Charge estimation method for Lithium Polymer batteries based on Extended Robust Kalman Filter

A lithium polymer battery state of battery charge (SOC) is a determinant for monitoring charging and discharging the battery factor. If we can control the operating status of the battery to prevent overload and underload, increasing the useful life of the lithium polymer battery. Based on the fact that the SOC level cannot be determined in form direct as it depends on the internal parameters of the battery, the temperature, and the level of aging. It is necessary to develop a technique to perform measurements in real-time voltage, current, and temperature of the battery. It determines a dynamic model of the battery and estimates the state of charge SOC from the robust extended Kalman filter.

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Simon Bolivar University, USB-SOLAR

Vehicle control system for an electric vehicle with an in-wheel motor and read powertrain (IRW)

The main objective of this work is to design and implement a Vehicle Control System (VCS) model oriented to a prototype of a competitive solar four-wheeled vehicle with an independent rear powertrain and front-wheel steering. This model adopts two permanent magnet synchronous machines embedded directly in the vehicle's drive wheels, making it unnecessary to integrate a conventional mechanical gear-based drive train, which provides a decentralized configuration and greater flexibility. Additionally, part of the Vehicle Driver Mode System (DMS).

Which is in charge of providing the interaction environment between the driver and the vehicle, is composed of the Vehicle Finite State Machine (VFSM) and a Graphical User Interface (GUI). Regarding the Stability Control System (SCS), a Direct Yaw Moment Control (DYC) is based on Sliding Mode Control (SMC). It allows selecting the tracking error (tracking yaw rate or lateral slip angle or a combination of both) and the type of control (Proportional or Proportional-Integral). As Electric Machine Control System (EMCS), a controller from the Kelly Controller line is selected, and together a Revolutions Acquisition System (RAS) is implemented that allows the acquisition of the revolutions of the electric machine through a Raspberry PI using a Client-Server architecture. The implementation uses the Python programming language and the Matlab-Simulink development environment.

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Simon Bolivar University, USB-SOLAR

email: 13-10673@usb.ve

Application of laboratory: Wien Filter

Interface Diagnostic mass separation

For the programming, the interface used interface CSS before and added a new option in MAIN DIAGNOSTICS Diagnostic mass separation this option controlled Beam profile Monitor, Efficiency measurement, and Mass Scanning. The interfaces are controlled.

In the scanning for Magnetic Field (Electric Field constant ) and scanning Electric Field (Magnetic Field constant) the option is selected for the user to fix the field (Magnetic or Electric) mass initial, mass final, exposure time, and step mass for analysis.

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INFN ISTITUTO NAZIONALE DI FISICA NUCLEARE LABORATORI NAZIONALI DI LEGNARO