Battery Cell Engineer | Caleb

Caleb interned as a cell engineer at Tesla in Palo Alto, California, USA. Keep reading to learn more about how he got there, what his job is like, and what skills to develop to land a similar position!

How did you get to where you are today?

Being persistent. Working for a variety of companies is important during your co-op. I always try to work hard at each co-op experience until I’ve left something behind that will benefit the group for years to come. It’s these projects, applications, tools, experimental rigs, SOPs, etc., that set you up with excellent interview topics for your next position. Bringing up this experience leaving lasting impact at each previous coop has helped me to be successful in my interviews and continue to receive offers to positions I truly want. School and marks matter, but what matters more is that you treat your previous coops as interviews for the next one. Take advantage of references and positive reviews related to your past work to project yourself to better and better roles.

What are the main responsibilities of your job? What project(s) have you taken on?

The main responsibility of my job as a cell engineer at Tesla is vendor management. I work to qualify cells that Tesla wishes to purchase and use in their products. This means designing test plans which quantify the performance of the cells in different conditions. Imagine simulating the operation of a battery at the (close-to) worst-case scenarios it might see in the field, from 40°C climates with reckless drivers to -20°C climates where consumers are traveling on long road trips. It’s important the cell’s performance is quantified at these conditions so that:

1. Tesla can justify that the cells meet the specifications of the electric vehicle or product, and
2. Hardware and firmware that supports the battery can accurately read battery life and protect the cells from degradation.

On the other side of the test plans, hundreds of thousands of data points from cycling, storing and abusing cells are returned to Tesla. This data must be processed, analyzed and evaluated against requirements for cell performance. More than half of my work is in programming data pipelines, scripts, macros, etc. for processing this data as efficiently as possible and presenting a story to my team from the test results. Examples of some questions that I’ll answer using the data I’ve analyzed are:

• Are we doing too much or too little to quantify the cell?
• Are there critical failures present at certain conditions?
• Do we have statistical evidence of a cell meeting said requirement?
• Are we accurately quantifying the limits of the cell at extreme conditions?

One other task I work on is test tracking. As opposed to analyzing data returned from the tests, the test progress should also be quantified so that the greater team knows how far along we are on a cell program, and where we can best allocate resources to ensure we meet our program deadlines. Working with labs and vendors to define test checkpoints and quantify their lead time is one way to achieve this. Then, many data points for similar tests across many different labs must be aggregated and presented in dashboards so that users responsible for different requirements can check in on their test status. This work has opened up a number of projects in front and back-end programming generating auto-updating, interactive dashboards with the tables and plots we need to accurately summarize test status across teams.

What skills do you need for this job? Any tips for getting a similar position to yours?

Programming, people, and electrochemistry skills. This position offers a somewhat unique balance of data analysis/programming skills and fundamental electrochemistry knowledge. Even if you don’t like programming (or aren’t great at it like me), you will greatly benefit by picking up programming/automation tips and tricks throughout your coops. I’ve learned so many useful tools and programming tricks in this position and in others that help to improve work efficiency dramatically. Data analysis tools like SQL queries, Tableau front-end displays, LabView UI, Excel VBA macros, Python pandas, and MATLAB scripts can all contribute to your work in so many different positions. I’ve used some in the past and I’ve learned some at Tesla, but you can really leave a lasting impact on your team or research group if you can unlock these skills for your team and remove the need to manually complete repeated tasks.