ENHANCING THE ACCURACY OF ESTIMATING THE STATE OF X FOR BATTERIES
Join Us to Revolutionize Battery Management
OUR LOCATION
We are located in Seoul, South Korea and Shenzhen, China
BatterMachine is an early-stage startup aiming to solve problems in the energy sector using AI technology. Our focus is on innovating battery management through machine learning. Numerous reports, including those from McKinsey, estimate the current market size for battery management at around 7 billion dollars, expected to grow to 30 billion dollars by 2030. These figures are based on scenarios without AI application; with AI, the market size could increase manifold. For instance, Google's DeepMind has discovered 380,000 new materials. Additionally, numerous news sources have reported that the chemical sector is among those most impacted by AI, suggesting that AI-integrated technologies will bring unprecedented productivity improvements in the energy sector. Traditional companies may struggle to maintain competitiveness due to internal resistance, political issues, and other factors.
Currently, we possess the battery machine learning framework technology (https://www.sciencedirect.com/science/article/pii/S2666546823000915 ) and technology for enhancing safety through collaboration between the battery management system in cars and the cloud (https://www.mdpi.com/2313-0105/9/5/264 ). Based on these core technologies, we plan to develop a machine learning framework for battery management algorithm developers, along with various applications (for electric vehicles, chargers, robots, drones, ESS, etc.) in battery management apps.
The market for AI-based battery management is expected to experience substantial growth over a long period. However, due to technical barriers, there are very few players in this field, most of whom are European companies that have received initial funding of around 100 million euros. Therefore, it seems that foreign investors already see market potential in AI-based battery management. From my previous experience as a director conducting technical research, I am convinced that battery management using machine learning is a definite technical direction. This belief, coupled with my research in this field over the last three years, which I believe has yielded competitive research results, is one of the reasons I decided to start a company. The market is expected to grow beyond 30 billion dollars by 2030, and I believe our technology is highly competitive. If we move quickly, there is a high possibility of leading the market and becoming a unicorn.
However, since this market is in its very early stages, there are many technical challenges to solve, requiring engineers or scientists who enjoy tackling new challenges and solving difficult problems. We are looking for individuals who want to be players, not just observers, in the disruptive innovation brought about by AI in the domains of batteries, energy, and electric applications.
JOIN OUR TEAM
Revolutionizing the Future of Battery Technology
At BatterMachine, we are always looking for talented individuals to join our team and help us enhance the accuracy of estimating the state of X for batteries. If you are passionate about technology and sustainability, and want to be part of a team that is making a difference, we would love to hear from you.
MACHINE LEARNING
Seoul, South Korea
(Background in Machine Learning): Initially, no prior knowledge of batteries is required. The primary focus will be on researching the latest developments in Machine Learning and swiftly applying these techniques to projects and real-world cases. This will involve collaborative research and development in predicting the internal state of batteries
BATTERY ALGORITHM
Seoul, South Korea
(Background in SOX algorithms): Must possess knowledge of the internal state of Li-ion batteries (SOC, SOH, SOE, SOP, RUL, etc.). The focus will be on preprocessing, feature selection, and labeling for applying machine learning algorithms. This role involves collaborative research and development in predicting the internal state of batteries.