AI 中文总结
研究电池电动汽车热管理优化问题，提出分层NMPC框架，上层平衡能耗与温度跟踪并转化为执行器命令序列，下层补偿干扰等，采用数据驱动神经网络模型，经高保真模拟器验证，相比基线策略能耗显著降低。
AI 中文摘要
本文研究电池电动汽车（BEV）中的节能热管理问题，目标是在确保乘客舒适度和电池热安全的同时延长行驶里程。提出了一种分层非线性模型预测控制（NMPC）框架，其监督层在较长时间范围内明确平衡能量消耗和车厢温度参考跟踪，并将这种权衡转化为集成加热、通风和空调（HVAC）系统以及电池热管理（BTM）系统的最优执行器命令序列。然后由下层NMPC执行这些命令序列，其在跟踪外部指定的车厢温度参考轨迹时引入局部校正以补偿干扰和建模不确定性。为实现对高度非线性和强耦合热动力学的预测控制，在两个控制层中都采用了数据驱动的神经网络模型作为预测模型。该方法通过与Stellantis N.V.合作在都灵理工大学开发的高保真BEV热模拟器进行开发和验证，并在WLTC驾驶循环中与基于工业规则（RB）的控制策略进行基准测试。仿真结果表明，与基线策略相比，能量消耗显著降低，同时满足舒适度和电池温度约束。
英文摘要
This paper addresses the problem of energy-efficient thermal management in Battery Electric Vehicles (BEVs), with the goal of extending driving range while ensuring passenger comfort and battery thermal safety. A hierarchical Nonlinear Model Predictive Control (NMPC) framework is proposed, in which the supervisory layer explicitly balances energy consumption and cabin temperature reference tracking over a long horizon and translates this trade-off into optimal actuator command sequences for the integrated heating, ventilation, and air conditioning (HVAC) system and the battery thermal management (BTM) system. These command sequences are then enforced by a lower-layer NMPC, which introduces local corrections to compensate disturbances and modeling uncertainty while tracking an externally specified cabin temperature reference trajectory. To enable predictive control of the highly nonlinear and strongly coupled thermal dynamics, data-driven neural network models are employed as prediction models within both control layers. The proposed approach is developed and validated using a high-fidelity BEV thermal simulator developed at Politecnico di Torino in collaboration with Stellantis N.V., and benchmarked against an industrial rule-based (RB) control strategy over the WLTC driving cycle. Simulation results demonstrate a significant reduction in energy consumption compared to the baseline strategy, while satisfying comfort and battery temperature constraints.
Journal ref2026 IEEE Conference on Control Technology and Applications (CCTA)
DOI：10.1109/CCTA62090.2026.11684180
