Smart Grids and Green Energy Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 383

Veröffentlichungsjahr: 2022

Beschreibung

SMART GRIDS AND GREN ENERGY SYSTEMS Green energy and smart grids are two of the most important topics in the constantly emerging and changing energy and power industry. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications. Smart grids and green energy systems are promising research fields which need to be commercialized for many reasons, including more efficient energy systems and environmental concerns. Performance and cost are tradeoffs which need to be researched to arrive at optimal solutions. This book focuses on the convergence of various technologies involved in smart grids and green energy systems. Areas of expertise, such as computer science, electronics, electrical engineering, and mechanical engineering are all covered. In the future, there is no doubt that all countries will gradually shift from conventional energy sources to green energy systems. Thus, it is extremely important for any engineer, scientist, or other professional in this area to keep up with evolving technologies, techniques, and processes covered in this important new volume. This book brings together the research that has been carrying out in the field of smart grids and green energy systems, across a variety of industries and scientific subject-areas. Written and edited by a team of experts, this groundbreaking collection of papers serves as a point of convergence wherein all these domains need to be addressed. The various chapters are configured in order to address the challenges faced in smart grid and green energy systems from various fields and possible solutions. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

1 Studies on Enhancement of Battery Pack Efficiency Using Active Cell Balancing Techniques for Electric Vehicle Applications Through MATLAB Simulations

1.1 Introduction

1.2 Influence of Lithium Ion Batteries

1.3 Cell Balancing

1.4 Block Diagram

1.5 SOC Control Using Passive Cell Equalization

1.6 Voltage Control Using Active Cell Equalization

1.7 Conclusion

References

2 Evaluation and Impacts of Minimum Energy Performance Standards of Electrical Motors in India

2.1 Introduction

2.2 A Review of IS 12615 Evaluation

2.3 A Scenario of ‘MEPS’ for Electric Motors From Around the World

2.4 Government Initiatives to Improve the Energy Efficiency of Electric Motors

2.5 Conclusion

References

3 Smart Power Tracking and Power Factor Correction in a PV System

3.1 Introduction

3.2 Literature Review

3.3 Smart Power Tracking

3.4 Perturb and Observe

3.5 Need for Power Factor Correction

3.6 Correction Method

3.7 Capacitive Bank

3.8 Simulation

3.9 Result and Output

3.10 Conclusion

References

4 Grid Connected Inverter for PV System Using Fuzzy Logic Controller

4.1 Introduction

4.2 Methodology

4.3 PV Module

4.4 DC-DC Converter

4.5 MPPT

4.6 Grid Connected PV System

4.7 Results and Discussion

4.8 Conclusion

References

5 An Experimental Investigation of Fuzzy-Based Voltage-Lift Multilevel Inverter Using Solar Photovoltaic Application

5.1 Introduction

5.2 Proposed SVLMLI

5.3 Design of FLC

5.4 FL Tuned PI Controller

5.5 Result and Discussion

5.6 Conclusion

References

6 Potentials and Challenges of Digital Twin: Toward Industry 4.0

6.1 Introduction

6.2 Industry 4.0

6.3 Digital Twin Technology

6.4 Potential and Challenges in Applying Digital Twin Technology

6.5 Research and Development Challenges

6.6 Future Scope of Digital Twin Technology

6.7 Conclusion

References

7 Real-Time Data Acquisition System for PV Module

7.1 Introduction

7.2 Description of Instrumentation Setup

7.3 Experimental Setup and Data Acquisition System

7.4 Experimental Results

7.5 Conclusion

References

8 Investigation of Controllers for “N” Input DC-DC Converters

8.1 Introduction

8.2 Role of Control Technique in Multivariable System

8.3 Controllers Employed in Multivariable System

8.4 Simulation Results and Discussion

8.5 Conclusion

References

9 Fuzzy Logic Controlled Dual-Input DC-DC Converter for PV Applications

9.1 Introduction

9.2 D

3

Converter Topology

9.3 Closed-Loop Controller

9.4 Experimental Verification

9.5 Conclusions

References

10 A Smart IoT-Based Solar Power Monitoring System

10.1 Introduction

10.2 Phases of System Implementation Process

10.3 Hardware Implementation and Results

10.4 Conclusions

References

11 Control of Multi-Input Interleaved DC-DC Boost Converter for Electric Vehicle and Renewable Energy

11.1 Introduction

11.2 Proposed Converter Topology

11.3 Control Strategy

11.4 Simulation Results

11.5 Conclusion

References

12 Maximum Power Point Tracking Techniques for Photovoltaic Systems—A Comprehensive Review From Real-Time Implementation Perspective

