The objective of this thesis is to simulate electrochemically a set of batteries, based on the configuration of the customer, then to develop and apply machine learning and neural network algorithms to forecast the performance and behaviour of batteries. The work begins with the data acquisition phase, executed through sensors and connectors from the client, and managed through a proprietary platform capable of real-time graphical display, data analysis tools, as well as integration and management capabilities for machine learning solutions. Subsequently, the data undergoes a cleaning and preprocessing phase. Initially, the quality of the acquired data is assessed; however, the raw data obtained from the sensors are typically noisy and contain gaps due to the client's inconsistent broadband connection.