F2008-06-112 STOP-START MICRO HYBRID: AN ESTIMATION OF AUTOMATIC ENGINE STOP AVAILABILITY Alastair Muncey1*, Aditya Dhand1, Baekhyun Cho1, Alan Walker1, Daniel Kok2, Thomas Hochkirchen2 1 AVL Powertrain UK Ltd. 2 Ford Motor Company KEYWORDS – Stop-Start, Usage profile, durability, inhibitor ABSTRACT:- One of the main features of a micro hybrid vehicle is the automatic stop and restart of the engine to avoid engine idling when the vehicle is at rest (1). However, in real world usage stopping the engine may not always be desirable for reasons of safety, comfort or other factors. The actual number of stops to be expected from a vehicle over it's lifetime is of crucial interest when developing the durability targets for a micro hybrid system. In this research reported here, a methodology to analyse the frequency of automatic engine stop events in real world usage has been developed. This takes into account the situations where the automatic engine stop is rendered unavailable (or inhibited) by the system controller. A statistical model has been constructed using a real world, real time, vehicle usage dataset measured for passenger cars and publicly available climate data combined with Monte Carlo analysis. This supports the prediction of occurrence of inhibitors during real world customer usage. The real world usage dataset, measured by an OEM in Europe, provided information regarding the vehicle speed profile, gear usage, engine warm up periods, ambient conditions as well as generic information about all the measured trips. Using the above mentioned sources, inhibiting conditions have been analysed individually, using simplified models where necessary. The simultaneous occurrence of inhibitors has been investigated and accounted for in the results. Conclusions: The methodology developed in this study provides a useful tool which supports the calculation of durability requirements as well as the initial calibration for micro hybrid systems in vehicle applications. Parametric studies show the routes to further system optimization. INTRODUCTION One of the common features of practically all proposed hybrid vehicle systems is the concept of shutting off the engine while the vehicle is stationary, in order to save fuel. This obviously will lead to an increase in the number of engine starts compared to traditional usage patterns which in turn leads to an increase in the stresses placed on components involved in the engine start event. In order to determine that these components can meet this increase in the number of start cycles reliably (and hence determine the trade off between component cost and fuel economy savings (2)) it is necessary to refine the durability testing that the systems are subject to. To set these new durability targets it is vital to first understand the number of start cycles that the stop start strategy will impose on the vehicle in the field. This paper discusses a methodology used to determine this number. ORIGINS OF THE METHODOLOGY

The simplest method of defining the lifetime number of starts is represented by the following formula:

S = s . L Where S is total lifetime number of starts s is starts/km on defined drive cycle (e.g. the NEDC emissions cycle) L is Lifetime distance target of the vehicle (km) This methodology is unsatisfactory for several reasons. Firstly it assumes that the only criterion for an engine stop to occur is vehicle speed (or lack of it). Secondly, it assumes that a single extrapolated drive cycle is representative of the lifetime usage of all vehicles. Unfortunately there is a large variability in customer usage which cannot be captured in a single drive cycle – It could represent the extreme or the "average" but not both. In order to optimize the system with respect to trade off between fuel economy targets and system cost/durability targets, we have to understand the whole spectrum. The solution here was to go back to the core real world usage data that would be used to create a generic cycle (3). This core dataset would need to contain the vehicle speed profiles of a significant number of vehicles in the relevant markets over a period of time. The issue of criteria for an engine stop to occur is also important– closer consideration of the vehicle system will suggest that there are many reasons why the vehicle may not want to shut off the engine. These include situations related to cabin comfort, battery condition, engine emissions, and many more. DESCRIPTION OF THE DATASETS The dataset (4) used in the analysis was based on data available from approximately one hundred passenger cars of two different size classes in two different major European markets. The data covered an entire calendar year with each vehicle instrumented for approximately one month, with data sampled at 1 Hz. In total over 15,000 journeys were recorded covering well over 120,000 km. As well as the vehicle dataset, information was also required about the environment the vehicle would operate in. This was obtained from a publicly available weather database, giving climate data from various geographical locations. Using these datasets, the spread of expected engine starts over the life of a vehicle was determined computationally using MATLAB™ . In this paper the combination of the datasets and the computational scripts are referred to as "the model". CONSTRUCTING THE CUSTOMER USAGE PROFILE Once the datasets had been defined and acquired they could be used to construct a large number of trips. From these trips the customer usage profile could be determined (Figure 1).

