Ryan M. Edwards

Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice

We propose a new concept of Connected Vehicle Diagnostics and Prognostics (CVDP) to address some of the challenges in vehicle system fault diagnostics and prognosis, such as the diagnostics of unexpected new faults, and infrequent or intermittent faults. As an initial practice, this concept has been implemented in the vehicle design validation process at GM. This paper presents the implementation details, and some case studies.

Vehicle Design Validation via Remote Vehicle Diagnosis: A feasibility study on battery management system

In recent years, passenger vehicle product development faces great challenges to maintain high vehicle quality due to the proliferation of Electronics, Control and Software (ECS) features and the resultant system complexity. Quickly detecting and trouble-shooting faults of integrated vehicle systems during the validation stage in a key to enhancing vehicle quality. In this paper, we present a feasibilty study of Vehicle Design Validation via Remote Vehicle Diagnosis (VDV-via-RVD) and its application in the validation of vehicle battery management system. After the discussion of the advantages and challenges of VDV-via-RVD, some preliminary experimental results are presented to demonstrate the concept.

Remote vehicle state of health monitoring and its application to vehicle no-start prediction

This paper reports a recent effort at GM to develop a remote vehicle diagnostics service under a previously proposed framework of Connected Vehicle Diagnostics and Prognostics. An algorithm development methodology combining the physics-based approach and the data-driven approach is presented to identify, select, and calibrate failure precursors to predict vehicle no-start due to battery failures. Initial results based on real field data are promising. Also presented is a proposed implementation solution that supports the cost and performance optimization of remote vehicle no-start prediction.