In this article, we explore four automotive signal processing application areas: engine and transmission control, safety and convenience, electronic steering and braking, and “infotainment.” For each application area, we survey current and forthcoming uses of signal processing technology, focusing on a few uses that are particularly noteworthy in each application area. We also highlight the forces driving these applications, such as government regulations, cost pressures, and the desire for differentiation among automakers.

Engine and transmission control
Signal processing is becoming more prevalent in engines and transmissions for several reasons. Government clean-air mandates are pushing manufacturers to design more fuel-efficient engines and transmissions. Fuel efficiency is also important to consumers, particularly in locations where fuel prices are high. And consumers also want strong performance. In parallel with these rising demands, automakers want to reduce their costs and shorten their design cycles. Signal processing can help achieve all of these goals.

Engine controllers

Engines have employed electronic controllers for many years. Until recently, these controllers relied heavily on static look-up tables to determine how to adjust engine parameters. Today, engine controllers use more sophisticated algorithms, such as those used to detect engine knock.

“Knocking” is uncontrolled fuel combustion that is detrimental to emissions, fuel economy, and engine longevity. Traditionally, engine controllers used conservative pre-defined settings to ensure knocking did not occur. However, engines operate most efficiently when they are on the edge of knocking, so this strategy limits engine power. To solve this problem, modern engine controllers employ signal processing algorithms to detect the warning signs of knock. This allows engines to operate on the edge of knocking.

Controllers are also beginning to use differential equations that model engine behavior. These equations allow the engine controller to fine-tune its response to engine conditions, rather than relying on a limited set of look-up table parameters. The model-based approach also reduces the amount of time spent on engine calibration. In the look-up table approach, calibration involves testing the engine under a broad range of conditions. This process requires an enormous amount of time and money. Determining the parameters for a model-based controller requires significantly less testing.

Engine controllers are also becoming more complex as they begin to control more aspects of the engine. In most current engines, the valves are operated by a purely mechanical system. Valve timing, however, is starting to become electronically controlled. Future engines may even have fully electronic valve trains where each valve is actuated by a solenoid. These increasingly sophisticated controls will call for additional signal processing.

Signal processing is also used to provide smooth control of various electric motors. One interesting motor-control application is the electrically boosted turbocharger. Engines can achieve higher efficiency through the use of a turbocharger, but turbochargers tend to suffer from “lag.” That is, there is usually a noticeable delay between the driver pressing the accelerator and the turbocharger delivering the additional power. By adding an electric motor to the turbocharger, engine designers can eliminate this lag.

Sophisticated electric motors are also used in hybrid vehicles that are powered by the combination of an internal combustion engine and an electric motor. Current vehicles have a single electric motor, but future vehicles may use separate motors for each wheel, greatly increasing the per-vehicle demand for motor controllers. Control of electric motors will also be important for the fuel-cell powered vehicles currently under development.