Real Life Drive Cycles: Experimental Results and Analysis - Assignment Example

Table of Contents 6.Real Life Drive Cycles – Experimental Results and Analysis 3 6 Introduction 3 6.2.Experimental Results and Data Analysis 4 6.2.1.Logged and Derived Parameters’ Behaviour 4 6.2.2.New European Drive Cycle 9 6.3.Real-life Drive Cycles 11 6.3.1.Analysis of Logged OBDII Data 11 6.3.2.Separating Logged Data into Specific Events 12 6.3.3.Determining Gear Usage via OBDII Data 13 6.3.4.Calculated Load to Monitor Fuel Consumption 15 6.3.5.Analysing Datalogged Events 16 6.3.6.Journey Gradient Profiling using OBDII Data 17 6.3.7.MATLAB Results for Real Life Driving Data 21 List of Figures Figure 6.‎6.1 - Plot of logged and derived parameters from real life driving between 0s and 200s 5 Figure 6.‎6.2 - Plot of logged and derived parameters from real life driving between 200s and 400s 6 Figure 6.‎6.3 - Plot of logged and derived parameters from real life driving between 400s and 600s 7 Figure 6.‎6.4 - Plot of logged and derived parameters from real life driving between 600s and 800s 8 Figure 6.‎6.5 - Plot of logged and derived parameters from real life driving between 800s and 1200s 8 Figure 6.‎6.6 - The New European Drive Cycle sourced from (Berry, 2007, p.132) 10 Figure 6.‎6.7 - Drive cycle analysis plotted data for the engine speed, vehicle speed and acceleration obtained from Nexiq 12 Figure 6.‎6.8 - Calculated Vehicle to Engine Speed Ratio Distribution 14 Figure 6.‎6.9 - Plots of engine speed and vehicle speed for urban drive cycle 16 Figure 6.‎6.10 - Map of hilly course 18 Figure 6.‎6.11 – Plot of engine load against cruise data for hilly course 19 Figure 6.‎6.12 - Inclination factor for hilly course 19 Figure 6.‎6.13 - Map of undulating course 20 Figure 6.‎6.14 - Plot of engine load against cruise data for undulating course 20 Figure 6.‎6.15 - Inclination factor for undulating course 21 Figure ‎6.16 - Fuel consumption against time for extra urban driving 23 Figure ‎6.17 – Fuel consumption against time for urban driving 25 Chapter 6 6. Real Life Drive Cycles – Experimental Results and Analysis 6.1. Introduction Real life driving parameter data was obtained through logging on an actual vehicle, a Golf Mk4 1.9 TDi. The data was acquired using the onboard OBDII interface and was later processed through MATLAB. A number of different parameter options were available for logging but four major parameters were logged including: engine speed (rpm); vehicle speed (km/hr); engine load (%); absolute throttle position (%). The logged data was utilized in turn to derive other parameters and measurements such as distance, acceleration, fuel consumption etc. The central contention was to compare between the NEDC and real life driving scenarios in order to derive parables. A fixed sampling rate was employed to make the logged data more comparable. Logged events and parameters were then classified in terms of vehicle behavior i.e. cruising, idling, acceleration, deceleration etc. in order to obtain logged runs that could be superimposed onto the NEDC. The gears in use were also tabulated in order to investigate their relationship to parameters. NEDC profiling was also carried out in terms of urban driving and extra urban driving. 6.2. Experimental Results and Data Analysis 6.2.1. Logged and Derived Parameters’ Behaviour Plots were obtained for logged parameters (vehicle speed, acceleration and gradient) as well as for derived parameters (fuel consumption). Parameters were logged and derived as per the NEDC recommendations for the entire test length. The various parameters of interest are plotted in the various plots provided below that have been separated with differentials of 200 seconds starting at 0 seconds and ending at 800 seconds followed by a plot between 800 seconds and 1200 seconds. It must be related that the vehicle was allowed to soak so that the engine temperature, engine lubricant temperature and coolant temperature achieved their normal values before testing began. This ensured that no variation would occur in measured parameters values as these temperatures changed over the testing time. The behavior of the vehicle along with logged parameters and derived fuel consumption are discussed below for greater clarity. Figure 6.‎6.1 - Plot of logged and derived parameters from real life driving between 0s and 200s As shown in the diagram above, the test starts with idling of the engine after which the vehicle is given some speed at around t = 20 seconds. There is a corresponding increase in acceleration and fuel consumption as well. There is a minor spike in fuel consumption during the initial idling regime but this may be considered as an outlier. The vehicle is then braked around t = 40 seconds and allowed to come to a rest. Here, the acceleration enters a negative regime while the fuel consumption decreases, but does not go to zero since the engine is still running. In comparison, NEDC requires an idling time of 40 seconds before moving the vehicle as shown in Figure 6.6. Subsequently, the engine is loaded to around 15% and then 