We are Universe Energy and we make batteries eternal.
Our mission is to make the next billion batteries out of used ones and reverse the need for mining. The problem is that batteries are challenging to reuse today because supply is constrained by expensive shipping, non-standard testing, and dangerous disassembly by hand. We streamline the supply of 2nd life batteries by scanning how good they still are and dismantling them. We automate this process for all types of EV batteries using a visual "brain scan" on the battery and robots to dismantle them at 50% of the cost. Once we have thousands of batteries flowing through our disassembly line, we’ll turn batteries into cheap grid storage packs. This prevents the need for new battery manufacturing, mining raw materials, and postpones recycling. Our vision is to become the world’s largest maker of clean batteries by making the next billion batteries from used ones. It will power a truly clean energy revolution and reduce 7 Gton of CO2 by 2050.
Before Bonobo disassembles the battery from pack to module, State-of-Health data is measured to determine the age, state, and value of the modules and cells in the battery pack. Therefore, it's important to characterize battery cells & their materials, determine degradation mechanisms, optimize cell performance, and estimate the lifetime of 2nd-life batteries. The material data is then collected, stored, and translated into a state of health, lifetime prediction, and battery valuation. It is compared with electrical testing data. The data informs the best next step for its 2nd life, which is either remanufacturing, repurposing, or recycling.
You will architect, develop and deploy the software & data platform to test, process, and measure the State of Health of batteries to collect from non-intrusive test methods like ultrasounds, X-rays, and CT scans. It merges and compares data from the battery management system and lifecycle testing from various sources. You will design and build the algorithm, pipeline, and infrastructure that analyzes, decides, and predicts data on battery health, lifetime, and residual value. This data will drive what to do with the battery next, like repairing, repurposing, or preparing for recycling.
How you will contribute
- Develop testing methods to obtain State of Health, battery value, and lifetime prediction through electrical, software, and non-intrusive pathways.
- Characterize battery cells, materials, determine degradation mechanisms and analyze cell performance, and estimate the lifetime of 2nd-life batteries.
- Obtain the data through electrochemical methods and non-intrusive testing methods like ultrasonic through to quickly characterize batteries.
- Merge, process, and compare data sets from testing, BMS, electrical testing methods, and ultrasound, X-ray and CT sources.
- Develop analytic and predictive algorithms on standardized State of Health, lifetime, and valuation models to decide on the next step in the battery's lifetime.
- Process test data, optimize model parameters, and develop lifetime prediction models for all types and scenarios of incoming 2nd life batteries.
- Use data to make decisions on whether to remanufacture, repurpose, or recycle the battery cells. Identify cells to maintain, replace, or discard.
- Work with partners in battery testing hardware and software & data vendors to launch first products in short development cycles.
The skills & experience that you bring
- At least an M.Sc. in Chemical Engineering, Materials Science, Chemical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering, Physics, Applied Mathematics, Statistics, Machine Learning or equivalent.
- 3-5 years Experience with lithium-ion batteries. Expertise in theory and application of electrochemistry. Apply battery testing methods and equipment such as electrical impedance, capacity, ultrasonic, X-ray & CT scans.
- 3-5 yrs experience in degradation mechanisms of batteries and battery materials. Strong background in numerical methods, especially in optimization and numerical solutions of ODEs and PDEs.
- 3+ yrs experience in developing empirical or physics-based models of dynamic systems, preferably in lithium-ion batteries with aging mechanisms. Hands-on experience with processing large datasets.
- 3+ yrs experience in developing production-ready software with proven skills in MATLAB, C/C++, Python, and related software tools.
- 3+ yrs experience in data science algorithms in Python, SQL, and Scala. Developed data & machine learning tools in Torch/TensorFlow.
- 1+ yrs experience in Non-intrusive testing methods applied to cell development like ultra-sonic, X-ray & CT scans in lab, engineering, and manufacturing environments.
How to hit a homerun
- Knowledge of battery engineering, electrical engineering, material science, and chemistry applied to the state of health, lifetime, and valuation models.
- Knowledge of server hardware to scale, such as data center network fundamentals, OS imaging, provisioning & distribution, and configuration deployment