Design and Implementation of an Automated Functional Testing System for NV11 Massage Seat Controller With Performance Optimization

ABSTRACT This paper addresses the growing need for efficient and precise automated testing of automotive electronic control units (ECUs), specifically focusing on the NIO NV11 massage seat controller. Traditional manual testing methods suffer from significant inefficiencies and accuracy limitations, while existing automated systems lack specialized load modeling and seamless integration with manufacturing execution systems (MES). The proposed solution aims to bridge these gaps through a comprehensive testing framework. The system integrates hardware and software components to enable end‐to‐end automation. The hardware core consists of an industrial computer (IPC) interfacing via the local interconnect network (LIN) bus, complemented by a Flash burning module, LIN communication interface, and programmable power supply. A custom test fixture facilitates uninterrupted transitions from functional verification to data uploading, while digital instrumentation ensures fine‐grained testing precision. The software architecture leverages intelligent algorithms for adaptive parameter adjustment and real‐time data analysis. Experimental results demonstrate notable performance improvements: testing time is reduced by ~30% compared to traditional methods, while error rates decrease by around 20%, ensuring high repeatability and accuracy. The system's modular design enables straightforward adaptation to other automotive ECUs, such as anti‐lock braking systems (ABS) and electronic stability programs (ESP), with minimal modifications. Industrial deployment has validated its ability to enhance testing efficiency, reliability, and flexibility in meeting evolving automotive quality control demands. This study contributes a robust automated testing framework that combines hardware‐software integration with intelligent algorithms, addressing critical gaps in existing solutions. The system's scalability and adaptability position it as a valuable asset for advancing ECU testing in the automotive industry, with future developments targeting AI‐driven predictive maintenance and expanded application scenarios. The abbreviations are shown in the “Abbreviations” section.