = trapz(pltData.time_s, pltData.speed_kph./3.6)/1000; % 1/3.6 for km/h to m/s and /1000 for km output
|
||||
pltData.peakPower_kw = max(pltData.ams_ptot);
|
||||
pltData.peakPowerMean_kw = max(movmean(pltData.ams_ptot,500));
|
||||
|
||||
pltData.maxSpeed_kph = max(pltData.speed_kph);
|
||||
pltData.startTime = time_hms(start);
|
||||
pltData.stopTime = time_hms(stop);
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user