12.1 Introduction

12.2 Conventional Electrical MPP Tracking Methods

12.3 Evolutionary Algorithm and Artificial Intelligence–Based MPP Tracking

12.4 Comprehensive Review on the Implementation Issues of MPPT

12.5 Commercial Products

12.6 Conclusion

References

13 Reliability Analysis Techniques of Grid-Connected PV Power Models

13.1 Introduction

13.2 Reliability Empirical Relations and Standards

13.3 Reliability Estimation of Grid-Connected PV Power Models

13.4 Conclusion

References

14 DC Microgrid: A Review on Issues and Control

14.1 Introduction

14.2 Challenges Incurred in DCMG

14.3 Control Strategies Adopted in DC Micro-Grid

14.4 Hierarchical Control

14.5 Conclusion

References

15 Maximizing Power Generation of a Partially Shaded PV Array Using Genetic Algorithm

15.1 Introduction

15.2 Literature Review

15.3 Proposed System Design

15.4 Design of SEPIC Converter

15.5 Comparison of Different Optimization Tools

15.6 Single-Phase Inverter

15.7 Simulation Results

15.8 Results and Discussion

15.9 Conclusion

References

16 Investigation of Super-Lift Multilevel Inverter Using Water Pump Irrigation System

16.1 Introduction

16.2 Proposed System Configuration

16.3 Design of Concentrator SPV Array

16.4 Principle of Particle Swarm Optimization

16.5 Result and Discussion

16.6 Conclusion

References

17 Analysis of Load Torque Characteristics for an Electrical Tractor

17.1 Introduction

17.2 Methodology

17.3 Dynamics of Draft Force

17.4 Power Train Calculation

17.5 MATLAB Simulation and Result

17.6 Motor Specifications

17.7 Conclusion and Discussion

References

18 Comparison of Wireless Charging Compensation Topologies of Electric Vehicle

18.1 Introduction

18.2 Types of Electric Vehicle Wireless Charging Systems (EVWCS)

18.3 Classification of Compensation Topologies

18.4 Simulation Diagram

18.5 Design Parameters of Circuit Used in Simulation

18.6 Results and Discussion

18.7 Conclusion

References

19 Analysis of PV System in Grid Connected and Islanded Modes of Operation

19.1 Introduction

19.2 Grid Connected Mode

19.3 Islanded Mode

19.4 Results and Discussion

19.5 Conclusion

References

Index

Also of Interest

Wiley End User License Agreement

List of Tables

Chapter 1

Table 1.1 Simulation specification of cells.

Table 1.2 Output voltages after cell equalization (using flyback converter).

Table 1.3 Output voltages after cell equalization (using flyback converter—two-c...

Table 1.4 Output voltages after cell equalization (using multi-winding transform...

Table 1.5 Comparative study of passive and active cell equalization.

Chapter 2

Table 2.1 Comparison of various international efficiency (IE) classes.

Table 2.2 MEPS-worldwide requirements for electric motors.

Chapter 5

Table 5.1 Comparison of MLI.

Table 5.2 Inverter switching method.

Table 5.3 Comparison between experimental and simulated result.

Chapter 7

Table 7.1 Calculated parameters of the reference module under uniform illuminati...

Table 7.2 Parameters of the reference module under partial shading condition.

Chapter 8

Table 8.1 Comparative analysis of state of art of controllers for multivariable ...

Chapter 9

Table 9.1 FLC rules proposed.

Table 9.2 Rules for battery control switches.

Chapter 11

Table 11.1 Simulation parameters for the proposed converter.

Table 11.2 Controller parameters.

Chapter 12

Table 12.1 Comparison of various MPP tracking techniques.

Table 12.2 Commercial inverters and MPPT methods used.

Chapter 13

Table 13.1 Reliability prediction models.

Table 13.2 Estimated failure rates of components using ALD software.

Chapter 14

Table 14.1 Comparison chart of DC Microgrid control strategy.

Chapter 15

Table 15.1 Comparison of Fuzzy, ANFIS, and GA-PID.

Chapter 16

Table 16.1 Design of PV array.

Table 16.2 Inverter switching method.

Table 16.3 Comparison of simulation and hardware result.

Table 16.4 Specifications of proposed converter.

Chapter 17

Table 17.1 Percentage contribution of vehicle components to total running resist...

Table 17.2 Parameters of ASABE.

Table 17.3 Parameters of tractor and farm implements.

Table 17.4 Major farm implements used considered for powertrain sizing.

Table 17.5 Motor specifications.

Chapter 18

Table 18.1 Parameters of circuit used in simulation.

Chapter 19

Table 19.1 Model specifications for PV in grid connected mode.

Table 19.2 Specifications for design of Boost converter.