Figure 1: Customer Usage Profile generation

The total trips were constructed as follows: •Each vehicle provided data for approximately 1 month; therefore the trip profiles for that month were extrapolated out for the entire year. This year was then repeated as many times as required to give the total design lifetime of the vehicle. •The temperature data consisted of max and min points for each day for 50 years. Ambient air temperatures were modelled by varying the temperature as a sine wave between a minimum assumed to be at 3 AM and a maximum assumed at 3 PM. •Engine coolant temperature was calculated with a simple model using idle time, average vehicle speed and coolant temperature at start of trip.(Coolant temperature was assumed to start at ambient air temperature) •The total trip profiles were then created by selecting each vehicle trip in turn, checking the start time and date and then allocating a random year to allow calculation of the ambient temperature during the trip. This process generated approx 200,000 trips in total, with each trip containing time indexed information on vehicle speed, ambient air temperature, and engine coolant temperature. ENGINE STOP INHIBITORS As mentioned earlier, there are many factors that the control system has to take into consideration when determining if the engine should be turned off during a stop. For ease of reference, each signal (or combination of signals) that could cause the system to NOT stop the engine is referred to as an inhibitor (it inhibits engine stop). Deciding on what these inhibitors are and how to handle them is a key part of determining a hybrid vehicle strategy. A preliminary study was conducted to estimate what inhibitors would be needed on a vehicle. This produced a list of over fifty, many of which would be rare events caused by the failure of another component or system on the vehicle. However, seven key inhibitors were identified as being the most likely causes of a stop inhibit in daily driving. A process was then determined for a way of estimating the inhibitors occurrence, using the data available in the customer usage profile. These inhibitors were as follows: Battery Temperature Limit, Air Conditioning request, Blower on Screen request, Heated Screen request, Cabin Heating, Vehicle Slow after Start, Engine Shutdown Delay /Pre-launch . A further category, "Non Transparent", was

Customer dataset

(Vehicles, Trips)

Temperature Data

@ Location

# of trips / day

Vehicle speed profile

Start time of trip

Initial engine coolant

Max. temperature.

Min. temperature.

Initial air temperature.

Customer Usage Profile

(Reconstructed Trips)

included to represent all other inhibitors. Each of these inhibitors is considered in detail below. Battery Temperature Limit Rationale: As the battery temperature drops the cranking performance of the system diminishes, due to the increase in the battery's internal resistance. To keep the cranking performance above a certain minimum level, it is thus necessary to limit the minimum battery temperature at which the system will be called upon to crank. Model: This inhibitor is triggered entirely off ambient air temperature. If the ambient air temperature drops below the threshold shown then the stop is inhibited. If the temperature subsequently rises then the stop is allowed (figure 2). In the real world, batteries have a large time constant and the battery temperature will only change slowly. However, because the air temperature model used in this model also changes very slowly it can be used as an acceptable surrogate.

Figure 2: Battery Temperature Inhibitor

A/C Request Rationale: It was assumed that Air Conditioning and Engine Stop were mutually exclusive (due to the need for the engine to run in order to drive the A/C compressor). Therefore whenever the driver demanded A/C, the engine would not stop. Model: As with the battery temperature, this inhibitor is triggered entirely off ambient air temperature. If the ambient air temperature rises above the hard threshold then the stop is inhibited. If the temperature subsequently falls then the stop is allowed. (figure 3)

Figure 3: A/C inhibitor

Blower on Screen request Rationale: It was assumed that if the driver or HVAC (Heating, Ventilation and Air conditioning) unit demanded full demist, then it indicated that there was potentially a problem with visibility and the engine should not stop. Model: This inhibitor assumes that stop start is inhibited when the driver turns the blowers on to the screen, presumably to demist it. It was modelled with the following method (figure 4)- At the start of the trip a random number is generated. This is then applied to a probability distribution centered on a pre-determined temperature value to determine the lower temperature threshold. This is assumed to be the air temperature that will trigger the driver to turning on the screen blower for the duration of that trip. During the trip, if the ambient air drops to this threshold value then the stop is inhibited for a duration determined by a second probability distribution. Only one blower event is allowed per trip.

Figure 4: Blower on Screen

Heated Screen request Rationale: Heated screens (especially front screens) are a large electrical load and would have a significant negative effect on battery life if supported by the battery alone during a stop event. It’s also likely that if the heated screen function is selected by the driver then there is a probability that the screens are either iced up or at least misted and so a delay to restoring full visibility would not be desirable. For both these reasons the heated screen function was set as an inhibitor. Model: Similar to the Blower on screens inhibitor, a threshold is determined prior to the trip although when the threshold is crossed, stops are inhibited for a fixed time. This inhibitor is assumed to cover both front and rear heated screens and can only be triggered once per trip.

Figure 5: Heated Screen Inhibitor

Cabin Heating Rationale: It is important that the trade-offs that the driver makes between comfort and fuel economy are carefully considered. One of these trade offs is the balance between cabin heating (which may require the engine to be running) and the engine stop request. The degree of heating required (and hence the required temperature of the engine) is assumed to be proportional to the ambient air temperature – a cold day will require greater heater performance than a warm one, so will have a higher engine temperature threshold before the inhibitor is raised. This also allows the system to ensure that the engine is up to temperature before a stop is allowed. Model: At each potential stop the ambient air and engine coolant temperature are cross referenced to determine whether the engine should be allowed to stop (figure 6).