20% through gear shifts and the vehicle is allowed to move around t = 60 seconds. The increase in vehicle speed is later mitigated around t = 110 seconds. This regime reenacts the previous observations on acceleration and fuel consumption. However, it must be noticed that compared to before, there are two distinct acceleration and deceleration regimes present for this section of testing indicating the presence of transients. The vehicle is loaded again around t = 125 seconds and is allowed to accelerate, cruise and then decelerate. The cruising regime for this section of testing could not achieve a stable speed since there are acceleration and deceleration transients that could not be satisfied in such a small amount of time. The deceleration transients just before t = 200 seconds present myriad transients and indicate the difficulty in using deceleration as a reliable measure for fuel consumption. Figure 6.‎6.2 - Plot of logged and derived parameters from real life driving between 200s and 400s The testing in the post 200 seconds regime is shown above. It is noticeable that the same testing regime has been initiated with nearly similar idling periods, cruising stints, acceleration and deceleration. Comparably, the acceleration and deceleration transients tend to become more complicated as the overall speed and the cruising speed are increased as shown earlier. Observations regarding all parameters (whether logged or derived) are comparable though not similar. The transients for the last acceleration, cruise and deceleration run are lesser than before and it could be attributed to driver behavior. As the driver gains more practice with such test, the number of transients could be expected to decrease as the driver’s cognition and expectation of such events improves. This also indicates that the NEDC is not totally realistic since it fails to account for human input into fuel consumption or the tabulation of other logged or derived parameters. Figure 6.‎6.3 - Plot of logged and derived parameters from real life driving between 400s and 600s Figure 6.‎6.4 - Plot of logged and derived parameters from real life driving between 600s and 800s Behavior for various logged and derived parameters for Figures 6.3 and 6.4 is similar and has been discussed before so requires little further elaboration. Figure 6.‎6.5 - Plot of logged and derived parameters from real life driving between 800s and 1200s The plot shown above depicts the various logged and derived parameters from the real life driving experiments. This plot differs from the other plots shown before since it depicts extra urban driving that is indicated by the consistency of vehicle speed over 300 seconds. After a brief period of idling between 800 seconds and 850 seconds, the vehicle is accelerated, stronger than any acceleration stints tested before. This increases the fuel consumption drastically, which keeps on tailing the acceleration. For a brief period, there is some deceleration that could be attributed to significant increases in engine load over a short period of time. Consequently, acceleration is kept near constant with slight variations over the remaining testing regime. However, there is a significant increase in the fuel consumption as the vehicle speed increases. As discussed in previous sections, the air drag increases proportional to the square of vehicle speed. The increase in fuel consumption with the increase in vehicle speed can be affirmed from the plot shown above. Finally, it must be noticed that the fuel consumption is related though not directly to the vehicle speed. Instead, it would be more appropriate to surmise that the fuel consumption and acceleration tend to follow each other closely. This is demonstrated with the increases and decreases in acceleration and fuel consumption during periods of vehicle acceleration and deceleration. However, this relationship is not defined similarly during periods of idling or cruising where the fuel consumption is constant or increasing although the acceleration is zero. As noticeable in the plot above, the deceleration between 1150 seconds and 1190 seconds, tends to produce the largest deceleration while fuel consumption tends to remain consistent but does not decrease to zero which proves the point listed above. 6.2.2. New European Drive Cycle The NEDC is depicted below in Figure 6.6 for reference and comparison to the plots above. It can be seen clearly from Figure 6.6, that the NEDC is essentially an idealised testing regime where the variations in vehicle speed are assumed to be constant for the advised four urban driving cycle runs. The testing runs performed under laboratory conditions and the resultant plots make two things abundantly clear – firstly that four urban driving cycle runs cannot be expected to resemble each other in real life driving conditions and secondly that expecting real life driving conditions to match some defined ideal standard is not possible in the provided regime. The small reaction times mean that slips such as different idling periods in the start of the test could cause significant differences to the tested fuel consumption. This could be addressed in the NEDC by allowing a larger testing period regime where small idling, acceleration, cruising and deceleration runs would not produce large offsets in the final tabulation of the fuel consumption. Figure 6.