Embedded impedance based state-of-charge estimation | Blazingprojects Postgraduate Thesis
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Embedded impedance based state-of-charge estimation

 

Table Of Contents


Chapter ONE

INTRODUCTION

  • 1.1Introduction
  • 1.2Background of Study
  • 1.3Problem Statement
  • 1.4Objective of Study
  • 1.5Limitation of Study
  • 1.6Scope of Study
  • 1.7Significance of Study
  • 1.8Structure of the Research
  • 1.9Definition of Terms

Chapter TWO

LITERATURE REVIEW

  • 2.1Overview of State-of-Charge Estimation
  • 2.2Historical Development of State-of-Charge Estimation
  • 2.3Impedance-Based State-of-Charge Estimation Techniques
  • 2.4Mathematical Models for State-of-Charge Estimation
  • 2.5Challenges in State-of-Charge Estimation
  • 2.6Applications of State-of-Charge Estimation
  • 2.7Comparison of Different State-of-Charge Estimation Methods
  • 2.8State-of-Charge Estimation in Electric Vehicles
  • 2.9State-of-Charge Estimation in Energy Storage Systems
  • 2.10Future Trends in State-of-Charge Estimation

Chapter THREE

RESEARCH METHODOLOGY

  • 3.1Research Methodology Overview
  • 3.2Research Design and Approach
  • 3.3Data Collection Methods
  • 3.4Sampling Techniques
  • 3.5Data Analysis Procedures
  • 3.6Validity and Reliability of Data
  • 3.7Ethical Considerations
  • 3.8Limitations of the Research Methodology

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

  • 4.1Analysis of State-of-Charge Estimation Techniques
  • 4.2Evaluation of Mathematical Models
  • 4.3Comparison of Experimental Results
  • 4.4Interpretation of Data Findings
  • 4.5Discussion on Implications of Findings
  • 4.6Practical Applications of State-of-Charge Estimation
  • 4.7Recommendations for Future Research
  • 4.8Conclusion of Findings

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

  • 5.1Summary of Research Findings
  • 5.2Conclusion and Interpretation
  • 5.3Contributions to the Field
  • 5.4Implications for Practice
  • 5.5Recommendations for Further Study
  • 5.6Conclusion and Final Remarks

Thesis Abstract

Estimating the battery state-of-charge is an important aspect of prolonging battery life and optimizing discharge cycles. In this thesis, a hybrid method for estimating state-of-charge for lead-acid batteries is developed and implemented in a motor controller.

The method uses a combination of impedance measurements and a linear regression model for estimation. Measurements are done at stand-still. The method does not require sensors that are external to the motor controller or auxiliary battery connections other than terminal connections. The final estimation model shows good results but exhibits a slight ambient temperature dependence.


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