Table 19.3 Model specifications for PV system in islanded mode.

Table 19.4 Calculated values for filter design.

List of Illustrations

Chapter 1

Figure 1.1 Typical BMS tasks.

Figure 1.2 Types of cell balancing.

Figure 1.3 Circuit diagram for passive cell balancing.

Figure 1.4 Circuit diagram for active cell balancing using flyback converter.

Figure 1.5 Circuit diagram for active cell balancing using multi-winding transfo...

Figure 1.6 Basic block diagram for passive cell balancing.

Figure 1.7 Basic block diagram for active cell balancing.

Figure 1.8 Simulation waveform for output SOC.

Figure 1.9 Voltage waveform of cell1, cell2, and cell3.

Figure 1.10 (a) Output voltage waveform of active cell balancing using flyback c...

Figure 1.11 (a) Output voltage waveform of active cell equalization using multi-...

Figure 1.12 (a) Output voltage waveform of active cell balancing using multi-win...

Chapter 2

Figure 2.1 Sector-wise electrical power consumption in the year 2018–2019.

Figure 2.2 Stages in the creation of the IS 12615 electrical motor efficiency st...

Chapter 3

Figure 3.1 Grid-connected PV power system.

Figure 3.2 P-V characteristics for perturb and observe algorithm.

Figure 3.3 Flow chart of perturb and observation algorithm.

Figure 3.4 Resistive, inductive, and capacitive load characteristics curve.

Figure 3.5 Capacitor bank.

Figure 3.6 Simulation.

Figure 3.7 Output of solar panel.

Figure 3.8 Output of MPPT.

Figure 3.9 Output of three-phase inverter.

Figure 3.10 Output of power factor correction.

Chapter 4

Figure 4.1 Block diagram.

Figure 4.2 Solar PV module.

Figure 4.3 Structure of DC-DC converter.

Figure 4.4 Fuzzy logic designer.

Figure 4.5 Fuzzy logic control design in MATLAB.

Figure 4.6 Grid connected PV system.

Figure 4.7 Grid connected PV system with fuzzy logic control.

Figure 4.8 Voltage gain.

Figure 4.9 Current gain.

Figure 4.10 Power output.

Chapter 5

Figure 5.1 Circuit diagram for SVLMLI.

Figure 5.2 Circuit diagram for ON state SVLMLI.

Figure 5.3 Structure of fuzzy logic controller scheme.

Figure 5.4 Membership function. (a) Reference signal. (b) Change of error signal...

Figure 5.5 Tuning PI with fuzzy controller.

Figure 5.6 Output voltage waveform.

Figure 5.7 Fuzzy-tuned PI settling waveform.

Figure 5.8 FFT analysis of closed-loop PI control.

Figure 5.9 FFT analysis of closed-loop fuzzy control.

Figure 5.10 Output voltage of inverter.

Figure 5.11 Inverter output voltage.

Figure 5.12 Hardware setup.

Chapter 6

Figure 6.1 The digital transformation of industries.

Figure 6.2 Digital transformation of various physical systems.

Figure 6.3 Digital twin conceptual architecture.

Figure 6.4 Digital twin with in industry 4.0 ecosystem [20].

Chapter 7

Figure 7.1 Schematic diagram of the data acquisition setup.

Figure 7.2 Flow chart for the developed data acquisition system.

Figure 7.3 Electronic load with control circuit for testing PV module.

Figure 7.4 I-V and P-V curve of the PV module traced by the proposed data acquis...

Figure 7.5 Experimental current and voltage waveform obtained by oscilloscope.

Figure 7.6 Partially shaded PV modules.

Figure 7.7 I-V and P-V curves of the PV module under the partial shading conditi...

Chapter 8

Figure 8.1 “N” input DC-DC converter control concept.

Figure 8.2 Voltage mode-controlled converter.

Figure 8.3 Current mode-controlled converter.

Figure 8.4 Multi-switch converter with PWM signals from the controller.

Figure 8.5 Hybrid converter for multivariable system.

Figure 8.6 (a) Multi-input DC-DC converter-I.

Figure 8.7 PID controlled converter input and output voltage waveforms (a) witho...

Figure 8.8 Current mode-controlled DC-DC converter. (a) Input voltage. (b) Outpu...

Figure 8.9 Closed-loop response of N input converter.

Chapter 9

Figure 9.1 Conventional multi-input converter.

Figure 9.2 Structure of the proposed system.

Figure 9.3 Bode plot of the proposed converter.

Figure 9.4 Impulse response.

Figure 9.5 Battery controller circuit.

Figure 9.6 Voltage gain comparison.

Figure 9.7 Closed-loop (input vs. output voltage).

