Technical Specialist, Battery Algorithms and Digital Twin, Ford Energy

About the company

Ford Motor Company

Automotive · 10,001+ employees

What you'll do

• Production-grade state estimation: SOC, SOH, Dynamic OCV, and power/energy capability estimators that meet defined accuracy, robustness, and compute targets across temperature, aging, and partial-cycle duty.
• A deployable digital twin architecture: a defined edge (BMS/EMS) and cloud split, with data, model, and interface specifications agreed with BMS, EMS, and Digital Services.
• A field-data feedback loop: a working pipeline from operating systems to model recalibration, with drift monitoring and a release process for algorithm updates.
• Prognostics the business can use: degradation and Remaining Useful Life (RUL) models with quantified uncertainty that support warranty, performance guarantees, and dispatch decisions.
• Early-warning diagnostics: fault and anomaly detection that contributes to the safety case, with false-alarm and missed-detection targets set and measured.
• A validation framework: test-to-field correlation, SIL/HIL coverage, and release criteria the whole organization trusts.

What you need

• Master's degree with 8+ years of experience, or PhD with 5+ years, in Electrical, Mechanical, Chemical, or Controls Engineering, Materials Science, or a related field.
• Hands-on experience developing and deploying battery algorithms on production systems (vehicle, stationary storage, or consumer device), meaning code or models that ran on real hardware or in a live fleet, not only in simulation. Includes at least two of: SOC, SOH, state of power, RUL/degradation, thermal estimation, or fault detection.
• Working knowledge of equivalent-circuit models and Kalman-family observers, and familiarity with reduced-order electrochemical or semi-empirical aging models.
• Solid understanding of Li-ion physics: electrochemistry, thermal behavior, and aging mechanisms (e.g., SEI growth, lithium plating, loss of active material).
• Proficiency in Python and MATLAB/Simulink, with working familiarity in C/C++ or another production language.
• Experience calibrating and validating models against cycling, characterization, and ideally field data, including quantifying estimation error.
• Technical leadership across functions without formal authority, and the ability to present trade-offs clearly to executives and non-specialists.
• PhD in a battery-relevant discipline, or a publication/patent record in state estimation, aging, or controls.
• Algorithms running in a deployed fleet: over-the-air updates, model-drift monitoring, and learning from field returns and warranty data.
• Stationary storage and LFP experience, including SOC/SOH estimation on flat OCV curves and long calendar-life behavior.
• Model-based design, auto-code generation, and SIL/HIL validation of BMS software.
• Machine learning for SOH/RUL or anomaly detection, with judgment on when physics-based, data-driven, or hybrid methods fit best.
• Digital twin or time-series data platforms (cloud, streaming, MLOps) and edge-to-cloud architecture.
• Knowledge of BESS safety standards and practices (e.g., UL 9540/9540A, IEC 62619, NFPA 855) and thermal-runaway early detection.
• Experience linking degradation models to warranty, levelized cost of storage, or dispatch optimization, and familiarity with PCS/EMS interactions and grid-service duty cycles.
• Prior experience in an OEM battery organization, or standing up an algorithm team or platform from scratch.
• Location &amp; Travel<ul><li>Location: Dearborn, MI / Hybrid
• Reports to: Manager BESS Platform<br>&nbsp;

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