Battery Algorithm Engineer
- Cupertino, CA
Posted: Sep 9, 2019
Role Number: 200002932
Envision what you could do here. At Apple, we believe new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
- Good chemical understanding of Li Ion and related cell chemistries.
- Good Electrical engineering background to understand impact of load behaviors on battery performance estimation.
- Excellent experience in developing solutions and validating them using control theories like Kalman filters, state observers and others.
- Experienced in various test tools like battery testers, EIS and others to evaluate algorithm performance in the batteries.
- Experience in writing efficient codes to analyze large data sets.
- Experience with detailed battery and cell modeling a plus.
- Expertise in C programming in a Unix-derivative environment is highly desirable.
- Excellent skills in communication, problem solving and critical thinking.
- Should use advanced analytical interpretation and discretion to provide creative solutions. High level of mathematical and modeling capabilities to develop and verify algorithms, and expertise in numerical analysis.
- Expert in using modeling and simulation tools like Spice, MATLAB, Simulink and other specialized tools.
- Relies on data to explain technical decisions.
- Self-starter with excellent time management skills.
- Mentors and provides technical direction.
We are looking for an individual who will help be a part of a team developing new class of algorithms, architectures and products to improve the utility of batteries. In this position you will be expected to use a combination of advanced chemical and electrical engineering and mathematics to develop and code. You will develop and code algorithms to estimate State of charge and State of Power of battery systems. Algorithm to predict lifetime cell mechanical and electrical performance. Models to estimate and optimize cell and pack performance in design phase. Develop and test methods to evaluate such new algorithms. New functions to help further the active health management goals based on new behavioral discoveries on cells from Cell Engineering to address cell degradation. Help reduce the complexity of estimation algorithms so that they to implement in resource constrained computing platforms
Education & Experience
BSEE is required. MSEE/PhD is preferred.
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