A Probabilistic Minimax Approach for Machine Learning-Based Optimization of Hybrid Energy Storage Systems for Electric Vehicle Charging
Keywords:
Bayesian Classifier, Feature vector, Prior Probability, Likelihood, Posterior Probability.Abstract
The increasing adoption of electric vehicles calls for effective and reliable energy management system that enhance performance, prolong battery lifespan and boost overall system efficiency. This research article represents an innovative control method for a hybrid energy storage system that integrates a battery with a supercapacitor, employing a Proportional Integral controller alongside a Bayesian classifier for smart energy allocation. In dynamic scenario, the PI controller maintains system stability by controlling the DC bus voltage. Using real time vehicle and load data, a Bayesian classifier is utilized to classify operational states. This classification makes it possible to share power appropriately, which lower battery stress and boosts energy efficiency. By efficiently managing power demand scenario the proposed control approach improves transient response, lowers voltage fluctuations, and prolongs batter life. According to the simulation results a Bayesian classifier perform better than conventional control methods in terms of system reliability, response time and energy economy. This research article shows how combining probabilistic machine learning technique with traditional control approaches can enhance energy management in electric vehicles, offering scalable and intelligent solution for electric vehicle systems of the future.
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