USER'S INSTRUCTIONS: The project work you are about to view is on "reliability assessment of batteries". Please, sit back and study the below research material carefully. This project topic "reliability assessment of batteries" have complete 5(five) Chapters. The complete Project Material/writeup include: Abstract + Introduction + etc + Literature Review + methodology + etc + Conclusion + Recommendation + References/Bibliography.Our aim of providing this "reliability assessment of batteries" project research material is to reduce the stress of moving from one school library to another all in the name of searching for "reliability assessment of batteries" research materials. We are not encouraging any form of plagiarism. This service is legal because, all institutions permit their students to read previous projects, books, articles or papers while developing their own works.

---

TITLE PAGE

 

BY

---
--/H2013/01430
DEPARTMENT OF ----
SCHOOL OF ---
INSTITUTE OF ---

---

APPROVAL PAGE

This is to certify that the research work, "reliability assessment of batteries" by ---, Reg. No. --/H2007/01430 submitted in partial fulfillment of the requirement award of a Higher National Diploma on --- has been approved.

By
---                                                     . ---
Supervisor                                                  Head of Department.
Signature……………….                           Signature……………….        

……………………………….
---
External Invigilator

---

DEDICATION
This project is dedicated to Almighty God for his protection, kindness, strength over my life throughout the period and also to my --- for his financial support and moral care towards me.Also to my mentor --- for her academic advice she often gives to me. May Almighty God shield them from the peril of this world and bless their entire endeavour Amen.

---

ACKNOWLEDGEMENT

The successful completion of this project work could not have been a reality without the encouragement of my --- and other people. My immensely appreciation goes to my humble and able supervisor mr. --- for his kindness in supervising this project.
My warmest gratitude goes to my parents for their moral, spiritual and financial support throughout my study in this institution.
My appreciation goes to some of my lecturers among whom are Mr. ---, and Dr. ---. I also recognize the support of some of the staff of --- among whom are: The General Manager, Deputy General manager, the internal Auditor Mr. --- and the ---. Finally, my appreciation goes to my elder sister ---, my lovely friends mercy ---, ---, --- and many others who were quite helpful.

---

PROJECT DESCRIPTION: This work "reliability assessment of batteries" research material is a complete and well researched project material strictly for academic purposes, which has been approved by different Lecturers from different higher institutions. We made Preliminary pages, Abstract and Chapter one of "reliability assessment of batteries" visible for everyone, then the complete material on "reliability assessment of batteries" is to be ordered for. Happy viewing!!!

---

TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
ACRONYM AND THEIR MEANING
CHAPTER ONE
1.0      INTRODUCTION
1.1      BACKGROUND OF THE PROJECT

• PROBLEM STATEMENT
• AIM AND OBJECTIVE OF THE PROJECT

CHAPTER TWO
LITERATURE REVIEW

• OVERVIEW OF THE STUDY
• OVERVIEW OF LITHIUM-ION BATTERY
• LITERATURE SURVEY OF THE STUDY
• BRIEF REVIEW ON THE HISTORY ON THE DEVELOPMENT OF LI‐ION BATTERIES
• RELATED WORK
• PROGNOSTICS AND HEALTH MANAGEMENT

CHAPTER THREE
3.0     METHODOLOGY
3.1      INTRODUCTION
3.2      DATA-DRIVEN ANALYSIS AND MODELING
3.3    ARTIFICIAL NEURAL NETWORKS
3.4      OVERVIEW OF THE DEEP LEARNING CONCEPT

• 3.5      EMPLOYMENT OF DEEP LEARNING TO PROGNOSTIC DATA

3.6 THE DEEP NEURAL NETWORK FRAMEWORK AND MODEL FOR PROGNOSTIC DATA

•  

CHAPTER FOUR

• RESULTS FOR SOH ESTIMATION
• RESULTS FOR RUL ESTIMATION
• DISCUSSION AND FUTURE WORK

CHAPTER FIVE

• CONCLUSION
• REFERENCES

ACRONYM AND THEIR MEANING
RUL = Remaining useful life
RNN = Recurrent neural network
CNN = Convolution neural network
DFF = Deep feed forward network
Seq2Seq = Sequence to sequence
HI = health indicator
LSTM = long-short term memory

1.0       INTRODUCTION

Reliability of an individual test has been pursued in a variety of ways aiming to indicate consistency, precision, repeatability, trustworthiness, and so on of a test. Reliability as degree of consistency of test scores, that is, repeatability, has been recommended by Berkowitz et al. (2000). However, different values of test–retest reliability of a single test may be obtained depending on the time gap between the administrations. Cronbach’s alpha is widely used to find reliability as a measure of internal consistency. However, it is necessary to check that the data fit the unidimensional model before calculating alpha (Trizano-Hermosilla and Alarado, 2016). Violation of assumption of tau-equivalence will underestimate α (Graham, 2006; Raykov, 1997). Working with data which comply with this assumption is generally not viable in practice (Teo and Fan, 2013). Wilcox (1992) showed how coefficient alpha is vulnerable to modest numbers of outlying observations and may substantially inflate alpha. Limitations of Cronbach’s alpha have also been reported by Hattie (1985) and Ritter (2010).

