  Lithium-ion batteries are playing a key role in the sustainable energy
transition. To fully exploit the potential of this technology, a variety of
modeling, estimation, and prediction problems need to be addressed to enhance
its design and optimize its utilization. Batteries are complex electrochemical
systems whose behavior drastically changes as a function of aging, temperature,
C-rate, and state of charge, posing unique modeling and control research
questions. In this tutorial paper, we provide insights into three battery
modeling methodologies, namely first principle, machine learning, and hybrid
modeling. Each approach has its own strengths and weaknesses, and by means of
three case studies we describe main characteristics and challenges of each of
the three methods.