‎6.6 - The New European Drive Cycle sourced from (Berry, 2007, p.132) As iterated above, there are differences between the proposed NEDC testing regime and what can actually be achieved. The differences between the NEDC testing regime and real life driving tests can be justified largely on basis of human inputs differences. The idealized NEDC does not require any human input unlike real life driving testing. Hence, the NEDC is not totally realistic since it fails to account for human input into fuel consumption or the tabulation of other logged or derived parameters. 6.3. Real-life Drive Cycles 6.3.1. Analysis of Logged OBDII Data The post-acquisition analysis of the data logged via OBDII on a Golf Mk4 1.9 TDi (130 PS) was done exclusively in Matlab. Four PIDs were monitored: Engine Speed (rpm), Vehicle Speed (km/h), Calculated Engine Load (%) and Absolute Throttle Position (%). Each PID value logged is time-stamped by the Nexiq on arrival. However, the transmission interval is non-deterministic and dependant on the ECU. In addition the each PID arrives independently, so the relationship between PIDs is also non-deterministic. The first stage of analysis was to homogenise the four PID streams acquired so they all have a constant sample rate. Linear interpolation was used to map all the PID data to a fixed sample rate (1 Hz). From this various parameters are easily calculated. The total distance is the trapezoidal piecewise integral of the vehicle speed. From this average speed can be calculated. Acceleration is calculated by piecewise differentiation of the vehicle speed data. Figure 6.‎6.7 - Drive cycle analysis plotted data for the engine speed, vehicle speed and acceleration obtained from Nexiq 6.3.2. Separating Logged Data into Specific Events The rest of the analysis follows the method of Holmén & Niemeier (1998) whereby the logged data is separated in events. Each event was determined to last for one linearised sample interval (1 second). Events are separated firstly into cruise, positive acceleration and negative acceleration. A cruise event is defined as occurring when the vehicle is non-stationary and the acceleration is below a set threshold. This was set to 0.5 ms-2 which provided a good balance of cruise/acceleration events in the datasets considered. Cruise events are further classified as high speed cruise (V > 40 mph), mid speed cruise (25 < V ≤ 40 mph) and low speed cruise (≤ 25 mph). An idle event was defined as occurring when the vehicle was stationary. In order to provide useful parameters for analysis the events where normalised to the total number of events logged. 6.3.3. Determining Gear Usage via OBDII Data Information on gear selection is not directly available via OBDII PIDs. It is possible however consider the ratio of vehicle speed to engine speed. There are however three potential unknowns required in addition to derive the gear result. These are gear ratios, differential ratio (for rear wheel drive) and tyre radius. Even with a priori knowledge of a given car gearbox/differential, there is no way of deriving tyre radius as these may be changed as aftermarket items. A more generic solution to determining gear ratio is thus preferential. It is possible to use a cluster type analysis in order to derive the current gear. An analysis of the distribution of vehicle to engine speed ratio will yield peaks at each valid gear. There will be some noise associated with actions during clutch depression (e.g. revving, coasting) or wheel spinning caused by traction loss. In order to establish gear positions a normalised distribution of vehicle to engine speed ratio was calculated (200 bin histogram). Calculating the ratio this way avoids the divide by zero that occurs when the vehicle is stationary. A threshold was then used effectively mask the random noise which is spread fairly across the distribution. Any value below this threshold is zeroed. Experimentally, a threshold value of 0.05 worked well for this purpose. The resulting maxima where then detected by for a sign change in the first order differential of the data. In this way it is possible to detect the current gear used during the drive cycle, although not the actual gear ratio. In the following example the four gears were detected and the vehicle-engine speed ratios were calculated as 0.0087, 0.0162, 0.0264 and 0.0359 for first to fourth gear respectively. The relatively small size of the first gear peak indicates little use which corresponds to the presence of a high torque (at low revs) diesel engine. The majority of pull away from stationary is done in second gear. Figure 6.