Figure 9.8 (a, b) Input and output membership functions.

Figure 9.9 Pulses for battery control switch.

Figure 9.10 Comparison of PI and fuzzy controller.

Figure 9.11 Hardware setup.

Figure 9.12 Proposed converter.

Figure 9.13 Pulses: (a) simulation and (b) experiment.

Figure 9.14 Output voltage: (a) simulation and (b) experiment.

Figure 9.15 Switch voltage S

1

and diode voltage: (a) simulation and (b) experime...

Figure 9.16 Inductor voltage: (a) simulation and (b) experiment.

Figure 9.17 Line regulation for changes in input: (a) battery and (b) PV input.

Chapter 10

Figure 10.1 PV Panel monitoring system with IoT.

Figure 10.2 Voltage divider circuit.

Figure 10.3 Implementation of data interface.

Figure 10.4 Hardware setup.

Figure 10.5 Light intensity graph plotted on ThingSpeak.

Figure 10.6 Temperature graph plotted on ThingSpeak.

Figure 10.7 Voltage graphs plotted on ThingSpeak.

Figure 10.8 Current graphs plotted on ThingSpeak.

Figure 10.9 Power graph plotted on ThingSpeak.

Figure 10.10 Output on LCD display.

Chapter 11

Figure 11.1 Block diagram of a new topology of the interleaved boost converter.

Figure 11.2 Multi-input interleaved DC-DC boost converter with coupled inductor.

Figure 11.3 (a, b) Block diagram of PI controller.

Figure 11.4 Simulation results of input voltages and output voltage.

Figure 11.5 Simulation result of inductor current.

Figure 11.6 Simulation results of load current and voltage.

Chapter 12

Figure 12.1 Constant voltage control method.

Chapter 13

Figure 13.1 Failure overview.

Figure 13.2 Typical reliability, failure rate, and MTBF plot.

Figure 13.3 Schematic diagram of typical grid-connected PV system.

Figure 13.4 Power models components of grid-connected PV system.

Figure 13.5 Reliability indices of power models components of grid-connected PV ...

Figure 13.6 Estimated failure rates of power models of grid-connected PV system.

Chapter 14

Figure 14.1 Classification of DC micro-grid control strategies.

Figure 14.2 (a) Centralized controller. (b) Distributed controller. (c) Decentra...

Figure 14.3 Decentralized droop control technique.

Figure 14.4 Distributed control technique.

Figure 14.5 Multi-layer hierarchical control technique.

Chapter 15

Figure 15.1 Equivalent model of PV cell.

Figure 15.2 Block diagram of fuzzy system.

Figure 15.3 Fuzzy logic. (a) Input 1. (b) Input 2.

Figure 15.4 Block diagram of ANFIS model.

Figure 15.5 ANFIS structure (a) input 1 (b) input 2.

Figure 15.6 Flow diagram of genetic algorithm.

Figure 15.7 PV module using INC method.

Figure 15.8 Single phase inverter.

Figure 15.9 GA-based PID controller (a) Output voltage waveform (b) Output AC vo...

Chapter 16

Figure 16.1 Block diagram for concentrator SPV super-lift Luo converter fed subm...

Figure 16.2 Double-lift circuit Luo converter. (a) Equivalent circuit for switch...

Figure 16.3 Circuit diagram for SLMLI (15 levels).

Figure 16.4 Flow chart for PSO algorithm.

Figure 16.5 Simulation voltage waveform for 15-level super-lift Luo converter.

Figure 16.6 Simulation current waveform for 15-level super-lift Luo converter.

Figure 16.7 Harmonic order vs. magnetic phase output voltage.

Figure 16.8 Harmonic order vs. magnetic phase output voltage.

Figure 16.9 Hardware model of concentrated SPV array.

Figure 16.10 Hardware SLMLI voltage waveform.

Figure 16.11 Hardware SLMLI current waveform.

Chapter 17

Figure 17.1 Illustration of rolling resistance force.

Figure 17.2 Representation of grading force acting on a vehicle.

Figure 17.3 Illustration of aerodynamic drag and lift forces.

Figure 17.4 Sweep plow.

Figure 17.5 Field cultivator.

Figure 17.6 Seed planter.

Figure 17.7 Disk horrow (tandem).

Figure 17.8 Variation of draft force with tillage depth.

Figure 17.9 Variation of draft force with operating velocity.

Figure 17.10 MATLAB simulation diagram for powertrain sizing.

Figure 17.11 MATLAB simulation diagram of Powertrain subsystem.

Figure 17.12 MATLAB simulation diagram for calculating torque and power for tran...

Figure 17.13 Modified NEDC drive cycle with reduction speed ranges applied to tr...

