# Problem Overview¶

This tutorial covers some of the more advanced capabilities of OpenMDAO. You should read and understand the simple tutorial problem before starting this one.

The objective of this tutorial problem is to design an automobile that performs “well” as measured by these three metrics:

• Time to accelerate from rest to 60 mph
• Fuel economy as measured by the EPA highway driving test
• Fuel economy as measured by the EPA city driving test

We will be designing a conventional automobile with a gasoline-fueled internal combustion engine and a 5-speed manual transmission. Our scope is preliminary conceptual design, so we need to choose simulation models with a compact set of design inputs and a quick execution time to allow exploration of the design space via multiple executions. An existing vehicle simulation was not available in the desired form, so we developed one based on physical first principles. The mathematical model for the engine came from open literature.

To simulate these performance metrics, we need a model of the vehicle’s power-train, including the engine, the transmission, and the rear differential. We also need the equations of motion for driving the vehicle. A logical way to divide the vehicle model is to break it into component models matching each of these subsystems: engine, transmission, and chassis (which includes the rear differential ratio). In a typical problem, each of these component models will be a separate implementation, possibly with different authors or vendors. The design variables for each subsystem are detailed below.

The engine, transmission, and chassis subsystems of a vehicle are each described by some kind of mathematical model that depends on a set of design variables (e.g., compression ratio.) There are also three simulation variables: throttle position, gear, and velocity. These variables are independent of any design and are used during simulation while the vehicle model is being “driven” to determine the metrics. There are also a couple of internal inputs, such as RPM and Power, that are provided by other components in the vehicle. These inter-dependencies define the connection order for vehicle components in terms of the Data Flow: Transmission -> Engine -> Chassis.

The full process model is shown below. Process Model for the Optimization Problem

The process model includes three drivers that performs the acceleration and fuel economy simulations and calculate the performance metrics. A typical problem might be to close an optimization loop around the vehicle model, driving one or more design variables to minimize the 0-60 acceleration time and maximize the EPA city and highway mileage.

## The Transmission Model¶

The transmission model must perform two tasks:

1. Provide a transformation from engine output torque to torque at the wheels
2. Calculate the engine RPM

The transmission modeled here is a 5-speed manual. Shifting occurs instantaneously when the simulation input CurrentGear is given a new value. When the clutch is engaged, the wheel rotation and the engine rotation are directly linked via the current gear ratio and the differential ratio, so the engine RPM can be calculated given the velocity. However, this direct linkage would cause the engine RPM to go to zero as the vehicle stops, so the clutch partially disengages for low speed operation (i.e., where the engine speed would drop below 1000 RPM) and sets the engine speed to 1000 RPM. This only occurs when the transmission is in first gear.

Transmission - Design Variables:

Variable Description Units
ratio1 Gear ratio in first gear
ratio2 Gear ratio in second gear
ratio3 Gear ratio in third gear
ratio4 Gear ratio in fourth gear
ratio5 Gear ratio in fifth gear
final_drive_ratio Gear ratio for vehicle’s differential
tire_circumference Circumference of the tire inch

Transmission - Simulation Inputs:

Variable Description Units
current_gear Current gear position
velocity Current vehicle velocity m/s

Transmission - Outputs:

Variable Description Units
torque_ratio Ratio of transmission output power to power at the wheel
RPM Engine rotational speed rpm

## The Engine Model¶

The engine model must provide two pieces of information:

1. Torque at engine output
2. Fuel burn under current load

We used a model published in a master’s thesis by S. Sitthiracha (1). It is a physics-based model of the Otto cycle in a 4-stroke spark-ignition internal combustion engine. This model allows the construction of a parametrized engine model with 10 design inputs covering the engine mechanical design (cylinder bore, stroke, connecting rod length, and compression ratio); intake valve design (diameter and lift); and the cycle timing (for both intake and spark). In the thesis, the model is implemented in Simulink and simulated using data from a family of Mercedes-Benz engines designed in 1969. The model includes the effects of burn duration, heat loss through the cylinder wall, losses due to friction and charge heating, and intake orifice flow. Some of these effects were derived from empirical data and are essentially valid over an engine speed ranging from 1000 RPM to 6000 RPM.

Sitthiracha’s model also includes the fuel type as a design variable. This introduces a half dozen parameters that are dependent on the fuel chemistry. To keep our model simple, we set these parameters to values appropriate for gasoline and did not provide them as design inputs for the engine model. However, it would not be difficult to modify the component code so any of these could be used as design variables.

Sitthiracha’s model contained a couple of errors in the equations, and a couple of factors needed to be adjusted to obtain good results. His model also assumed wide-open throttle, so the effect of a throttle was modeled as an additional restriction on the intake flow into the cylinder. For simulation, relating the throttle position to an actual physical foot position is not important. All that is needed is a continuum of throttle settings between closed and wide open. This model assumes that closed is 1% of open, but the simulation currently drives it using a minimum of 7%, which gives a more realistic performance.