‎6.8 - Calculated Vehicle to Engine Speed Ratio Distribution 6.3.4. Calculated Load to Monitor Fuel Consumption The ability to monitor fuel consumption is something that would be very useful to do via OBDII data. Unfortunately no quantifiable data is generally available for fuel flow in OBDII implementations. It is necessary therefore to try and infer the fuel consumed using other available parameters. Mass air flow can be used to try and determine fuel use in petrol engines, but a stochiometric air/fuel ratio must be assumed. This is invalid for high load (open loop) conditions. Furthermore this method is only viable for petrol engines, diesel engines are not throttled thus the airflow is proportional to only engine speed (simple volumetric displacement). OBDII provides a parameter for ‘Calculate Engine Load’ (Mode 01h, PID 04h) which yields a percentage figure which relates to the current engine load. For petrol engines it is calculated as ratio of airflow to maximum airflow (at any given engine speed). In diesel engines it is calculated as the ratio of fuel flow to peak fuel flow (at any given engine speed). The diesel method of determining calculated engine load means that is should give a good indication of fuel consumption. The petrol method is still limited by the potential for open loop excursions to result in under-reading. It may be possible to resolve open loop conditions through the monitoring of absolute throttle position (Mode 01, PID 11). A threshold may be set (e.g. > 80%) above which the engine is considered to be in open loop operation. Of course this would still not allow for the fuel flow to be corrected. The integrating the calculated engine load can provide an indication of overall fuel consumption for any single journey. This is not a relative value and is useful for comparing multiple drive cycles on any single car, but not able to derive an absolute ‘mpg’ value. 6.3.5. Analysing Datalogged Events The project aim is to try and reconstruct parts of the NEDC drive cycle from real logged data. To provide a starting point for analysis the NEDC data captured from the dyno rig was first analysed. To simplify matters at this early stage, only the EDE15 portion was looked at (this corresponds to the urban fuel consumption calculation). The logged engine speed and vehicle speed data is shown below. Figure 6.‎6.9 - Plots of engine speed and vehicle speed for urban drive cycle Two real drive cycles were also analysed having a similar duration to the EDE15. Both drive cycles were done from a cold start and are the routes were urban in nature. The first was a hilly course, the second more mildly undulating. The analysed results are shown in Annexure ‘A’. A cruise to acceleration ratio was also calculated to try and quantify what is likely to be an important driving style which impacts fuel consumption. The mpg values for the real drive cycles are taken from the onboard trip computer. It is very likely that this calculation will under read, some form of calibration will be necessary to confirm this. The mpg values appears to be higher (especially if under-read is assumed) for both the real drive cycles. This is explained by the fact average speeds are higher and the cruise to acceleration ratio is higher. Clearly the driving was faster and less ‘smooth’ than in an EDE15 cycle. 6.3.6. Journey Gradient Profiling using OBDII Data On major impact on fuel consumption is the gradients involved on a journey. Clearly a hilly course is likely to result in a worse mpg value for a given drive time. OBDII does not have access to any accelerometer or inclination data (assuming these sensors even exist on a particular model). A point of weakness in any comparison to the NEDC drive cycle is this lack of information. One potential method for determining hilliness is by using the engine load and derived cruise data. For any cruise condition (no acceleration) the engine load will be higher for a positive (uphill) gradient. Conversely it might be anticipated that any negative gradients will result in no engine load as the vehicle coasts. The situation is complicated slightly by the fact that engine load goes up slightly (~20% observed) if the engine is idling (clutch in) as the wheels are no longer turning the engine block and as such fuel is being injected to maintain engine operation. In order to test whether it is possible to gauge any gradient information from engine load and cruise data, two journeys were monitored. One was on a hilly course, the other mildly undulating. Ordnance survey mapping software was used to determine the actual gradient profiles of these two journeys. Both journeys started from the same point and the route/gradient profiles are hilly and mildly undulating respectively. An inclination factor, was then calculated for all categorised cruise data such that: where L is the calculated engine load and V is the vehicle speed. The denominator attempts to account for the approximately square law relationship between power and vehicle speed to do aerodynamic drag. Figure 6.‎6.10 - Map of hilly course Figure 6.‎6.11 – Plot of engine load against cruise data for hilly course Figure 6.‎6.12 - Inclination factor for hilly course Figure 6.‎6.13 - Map of undulating course Figure 6.