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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Ha, Vo Thanh
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Control & Systems Engineering Electrical & Electronics Engineering

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Machine learning-based lithium-ion battery life prediction for electric vehicle applications Ha, Vo Thanh; Vinh, Vo Quang; Truc, Le Ngoc
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1934-1941

Abstract

The actual and anticipated battlefield creates a model capable of accurately estimating the lifetime of lithium-ion batteries used in electric cars. This inquiry uses a technique known as supervised machine learning, more particularly linear regression. In lithium-ion batteries, modeling temperature-dependent per-cells is the basis for capacity calculation. When a sufficient quantity of test data is accessible, a linear regression learning method will be utilized to train this model, ensuring a positive outcome in forecasting battery capacity. The conclusions drawn in the article are derived from the attributes of the initial one hundred charging and discharging cycles of the battery, enabling the determination of its remaining power. This determination facilitates the swift identification of battery manufacturing procedures and empowers consumers to detect flawed batteries when signs of performance degradation and reduced longevity are observed. MATLAB simulations have demonstrated the accuracy of the projected results, exhibiting a margin of error of approximately 9.98%. With its capacity to provide a reliable and precise means of estimating battery lifespan, the developed model holds the potential to revolutionize the electric vehicle industry.
Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot Ha, Vo Thanh; Thi Thuong, Than; Ngoc Truc, Le
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7847

Abstract

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.
Advanced control structures for induction motors with ideal current loop response using field oriented control Ha, Vo Thanh; Lam, Nguyen Tung; Ha, Vo Thu; Vinh, Vo Quang
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1637.79 KB) | DOI: 10.11591/ijpeds.v10.i4.pp1758-1771

Abstract

Field oriented control (FOC) is widely used for high performance induction motor (IM) electrical drive systems. Typically, FOC uses linear controls and space vector modulation (SVM) to control the fundamental components of the stator voltages. This work shows that based on a fast and precise inner current loop response one may flexibly employ different advanced control methods, to achieve high performance outer loops (speed and flux control). In this paper, novel approaches based on dead-beat scheme for the current loop combining with exact linearization, backstepping controls, and fatness-based methods for the outer loop are proposed. By comparing with classical PI control, the proposed method shows the outstanding features of system response such as fast, accurate and decoupling properties. The performance evaluation is given by experimental results.
Torque control of PMSM motors using reinforcement learning agent algorithm for electric vehicle application Ha, Vo Thanh; Tuan, Duong Anh; Van, Tran Thuy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7852

Abstract

As electric vehicles (EVs) demand higher performance and efficiency, precise torque control in interior permanent magnet synchronous motors (IPMSMs) becomes increasingly vital. This paper introduces a reinforcement learning (RL)-based method to optimize torque control in IPMSMs. The RL agent is trained to regulate d-axis and q-axis currents, producing stator voltages to follow the desired motor speed. The control system includes an observation vector, voltage-based actions, and a specially designed reward function. Due to the nonlinear dynamics of the motor, training the agent requires significant computational effort. MATLAB/Simulink simulations are performed to compare the RL controller with a traditional PI controller. Results indicate that the RL controller delivers quicker and more accurate performance, although additional training is necessary to minimize overshoot.
Co-Authors Ha, Vo Thu Lam, Nguyen Tung Ngoc Truc, Le Thi Thuong, Than Truc, Le Ngoc Tuan, Duong Anh Van, Tran Thuy Vinh, Vo Quang