BMS multi-cell synchronous control strategy based on AI predictive equilibrium
CHEN Han
Abstract：In response to the problems faced by the battery management system(BMS)in the collaborative control of multiple cells,such as lag in balancing,low energy utilization efficiency,and insufficient system robustness,a multi-cell synchronous control strategy based on artificial intelligence(AI)predictive balancing is proposed.The strategy is centered on the concept of'prediction first,synchronous control',and constructs a control framework that integrates a long short-term memory network(LSTM)prediction model and multi-objective optimization algorithm.The results show that by intelligently sensing the evolution trend of cell states,the strategy has achieved a paradigm shift from traditional passive balancing to active predictive balancing.The proposed algorithm can significantly improve energy utilization efficiency and system robustness,effectively reduce energy consumption caused by balancing lag,providing key technical support for energy-saving and emission reduction of the power battery system of new energy vehicles.Through real-time and hardware feasibility verification,the strategy lays the foundation for industrialization implementation and provides a new paradigm for battery health management throughout the entire life cycle.
Keywords：AI predictive balancingbattery management systemmulti-cell synchronous controlLSTM predictive modelenergy utilization efficiency
Publication Date：2026-02-25
Online Publishing Date：2026-03-31(First online date of this platform, not the publication date of the document)
Pages：4( 25-28 )