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储能系统技术 储能系统 多物理场耦合 强化学习 ★ 4.0

分布式混合电推进飞机能量管理与控制优化及实验验证

Optimization and Experimental Validation of Energy Management and Control for a Distributed Hybrid Electric Propulsion Aircraft

语言:

中文摘要

提出改装通用航空飞机的分布式混合电推进系统(DHEPS),增加分布式推进单元提升整体性能。针对混合电地面试验台进行子系统建模和元件参数选择。提出分层控制框架,顶层基于状态机能量管理策略(SM-EMS)的监控控制器,底层开发多时间尺度机电耦合控制策略,集成分布式电机的强化学习磁场定向控制(RL-FOC)和扩展卡尔曼滤波(EKF)方法,以及发动机-发电机轴速自适应PI控制。巡航剖面仿真显示DHEPS相对基线飞机燃油消耗降低7.4%,能量比航程提升2.1%。RL-FOC和EKF方法显著改善响应时间和稳态精度,自适应PI控制最小化超调并缩短稳定时间。动态地面测试验证有效功率匹配和电压电流平衡,确认分层控制框架和DHEPS配置及SM-EMS的鲁棒性。

English Abstract

This article presents a distributed hybrid electric propulsion system (DHEPS) retrofitted for a general aviation aircraft, incorporating additional distributed propulsion units to enhance overall performance. Subsystem modeling and component parameter selection are conducted for a hybrid electric ground testbed. A hierarchical control framework is proposed, featuring a top-level supervisory controller based on a state machine-based energy management strategy (SM-EMS). At the bottom level, control strategies for electromechanical coupling across multiple time scales are developed, integrating reinforcement learning-based field-oriented control (RL-FOC) and extended Kalman filter (EKF) methods for distributed motors, as well as adaptive proportional-integral (PI) control for regulating the engine-generator shaft speed. Simulation results for a cruise flight profile demonstrate that the DHEPS achieves a 7.4% reduction in fuel consumption and a 2.1% improvement in energy-specific air range compared to the baseline aircraft. The RL-FOC- and EKF-based methods for distributed motors significantly improve response time and steady-state accuracy over conventional PI control, while the adaptive PI control minimizes overshoot and shortens settling time. Dynamic ground testing further validates effective power matching, voltage-current balance, confirming the robustness of the hierarchical control framework and the successful implementation of the DHEPS configuration and the SM-EMS.
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SunView 深度解读

该分布式混合电推进系统能量管理技术对阳光电源航空电气化和多源协调控制有重要参考价值。状态机能量管理策略可应用于ST储能系统的多模式运行优化,如峰谷套利和调频调峰场景切换。强化学习磁场定向控制对新能源汽车多电机驱动系统的协调控制有借鉴意义,可提升响应速度和稳态精度。该技术对PowerTitan大型储能系统的分层控制架构和多时间尺度优化有启发,可提升系统整体性能和能量效率。