Foundational ML & IoT Guidance Accelerates EV Development
Industry
Climate Tech,
Electrical Engineering,
Battery Technology, AI
Tech & TOOLS
AWS IoT Greengrass,
Quicksight, IoT Core and IoT Analytics, Docker
Teams & Services
AI/ML Engineers,
IoT Specialists,
Cloud Architects
milestones
Two-Year Blueprint for
ML & IoT, Infrastructure
Roadmap, Implementation,
ML Optimization
Industry
Climate Tech,
Electrical Engineering,
Battery Technology, AI
Tech & TOOLS
AWS IoT Greengrass, Quicksight, IoT Core and IoT Analytics, Docker
Teams & Services
AI/ML Engineers,
IoT Specialists,
Cloud Architects
milestones
Two-Year Blueprint for ML & IoT infrastructure Roadmap Implementation ML Optimization
ElectricFish builds AI-powered electric vehicle chargers with integrated energy storage to keep EVs moving and the grid stable. The challenge? Making it all work reliably at the edge and in the cloud.
The situation
ElectricFish is a climate technology company specializing in grid-integrated, rapidly deployable energy storage for electric vehicles. Their flagship product is the 350SquaredTM, an energy storage system equipped with two ultrafast charging ports and capacity for up to 48 hours of backup energy. Equally significant to their business is their AI-enabled software platform Stargazer, which manages their network of storage-integrated smart chargers to optimize both fast-charging and grid services, enhancing energy reliability and efficiency.
The challenge
To run its Stargazer platform, ElectricFish needed local inference on the edge that operates in a fault-tolerant scenario as well as the cloud. Loka collaborated with ElectricFish to develop and validate the initial cloud deployment of Stargazer, leveraging AI to enhance energy resilience and optimize operational efficiency.
Under the AWS Data Acceleration Program (DAPP), Loka worked with ElectricFish to design a scalable machine learning infrastructure tailored to their 350squared system. Our work involved creating an ML and IoT architecture using AWS IoT Greengrass, IoT Core and IoT Analytics, which enabled ElectricFish’s EV chargers and batteries to operate effectively across both edge and cloud environments.
The solution
Loka laid a solid foundation for ElectricFish’s Stargazer deployment, enabling rapid scalability, AI-driven optimization and IoT integration. With funding from the California Energy Commission’s Realizing Accelerated Manufacturing and Production (RAMP) program, ElectricFish is further building on this innovative solution to support California’s grid resilience and renewable energy objectives.
What we delivered
Customized Architecture: Loka provided ElectricFish with a tailored ML and IoT architecture that supports predictive analytics, real-time monitoring and automated insights via AWS IoT and Quicksight.
Initial Implementation: We outlined and helped implement a roadmap that included setting up edge devices, Docker environments and data pipelines to AWS IoT, enabling seamless real-time data flow and analytics for ElectricFish’s system.
AI-Driven Optimization: By optimizing MLOps practices, we helped ElectricFish achieve a scalable and resilient infrastructure, aligning with AWS best practices to maximize their Stargazer system’s potential.
Project results
Accelerated time to market by 6-12 months.
Supported implementation of edge and cloud implementation roadmap for real-time analytics.
Optimized ML to build a scalable, fault-tolerant infrastructure.
Created a customized ML and IoT architecture that ElectricFish followed for two years and counting.
Loka’s understanding of AI and AWS IoT solutions gave us a blueprint for growth that we’ve been following for the last year. Their support ensured that we quickly established a reliable and resilient foundation for our grid-integrated energy storage solutions.
