Artificial Intelligence is transforming every industry, and renewable energy is no exception. State-of the-art machine learning capabilities (e.g., deep learning) from the likes of Google, Microsoft and AWS, are readily available over the cloud to businesses of large and small. By applying such powerful technologies to ever growing varieties and volumes of data, these organizations can easily analyze the past, optimize the present and predict the future. In doing so, they are using AI in transforming their own businesses by, for example, improving supply chain transparency and efficiency, reducing product defects and downtime, and increasing product safety and salespeople productivity, and improving customer experience.
Energy storage holds the key to overcoming the intermittency challenge of solar and wind and, therefore, to the ultimate future of renewables. Fire safety, along with high cost and limited cycle-life and c-rates are considered among top barriers to the broad adoption of battery technologies. While battery storage is a living system, it is commonly treated as a black box. As such, nobody knows what’s happening inside the box, which is what makes batteries unsafe.
With our unique, AI-embedded system design, we turn this black box into a clear box. In doing so, we can continuously analyze subtle changes occurring in battery cells, and infer about the real-time safety status of the battery. We can predict the battery fire risk long before such an event would actually occur. In that case, the system recommends replacing questionable battery modules to prevent potential fire hazards as part of routine maintenance.