Controls Engineer - Propulsion and Energy Controls

Ford Ford · Auto · Dearborn, MI +1 · Research and Advance Engineering

Controls Engineer for Ford's Research and Advanced Engineering team, focusing on developing control systems for electrified vehicle powertrains. The role involves algorithm design, simulation, virtual and physical validation, dynamic system modeling, and integrating connected vehicle data with machine learning models for energy management. It emphasizes applying modern software practices and AI integration for future vehicle programs.

What you'd actually do

  1. Develop, simulate, and implement advanced embedded control algorithms (including feedback controls, state estimation, and Model Predictive Control) to optimize vehicle efficiency, performance, and drivability.
  2. Leverage Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) environments to rigorously test and validate your designs before they hit the road.
  3. Utilize physics-based and data-driven CAE models of electric motors, batteries, engines, and mechatronic actuators to analyze complex system interactions and optimize parameters.
  4. Collaborate with systems engineering and cloud teams to integrate connected vehicle data (traffic, route, weather) and machine learning models for advanced energy prognostics.
  5. Document and present your research to global engineering partners, facilitating the seamless transfer of advanced technology into production vehicle programs.

Skills

Required

  • M.S. or equivalent in mechanical, aerospace, electrical, systems engineering or similar field with a control systems focus
  • 6+ years of experience in dynamic systems modeling and real-time controls development for automotive or similar complex electromechanical systems
  • 6+ years in roles requiring understanding of automotive powertrain (including hybrid electrical vehicle) systems and controls, and vehicle dynamics with fundamental knowledge of general dynamics, hydraulics, electric machines, batteries, internal combustion engines, and mechatronic actuators
  • 6+ years in roles demonstrating the ability to develop controls software for embedded targets using C or C++
  • 6+ years in roles demonstrating the ability to use Matlab/Simulink or Python for system modeling, simulation, scripting, and data analysis
  • 6+ years of experience working in Linux, Unix and Windows environments
  • 6+ years of experience with unit testing platforms for tools development (pytest, Google Test, Ceedling)
  • 6+ years experience with standard systems engineering tools including boundary diagrams, fault tree analyses, FMEAs, P-diagrams, etc.
  • 6+ years in roles demonstrating excellence in problem-solving (with a passion for new technology development)
  • 6+ years of experience in roles requiring effective communication, documentation, and interpersonal skills in a team environment and the ability to work well with others as part of a diverse global team

Nice to have

  • Ph.D. or equivalent in mechanical, aerospace, electrical, systems engineering or similar field
  • 3+ years of experience in distributed systems engineering including requirements management, function decomposition and allocation, failure mode avoidance, robustness analysis, and system validation and verification
  • Experience with modern build systems (Bazel, Gradle) and continuous integration and continuous delivery systems (Jenkins, GitHub Actions)
  • Experience with longitudinal controls for xEV systems (series hybrid, parallel hybrid, mild hybrid, battery electric, fuel cell)
  • Familiarity with ISO26262 engineering tools including Hazard Analysis, Functional Safety Concept, Safety Requirements Specification, Functional Safety Requirements, Technical Safety Requirements
  • Hands-on experience with distributed system prototyping, troubleshooting, and commissioning
  • Experience using Matlab/Sim

What the JD emphasized

  • delivering propulsion systems for electrified vehicles of the future
  • develop control systems for our newest powertrain technologies
  • developing technologies for future vehicle programs
  • integrate connected vehicle data (traffic, route, weather) and machine learning models for advanced energy prognostics

Other signals

  • integrating machine learning models
  • AI coding assistants
  • predictive, self-optimizing energy management systems