To effectively mitigate the error sources identified in the article, the integration of emerging technologies like digital twins, artificial intelligence (AI), and sensor fusion can be highly beneficial.
Digital Twins: By creating digital replicas of the physical batteries, digital twins can simulate various operating conditions and predict potential errors in SOP estimation. This allows for proactive maintenance and optimization strategies to be implemented, reducing the impact of errors on battery performance.
Artificial Intelligence: AI algorithms can analyze large amounts of data from battery sensors and historical performance to identify patterns and trends related to error sources. Machine learning models can then be trained to predict and correct errors in SOP estimation in real-time, improving the accuracy of the system.
Sensor Fusion: Integrating data from multiple sensors that measure different aspects of battery performance can enhance the overall accuracy of SOP estimation. By combining data from temperature sensors, current sensors, voltage sensors, and more, a more comprehensive understanding of the battery's state can be achieved, reducing the impact of individual sensor errors.