The design variables in this problem allow for some significant modification to the engine design. This strongly affects the engine weight, so we need to estimate this. A report by Shikida (2) contains some empirical data taken from a sampling of engines present in the Japanese market in 2000. This data maps engine displacement and weight vs power. Displacement is essentially a measurement of the engine size and can be calculated from the design parameters, so the engine model uses a linear fit between engine weight and displacement to estimate the engine weight and provide it as an output.

Engine - Design Variables:

Variable Description Units
stroke Length of compression zone in cylinder mm
bore Bore (cylinder diameter) mm
conrod Connecting rod length mm
comp_ratio Volumetric ratio of compression
spark_angle Spark angle with respect to top dead center deg
n_cyl Number of Cylinders
IVO Intake valve open before top dead center deg
IVC Intake valve close after bottom dead center deg
L_v Maximum valve lift mm
D_v Intake valve diameter mm

Engine - Simulation Inputs:

Variable Description Units
RPM Engine rotational speed (1000-6000) rpm
throttle Throttle position

Engine - Outputs:

Variable Description Units
power Power produced by engine kW
torque Torque produced by engine N*m
fuel_burn Fuel burn rate L/sec
engine_weight Engine weight estimate kg
overspeed True if engine RPM is over 5000
underspeed True if engine RPM is under 1000

References:

1. Sitthiracha, S., “An Analytical Model of Spark Ignition Engine for Performance Prediction,” Master’s Thesis, King Mongkut’s Institute of Technology, North Bangkok, 2006.

2. Shikida, T., Nakamura, Y., Nakakubo, T., and Kawase, H., “Development of the High Speed 2ZZ-GE Engine,” SAE 2000-01-0671, SAE World Congress, 6-9 Mar. 2000.

## The Chassis Model¶

The chassis model must provide the vehicle acceleration given the torque produced by the engine and scaled by the transmission. The equation used for the model is the sum of the forces acting on the vehicle in the forward direction. These forces include both the rolling friction associated with the tires and the vehicle drag which is proportional to the square of velocity.

Chassis - Design Variables:

Variable Description Units
mass_vehicle Vehicle mass kg
Cf Rolling friction coefficient
Cd Drag coefficient
area Front profile area m*m

Chassis - Simulation Inputs:

Variable Description Units
mass_engine Engine mass estimate kg
velocity Current vehicle velocity m/s
torque_ratio Ratio of transmission output power to power at the wheel
tire_circumference Circumference of the tire m

Chassis - Outputs:

Variable Description Units
acceleration Vehicle instantaneous acceleration m/(s*s)

## Simulating the Acceleration Test (0-60)¶

The procedure for simulating the maximum acceleration is straightforward. The vehicle is commanded at wide open throttle, and the resulting acceleration is integrated until the velocity reaches 60 mph. A time step of 0.1 seconds is used for simulation, which is small enough that a simple (and efficient) trapezoidal integration was adequate. Gears are shifted at the red line, which is the 6000 RPM limit of the engine model.

Shifting at the red line is not always optimal, though it is optimal for the default engine given here. The optimal shifting RPMs are dependent on the engine’s torque curve as well as the gear ratios, so creating a generalized yet more optimal shifting procedure would be more numerically intensive, though it could be done.

## Simulating the EPA Mileage Tests¶

The EPA mileage tests give an estimate of the fuel consumed while driving a predetermined velocity profile that represents a particular class of driving, the two most well-known of which represent typical city driving and highway driving. These tests aren’t actually performed on the open road but are instead done in the EPA testing garage with the tires on rollers and a hose connected to the exhaust pipe to measure the composition of the exhaust gases. The test still uses a driver who must follow a velocity profile given on a computer screen. The actual velocity profiles are available on the EPA website as follows: EPA City Driving Profile EPA Highway Driving Profile

To simulate these tests, the vehicle model must follow the EPA velocity profiles. That is, the time history of the gear and throttle position must be found that allows the vehicle to follow these profiles. The fuel consumed is captured over the profile so that the mileage estimate can be calculated. This can be summarized by the following procedure:

1. Determine acceleration required to reach next velocity point
2. Determine correct gear
3. Solve for throttle position that matches the required acceleration
4. For that gear and throttle setting, calculate fuel burn

The trickiest part of the entire simulation is determining the right gear. The simulation has to test the acceleration at min and max throttle to determine if the required acceleration is possible in that gear. The simulation also has to make sure the engine RPM lies within its min and max values. For low speed (under 10 mph), the transmission is always set to first gear.

Once the gear is determined, a bisection method is used to find the throttle position that matches the required acceleration within a small tolerance. This solution method converges quickly, especially when applied over a linear range of the torque curve. However, the EPA profiles are long, with many calculation points, so simulating these driving profiles consumes much more CPU time than the acceleration test. 