Figure 17.14 Power requirement of NEDC drive cycle.

Figure 17.15 Motor torque for the NEDC drive cycle.

Chapter 18

Figure 18.1 Wireless electric vehicle charging system (WEVCS).

Figure 18.2 Types of electric vehicle wireless charging systems.

Figure 18.3 Compensation topology.

Figure 18.4 Compensation topologies: (a) Series-Series; (b) Series-Parallel; (c)...

Figure 18.5 Hybrid topologies: (a) Series-CLC; (b) Series-LCL; (c) CCL-Series; (...

Figure 18.6 Series-Series topology simulation model.

Figure 18.7 Series-Series topology simulation model.

Figure 18.8 Input current waveform.

Figure 18.9 Transmission coil voltage after compensation.

Figure 18.10 Receiver coil voltage before compensation.

Figure 18.11 Receiver coil voltage after compensation.

Figure 18.12 Output voltage waveform.

Figure 18.13 Receiver coil voltage before compensation.

Figure 18.14 Transmission coil voltage before compensation.

Figure 18.15 Transmission coil voltage after compensation.

Figure 18.16 Output voltage waveform.

Chapter 19

Figure 19.1 Block diagram of PV system in grid connected mode.

Figure 19.2 Circuit diagram of boost converter.

Figure 19.3 Irradiation curve for PV input.

Figure 19.4 Flowchart of MPPT algorithm.

Figure 19.5 Control loop to generate d-axis reference current.

Figure 19.6 Circuit to generate d-axis modulation signal.

Figure 19.7 Circuit to generate q-axis modulation signal.

Figure 19.8 Circuit to generate final modulation signal for PWM generator.

Figure 19.9 Block diagram of PV system in islanded mode.

Figure 19.10 Control loop to obtain modulation index.

Figure 19.11 Circuit to obtain final modulation signal.

Figure 19.12 Waveforms for PV system in grid connected mode. (a) Grid currents, ...

Figure 19.13 Grid currents and DC bus voltage for PV system in grid connected mo...

Figure 19.14 Waveforms for PV system in islanded mode. (a) Inverter voltage, (b)...

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

Also of Interest

End User License Agreement

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Scrivener Publishing

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Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Smart Grids and Green Energy Systems

Edited by

A. Chitra

V. Indragandhi

and

W. Razia Sultana

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 9781119872030

Cover images: Background, Disconnectors, Fedecandoniphoto | Transmission Tower, Roncivil | Dreamstime.com Cover design by Kris Hackerott

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Preface

The ecological benefits of a greenhouse gas emission-free system are tremendous. Recent trends in energy are heading in that direction. In such circumstances, smart grids and green energy systems are emerging as two of the most important parts to this progress. The demand for renewable energy is increasing every day. Hence, the technology needs to be supported from an academic perspective to service industrial demands.

This book aims to bring together the research that has been carried out in the field of smart grids and green energy systems. Smart grids and green energy systems involve the expertise from various areas of research, such as electrical engineering, electronics, computer engineering, and mechanical engineering. This book will serve as a point of convergence wherein all these domains need to be addressed.

The various chapters are configured in order to address the challenges faced in smart grids and green energy systems from various fields and possible solutions. The outcome of this book can serve as a potential resource for industry professionals, engineers, and researchers working in the domain of smart grids and green energy systems.

The contributions for this book have been chosen from various esteemed national and international institutions. We would like to extend our sincere thanks to all the valuable contributors for the wonderful research contribution. Our sincere thanks to Vellore Institute of Technology, for providing all the support to make this book a success. We would like to extend our heartfelt gratitude to the Wiley-Scrivener team for providing endless support in making this book into a reality.

1Studies on Enhancement of Battery Pack Efficiency Using Active Cell Balancing Techniques for Electric Vehicle Applications Through MATLAB Simulations

B. Akhila and S. Arockia Edwin Xavier*

Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India

Abstract

Battery management system (BMS) is one of the most vital parts of electric vehicles, and their precision and accuracy in every aspect are required. Balancing of cells in battery packs is an important task of BMS when it comes to increasing the lifetime of the whole battery pack, end of life (EOL), and the reliability. For balancing individual cells of a battery pack, there are two main methodologies: the passive balancing technique and the active cell balancing technique. In passive cell balancing, state of charge (SOC) is considered for equalization and voltage in case of active cell balancing. This work analyzes which of the two methods is efficient. Moreover, there are several methods that come under active cell balancing method and this work concentrates more on the usage of flyback converter technique and multi-winding transformer method. The proposed cell balancing is analyzed by simulations and results.

