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具有电池储能的三结光伏电动汽车的鲁棒优化与功率管理

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage.

作者信息

Hamed Salah Beni, Hamed Mouna Ben, Sbita Lassaad, Bajaj Mohit, Blazek Vojtech, Prokop Lukas, Misak Stanislav, Ghoneim Sherif S M

机构信息

Physic Department, High School of Engineers of Tunis, Tunis 1008, Tunisia.

Electrical Department, National Engineering School of Gabes, Gabes 6029, Tunisia.

出版信息

Sensors (Basel). 2022 Aug 16;22(16):6123. doi: 10.3390/s22166123.

DOI:10.3390/s22166123
PMID:36015883
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9412334/
Abstract

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input-output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

摘要

本文重点介绍了一种强大的优化和功率管理算法，该算法可监控能量传输流，以满足光伏（PV）电动汽车的需求，即使在牵引系统运行时也是如此。所研究系统的功率级由作为主要能源的三结光伏发电机、作为辅助能源的锂离子电池和一辆电动汽车组成。通过使用能量转换阶段来实现输入输出信号适配。一个双向DC-DC降压-升压变换器将电池连接到直流母线。两个单向升压变换器在光伏发电机和直流母线之间进行接口连接。其中一个采用最大功率点跟踪（MPPT）算法进行控制，以达到最大功率点。另一个用于控制直流母线上的电压。这些变换器通过同一个直流母线经由一个三相逆变器连接到电动汽车。考虑到这些元件的非线性行为，建立了动态模型。由于光伏发电机的非线性动态特性、计量条件变化和负载变化，开发了一种强大的非线性MPPT算法。通过与两种经典的扰动观察法（P&O）和模糊逻辑MPPT算法进行对比研究，有效地突出了MPPT算法的高性能。已开发出一种基于李雅普诺夫函数的非线性控制方法，以同时调节直流母线电压并控制电池的充电和放电操作。提出了一种基于能量管理规则的策略，以有效地监控功率流。在Matlab/Simulink环境中实现并验证了所构思的系统、能量管理和控制算法。在不同的运行条件下展示并讨论了获得的结果。

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