Battery Cell Electrochemistry Analysis Engineer

General Motors•Warren, MI

2d•Hybrid

About The Position

Global Virtual Engineering (GVE) at General Motors is looking for driven individuals to become Computer Aided Engineers (CAE) and lead the future of virtual engineering. As a company we are focused on using virtual tools as the primary driver to enable speed and built in quality. This position is a highly technical, interdisciplinary role focused on advancing the future of virtual engineering by developing, integrating, and deploying multi-physics battery cell and pack models. The role combines electrochemical modeling, data science, battery design optimization, and cross-functional technical leadership to deliver models and tools that guide electrochemical, thermal, mechanical, electrical, and safety-related design decisions for electric vehicles. This involves characterization & optimization of battery cell performance for vehicle range, charging capability, and battery aging effects.

Requirements

BSE Chemical, Electrical, Computer Science, or Mechanical Engineering Required; Chemical Engineering preferred.
Computer Science
Strong understanding of modeling processes and tools especially physics based degradation mechanisms and transport phenomena
5+ years of experience in industry executing battery modeling & analysis

Nice To Haves

Advanced degree (MS or greater) in Chemical Engineering, Electrochemical Engineering, Materials Engineering, Mechanical Engineering, or Electrical Engineering preferred.
Solid understanding and working knowledge of fundamental physics, equations and numerical methods underpinning all software used

Responsibilities

Develop, maintain, and calibrate physiochemical, electrochemical, thermal, mechanical, and safety models for current and future battery chemistries.
Use COMSOL, MATLAB/SIMULINK, SABER, Python, and PyBaMM to implement multi-scale and multi-dimensional cell models.
Collaborate with test teams to enhance test protocols to support model development.
Participate in development of material test methods and provide electrochemical parameter database.
Incorporate cell performance variation, aging mechanisms, and thermal behavior into simulation frameworks.
Document models, assumptions, and intended model usage.
Support electric vehicle execution by releasing models integrated into full vehicle co-sims supporting calibration, diagnostics, battery state estimation, controls development and warranty.
Develop methods to gain data insights applying statistics and machine learning to cell performance data.
Improve analysis efficiency by applying variation and statistical methods paired with ML learning.