‎6.14 - Plot of engine load against cruise data for undulating course Figure 6.‎6.15 - Inclination factor for undulating course There is without doubt some correlation between the results for and the calculated inclination factor for both examples. The peak values do correlate with gradients and the peak excursions are lower for the mildly undulating course. Clearly there is a lot of noise in the data, and a lot of missing data due to acceleration events. There may be scope using more advanced signal processing to derive better data. A more advanced correction factor for the effects of drag with vehicle speed would be a good starting point. 6.3.7. MATLAB Results for Real Life Driving Data Real life driving data for a number of parameters was tabulated and plotted for interpretation. Among other variables extracted from real life driving, one of the more important parameters was fuel consumption. The current research is largely concerned with tabulation of fuel consumption with various kinds of driving conditions so an examination of fuel consumption was required. The frameworks of investigation developed in preceding chapters were then utilised in order to interpret and understand the trends available in the fuel consumption plots. In order to make things simpler, two different real life driving conditions have been evaluated below. The first situation depicts largely extra urban driving while the second situation depicts mostly urban driving. The primary means of analysing the fuel consumption against time plot was to decompose the various time bound phases according to the algorithm provided in Chapter Four previously. The plot provided below depicts the fuel consumption experienced during extra urban driving for a total testing period of approximately 1017 seconds. A look at the plot provided below reveals that the vehicle experienced several gear change regions during the course of the testing. This is reinforced by the presence of multiple peaks and troughs in close proximity on the plot. Based on phase type breakdowns of the NEDC, or any other driving testing cycle, the entire driving cycle can be broken down into phases consisting of: Acceleration; Deceleration; Constant speed; Braking. For the sake of explaining the plot behaviour, it is notable that as the vehicle would have accelerated, it would have required the greatest amount of fuel and hence it would have shown the steepest growth in fuel consumption. In a comparable manner, a decelerating vehicle would tend to exhibit a downtrend in fuel consumption although it would not possess a sharp rate of decline as it did for the acceleration phase. Figure ‎6.16 - Fuel consumption against time for extra urban driving An examination of the plot provided above also reveals that there are regions where the vehicle experiences constant speed. These regions are depicted by near straight plot features with some instantaneous increases and decreases in fuel consumption. This would tend to indicate that vehicle fuel consumption is an instantaneous affair since the plot does not offer completely flat lines during the constant speed phases. Finally, the plot reveals areas where the vehicle was idling. In comparison to other variables tabulated for the purposes of this study, fuel consumption does not approach zero comparable to vehicle velocity or acceleration since fuel consumption remains in place, even during idling. Table ‎6.1 - Summary of salient characteristics for extra urban real life driving Calculated consumption: 0.4051 g/s Total time: 1017.574 s Total distance: 8.931605 km Total consumption (mass): 412.2192 g Fuel density: 860 kg/m3 (g/l) Total consumption (volume): 0.479325 L Consumption: 5.366613 l/100 km Based on the plot provided above and the resulting findings, the average fuel consumption for the extra urban testing regime was 0.4051 g/s that in turn has led to a fuel consumption of approximately 5.34 l/100 km (alternatively 18.63 km/l). Arguably, this fuel consumption figure represents a high value when compared to similar real life driving conditions. On the other hand, using the plot provided below for urban driving in real life conditions, it becomes clear that fuel consumption can again be divided into the same four phases as above. The urban driving cycle represents sharper peaks in fuel consumption indicating far sharper acceleration regimes, such as after pulling out from a traffic light. In a similar manner, there are a lot of idling patches available, indicated by low fuel consumption that are complemented by some constant speed patches where the fuel consumption shows greater stability than that achieved in the extra urban driving regime. Figure ‎6.17 – Fuel consumption against time for urban driving Table ‎6.2 - Summary of salient characteristics for urban real life driving Calculated consumption: 0.3775 g/s Total time: 756.678 s Total distance: 6.323837 km Total consumption (mass): 285.6459 g Fuel density: 860 kg/m3 (g/l) Total consumption (volume): 0.332146 L Consumption: 5.252293 l/100 km Annexure A – Analysis of two real drive cycle Read More