Real-time analytical equivalent factor ECMS for multi-mode hybrid electric vehicles

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI:10.1016/j.segan.2026.102145

Wei Wang , Zhenjiang Cai , Yi Tian , Jian Wang

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Abstract

This study develops a real-time optimization framework for the Equivalent Factor (EF) in multi-mode HEV energy management systems. Leveraging Pontryagin’s Minimum Principle (PMP), a convex optimization problem for the EF s(t) is formulated. Closed-form solution of Karush-Kuhn-Tucker (KKT) conditions yields near-optimal analytical EF solutions with precise time-varying boundaries. The key advantage of this work lies in obtaining an analytical solution through a standard convex optimization and KKT framework without requiring any adaptive mechanism. The proposed Online Analytical EF-based ECMS (OEF-ECMS) replaces heuristic adaptive tuning mechanisms (e.g., PI controllers) with deterministic analytics, eliminating parametric dependencies while meeting real-time control requirements. Simulations demonstrate OEF-ECMS’s superiority over conventional Adaptive ECMS (A-ECMS) in analytical efficiency through online generation of near-optimal EF solutions and significant fuel economy improvements under dynamic operating conditions.

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多模混合动力汽车实时分析等效因子ECMS

本研究开发了多模式HEV能量管理系统中等效因子（EF）的实时优化框架。利用庞特里亚金最小值原理（PMP），给出了一个EF (t)的凸优化问题。Karush-Kuhn-Tucker （KKT）条件的闭型解产生具有精确时变边界的近最优解析EF解。这项工作的关键优势在于通过标准凸优化和KKT框架获得解析解，而不需要任何自适应机制。提出的基于在线分析ef的ECMS （OEF-ECMS）用确定性分析取代启发式自适应调谐机制（例如PI控制器），在满足实时控制要求的同时消除了参数依赖性。模拟结果表明，OEF-ECMS在分析效率方面优于传统的自适应ECMS (A-ECMS)，在线生成接近最优的EF解决方案，并在动态运行条件下显著提高燃油经济性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology

CiteScore

7.90

自引率

13.00%

发文量

206

审稿时长

49 days

期刊介绍： Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.