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Tesla Battery Algorithms Engineer Vehicle Controls United States, California, Palo Alto 144715668
06.04.2025
What You’ll Do What You’ll Bring
- Use a variety of cell and battery pack models, fleet data, and laboratory test data to create state-of-the-art feedback control and estimation algorithms for Tesla’s high voltage battery packs
- Own a subset of the algorithms that run on the BMS in millions of Tesla vehicles worldwide – these algorithms span cell capacity, impedance, energy, fast charging, and degradation estimation to diagnostics of sensors, cells and battery pack components
- Research, design, prototype, and prove functionality of novel battery estimation and control approaches to maximally extract performance from our cells
- Create physics-based models of cell and pack elements of the appropriate fidelity to analyze problems; use these models to design estimation and control algorithms
- Prototype and test your algorithms, and work with Firmware Engineers where required to productionize your algorithms in our codebase
- Build fleet data pipelines and tools to monitor performance of your algorithms in our fleet, identify areas for improvement, and test efficacy of proposed solutions
- Work cross-functionally with Cell Modeling, Cell Qualification, Firmware, and Battery Safety organizations to advance state-of-the-art of BMS algorithms at Tesla
- Degree in Mechanical Engineering, Electrical Engineering, Materials Science, or equivalent in experience and evidence of exceptional ability
- Excellent background in linear systems analysis, state-estimation, and first principles design and analysis of control systems
- Proficiency with analyzing large datasets and algorithm performance in MATLAB/Python
- Excellent understanding of the electrochemical physics of lithium-ion cells, and experience with modeling these physics across a range of fidelities
- Ability to ground-up design, analyze, and implement battery management algorithms in real-world applications, demonstrated by strong examples
- Strong understanding of battery diagnostics and anomaly detection algorithms
- Demonstrated ability to collaborate cross-functionally, work hands-on, and execute on open-ended projects in a fast-paced, resource-constrained environment
- Basic proficiency with embedded software development in C/C++
- Experience productionizing physics model-based fast-charging algorithms, preferred
- Experience in executing tests on cells in a laboratory environment, preferred
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