Alberto Aniballi

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About

Alberto Aniballi is at the forefront of embedded artificial intelligence, specializing in tiny machine learning (TinyML) for battery management systems. His research centers on developing hardware-accelerated algorithms for precise state-of-charge estimation in lithium-ion batteries, a critical challenge for electric mobility, from motorbikes and cars to humanoid robots. Aniballi’s most cited work, "Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated" (2024), introduces a novel approach that deploys lightweight neural networks directly onto resource-constrained microcontrollers, enabling real-time, energy-efficient monitoring without cloud dependency. This contribution addresses the growing need for smarter, safer battery architectures in custom series-parallel cell configurations. With 14 citations in under a year, his paper is gaining traction among researchers seeking to bridge machine learning and edge computing for sustainable energy systems. Aniballi’s work is particularly notable for its practical impact on electric mobility, where accurate state-of-charge estimation extends battery life and enhances safety. As a rising voice in the TinyML community, he exemplifies how compact, hardware-aware models can democratize AI for critical infrastructure, making him a key figure to watch in the evolution of intelligent energy storage.