Figure 6: Cabin Heating Inhibitor

Vehicle Slow after Start Rationale: In order to prevent engine stop in slow speed situations, such as parking manoeuvres or very slow queuing traffic, an inhibitor was investigated that would require the vehicle to exceed a minimum speed before enabling another stop event. Model: After a restart the stop is inhibited until the vehicle exceeds a certain minimum speed. Once this speed is exceeded, this inhibitor is lifted until the next stop.

Figure 7: Vehicle Slow after Start

Engine Shutdown Delay and Pre-launch Rationale: Engine Shutdown Delay and Pre-launch are two separate concepts that are easily combined in one inhibitor. Shutdown delay is a calibratable value that can be implemented to improve drivability dependent on stop strategy i.e. the engine is switched of X seconds after the vehicle has come to a halt. Prelaunch is a short delay added to reflect the time it takes for the engine to restart prior to the vehicle starting to move. In most cases (e.g. when the traffic light changes to amber and the driver puts the car in to gear) the engine will be restarted before the driver wishes the vehicle to pull away. Model: Shutdown Delay and Prelaunch are very simple to model as they are implemented purely as delays at the start and end of the stop event. In short stop events this can completely eliminate the stop. (Figure 8)

Figure 8: Shutdown Delay and Prelaunch

Non Transparent: Rationale: As mentioned, there are many possible inhibitors that occur rarely. There is a need to determine a way to account for these other inhibitors. Model: When a stop is detected and no other inhibitor is active, then there is a small chance that it will be inhibited by "other causes". This is modelled by generating a random number and seeing if it is below a pre-set threshold. If it is, then the stop is inhibited. CALIBRATING THE MODEL All eight inhibitors mentioned above will vary in effect depending on what thresholds are calibrated. This is most obvious in the inhibitors based on temperature, but equally valid on the other inhibitors too. The actual values of the calibration will depend on the proposed system, but one of the uses of the model is that the effect of different calibrations can be analysed. (Note that calibration includes not just the temperature or speed thresholds, but also the mean and sigma values of the probability density functions used, and the type of functions themselves) Aside from the calibration of the individual inhibitors the other key variable in the model is the geographical source for the climate data. Varying the dataset used for the climate numbers will have a significant effect on the number of stops, although the exact effect will depend on how the individual inhibitors are calibrated. RESULTS Once the model is fully calibrated and run then it will deliver the number of stops each vehicle will endure over its lifetime (The model will need to be checked that each vehicle lifetime is limited by time or mileage, depending on how the target is defined). These numbers can then be ranked to determine the percentile values of the number of stops. From this a durability target can be determined, based on a general percentile target (e.g. 95th or 98th percentile customer). SCOPE FOR FUTURE DEVELOPMENT The authors believe that this model strikes a useful balance between simplicity and accuracy. However, there are areas with in the proposed methodology that are knowingly constrained by the available data. One example is using air temperature as a surrogate for battery temperature when in reality the battery temperature is far slower to change – A more accurate battery temperature model would be a definite improvement. Similarly the visibility related inhibitors

(heated screens and demist) could benefit from greater data on the conditions (local temperature and humidity) that cause misting and frosting of screens, and all inhibitors that involve the driver would benefit from more knowledge of how customers actually use the vehicle. Finally there is room for some debate as to whether extrapolating the drive cycles of cars in their first year of ownership over several years is accurate. There is data available that suggests that annual mileages vary significantly with the age of the vehicle (5). However these are all containable as separate projects and none are expected to increase the number of stops. The model has been designed at all points to be conservative and to err on the side of higher numbers of stops, since this will result in higher durability targets and hence minimise the chance of early failures in the field. CONCLUSION A methodology has been developed to take available data and estimate the effects of a stop start system in terms of the number of stops the system will experience over the life of a vehicle. The methodology is simple to construct, easy to calibrate and allows analysis of the effects of various system calibrations. It is designed to be conservative in the numbers it produces but can be easily upgraded as more accurate data or sub-models become available. REFERENCES 1. D. Kok, P. Schmitz, U. Kramer, R. Busch, D. Kees: "Micro-Hybrid technology – Maximum customer benefit at minimum cost?" 19th International AVL Conference "Engine and Environment", September 2007 2. Hochkirchen, Thomas: Customer Benefit, Reliability, System Cost – the Magic Triangle of Micro-Hybrid Technology. AutoTechnology. International Journal for Engineering, Production and Management Vol. 7, February 2007 (pp. 52-55). 3. Hochkirchen, T , Ploumen, S. :"Traffic Legos – Towards a better understanding of Real-World Customer Usage Profiles"; FORD Research and Advanced Engineering Technical reports, SRR 2005 -0056 4. Hochkirchen, T.: "Real-World European Customer Usage Profiles –FORD Data Sources and their Answers to Stop/Start related Questions"; Ford Research and Advanced Engineering Technical reports, SRR 2003 -0209 5. André, M. et. al. (1999): Driving statistics for the assessment of pollutant emissions from road transport. INRETS report LTE 9906.