Battery management systems are critical for electric vehicles, as they directly impact performance, safety, and how long the battery lasts. One of the biggest challenges is accurately estimating the State of Charge (SoC) - essentially how “full” the battery is. Energy management in electric vehicles relies heavily on robust tracking of power reserve. Traditional monitoring strategies are unable to handle dynamic and volatile behaviour of energy storage devices and do not provide for diverse operational scenarios. Our work utilized a high-end adaptive learning approach called PPO, which learns from operational data directly without structural assumptions beforehand. The approach capitalizes on real-world transportation data (electric factors, distances, and timing parameters) in envisioning reserve measurement as a perpetual challenge to learn. We constructed a real-time simulation world in which an AI system engages with virtual power cells to come up with maximum evaluation strategies. The architecture contains two networks of computation: a primary estimator which calculates current reserves of energy as a function of situation inputs, and a feedback evaluator providing prediction accuracy ratings to enhance the performance of the main network. This learning is obtained through gradual interaction with the simulated environment. Experimental validation indicated that our adaptive approach demonstrates improved performance under various operational conditions and addresses significant limitations inherent in existing methods. Future directions include enhancing system resiliency, extensibility, and tractability. Our findings suggest that adaptive learning frameworks present a promising direction for the development of power management technologies for electric mobility solutions.
Mortabit et al. (Mon,) studied this question.