With relatively few product introductions and a relatively long design cycle, the automotive sector is a good example of one industry pressed to make real change in the way things are designed. Complex electronic systems have to be designed much faster, and new smart features need to be developed to engage occupants, deliver better mileage results, and create a more efficient, safe driving experience.

In one example from GM, the challenge was to get to market much faster. In another example from Ford, the idea was to bring unique information into a car as part of an integrated, compelling experience.

Getting to hybrid, faster

GM has been pushing to deliver more hybrid platforms, but that’s a task that is much easier said than done. Hybrids present a complex electromechanical control system problem with a significant compute component. GM’s Two-Mode Hybrid power train is designed to optimize fuel efficiency in both city and highway driving. It combines a conventional engine with two 60 kW electric motors integrated into an automatic transmission (Figure 1) and includes new components such as battery and power electronics.

Figure 1: GM Two-Mode transmission (click graphic to zoom by 1.9x)

It was imperative for GM to get the Two-Mode Hybrid power train into production quickly, and to do that, they turned to math and simulation-based tools from The MathWorks. Using Model-Based Design, GM designed the power train prototype within 9 months, shaving 24 months off the expected development time. The complex control system is currently in production in the GMC Sierra Hybrid, GMC Yukon Hybrid, Chevy Tahoe Hybrid, Chevy Silverado Hybrid, and Cadillac Escalade Hybrid vehicles.

“Model-Based Design helps us work at a higher level of abstraction, allowing us to verify designs early,” remarks Larry Nitz, GM executive director of hybrid and electric power trains. “This ability to simulate and correct systems before committing to hardware allows us to try new control strategies virtually, while the use of production code generation accelerates design iterations and eliminates translation errors common in hand coding.”

“The timeline GM gave itself to design, verify, and deliver the Two-Mode Hybrid to the market was very aggressive and required engineering work that is traditionally done on the road through iterations with prototype hardware to move to the desktop,” says Steve Toeppe, senior manager of automotive engineering at The MathWorks. “GM’s integration of Model-Based Design into its development process – from early verification of specifications, through testing the designs in HIL simulators, and ending on production vehicles – was exciting to watch.”

According to The MathWorks, GM used MATLAB, Simulink, and Stateflow to design the control system architecture and model all the control and diagnostic functions. Real-Time Workshop Embedded Coder provided the capability to generate production code from the models, and Real-Time Workshop and Hardware-In-the-Loop (HIL) simulators helped verify the control system.

Getting beyond navigation, smarter

Ford is striving to integrate more intelligence into their dashboards with the SYNC system, which is about much more than just infotainment. For their traffic solution in SYNC, Ford partnered with INRIX to deliver real-time, historical, and predictive information.

The Ford mission behind SYNC sees a car as an edge device participating in a much larger cloud. Jim Buczkowski, director of electrical systems engineering for Ford, put it this way: “We think of it as ‘beamed in,’ ‘brought in,’ and ‘built in.’” He went on to explain that “beamed in” means connected to the cloud via 3G or other technology. “Brought in” means to integrate the user’s mobile device of choice and augment content using things like USB sticks. “Built in” refers to the infrastructure in the car to communicate with the car’s systems, the mobile device, and the driver and occupants.

By beaming in traffic information from the cloud, Ford gets access to a much more robust INRIX solution that outshines a simple mapping and navigation solution (see Figure 2). INRIX knows not only about routes, but also about traffic history, current congestion, and using proprietary models to predict the best available route for the situation.

Figure 2: INRIX triangle (click graphic to zoom by 1.3x)

With INRIX technology, Ford SYNC users get answers to questions like “Traffic Ahead,” “Traffic Around Me,” and “Fastest Route Home.” INRIX claims to provide true real-time information by blending real-time road sensor data with billions of real-time data points from GPS-enabled commercial and consumer devices in taxis, service vehicles, airport shuttle services, cars, and long-haul trucks.

The INRIX solution is super-sophisticated. Here’s a sample from their website listing the types of things they look at in their Traffic Fusion Engine:

·    Detection of malfunctioning traffic sensors: Particularly in the case of public DOT loop detector and toll-tag reader data, feeds and the physical sensors and communications infrastructure are prone to failures and long lead repair times. INRIX detects incorrectly functioning sensors in near real time, flagging them to be ignored as a data source for the Fusion Engine.

·    Geospatial filtering: Due to its unique relationships with data suppliers, INRIX obtains location and time information, speed, and heading for each vehicle together with additional metadata that provides context for the vehicles’ current status, allowing high-accuracy geospatial filtering of probe-derived data.

·    Collaborative filtering and outlier detection: INRIX combines data from all sources, allowing collaboration between data points that agree with one another to identify statistical outlier data points and thus compute a high-confidence estimate of real-time conditions together with an error estimate.

·    Optimizing spatial granularity: Due to the high density of raw data that INRIX receives, it is frequently able to determine real-time conditions at or below the resolution of a single TMC segment. In real-time, the Traffic Fusion Engine can adjust the spatial granularity of the data it computes to maximize the statistical confidence in the data.

·    Never low-confidence data: If, due to insufficient data at a given location, INRIX can’t meet its error threshold for real-time data, no information is reported on the road segment in question.

While a driver has no idea all this is going on, it’s a great example of the type of intelligent embedded computing system that’s changing the car experience and improving energy efficiency today.