Keywords: Active cell balancing, battery cell balancing, battery management system, passive cell balancing

1.1 Introduction

Electric automotives were prevalent in the 19th century because of their ease of use and comfort. However, the invention of combustion engines dominated over their electrical counterpart. The EVs are reappearing in the automotive industries due advancement in the technologies and the consideration of environmental impacts.

Although the electric vehicles are slowly taking over the industries, still there are some uncertainties in the vehicle systems and dubiousness among the customers in purchasing. Charging time, range, safety, comforts, and many other factors are in the considerations list. One of the main systems manufacturers that are focusing more especially on electrical side is the battery management system (BMS) (Figure 1.1). Increasing their lifetime despite the surroundings where they are subjected to is the main objective. In this work, we are concentrating on one of the main tasks of BMS, the balancing [1].

1.2 Influence of Lithium Ion Batteries

Li-ion battery systems are becoming prevalent for the propulsion of electric vehicles because of their lighter weight, high energy density, extended lifetime, fast charge capability, low level of self-discharge, and eco-friendliness. Li-ion battery cell level and pack variables are needed to be maintained accurately for safe operation [15, 16]. BMS acts as a brain of a battery pack that incorporates monitoring of temperature, voltage, current, and state of charge (SOC).

Figure 1.1 Typical BMS tasks.

1.3 Cell Balancing

Not every cell is manufactured evenly. Researchers has found that lifetime of a battery pack in an EV is dependent on health of each cell, apart from the whole pack reaching its end of life (EOL). Therefore, it becomes important to monitor and control each and every cell. The cell that has lost its ideal characteristics of charging and discharging can be removed from the pack and replaced with new one. To maintain their shelf life, balancing of each cell becomes important [4].

1.3.1 Types of Cell Balancing

Cycling a battery pack eventually causes individual cells to become out of balance. Therefore, cell balancing becomes one of the important tasks of BMS [5]. The weakest cell limits the amount of charge that can be drawn and the strongest cell limits the extent to which it can be charged. Thus, balancing becomes important. There are two types of cell balancing techniques as shown in Figure 1.2 [2, 3].

1.3.2 Passive Cell Balancing

Passive cell balancing is a dissipative method in which the parallel resistors bleed excess charge from individual cell as shown in Figure 1.3. Bleeding here means that they dissipate the excess charge through this resistor.

1.3.3 Active Cell Balancing

Active cell balancing is a non-dissipative method in which the charge is redistributed equally among the cells. The cell with the higher charge will be redistributed to the cell with weakest SOC or voltage [8, 9].

The two methods of active cell equalization discussed in this work uses flyback converter and other method uses multi-winding transformer.

The operation of flyback converter is very simple. Here, the windings of inductor 1 and inductor 2 act individually rather than acting as a single transformer. This makes the flyback converter topology stand out from other transformers. When the current passes through the switch (SW), magnetic field is set up, which is stored in the core as energy. This reverse biases the output diode. This magnetic field is collapsed when SW is in open condition and transfers the stored energy to the secondary winding that makes the flyback diode forward biased. Finally, the energy is eventually sent to the load as shown in Figure 1.4.

Figure 1.2 Types of cell balancing.

Figure 1.3 Circuit diagram for passive cell balancing.

Figure 1.4 Circuit diagram for active cell balancing using flyback converter.

Figure 1.5 Circuit diagram for active cell balancing using multi-winding transformer.

While using the multi-winding transformer, the whole battery current is transferred to the transformer and the output of the transformer is rectified and brought inside the cell that has lowest SOC/voltage level through corresponding semiconductor switches as shown in Figure 1.5 [12].

1.3.4 Why Cell Balancing is Important

Cycling a battery pack eventually causes individual cells to become out of balance. Therefore, cell balancing becomes one of the important operations of the BMS. Not all cells behave alike because of columbic efficiency difference.

Weakest cell limits the amount of charge that can be drawn. Strongest cell limits the extent to which it can be charged.

1.4 Block Diagram

The entire block shown in Figure 1.6 consists of battery cells, resistors, MOSFET switches, and a switch control signal block. The battery cell used is of 7.2 V and 5.4 Ah. The switch control signal is the MATLAB program to trigger the gate terminal of the MOSFET switch.

In active cell balancing technique as shown in Figure 1.7, the voltage of the higher cell is redistributed to the cell that has lowest charge through storage element [11]. Switch control signal is used to monitor which cell is of lowest voltage and targets it to provide it with the necessary energy. The software used in this project is MATLAB/SIMULINK. With the help of this software output waveforms of SOC, current and voltage waveforms of the passive cell balancing and active cell balancing technique is obtained. MATLAB program is written which is fed to the function block of the MATLAB in order to trigger the gate pulse of the MOSFET switch with which we control the charging and discharging cycle of each battery cell.

