当前动力电池管理系统 BMS 面临动态工况适应性与全生命周期管理的双重挑战，传统方法在极端工况下状态估计误差超 15%，早期健康状态预测准确率不足 80%。文章融合智能化控制与能效优化技术，通过深度学习、边缘计算及数字孪生技术，推动 BMS 从被动防护向主动优化升级。研究表明，智能化技术使低温充电效率提升 20% 以上，模型预测控制等智能算法结合数字孪生模型，将充放电效率提升 15%~20%。该技术能为新能源汽车电池可靠性与经济性研究提供技术支撑。

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.