Silva, E., "Connected Vehicle Data Applied to Remote Diagnostics Methods for Heavy Duty Trucks," SAE Technical Paper 2015-01-2879, 2015, doi:10.4271/2015-01-2879.
In recent years the commercial vehicle industry, specifically the heavy duty truck product line, has seen a rapid increase in the replacement of pure mechanical systems by electronic controlled systems. Engine, transmission, brakes, lighting, clusters, etc. are all monitored and/or controlled electronically.The adoption of electronic systems created a substantial change in the complexity of the heavy duty trucks systems. Currently Diagnostic Trouble Codes (DTC) displayed on instrument clusters, in the majority of the cases, are no longer generated by a single sensor/component failure, instead these DTCs are triggered by a system monitor flag, as the result of a below average performance or a failure of an entire system. This new level of complexity makes it very difficult for the current diagnostic methods and tools, to identify what is causing the equipment to operate below ideal conditions.As more and more Original Equipment Manufacturers (OEMs) are offering standard connectivity devices on their vehicles, these devices are becoming an alternative to this growing concern because they can be utilized to capture and supply equipment operating data to support diagnostics methods and tools with critical information about an active DTC and its consequences for the operation/health of the vehicle.The objective of this paper is to demonstrate the potential of the connected vehicle technology data  to drive the development of a diagnostic methodology and applicable tools that better suit the system monitoring strategy applied to the commercial vehicle industry.A telematics monitoring solution was used to develop this remote diagnostics concept, which also includes an integrated process of communication between OEM, the repair location, and vehicle owner. Utilization of the data collected from the trucks, in association with a process focused on customer support, made it possible to demonstrate the capabilities of this new diagnostics methodology to improve truck availability and customer uptime.