Development of a HIL Based Methodology for Over-The-Air Updates Validation

Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent e integration of Hardware-in-the-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of updates before they are applied in the field. We present a structured approach of combining HIL systems with OTA workflows, wherein a virtual vehicle environment is simulated in real time to reproduce a replica of the actual operating conditions for the target ECU under test. The OTA update is injected through a simulated or physical OTA backend and transmitted to the control unit interfaced within the HIL loop. This setup facilitates the emulation of update scenarios including firmware re-flashing, configuration updates, security checks, and rollback mechanisms under controlled, observable conditions. Key advantages include the ability to inject faults, monitor system response, verify communication integrity, and perform automated regression tests without risking physical prototypes or production vehicles. This integration of OTA and HIL not only enhances pre-deployment validation but also lays the foundation for continuous development and in-field update strategies in connected EV platforms. The proposed framework can be scaled to multiple ECUs and integrated with CI/CD pipelines for automated nightly testing. Future work will explore combining this setup with Digital Twin environments and Machine Learning-based anomaly detections.