Subhash Kumar Ram

Dielectric barrier discharge (DBD)-based excimer sources have found diverse applications in biomedical, agriculture, pharmacy, surface treatment, and industrial processes. The radiation ability of DBD-based 172-nm excimer radiation source to break molecular bonds in organic matter is beneficial for processes, such as ultracleaning and surface activation, improving adhesion. The high-voltage pulsed power modulator plays a critical role in generating efficient plasma discharge for a 172-nm VUV excimer lamp. The precise control of output pulsed amplitude voltage and pulse repetition frequency (PRF) is required for generating efficient and uniform plasma inside the plasma tube. In this article a two switch forward converter (S $^{{2}}$ FC) topology with snubber at the output is proposed to generate unipolar pulse output for 172-nm VUV excimer source. The developed prototype of high-voltage unipolar impulse power modulator (HVU-IPM) has a 32-kHz PRF and a 1- $\mu $ s pulsewidth with adjustable output pulse voltage. The mathematical model for calculating the DBD pulse voltage and current is developed through mode analysis of the proposed HVU-IPM system. The experimental verifications of the developed prototype of HVU-IPM system are carried out to evaluate the system performance. The DBD plasma generated by the developed HVU-IPM remains stable across a broader range of operating parameters, such as gas pressure and applied voltages, and generates VUV light of absolute irradiance up to 11.24 mW/cm $^{{2}}$ . The achieved rise time/fall time of output pulse is below 400 ns.

In this article, the design and development of a fast rise time high-voltage bipolar impulse generator (HV-BIG) system for a dielectric barrier discharge (DBD)-based coaxial 222 nm excimer radiation source has been carried out for effective plasma generation. Full bridge topology utilizing silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) along with a step-up transformer has been realized for the development of the HV-BIG system. Sectionalized windings were designed to realize the primary and secondary winding of high-voltage transformers. The fabricated high-voltage transformer features a uniform distribution of the primary and secondary windings to minimize leakage inductance. A control algorithm has been implemented to generate 180 $^{\circ}$ phase-shifted gating pulses with suitable pulse repetition frequency and pulsewidth for the full bridge converter. The developed laboratory prototype of the HV-BIG system was experimentally verified with the DBD-based Krypton/Chlorine (Kr/Cl $_{2})$ excilamp to generate 222 nm radiation with output pulse voltage ranging from $\pm$ 2.85 to $\pm$ 8.25 kV. The HV-BIG system has been experimentally tested to generate variable high-voltage bipolar pulse output voltage with a pulse repetition frequency of 31.5 kHz and tunable at two distinct pulsewidth of 1.0 and 1.2 $\mu $ s.

This article presents a model-based approach to assess the battery performance of a two-wheeler EV drive train system for various user driving patterns using the selected urban drive cycles. The battery pack is one of the most expensive parts of an EV, and its life is heavily dependent on its usage pattern. The impact of the user’s driving behaviour on the performance parameters of the EV battery pack needs to be investigated. Thus, a two-wheeler EV drive train model was developed in MATLAB with a 5 kW motor, a 4.32 kWh battery, vehicle dynamics, and the power train control algorithms for in-depth analysis of battery performance. The validity of the developed model was tested against various state-of-the-art drive cycles for a duration of 3600 s. Numerous user driving behaviours, such as aggressive, moderate, and slow driving behaviours, were modelled with modified drive cycles, which were used to assess the two-wheeler battery pack performance. An optimum speed range, which ranges from 21 km/h to 34 km/h for different drive cycles, was identified, and these speed ranges minimised the battery energy consumption for the selected drive cycles with the modified drive cycle models.

Accurate estimation of State of Charge (SoC) in Li-ion batteries using data-driven techniques is challenging due to the tedious and resource-consuming nature of real-time data generation. To address this issue, this paper proposes a data-light model for SoC prediction. The model is based on a hybrid architecture combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. To optimize data usage, the model is trained with progressively decreasing amounts of data. Additionally, the number of nodes in the hybrid model is optimized to enhance the accuracy of SoC prediction. Experimental results demonstrate that the proposed hybrid model achieves accurate SoC estimation with significantly reduced data requirements, making it a promising solution for real-time Li-ion battery monitoring and management.

