Tight vehicle emission regulations and rising fuel prices have heightened the demand for fuel-efficient cars. At the same time, customers continue to expect the same vehicle performance that they had when fuel prices were lower. These conflicting demands present an optimisation challenge for automotive manufacturers: how to minimise fuel consumption and CO2 emissions without sacrificing performance?
In the distant past, automobile manufacturers tackled the problem by optimising the power efficiency of each powertrain component separately. During the 1970s fuel crisis, large automakers began developing in-house computer simulation models to achieve optimal system-level performance. Despite this move towards system-level optimisation, it is still common in some emerging markets to optimise individual components. This piecemeal approach misses a large opportunity to reduce vehicle-level fuel consumption by coordinating the operating points of the components.
Model-Based Design with MATLAB and Simulink enables all automakers and suppliers to achieve optimisation results once reserved for a few large automakers with the resources to develop large internal simulation models and optimisation programs. By using a system model that incorporates the engine, transmission, axle ratio, driver, and vehicle, engineers can precisely match powertrain components and optimise hardware variables, such as axle ratios, and calibration parameters, such as shift schedules, simultaneously. Instead of rough estimates of fuel economy impact derived from expensive technology alternatives, they then have hard metrics upon which to base crucial hardware-selection decisions.
For example, suppose we want to optimise the powertrain for an economy car with a five-speed, dual-clutch transmission (DCT) and a turbo-charged, 2l, 4-cylinder engine (table 1). The goal is to use as little fuel as possible over a Federal Test Procedure (FTP75) drive cycle while maintaining a minimum performance threshold of 10 seconds for the 0–100 kph acceleration time (the time it takes to reach 100 kph from a standing start).
Table 1: Vehicle characteristics.
To find the combination of gear shift schedule calibrations and axle ratio that meets these requirements, we test a range of axle ratios. For each ratio, we use numerical optimisation to find the most fuel-efficient shift schedule calibration for the FTP75 cycle, as well as a separate shift schedule calibration that minimises 0–100 kph acceleration time. In keeping with current practice, the production powertrain controller chooses which of the two optimal shift schedules to use depending upon the magnitude of the torque demand sent by the driver through the accelerator pedal. Fuel cost and 0–60 kph acceleration time corresponding to each axle ratio are then plotted together to form a trade-off graph of fuel economy and performance vs. axle ratio (figure 1).
Figure 1: Simulation results showing optimal FTP75 fuel consumption and optimal 0–100 kph time for 7 axle ratio values. The blue line plots fuel cost as a function of axle ratio. The green line plots acceleration performance measured as 0–100 kph time against axle ratio.
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