Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 611-618.DOI: 10.11772/j.issn.1001-9081.2023020197

• Frontier and comprehensive applications • Previous Articles    

Optimization strategy of tandem composite turbine energy storage based on self-adaptive particle swarm optimization algorithm

Zhen WANG, Shanshan ZHANG(), Binyang WU, Wanhua SU   

  1. State Key Laboratory of Engines,Tianjin University,Tianjin 300072,China
  • Received:2023-02-28 Revised:2023-03-29 Accepted:2023-04-10 Online:2023-08-14 Published:2024-02-10
  • Contact: Shanshan ZHANG
  • About author:WANG Zhen, born in 1998, M. S. candidate. His research interests include waste heat recovery, energy storage.
    WU Binyang, born in 1986, Ph. D., associate professor. His research interests include artificial intelligence prediction of combustion.
    SU Wanhua, born in 1941, M. S., professor. His research interests include diesel electric control fuel system.
  • Supported by:
    National Natural Foundation of China Innovative Research Group Project(51921004)


王震, 张珊珊(), 邬斌扬, 苏万华   

  1. 天津大学 内燃机燃烧学国家重点实验室,天津 300072
  • 通讯作者: 张珊珊
  • 作者简介:王震(1998—),男,吉林吉林人,硕士研究生,主要研究方向:余热回收、能量存储
  • 基金资助:


A new Maximum Power Point Tracking (MPPT) method, based on Self-Adaptive Particle Swarm Optimization (SAPSO), was proposed to address the energy storage challenge in engine tandem composite turbine power generation systems. A Hybrid Energy Storage System (HESS) was introduced to augment the power capture capability of the generation system and replace single battery storage, achieving efficient and stable electrical energy storage. A control simulation model of energy storage optimization based on tandem composite turbine power generation was established using Matlab/Simulink software. The power tracking performance for various control methods and the energy storage characteristics of hybrid energy storage systems were compared and analyzed under predetermined operating conditions. Simulation results reveal that the proposed SAPSO-MPPT method outperforms the conventional P&O (Perturbation and Observation) control method, increasing power generation by 190 W and reducing response time by 0.15 s. Additionally, HESS could effectively track the demand power on the busbar, achieving power recovery efficiency of 95.3% . Finally, a test platform for the tandem composite turbine power generation system was developed using a modified Y24 engine bench to validate the fuel-saving potential of the proposed energy storage optimized control strategy. The test findings indicate that the suggested SAPSO-MPPT+HESS energy storage optimization strategy improves energy recovery efficiency by 0.53 percentage points compared to the original engine.

Key words: Self-Adaptive Particle Swarm Optimization (SAPSO) algorithm, tandem composite turbine power generation system, Maximum Power Point Tracking (MPPT), Hybrid Energy Storage System (HESS)


针对发动机串联复合涡轮发电系统储能困难等问题,提出了一种基于自适应粒子群优化(SAPSO)算法的最大功率点追踪(MPPT)方法,增强发电系统功率的捕获能力。此外,采用混合储能系统(HESS)替代单一蓄电池储能,实现电能的高效、稳定存储。通过Matlab/Simulink软件,建立了基于发动机串联复合涡轮发电的储能优化控制仿真模型,对比分析了不同控制方法在设定工况下的功率追踪性能以及混合储能系统的储能特性。仿真结果表明,相较于传统扰动观测法(P&O)控制方法,在所提的SAPSO-MPPT方法下,发电功率提高了190 W, 响应时间缩短了0.15 s。同时,HESS能够有效追踪母线上的需求功率,电能回收效率高达95.3% 。最后,基于Y24型改装发动机台架搭建了串联复合涡轮发电系统实验平台,对所提储能优化控制策略的节油潜力进行了实验验证。结果表明,SAPSO-MPPT+HESS储能优化策略能够有效提高排气能量回收效率,优化后系统总热效率比原发动机提高了提高0.53个百分点。

关键词: 自适应粒子群优化算法, 串联复合涡轮发电系统, 最大功率点追踪, 混合储能系统

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