based on FT23-Ultra script. fixed for FT24 and FT25
This commit is contained in:
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assets/FT_icon.png
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assets/FT_icon.png
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assets/FT_logo.png
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assets/FT_logo.png
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79
assets/loadvector.m
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assets/loadvector.m
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function [pltData,laps_strArray] = loadvector(filepath)
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% Load dataset:
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fprintf("Loading: %s\n",filepath)
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data = vector2timetable(filepath,"ABX*","Shunt*","AMS_Status*","INV*","XSens*");
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fprintf("Finished loading\n------------------------\n")
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% get date / time
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date = dateshift(data.Time(1),'start','day');
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fprintf("Date: %s\n",date)
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fprintf("Start: %d:%d:%02.0f\n",hour(data.Time(1)),minute(data.Time(1)),second(data.Time(1)))
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fprintf("End: %d:%d:%02.0f\n",hour(data.Time(end)),minute(data.Time(end)),second(data.Time(end)))
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fprintf("------------------------\n")
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% get lapcounter / laptrigger
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laps_counter = data.ABX_Driver_ABX_Lapcounter;
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timestamp_hms(1) = data.Time(1) - date;
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timestamp_index(1) = 1;
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j = 2;
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for i = 2:length(laps_counter)
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if laps_counter(i) ~= laps_counter(i-1)
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timestamp_hms(j) = data.Time(i) - date;
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timestamp_index(j) = i;
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j = j + 1;
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end
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end
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% calculate laptimes
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timestamp_s = seconds(timestamp_hms);
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for i = 1:length(timestamp_s)-1
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laps_s(i) = timestamp_s(i+1) - timestamp_s(i);
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end
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% if laps could be detected find fastest lap and create list of
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% laptimes, otherwise only "Entire stint" will be selectable
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laps_strArray(1) = sprintf("Entire stint");
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if (exist("laps_s","var") == true)
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% create list of laptimes
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for i = 1:length(laps_s)
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if laps_s(i) == min(laps_s)
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laps_strArray(i+1) = sprintf("Lap %d: %.3fs (best)",i,laps_s(i));
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else
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laps_strArray(i+1) = sprintf("Lap %d: %.3fs",i,laps_s(i));
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end
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end
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% display fastest lap
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[laps_best_time, laps_best] = min(laps_s);
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fprintf("Fastest lap: %.2fs (%d)\n",laps_best_time,laps_best)
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end
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% create time arrays in hh:mm:ss and just seconds
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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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% calcualte energy consumption and total distance
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power_kw = data.Shunt_Voltage1_Shunt_Voltage1.*data.Shunt_Current_Shunt_Current/1000;
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power_regen_kw = power_kw;
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power_regen_kw(power_regen_kw > 0) = 0;
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power_used_kw = power_kw;
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power_used_kw(power_used_kw < 0) = 0;
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energy_kwh = trapz(time_s, power_kw)/3600;
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energy_regen_kwh = trapz(time_s, power_regen_kw)/3600;
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energy_used_kwh = trapz(time_s, power_used_kw)/3600;
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fprintf("Energy used: %.2fkWh\n",energy_used_kwh)
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fprintf("Energy regen: %.2fkWh\n",energy_regen_kwh)
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fprintf("Energy total: %.2fkWh\n",energy_kwh)
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distance_km = trapz(time_s, movmean(data.ABX_Driver_ABX_Speed,10))/1000;
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fprintf("Distance driven: %.2fkm\n",distance_km)
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fprintf("------------------------\n")
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pltData(1) = sortdata(data,1,length(data.Time));
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% sort data for plotting:
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for i = 1:length(timestamp_index)-1
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pltData(i+1) = sortdata(data,timestamp_index(i),timestamp_index(i+1));
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end
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end
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21
assets/savePlot.m
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21
assets/savePlot.m
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function [outputArg] = savePlot(figure,path,format)
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switch format
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case "png"
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savepath = sprintf("%s.png",path);
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exportgraphics(figure,savepath);
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case "pdf"
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savepath = sprintf("%s.pdf",path);
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exportgraphics(figure,savepath,'ContentType','vector');
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case "fig"
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saveas(figure,path,"fig");
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case "m"
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saveas(figure,path,"m");
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otherwise
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error("invalid filetype");
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end
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% return null (not relevant for plots!)
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outputArg = [];
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end
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122
assets/sortdata.m
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assets/sortdata.m
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function [pltData] = sortdata(data,start,stop)
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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(start:stop)-time_s(start);
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pltData.time_hms = time_hms(start:stop);
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% distance calculation
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speed_mps = movmean(data.ABX_Driver_ABX_Speed(start:stop),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)*(pltData.time_s(i)-pltData.time_s(i-1)) + pltData.distance(i-1);
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end
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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.ABX_Driver_ABX_APPS_percent(start:stop);
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pltData.speed_kph = 3.6*movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % same filter as for distance calculation ???
