ArticleSensors (Basel, Switzerland)2022
Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Integration of fast charging EV infrastructure with high gain Z-source converters and hybrid optimized MPPT algorithm.Scientific reports · 2025Article
- Enhanced state of charge estimation in electric vehicle batteries using chicken swarm optimization with open ended learning.Scientific reports · 2025Article
- Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach.Scientific reports · 2024Article
- Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience.Scientific reports · 2024Article
- Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.Scientific reports · 2024Article
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Authors and funding
8 authors.
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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.
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