Battery health and energy saving are crucial factors to consider for any electric vehicle powertrain systems. A battery that fails unexpectedly can be a major inconvenience, leading to huge performance degradation, costly repairs and lost time. To address this issue, AI-powered algorithms have been developed to monitor battery State of Health and operating conditions in Real-Time. These algorithms provide alerts for battery issues and even predict potential failures before they occur.
Our battery performance and health predictor algorithm has been developed to release power and usable energy increase. Implementing advanced State of Health estimation and reliable safety logics, this AI-based system makes the Electric Powertrain Management System more powerful.
AI algorithms need a huge amount of data to well train the models. Indeed, data acquisition and management for AI based applications are usually critical issues for the model development. This is the reason why, as explained in our recent news, R&D engineers’ efforts have been firstly dedicated to the implementation of reliable and accurate models. In our approach, simulation results can be a source of data to train the AI model, improving and speeding up its training process.
A sneak peek of our Battery Performance Algorithm application is here reported. In the experiment performed, a battery pack internal resistance variation has been triggered. The algorithm analyses the input signals during a specified time buffer and from that moment on, it is able to estimate an overall warning index related to the operating and health conditions of the battery pack.
Even if the battery electrical and thermal features continue to be inside the acceptable range, our AI algorithm detects the anomaly operation and it is able to trigger both alarms and safety logics if needed.
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