1.5 SOC Control Using Passive Cell Equalization

The simulation model has three lithium ion batteries of 7.2 V and 5.4 Ah each in parallel with MOSFET-resistor pair as shown in Table 1.1. The gate terminal of the MOSFET is triggered according to the MATLAB coding. Thus, we are monitoring which cell is to be drained and which should not. Each cells are set to different initial SOC for better comparative studies and analysis. Components and elements are used from the library browser of the simulink window. The codes are fed in the respective MATLAB function block. SOCs being the input for the code and output of the code will be fed to the gate terminal for triggering purpose.

Figure 1.6 Basic block diagram for passive cell balancing.

Figure 1.7 Basic block diagram for active cell balancing.

Table 1.1 Simulation specification of cells.

Specifications

Cell 1

Cell 2

Cell 3

Nominal voltage

7.2 V

7.2 V

7.2 V

Rated capacity

5.4 Ah

5.4 Ah

5.4 Ah

Initial SOC

15%

30%

60%

The output of the code will be either 1 or 0 (high or low), and it will decide which of the three gate terminal should be turned on from which the discharging cycle will occur.

1.5.1 Equalization Results

In the simulation setup, we have three lithium ion cells in parallel with MOSFET-resistor pair. With the help of bus selector, SOC and current are measured. The SOC plot of 30% initial SOC is found to be balanced at 1,250 s and 60% initial SOC is found to be balanced at 3,550 s. Finally, the SOC of all three cells attains the balanced state of 15% SOC as shown in Figure 1.8. The ifelse statement continuously operates until it reaches the minimum SOC in each cell. Here, the SOC is taken as the input for the function block where set of nested ifelse code is available. The output of the program will be fed to the gate terminal of the MOSFET (1 or 0). The cell will be draining accordingly (Figure 1.9) until it reaches a preset value of SOC (minimum of all three cells).

Figure 1.8 Simulation waveform for output SOC.

Figure 1.9 Voltage waveform of cell1, cell2, and cell3.

1.6 Voltage Control Using Active Cell Equalization

1.6.1 The Flyback Converter Method

The model uses two capacitors as cells each of 5-F capacitance and the initial voltage of first cell is set to be 6 V and the second cell voltage is set to be 4 V [13]. Flyback diode id reverse biased when the MOSFET switch is closed and current passes through the primary winding as it sets up a magnetic field. When the MOSFET switch is opened, the magnetic field collapses thus forward biasing the diode in the secondary side as a result they transfer energy.

From the output voltage waveform as shown in Figures 1.10a and b of the flyback converter equalization topology, it is evident that the balancing of cells occurs only when needed, and the charges are redistributed accordingly (Table 1.2). In addition, there is no waste of energy through heat dissipation, since there is no bleeding resistor.

Table 1.2 clearly shows the cell that has been balanced to a certain point with little deviations and very less transients. From the results, it can be said that this is one of the reliable and comparatively less charge wastage method because of the effective redistribution of the voltage among every cells of the battery pack.

When it comes to on-road applications, the charge/voltage redistribution should be done carefully and effectively since a little amount of charge wastage causes significant impact on the efficiency of the whole vehicle. Thus, maintaining little to no charge wastage at the balancing system becomes a crucial and essential task.

Figure 1.10 (a) Output voltage waveform of active cell balancing using flyback converter. (b) Output voltage waveform of active cell balancing using flyback converter.

Table 1.2 Output voltages after cell equalization (using flyback converter).

Cell no.

Capacitance (F)

Initial cell voltage (V)

Balanced voltage (V)

1

cell1

5

6

5.31

cell2

5

4

4.502

2

cell1

5

10

7.472

cell2

5

5

7.471

1.6.2 The Multi-Winding Transformer Method

The multi-winding transformer model is analyzed with two cells and three cells topology respectively to observe their behavior and deviations from ideal state when number of cells is multiplied as shown in Figures 1.11 a and b. The model uses capacitors of 5 F and diodes of 0.001-ohm resistance and 0.8-V forward voltage. Here, the entire battery current is transferred to transformer through the switch.

From the output waveforms, it is evident that the cell is getting balanced, but there are unnecessary discharges too (Table 1.3). Some of the cells need not be balanced since their voltages are nearly equal to the lowest charge cell. Despite that, they are being discharged and charged too. In addition, there are some transients in the beginning as shown in Figures 1.12a and b.

Table 1.3 Output voltages after cell equalization (using flyback converter—two-cell topology).

Cell no.

