Currently， the power Battery Management System （BMS） is confronted with dual challenges of dynamic working condition adaptability and full life cycle management. Traditional methods have a state estimation error of more than 15% under extreme working conditions and an accuracy rate of early health state prediction of less than 80%. This article integrates intelligent control and energy efficiency optimization technologies. Through deep learning，edge computing and digital twin technologies， it promotes the upgrade of BMS from "passive protection" to "active optimization". Research shows that intelligent technologies can increase the efficiency of low-temperature charging by more than 20%. Intelligent algorithms such as model predictive control，combined with digital twin models，can enhance the charging and discharging efficiency by 15% to 20%. This technology can provide technical support for the research on the reliability and economy of new energy vehicle batteries.