The electronic content of vehicles—both for control and infotainment—is estimated to be growing at around 10% per year. Supplying data to those control systems are dozens of onboard sensors, of which typically 60% are contactless types.
This huge dependency is fine—provided they are working correctly and have the quality and durability to withstand the often hostile environments in which they must operate. That capability will in turn support whole-vehicle quality and durability.
Automotive sensor supplier Hamlin is well aware of the roles of its products and the need to reduce the risk of any incorrect operation. The company has developed magnetic modeling systems that can simulate the magnetic environment of the car or its subassembly.
“The sensor is a critical element of the control system and is often now a custom item to meet specific packaging and performance requirements,” said Mark Pickhard, Hamlin’s Global Technology Manager. “So it follows that there are increasing demands on their design and cost-effective integration.”
Modeling and simulation are already well established in the physical design and production of such sensors, but Hamlin has now taken this work further. Pickhard explained that there are many types of sensors that employ some form of magnetic field, ranging from the electromechanical reed switch to the Hall effect sensor. These include AMR (anisotropic magnetoresistive), GMR (giant magnetoresistive), and other magnetic sensing technologies, each having “unique” advantages that provide improved cost or performance in many circumstances.
“To analyze the performance fully and optimize the design of such sensors, it is essential to understand and manipulate magnetic fields,” he said.
Hamlin is an established supplier of safety-critical sensors, notably its safing sensor of which it has delivered more than 300 million units since the first use by Mercedes-Benz in 1983. The company’s custom position and movement sensor-design capability, particularly for engine-management applications, includes a highly developed and proven magnetic-simulation capability.
Pickhard said of Hamlin’s latest simulation work: “There are many techniques for deriving magnetic solutions, ranging from the straightforward ‘suck-it-and-see’ approach on the test bench, to detailed simulation and modeling using finite element analysis [FEA] as well as sophisticated custom mathematical formulae. Each technique has its strengths and weaknesses, but all can help us optimize the design of a custom sensor for its specific application.”
The design methodology developed by Hamlin is a three-stage process comprising concept exploration and selection, optimization, and analysis. For speed and simplicity, the company’s design team uses its calculation toolset, but if the design is beyond the scope of that—for example, if the design contains sufficient ferrous material to significantly affect the magnetic fields—FEA is used.
“FEA is the ultimate in flexibility, but with its use goes the caveat of ‘garbage in, garbage out,’” Pickhard stated. "It is slower than using closed-form formulae, but it can analyze arbitrary shapes and materials, a capability sometimes required for custom sensors.”
Tools used include Mathematica commercial software by Wolfram Research, which can do symbolic or numeric calculations and includes plotting and programming capabilities. It is useful for visualization and to automate modeling and is used by Hamlin for closed-form formulae.
For more complex analyses, Hamlin uses Amperes from Integrated Engineering Software. The program is a 3-D magnetic field solver that calculates force, torque, flux linkage, and inductance as well as magnetic field strengths. It includes a parametric solver that can vary geometry, materials, and sources to help fine-tune designs.
“However, to make full use of these design tools, it is essential to have a full understanding of magnetics, including interference sources such as the Earth’s field, magnetic circuit concepts, and other issues,” stressed Pickhard. “It is also important to understand the unusual characteristics of magnetic materials; for example, the operating temperature range of a magnet is affected by its shape.”
An example given by Pickhard is how magnetic modeling helped to finalize the design of a mass-production custom sensor to check liquid level. The float diameter was restricted, so it was necessary to find a magnet configuration with low weight and low sensitivity to tolerances. An initial design was created and optimized with the required features that also minimized the effect of lateral movement and maximized the activation zone.
“The design was analyzed to predict performance with manufacturing and end-use environmental variations including magnet strength, Hall sensitivity variation, and mechanical tolerances across a wide temperature range,” said Pickhard. “The effect of varying external magnetic fields was also considered. A design was achieved and subsequent tests on physical units validated the modeling work satisfactorily.”