AI-powered digital twin models have the potential to improve electric vehicles' battery range, efficiency, safety, and lifetime. This could lead to multiple new applications.
As batteries continue to be the most expensive element in an electric vehicle (EV), AI-powered digital twins have a high potential to improve estimations of the battery’s state of health (SOH) and state of charge (SOC) for improved efficiency, lifetime and cost. Battery digital twins adapt to ongoing changes in battery health due to operating conditions and provide updated figures back to the Battery Management System for continuously improving control decisions.
Carmakers can use the technology to provide the driver insights, such as range and speed recommendations. In addition, adaptive battery control can improve the battery’s performance and safely extend its lifespan. It also has the potential to reduce warranty costs for the carmaker.
Another potential application of the digital twin technology is EV fleet management, providing fleet operators with invaluable usage insights, such as vehicle charging times and battery predictive diagnostics. Battery care centers can also use this in-depth information to reduce downtimes with rapid diagnostics, and EV charging station operators can effectively optimize their charging service and energy efficiency.
Photo by Markus Spiske on Unsplash
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