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pltData.steering_deg = data.ABX_Driver_ABX_Steering_Angle(start:stop);
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%% AMS:
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pltData.ams_soc = data.AMS_Status_SOC(start:stop);
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pltData.ams_tmax = data.AMS_Status_Max_cell_temp(start:stop);
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pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1(start:stop);
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pltData.ams_itot = data.Shunt_Current_Shunt_Current(start:stop);
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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.ABX_Driver_ABX_BrakeP_F(start:stop);
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pltData.brakePRear_bar = data.ABX_Driver_ABX_BrakeP_R(start:stop);
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% brake disc temperatures
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pltData.brakeTFrontLeft_degC = data.ABX_BrakeT_ABX_BrakeT_FL(start:stop);
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pltData.brakeTFrontRight_degC = data.ABX_BrakeT_ABX_BrakeT_FR(start:stop);
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pltData.brakeTRearLeft_degC = data.ABX_BrakeT_ABX_BrakeT_RL(start:stop);
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pltData.brakeTRearRight_degC = data.ABX_BrakeT_ABX_BrakeT_RR(start:stop);
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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.ABX_Dampers_ABX_Damper_FL(start:stop); %Heave_F
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pltData.damper_FR_mm = data.ABX_Dampers_ABX_Damper_FR(start:stop); %Roll_F
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pltData.damper_RL_mm = data.ABX_Dampers_ABX_Damper_RL(start:stop); %Heave_R
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pltData.damper_RR_mm = data.ABX_Dampers_ABX_Damper_RR(start:stop); %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
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% Acceleration
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% pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX(start:stop),100)/9.81;
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% pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY(start:stop),100)/9.81;
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% Rate of turn
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% pltData.rot_roll_degps = movmean(data.XSens_RateOfTurn_XSens_gyrX(start:stop),100);
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% pltData.rot_pitch_degps = movmean(data.XSens_RateOfTurn_XSens_gyrY(start:stop),100);
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% pltData.rot_yaw_degps = movmean(data.XSens_RateOfTurn_XSens_gyrZ(start:stop),100);
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%% Inverters
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% inverter temperatures
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pltData.invL_temp = data.INV_L_TxPDO_1_T_Inv_L(start:stop);
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pltData.invR_temp = data.INV_R_TxPDO_1_T_Inv_R(start:stop);
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% motor temperatures
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pltData.motL_temp = data.INV_L_TxPDO_1_T_Mot_L(start:stop);
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pltData.motR_temp = data.INV_R_TxPDO_1_T_Mot_R(start:stop);
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% motor velocities
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pltData.motL_vel_rpm = 60*data.INV_L_TxPDO_4_Velocity_L(start:stop);
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pltData.motR_vel_rpm = 60*data.INV_R_TxPDO_4_Velocity_R(start:stop);
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% inverter torque demand
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pltData.invL_torqueDemand = data.INV_L_TxPDO_3_DemandedTorque_L(start:stop)/10; % /10 to match autobox torque
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pltData.invR_torqueDemand = data.INV_R_TxPDO_3_DemandedTorque_R(start:stop)/10;
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% inverter actual torque
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pltData.invL_torqueActual = data.INV_L_TxPDO_3_ActualTorque_L(start:stop)/10;
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pltData.invR_torqueActual = data.INV_R_TxPDO_3_ActualTorque_R(start:stop)/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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pltData.energy_kwh = trapz(pltData.time_s, pltData.ams_ptot)/3600;
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pltData.energy_regen_kwh = trapz(pltData.time_s, power_regen_kw)/3600;
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pltData.energy_used_kwh = trapz(pltData.time_s, power_used_kw)/3600;
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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
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pltData.peakPower_kw = max(pltData.ams_ptot);
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pltData.peakPowerMean_kw = max(movmean(pltData.ams_ptot,500));
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pltData.maxSpeed_kph = max(pltData.speed_kph);
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pltData.startTime = time_hms(start);
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pltData.stopTime = time_hms(stop);
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end
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122
assets/sortdata_XSens.m
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122
assets/sortdata_XSens.m
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@ -0,0 +1,122 @@
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function [pltData] = sortdata(data,start,stop)
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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(start:stop)-time_s(start);
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pltData.time_hms = time_hms(start:stop);
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% distance calculation
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speed_mps = movmean(data.ABX_Driver_ABX_Speed(start:stop),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)*(pltData.time_s(i)-pltData.time_s(i-1)) + pltData.distance(i-1);
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end
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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.ABX_Driver_ABX_APPS_percent(start:stop);
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pltData.speed_kph = 3.6*movmean(data.ABX_Driver_ABX_Speed(start:stop),50); % same filter as for distance calculation ???
