"Leveraging Python-CAN and UDS for Cost Effective Automotive ECU Re-Programming"

Electronics with Software plays a crucial role to realize innovations in automotive industry. Advancement in automotive features has been fast paced, with rising number of ECUs (Electronics Control Unit) architecture increasing the complexity and cost pressure. While ECUs have established themselves the core unit to process and control vehicle including diagnostics. Hence ECU programming is vital for every player contributing to automotive development. Programming of an ECU involves several files like Bootloader, Application and calibration files, which are programmed over CAN bus using few of the widely used licensed tool such as Vector’s V-Flash, INCA-Flash, FlashR, etc. Despite the existence of major tools several organizations look for alternatives due to the high cost involved with license and CAN Hardware dependencies of manufacturers. Throughout this research various programming tools and CAN hardware compatibility were evaluated in combination, including time to program. The gap discovered was the premium cost of the tool, dependency on CAN hardware and existence of monopoly with distributors including cost for OEM specific programming sequence. The research work on this paper offers a programming tool capable of interfacing with several CAN hardware like V-CANoe, P-CAN, Kvaser, etc., and flexibility in defining the programming sequence. The tool is developed on python and UDS (ISO - 14229) on CAN integration architecture, programming the bootloader sectors as well as Application. The programming tool has a modest requirement of any CAN hardware to be connected during programming, it is thoroughly tested on capability to program various vehicle ECUs, with time and reliable performance compared including checksum and software version verification over a GUI, parallelly recording the programming logs. Ours is a noble attempt to reduce dependencies and cost of tool, ensuring a robust and transparent tool to verify programming sequence. In doing so we were able to save around 87% in cost.