Model Predictive Fault Tolerant Control of Bidirectional AC/DC Converter with Voltage Balance of Split Capacitor

Jin, Nan; Zhao, Chongyan; Guo, Leilei

doi:10.23919/ipec.2018.8507868

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[34], the same bidirectional converter is controlled by a hybrid SVM (HBSVM) strategy; the authors include a vector plane distribution for reduced DC-link capacitor currents to achieve a reduced ripple voltage on the DC capacitors. Model predictive control (MPC) has been widely used in this converter to minimize power quality issues during leg failure (see [35][36][37][38], for instance) by analyzing disturbances that cause changes in the converter behavior or the control system itself. Additionally, by finite-state model predictive direct power control (MPDPC), an optimal voltage vector has been selected by a power prediction model, and a cost function to achieve flexible switching between inverter and rectifier modes [39][40][41].…”

Section: Leg-level Fault Tolerance Techniquesmentioning

confidence: 99%

An Overview on Fault Management for Electric Vehicle Onboard Chargers

et al. 2022

View full text Add to dashboard Cite

Onboard charging systems (OBCs) convert AC power from an external charging source into a DC voltage used to charge the battery pack of an electric vehicle (EV). OBCs are versatile since they can convert energy from almost every AC source, including standard household electrical receptacles, without needing wall chargers or charging stations. Since the same motor-drive electronics are reconfigured for onboard charging, weight and cost barely increase. However, the power quality and reliability of the OBCs are essential elements for proper grid interconnection. This article reviews the failures of power electronic converters that can be used for onboard charging and their most prominent fault-tolerance techniques. The various fault-tolerance methods are evaluated and compared in terms of complexity, cost, and performance to provide insights for future developments and research directions.

show abstract