Battery State Estimation Senior SW Engineer

General Motors•Milford, MI

3d•Hybrid

About The Position

Design, develop, and productionize robust battery state estimation algorithms for SOC, SOH, and SOP using physics-based, model-based, and hybrid data-driven estimation techniques across multiple battery chemistries. Develop observers and filters (e.g., equivalent circuit and electrochemical informed models, Kalman filter based approaches) that remain accurate across temperature extremes, power transients, sensor noise, and battery aging. Incorporate calendar and cycle aging effects into estimation logic so outputs remain truthful throughout the battery lifecycle and across chemistries and pack architectures. Implement estimation algorithms as production quality embedded software in C/C++, meeting GM standards for safety, cybersecurity, and coding discipline, including MISRA compliance. 10% domestic travel required. Hybrid Work Policy - 3 days In-office, 2 days remote - Must be able to report to local office.

Requirements

Bachelor's degree in Mechanical Engineering, Electrical Engineering, Chemical Engineering, Computer Engineering, or related field of study
Five (5) years of experience as a Software Engineer, Design Engineer, Automotive Engineer, or related occupation.
Two (2) years of experience with: Delivering embedded software for high voltage battery systems
Two (2) years of experience with: SOC, SOH, and SOP estimation algorithms deployed in vehicles
Two (2) years of experience with: C/C++ for embedded systems development
Two (2) years of experience with: Agile/Scrum environments and cross functional automotive programs
Two (2) years of experience with: Leveraging Battery Data to generate actionable insights that improve battery performance, reliability, and lifecycle characteristics.

Responsibilities

Design, develop, and productionize robust battery state estimation algorithms for SOC, SOH, and SOP using physics-based, model-based, and hybrid data-driven estimation techniques across multiple battery chemistries.
Develop observers and filters (e.g., equivalent circuit and electrochemical informed models, Kalman filter based approaches) that remain accurate across temperature extremes, power transients, sensor noise, and battery aging.
Incorporate calendar and cycle aging effects into estimation logic so outputs remain truthful throughout the battery lifecycle and across chemistries and pack architectures.
Implement estimation algorithms as production quality embedded software in C/C++, meeting GM standards for safety, cybersecurity, and coding discipline, including MISRA compliance.