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<p>As an advanced integrated airborne electromechanical system, the Adaptive Power and Thermal Management System (APTMS) extracts air and shaft power from aircraft engines, which significantly affects fuel consumption and engine stability. In a dynamic flight environment, the solution space under the aforementioned multi-source combined power exhibits a multi-peak distribution, and the solution space changes in response to environmental variations, posing significant challenges for energy management. To address these challenges, this paper proposes an energy optimization management method for APTMS based on Multi-Strategy Particle Swarm Optimization (MSPSO). Firstly, a coupling model of the aircraft engine and APTMS considering multi-source energy extraction is established to analyze the impact of energy extraction on the performance of the engine-APTMS coupling system. Subsequently, the MSPSO is employed to solve the energy management problem, with the objective of minimizing fuel consumption while subject to constraints such as stability margin and cooling requirements. At the algorithmic level, the MSPSO incorporates the evolutionary model of double subgroup co-evolution and the combined mutation strategy of Gauss combined with Cauchy to enhance optimization accuracy and overcome the problem of getting trapped in local optima within the multi-peak solution space. Additionally, the algorithm leverages historical optimization data and employs a dynamic particle update strategy based on the fitness variations of environmental monitoring particles to effectively adapt to the dynamic flight environment. Finally, the proposed method is validated through simulations to demonstrate its advancement and effectiveness. The numerical simulation results demonstrate that the proposed method achieves a reduction in average fuel consumption of over 0.0057 kg/s compared to the traditional energy management scheme. Furthermore, when compared to the basic particle swarm optimization algorithm, this method achieves a minimum improvement of 0.81% in energy efficiency optimization value.</p>

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Multi-Source Energy Optimization Management Method for Adaptive Power and Thermal Management System using Multi-Strategy Particle Swarm Optimization

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Jiecheng Fu,
  • Fengying Zheng,
  • Jingyang Zhang,
  • Weidong Chen

摘要

As an advanced integrated airborne electromechanical system, the Adaptive Power and Thermal Management System (APTMS) extracts air and shaft power from aircraft engines, which significantly affects fuel consumption and engine stability. In a dynamic flight environment, the solution space under the aforementioned multi-source combined power exhibits a multi-peak distribution, and the solution space changes in response to environmental variations, posing significant challenges for energy management. To address these challenges, this paper proposes an energy optimization management method for APTMS based on Multi-Strategy Particle Swarm Optimization (MSPSO). Firstly, a coupling model of the aircraft engine and APTMS considering multi-source energy extraction is established to analyze the impact of energy extraction on the performance of the engine-APTMS coupling system. Subsequently, the MSPSO is employed to solve the energy management problem, with the objective of minimizing fuel consumption while subject to constraints such as stability margin and cooling requirements. At the algorithmic level, the MSPSO incorporates the evolutionary model of double subgroup co-evolution and the combined mutation strategy of Gauss combined with Cauchy to enhance optimization accuracy and overcome the problem of getting trapped in local optima within the multi-peak solution space. Additionally, the algorithm leverages historical optimization data and employs a dynamic particle update strategy based on the fitness variations of environmental monitoring particles to effectively adapt to the dynamic flight environment. Finally, the proposed method is validated through simulations to demonstrate its advancement and effectiveness. The numerical simulation results demonstrate that the proposed method achieves a reduction in average fuel consumption of over 0.0057 kg/s compared to the traditional energy management scheme. Furthermore, when compared to the basic particle swarm optimization algorithm, this method achieves a minimum improvement of 0.81% in energy efficiency optimization value.