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implemented presets til sortdata, seasonal presets still need to be adapted

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v.chau 2025-08-20 21:15:46 +02:00
parent d8306475ad
commit 0a7769f31f
2 changed files with 56 additions and 54 deletions
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app2.mlapp
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season/FT23/sortdata.m
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@ -1,43 +1,44 @@
function [pltData] = sortdata(data,start,stop)
function [pltData, stats] = sortdata(data)
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);
pltData.time_s = time_s -time_s(1);
pltData.time_hms = time_hms;
% distance calculation
speed_mps = movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % TODO: test different speed filters for distance calculation
speed_mps = movmean(data.ABX_Driver_ABX_Speed,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);
pltData.distance(i) = speed_mps(i)*(time_s(i)-time_s(i-1)) + pltData.distance(i-1);
end
pltData.distance = pltData.distance.'
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);
pltData.app_percent = data.ABX_Driver_ABX_APPS_percent;
pltData.speed_kph = 3.6*movmean(data.ABX_Driver_ABX_Speed,50); % same filter as for distance calculation ???
pltData.steering_deg = data.ABX_Driver_ABX_Steering_Angle;
%% 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);
pltData.ams_soc = data.AMS_Status_SOC;
pltData.ams_tmax = data.AMS_Status_Max_cell_temp;
pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1;
pltData.ams_itot = data.Shunt_Current_Shunt_Current;
% 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);
pltData.brakePFront_bar = data.ABX_Driver_ABX_BrakeP_F;
pltData.brakePRear_bar = data.ABX_Driver_ABX_BrakeP_R;
% 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);
pltData.brakeTFrontLeft_degC = data.ABX_BrakeT_ABX_BrakeT_FL;
pltData.brakeTFrontRight_degC = data.ABX_BrakeT_ABX_BrakeT_FR;
pltData.brakeTRearLeft_degC = data.ABX_BrakeT_ABX_BrakeT_RL;
pltData.brakeTRearRight_degC = data.ABX_BrakeT_ABX_BrakeT_RR;
% calculate brake bias [%]
minBrakeP = 5; % minimum brake pressure to avoid artifacts due to sensor noise near 0 bar
brakePFront = pltData.brakePFront_bar;
@ -50,73 +51,74 @@ function [pltData] = sortdata(data,start,stop)
%% 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);
pltData.damperHeaveFront_mm = data.ABX_Dampers_ABX_DamperHeave_F;
pltData.damperRollFront_mm = data.ABX_Dampers_ABX_DamperRoll_F;
pltData.damperHeaveRear_mm = data.ABX_Dampers_ABX_DamperHeave_R;
pltData.damperRollRear_mm = data.ABX_Dampers_ABX_DamperRoll_R;
% 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);
for i = 2:length(time_s)
timestep = time_s(i)-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);
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;
pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX,100)/9.81;
pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY,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);
pltData.rot_roll_degps = movmean(data.XSens_RateOfTurn_XSens_gyrX,100);
pltData.rot_pitch_degps = movmean(data.XSens_RateOfTurn_XSens_gyrY,100);
pltData.rot_yaw_degps = movmean(data.XSens_RateOfTurn_XSens_gyrZ,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);
pltData.invL_temp = data.INV_L_TxPDO_1_T_Inv_L;
pltData.invR_temp = data.INV_R_TxPDO_1_T_Inv_R;
% 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);
pltData.motL_temp = data.INV_L_TxPDO_1_T_Mot_L;
pltData.motR_temp = data.INV_R_TxPDO_1_T_Mot_R;
% 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);
pltData.motL_vel_rpm = 60*data.INV_L_TxPDO_4_Velocity_L;
pltData.motR_vel_rpm = 60*data.INV_R_TxPDO_4_Velocity_R;
% 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;
pltData.invL_torqueDemand = data.INV_L_TxPDO_3_DemandedTorque_L/10; % /10 to match autobox torque
pltData.invR_torqueDemand = data.INV_R_TxPDO_3_DemandedTorque_R/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;
pltData.invL_torqueActual = data.INV_L_TxPDO_3_ActualTorque_L/10;
pltData.invR_torqueActual = data.INV_R_TxPDO_3_ActualTorque_R/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));
stats.energy_kwh = trapz(time_s, pltData.ams_ptot)/3600;
stats.energy_regen_kwh = trapz(time_s, power_regen_kw)/3600;
stats.energy_used_kwh = trapz(time_s, power_used_kw)/3600;
stats.distanceTotal_km = trapz(time_s, pltData.speed_kph./3.6)/1000; % 1/3.6 for km/h to m/s and /1000 for km output
stats.peakPower_kw = max(pltData.ams_ptot);
stats.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);
stats.maxSpeed_kph = max(pltData.speed_kph);
% pltData.startTime = time_hms(1);
% pltData.stopTime = time_hms(end);
%% struct2timetable
pltData = table2timetable(struct2table(pltData), "RowTimes", data.Time);
end