128 lines
5.8 KiB
Matlab
128 lines
5.8 KiB
Matlab
function [pltData, stats] = sortdata(data)
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date = dateshift(data.Time(1),'start','day');
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time_hms = data.Time-date;
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time_s = seconds(time_hms);
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time_s = time_s - time_s(1); % set beginning of session to 0
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% x-Axis:
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pltData.time_s = time_s;
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pltData.time_hms = time_hms;
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% distance calculation
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speed_mps = movmean(data.FTCU_Driver_FTCU_Speed,50); % TODO: test different speed filters for distance calculation
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pltData.distance(1) = 0;
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for i = 2:length(speed_mps)
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pltData.distance(i) = speed_mps(i)*(time_s(i)-time_s(i-1)) + pltData.distance(i-1);
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end
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pltData.distance = pltData.distance.';
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pltData.xAxis = pltData.time_s; % set time as default x-Axis for plotting
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%% Misc
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pltData.app_percent = data.FTCU_Driver_FTCU_APPS_Percent;
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pltData.speed_kph = 3.6*movmean(data.FTCU_Driver_FTCU_Speed,50); % same filter as for distance calculation ???
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pltData.steering_deg = data.FTCU_Driver_FTCU_Steering_Angle;
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%% AMS:
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pltData.ams_soc = data.AMS_Status_SOC;
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pltData.ams_tmax = data.AMS_Status_Max_cell_temp;
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pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1;
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pltData.ams_itot = data.Shunt_Current_Shunt_Current;
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% calculations:
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pltData.ams_ptot = pltData.ams_utot.*pltData.ams_itot/1000;
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%% Brakes
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% brake pressure
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pltData.brakePFront_bar = data.FTCU_Driver_FTCU_Brake_Pressure_F;
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pltData.brakePRear_bar = data.FTCU_Driver_FTCU_Brake_Pressure_R;
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% brake disc temperatures
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pltData.brakeTFrontLeft_degC = data.Sensornode_F_10Hz_BDTS_FL;
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pltData.brakeTFrontRight_degC = data.Sensornode_F_10Hz_BDTS_FR;
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pltData.brakeTRearLeft_degC = data.Sensornode_R_10Hz_BDTS_RL;
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pltData.brakeTRearRight_degC = data.Sensornode_R_10Hz_BDTS_RR;
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% calculate brake bias [%]
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minBrakeP = 5; % minimum brake pressure to avoid artifacts due to sensor noise near 0 bar
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brakePFront = pltData.brakePFront_bar;
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brakePFront(brakePFront < minBrakeP) = minBrakeP;
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brakePRear = pltData.brakePRear_bar;
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brakePRear(brakePRear < minBrakeP) = minBrakeP;
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pltData.brakeBias_perc = 100*brakePFront./brakePRear; % check calculation!
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%% Cooling system
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%% Dampers
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% damper positions
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pltData.damper_FL_mm = data.Sensornode_F_100Hz_2_DS_FL; %Heave_F
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pltData.damper_FR_mm = data.Sensornode_F_100Hz_2_DS_FR; %Roll_F
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pltData.damper_RL_mm = data.Sensornode_R_100Hz_DS_RL; %Heave_R
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pltData.damper_RR_mm = data.Sensornode_R_100Hz_DS_RR; %Roll_R
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% calculate damper velocities
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pltData.velocity_FL_mmps(1) = 0;
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pltData.velocity_FR_mmps(1) = 0;
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pltData.velocity_RL_mmps(1) = 0;
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pltData.velocity_RR_mmps(1) = 0;
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for i = 2:length(pltData.time_s)
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timestep = pltData.time_s(i)-pltData.time_s(i-1);
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pltData.velocity_FL_mmps(i) = pltData.damper_FL_mm(i)-pltData.damper_FL_mm(i-1)/timestep;
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pltData.velocity_FR_mmps(i) = pltData.damper_FR_mm(i)-pltData.damper_FR_mm(i-1)/timestep;
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pltData.velocity_RL_mmps(i) = pltData.damper_RL_mm(i)-pltData.damper_RL_mm(i-1)/timestep;
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pltData.velocity_RR_mmps(i) = pltData.damper_RR_mm(i)-pltData.damper_RR_mm(i-1)/timestep;
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end
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% filter damper velocities ??? bessere Berechnung über mittelwert aus mehreren werten? Vorfilterung?
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pltData.velocity_FL_mmps = movmean(pltData.velocity_FL_mmps,100).';
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pltData.velocity_FR_mmps = movmean(pltData.velocity_FR_mmps,100).';
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pltData.velocity_RL_mmps = movmean(pltData.velocity_RL_mmps,100).';
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pltData.velocity_RR_mmps = movmean(pltData.velocity_RR_mmps,100).';
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%% IMU - VN200, no need to implement didnt work well anyway
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% Acceleration
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pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX,100)/9.81;
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pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY,100)/9.81;
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% Rate of turn
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pltData.rot_roll_degps = movmean(data.XSens_rateofturn_XSens_gyrX,100);
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pltData.rot_pitch_degps = movmean(data.XSens_rateofturn_XSens_gyrY,100);
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pltData.rot_yaw_degps = movmean(data.XSens_rateofturn_XSens_gyrZ,100);
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%% Inverters
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% inverter temperatures
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pltData.invL_temp = data.INV_1_TxPDO_1_T_Inv_1; % not sure if 1 is left and 2 is right
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pltData.invR_temp = data.INV_2_TxPDO_1_T_Inv_2;
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% motor temperatures
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pltData.motL_temp = data.INV_1_TxPDO_1_T_Mot_1; % not sure if 1 is left and 2 is right
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pltData.motR_temp = data.INV_2_TxPDO_1_T_Mot_2;
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% motor velocities
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pltData.motL_vel_rpm = 60*data.INV_1_TxPDO_4_Velocity_1; % not sure if 1 is left and 2 is right
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pltData.motR_vel_rpm = 60*data.INV_2_TxPDO_4_Velocity_2;
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% inverter torque demand
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pltData.invL_torqueDemand = data.INV_1_TxPDO_3_DemandedTorque_1/10; % /10 to match autobox torque % not sure if 1 is left and 2 is right
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pltData.invR_torqueDemand = data.INV_2_TxPDO_3_DemandedTorque_2/10;
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% inverter actual torque
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pltData.invL_torqueActual = data.INV_1_TxPDO_3_DemandedTorque_1/10; % not sure if 1 is left and 2 is right
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pltData.invR_torqueActual = data.INV_2_TxPDO_3_DemandedTorque_2/10;
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%% Wheelspeed
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%% Statistics:
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power_regen_kw = pltData.ams_ptot;
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power_regen_kw(power_regen_kw > 0) = 0;
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power_used_kw = pltData.ams_ptot;
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power_used_kw(power_used_kw < 0) = 0;
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stats.energy_kwh = trapz(time_s, pltData.ams_ptot)/3600;
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stats.energy_regen_kwh = trapz(time_s, power_regen_kw)/3600;
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stats.energy_used_kwh = trapz(time_s, power_used_kw)/3600;
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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
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stats.peakPower_kw = max(pltData.ams_ptot);
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stats.peakPowerMean_kw = max(movmean(pltData.ams_ptot,500));
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stats.maxSpeed_kph = max(pltData.speed_kph);
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% pltData.startTime = time_hms(1);
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% pltData.stopTime = time_hms(end);
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%% struct2timetable
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pltData = table2timetable(struct2table(pltData), "RowTimes", data.Time);
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end
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