Transportation systems based on electric vehicle (EV) technology are regarded as the most environmentally sustainable system for modern transport industries. The maximum benefits of EVs can only be realized when the energy required to charge the EVs is obtained from renewable energy sources. This paper presents design and implementation of solar PV-based multi-functional e-Mobility charging system for two and three-wheeler EVs. The charging system is powered by 3 kWp solar PV panels consisting of solar battery charger to charge the auxiliary batteries and an e-Mobility charger to charge the EVs. It proposes a decentralized architecture for solar battery charging systems and EV charging system. The decentralized interleaved architecture does not require inter-converter communication and prevents the possibility of a single point of failure. Interleaving of converters also allows to have a high step-down conversion ratio, reduction in size, weight, and volume of filters, and high output current with low ripple current. To achieve a controlled and regulated output voltage, a digitally controlled algorithm is implemented in TMS320F28027 microcontroller. The proposed system can charge multi-chemistry batteries with multiple output voltage levels. The proposed architecture is experimentally validated and presented in this work.

Battery health is a critical factor influencing the widespread adoption of Electric Vehicles (EVs). The SoH serves as a reliable indicator of a battery's capacity degradation over time, directly impacting its performance and overall lifespan. In this research paper, various ensemble based techniques have been used to estimate the state of Health (SoH) of battery. To assess the effectiveness of the proposed approach, a detailed comparison of several state-of-the-art regression models is conducted. Specifically, the performance of Bagging Regression, Gradient Boost Model, Stacking Regression Model, xGBoost Model, and Extra Trees Model is evaluated in predicting the SoH of batteries. R-squared (R2) score is utilized as the metric for assessing the accuracy and predictive capability of the developed models. The performance of the developed models are evaluated using progressively reduced amounts of data, demonstrating its effectiveness even with limited available data. Through extensive analysis of the developed models, efficacy of the data-driven technique is demonstrated in estimating battery SoH accurately. Certain models outperform others in terms of R2 score, indicating superior predictive capabilities for battery health estimation. The results offer valuable insights for selecting the most suitable regression model for battery SoH estimation, contributing to enhanced EV battery management and optimization.

The charging infrastructure plays a crucial role in the widespread adoption of electric vehicles (EVs) by providing accessible, convenient, and technologically compatible charging solutions to extend the driving range and contribute to environmental sustainability. The DC-DC converters and their control are essential components utilized in charging infrastructure with efficient conversion of input voltage to optimal levels. This paper presents the design and implementation of an interleaved DC-DC buck converter for Lithium-ion battery charging applications. The incorporation of interleaving in the DC-DC buck converter enhances the overall both system performance. The interleaved architecture eliminates the need for inter-converter communication and effectively avoids any potential single point of failure. To achieve controlled and regulated output voltage, closed-loop control algorithm is implemented in TMS320F28027 microcontroller. The hardware of the interleaved DC-DC buck converter is tested on resistive load as well as on 48 V, 30 Ah Li-ion battery. The charging of the 48 V, 30 Ah Li-ion battery is carried out in Constant Current-Constant Voltage (CC-CV) mode. The experimental result of the developed interleaved DC-DC buck converter is presented in this work.

This paper presents an FPGA-controlled three-phase inverter programmed for unbalance compensation in the output voltage of the three-phase inverter. The inverter output voltage becomes unbalanced because of the unbalanced load currents drawn by the unbalanced lads of the inverter. The unbalance compensation method mainly uses fundamental harmonic observers, which are designed to decompose a highly distorted voltage and current signal. These observers are embedded into the FPGA. FPGA is used along with other PWM-type controllers and input-output interfaces. The three-phase inverter fundamental output voltage is regulated by using a feedback control system, and a decoupled channel control mechanism has been employed to perform unbalance compensation. Two Cyclone FPGA chip implements the various signal processing steps in the closed loop inverter control methods.