Net
Hidden Markov Models (HMM) are a type of probabilistic graphical model applied to modelling the sequences of observations or events in areas such as speech recognition, natural language processing, text analysis, computational biology and other areas. EatonNet applies HMM to model temporal sequences for electrochemical processes, in particular for battery and fuel cell systems. This enables EatonNet to more accurately predict the battery's future states, including the energy level, enabling improved control and optimization of battery operations. Additionally, EatonNet can be used to analyse complex temporal data and identify important correlations, allowing users to make more informed decisions and develop better strategies for battery operations. Finally, EatonNet can be used to detect anomalies in battery behaviour and take necessary early preventive actions.