M.A. Hannan

Voltage equalization control algorithm for monitoring and balancing of series connected lithium-ion battery

Lithium-Ion (Li-Ion) batteries are commonly used as automobile energy storage systems for powering applications due to their lucrative features. However, a battery management system with individual cell monitoring and balancing of Li-Ion batteries for long use and casualties protection are still major issues in electric vehicle applications. This paper deals with the development of a voltage equalization control algorithm for individual cell monitoring and balancing of series connected Li-Ion battery cells. The developed states and sequences of the control algorithm manage the whole processes of battery cell monitoring, charging, and discharging, respectively. A charge equalization model is implemented with series connected 10 Li-Ion battery cells utilizing the developed control algorithm. Results show that charging and discharging, and cell balancing performance of the control algorithm are capable of quickly responding to reach the state of charge difference of 2.5% among all cells, defending the exist...

A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive

This paper presents a random forest (RF) regression based implementation of space vector pulse width modulation (SVPWM) for a two-level inverter to improve the performance of the three-phase induction motor (TIM) drive. The RF scheme offers the advantage of rapid implementation and improved prediction for the SVPWM algorithm to improve the performance of a conventional space vector modulation scheme. In order to show the superiority of the proposed RF technique to other techniques, an adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) based SVPWM schemes are also used and compared. The proposed speed controller uses a backtracking search algorithm to search for the best values for the proportional-integral controller parameters. The robustness of the RF-based SVPWM is found superior to the ANFIS and ANN controllers in all tested cases in terms of damping capability, settling time, steady-state error, and transient response under different operating conditions. The prototype of the optimal RF-based SVPWM inverter controller of induction motor drive is fabricated and tested. Several experimental results show that there is a good agreement of the speed response and stator current with the simulation results which are verified and validated the performance of the proposed RF-based SVPWM inverter controller.

An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system

This paper deals with the backtracking search algorithm (BSA) optimization technique to solve the design problems of multi-machine power system stabilizers (PSSs) in large power system. Power system stability problem is formulated by an optimization problem using the LTI state space model of the power system. To conduct a comprehensive analysis, two test systems (2-AREA and 5-AREA) are considered to explain the variation of design performance with increase in system size. Additionally, two metaheuristic algorithms, namely bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) are accounted to evaluate the overall design assessment. The obtained results show that BSA is superior to find consistent solution than BFOA and PSO regardless of system size. The damping performance that achieved from both test systems are sufficient to achieve fast system stability. System stability in linearized model is ensured in terms of eigenvalue shifting towards stability regions. On the other hand, damping performance in the non-linear model is evaluated in terms of overshoot and setting times. The obtained damping in both test systems are stable for BSA based design. However, BFOA and PSO based design perform worst in case of large power system. It is also found that the performance of BSA is not affected for large numbers of parameter optimization compared to PSO, and BFOA optimization techniques. This unique feature encourages recommending the developed backtracking search algorithm for PSS design of large multi-machine power system. BSA technique is proposed to alleviate PSS design for large power system.Stability analysis is done based on LTI State Space model of power system.Damping performance is explained in two benchmark systems for BSA, BFOA and PSO.BSA based design shows fast damping while BFOA and PSO perform worst.

Lithium-Ion Battery Charge Equalization Algorithm for Electric Vehicle Applications

The lithium-ion batteries are commonly used in electric vehicle (EV) applications due to their better performances as compared with other batteries. However, lithium-ion battery has some drawbacks such as the overcharged cell which has a risk of explosion, the undercharged cell eventually reduces the life cycle of the battery, and unbalanced charge in series battery gradually reduces overall charge capacity. This paper presents a battery charge equalization algorithm for lithium-ion battery in EV applications to enhance the batterys performance, life cycle, and safety. The algorithm is implemented in series-connected battery cells of 15.5 Ah and 3.7 V nominal each using a battery monitoring integrated circuit for monitoring and equalization of an 8-cell battery pack using a bidirectional flyback dc–dc converter as the channel for charging and discharging of the battery cell. The obtained results show that the developed charge equalization controller algorithm performs well in equalizing both undercharged and overcharged cells, and equalizes the cell within the safety operation range of 3.81 V. To validate the charge equalizer performance, the proposed algorithm outperforms with other studies in terms of balancing, equalization speed, low power loss, and efficiency. Thus, the proposed battery charge equalization algorithm proves an effective and automated system to modularize the battery charge that improves the safety and life cycle of battery.

Toward Optimum Topology Protocol in Health Monitoring

Sensor technology has opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compared to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure at a lower price and greater range. This paper addresses the coverage area and lifetime-related issues arise in the building structural monitoring system. The monitoring system consists of a large number of sensor nodes for collecting structural health information. Numerous domestic buildings, especially long-span buildings, have lower frequency response that is challenging to accurately measure using a number of deployed sensor nodes. The way the sensor nodes are connected plays an important role in providing signals with required strengths. Out of many topologies, the dense and sparse topology was extensively used in sensor network applications for collecting health information. The lifetime of the wireless sensor network is a fundamental issue because it determines the whole system aliveness. Network lifetime is one of the most important performance indicators for real-life application. The objective of this article is to investigate the network lifetime and compare the computational results of different kinds of transmission construction protocols to find the optimum lifetime protocol for extending the monitoring system lifetime. The proposed dense topology sensor network maximizes the network lifetime and minimizes the system cost. The result shows that the dense topology would be a good choice for monitoring building structural health damage.

Optimal CC-CV charging of lithium-ion battery for charge equalization controller

This paper proposes a lithium-ion battery charging technique for the charge equalization controller based on the particle swarm optimization (PSO) algorithm. A flyback DC-DC converter is utilized to perform the charge equalization and battery charging. The charging of lithium-ion battery is executed by constant current-constant voltage (CC-CV) charge PI control process. In the proposed technique, the PSO algorithm is used to attain the best and optimal values of the PI controller parameters. The optimized PI controller regulates the PWM signal to the MOSFET switching drive of the converter for quality CC-CV charging of the lithium-ion battery, so that it reduces the memory effect, and thus, enhances the performance of lithium-ion battery and equalization. The proposed charging technique for efficient charge equalization controller is applicable in energy storage applications toward the sustainable electric vehicle development.

Optimized speed controller for induction motor drive using quantum lightning search algorithm

This paper presents an improve proportional-integral-derivative (PID) controller design technique for controlling a three-phase induction motor (TIM) speed drive using quantum lightning search algorithm (QLSA). This proposed controller avoids the exhaustive conventional trial- and-error procedure for obtaining PID parameters. Objective function using in the proposed controller is mean absolute error (MAE) to enhance the TIM speed performance under sudden change of the speed and load conditions. The QLSA is used to improve two controller system PID and PI controllers in the TIM drive. Moreover, the QLSA algorithm comperes with three optimization algorithms, namely, lightning search algorithm (LSA), the backtracking search algorithm (BSA), the particle swarm optimization (PSO). Designed and validated the simulation model by using a MATLAB/Simulink environment. Results show that the QLSA-based PID and PI speed controller is achieved better results than the other optimization controllers through reduce of damping capability, enhance the transient response, minimize the MAE, root mean square error (RMSE) and standard division (SD) of the speed response.