Capacitance (F)

Initial cell voltage (V)

Balanced voltage (V)

1

cell1

5

6

1.161

cell2

5

4

2.125

2

cell1

5

15

4.102

cell2

5

10

4.721

Figure 1.11 (a) Output voltage waveform of active cell equalization using multi-winding transformer (two-cell topology). (b) Output voltage waveform of active cell equalization using multi-winding transformer (two-cell topology).

Figure 1.12 (a) Output voltage waveform of active cell balancing using multi-winding transformer (two-cell topology). (b) Output voltage waveform of active cell balancing using multi-winding transformer (two-cell topology).

1.7 Conclusion

The waveforms show us how the battery behaves during the process of balancing. Since cell balancing is one of the important BMS tasks that increase shelf life of the whole pack, it is important to analyze their behavior in various circumstances. This work has figured out their behavior without the considerations of thermal aspects.

The passive cell balancing technique means equalizing the SOC of the cells by the dissipation of energy from higher SOC cells and formulates all the cells with similar SOC equivalent to the lowest level cell SOC. Active cell balancing is more efficient than the passive method because the charge redistribution [6] and balancing (energy transfer) occurs only when it is needed (Table 1.5). Among two active cell balancing methods, the flyback converter-based equalization is found to be more efficient because in multi-winding transformer method (Table 1.4) increase in number of cells cause deviations in the output voltage and unnecessary voltage dip during the initial time period [14]. However, in active cell balancing method, there is no wastage of energy in the form of heat dissipation through the bleeding resistor unlike passive cell equalization method.

Establishing a suitable equalizing system will protect the individual cell voltage variation that may vary over time, the overall battery capacity and the electric driving range of electric vehicles can be enhanced, and the lifetime of the whole battery pack will be significantly increased [7]. The future scope of the cell balancing techniques can be done with the help of smart intelligent techniques. This can include some of the soft computing techniques like fuzzy logic and genetic algorithm [10].

Table 1.4 Output voltages after cell equalization (using multi-winding transformer—two-cell topology).

Cell no.

Capacitance (F)

Initial cell voltage (V)

Balanced voltage (V)

1

cell1

5

6

2.472

cell2

5

4

0.4719

cell3

5

2

1.89

2

cell1

5

15

9.072

cell2

5

10

4.072

cell3

5

5

2.815

Table 1.5 Comparative study of passive and active cell equalization.

Passive cell balancing

Active cell balancing

Degree of difficulty in circuitry implementation

Easier implementation

Complex circuit

Energy

Some energy is wasted in the form of heat

Transfers the energy to neighboring cells instead of wasting

Components

Needs less components

Demands additional components

Cost

Cost effective

Increased cost

Efficiency

Less efficient due to heat dissipation through bleeding resistors

More efficient because the energy is transferred within the cells rather than wasting it

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*

Corresponding author

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[email protected]

2Evaluation and Impacts of Minimum Energy Performance Standards of Electrical Motors in India

S. Manoharan1*, G. Sureshkumaar2, B. Mahalakshmi3 and V. Govindaraj2

1Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, India

2Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, India

3Dept. of Physics, Hindustan College of Engineering and Technology, Coimbatore, India

Abstract

The demand for electricity is rapidly increasing, posing a significant challenge to the entire power industry in meeting the unprecedented demand. The inefficiency of the low-quality motors leads to increased electric power consumption and the associated energy cost. As a result, improving the motor’s efficiency must be a component of any overall energy saving strategy. Induction motors are widely employed as driving power in most industries, accounting for over 65% of total power utilization. These motors’ energy-efficient design saves a lot of energy and lowers operating costs. The majority of energy consumed in industries is used to power various motors, which consumes a considerable amount of energy that may be greatly decreased by replacing the normal motor with a high efficiency motor. The goal of this study is to shed light on India’s existing Minimum Energy Performance Standards (MEPS) for asynchronous motors by applying IS 12615: 2018, which is founded on the International Test Standard IEC 60034-2-1. This document also looks at the Indian government’s energy-saving programs, such as the National Motor Replacement Program (NMRP), as well as the challenges that must be addressed and the path forward to implement this mission.

Keywords: Minimum Energy Performance Standards (MEPS), IE2, IE3, IE4, energy demand, efficiency, National Motor Replacement Program (NMRP)

2.1 Introduction

India’s energy demand will grow faster than any other country over the next two decades, according to the International Energy Agency (IEA), with India overtaking the European Union (EU) as the world’s third largest energy consumer by 2030. Under present policies, India’s energy needs will grow three times faster than the global average, according to the IEA’s India Energy Outlook 2021. India is currently the world’s fourth largest energy consumer, trailing only China, the United States, and the EU. Industry’s increased use of electrical motors and other machinery has also contributed to an increase in electricity demand [1].