模型预测控制
电池(电)
汽车工程
能源管理
控制器(灌溉)
燃料效率
电池组
MATLAB语言
计算机科学
工程类
控制工程
控制理论(社会学)
功率(物理)
控制(管理)
能量(信号处理)
农学
统计
物理
数学
量子力学
人工智能
生物
操作系统
作者
Kai Li,Hong Chen,Shengyan Hou,Lars I. Eriksson,Jinwu Gao
出处
期刊:Energy
[Elsevier]
日期:2023-09-01
卷期号:278: 127726-127726
被引量:5
标识
DOI:10.1016/j.energy.2023.127726
摘要
Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.
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