Electric vehicles contribute a major role in building an eco-friendly environment. Li-ion batteries are most widely used in electric vehicles. It is very important to maintain the operation of Li-ion batteries within their “safety operation area (SOA)”. Hence implementing a battery management system (BMS) becomes a necessity while using Li-ion batteries. This paper proposes an intelligent BMS for electric vehicles using proportional integral derivative (PID) control action along with artificial neural network (ANN). It prefers the improved pulse charging technique. The design consists of a battery pack containing four 12 V Li-ion batteries, MOSFETs, Arduino Uno, a transformer, a temperature sensor, a liquid-crystal displays (LCD), a cooling fan, and four relay circuit are used. Arduino Uno is used as a master controller for controlling the whole operation. Using this design approximately 38 minutes are required to fully charge the battery. Implementation results validate the system performance and efficiency of the design.
Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.
Modular multilevel converter (MMC) modules have popped up as among the best choices for medium and high-powered uses. This paper proposes a control scheme for the entire frequency range of operation for the MMC, focusing on supplying a three-phase machine. The machine is required to be controlled in the outer as well as the inner loop. Standard field oriented control (FOC) manages the three-phase machine in the outer closed loop while the inner control has to come up against the problem of energy balancing. That is unevenly distributed and stored in the capacitance of the upper and lower arms of the converter. There are two operating methods used in the inner control loop: a low-frequency method is used for start-up and low-speed operation, and a high-frequency method is for higher speed. In low-frequency mode (LF-mode), a special control strategy has to be implemented to minimize the energy oscillation in the capacitances of the converter arms. It makes utilization of the 3-phase machine's common mode voltage (Vc) as well as internal circulatory currents to verify a symmetrical energy distribution inside this MMC arms and also to avert whatever AC currents inside the DC source.
Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.
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