This paper comprehensively reviews bidirectional DC-DC converters and their control techniques for Electric Vehicle applications. A classification is proposed based on the three power ratings levels of the SAEJ1772 standard. Circuit topologies are compared based on power rating, switching frequency, static voltage gain, operating modes, number of components and power switch material. High switching-frequency topologies that include emerging Wide Bandgap (WBG) devices are also discussed. Cost comparison of traditional Si power switches and WBG devices employed in the reviewed topologies is also included. Traditional control methods for power converters in EVs are presented while also considering the emergence of Artificial Intelligence algorithms applied in systems controls that offer alternative methods to improve the efficiency of bidirectional DC-DC converters.

This study undertakes a comprehensive review of control techniques applicable to DC-DC power converters, categorized into Traditional Control (TC) methods and those based on Artificial Intelligence (AI). Succinct descriptions of prevalent strategies in both classifications are furnished, shedding light on their fundamental principles. Further, the current progress in the field is evaluated, anchoring the discussion in the provided categorization. In assessing the merits and potential drawbacks of each method, specific emphasis is laid on the target converter topology. Predominant topologies such as the elementary buck, boost, bidirectional buck-boost, and dual-activebridge (DAB) are scrutinized. To furnish a thorough analysis and facilitate comparison of principal control methods, simulations of four fundamental off-the-shelf algorithms are undertaken, employing a 1 MHz switching frequency.

Los vehículos eléctricos presentan una alternativa viable para reducir las emisiones de gases tóxicos en las concentraciones urbanas y para disminuir los efectos de los gases de invernadero. La batería de los vehículos eléctricos debe ser monitoreada con precisión para asegurar su funcionamiento adecuado y seguro. Para esto, es necesario desarrollar algoritmos eficientes que permitan estimar de forma precisa el estado de carga mediante dispositivos embarcados en el vehículo. En este trabajo, se utiliza un conjunto de datos de ciclado de una batería de Litio para entrenar una red neuronal para la estimación del estado de carga. Se realiza una optimización bayesiana para establecer la mejor arquitectura de red neuronal y se valida el comportamiento frente a las mediciones reales que ofrece el conjunto de datos. Para su utilización en un dispositivo embarcado, la red neuronal se valida con un modelo de hardware-en-lazo (HIL) en un FPGA con aritmética de punto fijo. Después del entrenamiento se observa un error promedio cuadrático menor al 2% y una precisión promedio del 97.5%.

The Xanab field is located off the coast of Tabasco state in the southeastern Gulf of Mexico. In this field, oil reserves accumulate in dolomitized carbonate rocks at the Upper Jurassic Kimmeridgian formations. While drilling the Xanab 11 well, a narrow margin of 0.2 g/cm3 was expected between the reservoir and fracture pressures, based on correlative well data. Given this condition, conventional drilling methods could cause severe kicks and drilling fluid losses. A managed pressure drilling technique (MPD) makes it possible to exert and maintain a constant bottomhole pressure (BHP) by applying a backpressure in the annular space to accurately choke the fluid flow returning from the well with a closed and pressurized surface-control system. In the event that a kick is detected, the BHP can be quickly increased by applying a backpressure with the choke at the wellhead to immediately control the kick. Similarly, if fluid losses are encountered, the downhole pressure can be quickly reduced by decreasing the backpressure, thus adapting to the requirements of the well. This paper describes the application of the MPD technique during the 5-7/8 in. hole section of the Xanab 11 well. The MPD technique made it possible to work within a narrow operational window and to identify formation tops through drill breaks, which are usually followed by severe fluid losses and kicks. Abnormal pressures were detected earlier, and surface conditions were adjusted to prevent well influx scenarios that could result in severe kicks or blowouts. Consequently, only two kicks occurred. In addition, fluid losses were identified earlier, which enabled preventive measures to be applied rapidly to avoid sticking. The MPD technique enabled a reduction of 44% in drilling scheduled times; it also resulted in a decrease in the volume of fluid lost to the formation and in non-productive time (NPT), as compared to conventionally drilled correlative wells. In addition, the amount of time required for production well clean up was only six hours, which helped joining the reserves to production faster. The use of a closed system prevented gasification events at the drill floor, mud shakers, and pits, thereby creating safer working conditions for the personnel involved. Technical adjustments were made to fit the onshore MPD equipment to the limited space available in the offshore work environment. Introduction: The Xanab field is located off the coast of Tabasco in the southeastern Gulf of Mexico (Fig. 1a). Its structure is presented as an elongated anticline, bounded by two faults (Drilling Program for Offshore Development, Well Xanab 11). From logs run on the Xanab 101, 1DL, 1, and 31 wells, a pressure ramp occurs at approximately 4900 m at the entrance to the Lower Miocene zone, which resulted in a narrow operational window. Fig. 1b shows the 3D view of the trajectory of the Xanab 1 DL, Xanab 31, and Xanab 11 wells. A review of the drilling records and events logs of the correlative Xanab 1DL and Xanab 31 wells in the 5-7/8 in. hole section was performed to understand the well trajectory plans, well geometry, average mud weights, kick frequency, severity of the influxes, events from the well, maximum annular pressure increase as a result of an influx, number of well control events, total number of drilling days, and average rate of penetration per day per section to drill. This data was then placed in individual charts for each well to show the depth vs. the number of days and annular pressure.