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High Performance, reduced sensor model uses AI in BMS for Electric and Hybrid Vehicles
Deep Learning AI solution takes advantage of ST SPC5-STUDIO to accurately predict Li-Ion State of Health and Charge, using less sensors and optimized CPU resources
The presented platform involves a Machine Learning approach and demonstrates how data-driven approaches meet the challenge of reaching high accuracy while being light and computationally efficient. The architecture of the solution ensure the easiness of use and stands as a ready to use tool for R&D forces designing the next generation of AI-driven Battery Management Systems and constantly dealing with timings and budgetary constraints.