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pltData.steering_deg = data.ABX_Driver_ABX_Steering_Angle(start:stop);
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%% AMS:
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pltData.ams_soc = data.AMS_Status_SOC(start:stop);
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pltData.ams_tmax = data.AMS_Status_Max_cell_temp(start:stop);
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pltData.ams_utot = data.Shunt_Voltage1_Shunt_Voltage1(start:stop);
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pltData.ams_itot = data.Shunt_Current_Shunt_Current(start:stop);
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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.ABX_Driver_ABX_BrakeP_F(start:stop);
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pltData.brakePRear_bar = data.ABX_Driver_ABX_BrakeP_R(start:stop);
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% brake disc temperatures
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pltData.brakeTFrontLeft_degC = data.ABX_BrakeT_ABX_BrakeT_FL(start:stop);
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pltData.brakeTFrontRight_degC = data.ABX_BrakeT_ABX_BrakeT_FR(start:stop);
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pltData.brakeTRearLeft_degC = data.ABX_BrakeT_ABX_BrakeT_RL(start:stop);
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pltData.brakeTRearRight_degC = data.ABX_BrakeT_ABX_BrakeT_RR(start:stop);
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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.damperHeaveFront_mm = data.ABX_Dampers_ABX_DamperHeave_F(start:stop);
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pltData.damperRollFront_mm = data.ABX_Dampers_ABX_DamperRoll_F(start:stop);
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pltData.damperHeaveRear_mm = data.ABX_Dampers_ABX_DamperHeave_R(start:stop);
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pltData.damperRollRear_mm = data.ABX_Dampers_ABX_DamperRoll_R(start:stop);
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% calculate damper velocities
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pltData.velocityHeaveFront_mmps(1) = 0;
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pltData.velocityRollFront_mmps(1) = 0;
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pltData.velocityHeaveRear_mmps(1) = 0;
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pltData.velocityRollRear_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.velocityHeaveFront_mmps(i) = pltData.damperHeaveFront_mm(i)-pltData.damperHeaveFront_mm(i-1)/timestep;
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pltData.velocityRollFront_mmps(i) = pltData.damperRollFront_mm(i)-pltData.damperRollFront_mm(i-1)/timestep;
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pltData.velocityHeaveRear_mmps(i) = pltData.damperHeaveRear_mm(i)-pltData.damperHeaveRear_mm(i-1)/timestep;
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pltData.velocityRollRear_mmps(i) = pltData.damperRollRear_mm(i)-pltData.damperRollRear_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.velocityHeaveFront_mmps = movmean(pltData.velocityHeaveFront_mmps,100);
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pltData.velocityRollFront_mmps = movmean(pltData.velocityRollFront_mmps,100);
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pltData.velocityHeaveRear_mmps = movmean(pltData.velocityHeaveRear_mmps,100);
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pltData.velocityRollRear_mmps = movmean(pltData.velocityRollRear_mmps,100);
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%% IMU
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% Acceleration
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pltData.acc_long_g = movmean(data.XSens_Acceleration_XSens_accX(start:stop),100)/9.81;
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pltData.acc_lat_g = movmean(data.XSens_Acceleration_XSens_accY(start:stop),100)/9.81;
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% Rate of turn
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pltData.rot_roll_degps = movmean(data.XSens_RateOfTurn_XSens_gyrX(start:stop),100);
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pltData.rot_pitch_degps = movmean(data.XSens_RateOfTurn_XSens_gyrY(start:stop),100);
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pltData.rot_yaw_degps = movmean(data.XSens_RateOfTurn_XSens_gyrZ(start:stop),100);
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%% Inverters
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% inverter temperatures
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pltData.invL_temp = data.INV_L_TxPDO_1_T_Inv_L(start:stop);
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pltData.invR_temp = data.INV_R_TxPDO_1_T_Inv_R(start:stop);
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% motor temperatures
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pltData.motL_temp = data.INV_L_TxPDO_1_T_Mot_L(start:stop);
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pltData.motR_temp = data.INV_R_TxPDO_1_T_Mot_R(start:stop);
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% 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
|
||||
|
||||
21
assets/vector2timetable.m
Normal file
21
assets/vector2timetable.m
Normal file
@ -0,0 +1,21 @@
|
||||
function [data] = vector2timetable(data_path, varargin)
|
||||
%VECTOR2TIMETABLE Load vector datalogger data and convert it to a timetable
|
||||
% Specify the path to the data in DATA_PATH, and optionally specify names
|
||||
% of signals to load.
|
||||
raw_data = load(data_path, varargin{:});
|
||||
fn = fieldnames(raw_data);
|
||||
base_ts = datetime(0, 1, 0);
|
||||
for i=1:numel(fn)
|
||||
var = raw_data.(fn{i});
|
||||
timestamps = days(var(:,1)) + base_ts;
|
||||
tt{i} = array2timetable( ...
|
||||
var(:,2), ...
|
||||
"RowTimes",timestamps, ...
|
||||
"VariableNames",{fn{i}} ...
|
||||
);
|
||||
end
|
||||
data = synchronize(tt{:},"union","nearest");
|
||||
% union -> union of the row times
|
||||
% method -> fill gaps in output with nearest neighbor in the input timetable
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user