Consequently, multiple values of the error variance and reliability can be obtained for the same test even if the sample remains unchanged. Reliability that conforms to the theoretical definition cannot be computed since true scores of individuals taking the test are not known. Moreover, estimates of reliability are themselves vulnerable to measurement error (Vacha-Haase et al., 2000).
The above are also applicable for estimating reliability of a battery of tests, where battery score is taken as sum or weighted sum of component tests scores. Reliability of the battery can be influenced significantly by method of selection of weights to arrive at the battery scores and methods of estimating reliability of component tests, since sources of errors of individual tests may get manifold for the battery. For estimation of reliability of a battery, Lubans et al. (2011) used test–retest approach.
Berchtold (2016) opined that while reliability is the ability of a measure applied twice upon the same respondents to produce the same ranking on both occasions, agreement requires not only to preserve the relative order of the respondents in the two sets of measurements but also the same exact result that each respondent obtains on the two testing situations. Battery reliability using weighted sum of component tests was attempted by researchers like Rudner (2001), Webb et al. (2007), and others using different approaches and scaling.

However, the approaches gave rise to different values of battery reliability, especially when individual components measure multidimensional constructs. Rudner (2001) indicated that adding raw scores fails to recognize the relative importance of components to the overall composite; weights proportional to reliability of the components may results in lower SEM of the composite score in comparison to simple summative scores. In case of availability of pre-specified, quantifiable external criterion, weights of component tests may be found by linear multiple regressions with the criterion. Such method of finding weights may result in maximum validity of the composite score.
Thus, need is felt to estimate reliability of a battery as a function of reliability of each component test along with estimation of true score variance of the battery, considering battery score as a weighted sum of component tests and avoiding scaling of scores of component tests. Also, Satyendra (2020) make a Reliability test of a battery and this research intend to study the reliability assessment of batteries.

1.2       STATEMENT OF PROBLEM
This study wants to assess the reliability of batteries and bridge practical knowledge gap in computation of the reliability of a battery. Satyendra (2020) make a Reliability test of a battery through the computation of the reliability of a battery with summative scores weighted scores.
1.3       AIM AND OBJECTIVES
This research, aim to assess the reliability of batteries.
The objectives of this study are as follows;

• To assess various batteries test
• To examine the reliability of various batteries
• To justify various reliability computational method of batteries.

CHAPTER FIVE

5.1                                            CONCLUSIONS

This work aims to accomplish two tasks. First, a complete benchmarking of the data-driven model by using a machine learning algorithm with the battery prognostic data is made. Second, a preliminary data-driven model is developed by using a deep learning algorithm for the prognostic data. This paper has achieved its goal to aid, as a benchmark, the prognostic data-driven model for battery data using machine learning algorithms, and based on the results from the case studies, it shows that the deep learning algorithm provides a promising outcome for predicting and modeling the prognostic data, especially in the battery prognostic and health management applications. Based on the accuracy archived, we also believe that the traditional physics-based model may be replaced by data-driven models in the near future, in various fields and applications. The reliable data-driven model has many advantages over a traditional physics-based model. The first major advantage is that it overcomes the complexity of the physics-based model. This attribute of less complexity in a data- driven model helps to reduce the involvement of the domain experts in particular fields. In the future, the predictive model might be able to be generated and constructed without any opinion or knowledge from experts at all. The second advantage is that data-driven models can be employed in real-time situations, due to the shorter computational time needed, when compared to physics-based models in general. The last point is that the data-driven model is more cost-effective to construct and to employ in real applications. As an example, a data-driven model can be generated and monitored by using only regular personal computing devices, without the need for exclusive and excessive resources. This future trend of data-driven models is in line with the recent achievement of deep learning algorithms and artificial intelligence. These methodologies are believed to be the main approaches in the further development of data-driven models. However, the accuracy of prediction and the higher performance of using deep learning algorithms also comes with the drawback of higher computational time. With rapid advancements in technology, the computational time could be substantially reduced. The future direction of this work will focus on developing a hybrid-deep learning model that could be universally applicable to multiple types of prognostic data.

 

REFERENCES

Berchtold A (2016) Test–retest: Agreement or reliability? Methodological Innovations 9: 1–7.
Berkowitz D, Wolkowitz B, Fitch R, et al. (2000) The Use of Tests as Part of High-Stakes Decision Making for Students: A Resource Guide for Educators and Policy Makers. Washington, DC: US Department of Education.
Graham JM (2006) Congeneric and (essentially) tau-equivalent estimates of score reliability what they are and how to use them.
Hattie J (1985) Methodology review: Assessing uni-dimensionality of tests and items. Applied Psychological Measurement 9(2): 139–164.
Lubans DR, Morgan P, Callister R, et al. (2011) Test–retest reliability of a battery of field-based health-related fitness measures for adolescents. Journal of Sports Sciences 29(7): 685–693.
Raykov T (1997) Scale reliability, Cronbach’s coefficient alpha, and violations of essential tau-equivalence with fixed congeneric components. Multivariate Behavioral Research 32: 329–353.
Ritter N (2010) Understanding a widely misunderstood statistic: Cronbach’s alpha. Paper presented at southwestern educational research association (SERA) conference, New Orleans, LA, 17–20 February.
Rudner L (2001) Informed test component weighting. Educational Measurement: Issues and Practice 20: 16–19.
Satyendra (2020); Reliability of test battery. Methodological Innovations; journals.sagepub.com/home/mio
Teo T and Fan X (2013) Coefficient alpha and beyond: Issues and alternatives for educational research. Asia Pacific Education Review 22: 209–213.
Trizano-Hermosilla I and Alarado JM (2016) Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology 7: 769.
Vacha-Haase T, Kogan LR and Thompson B (2000) Sample compositions and variabilities in published studies versus those in test manuals: Validity of score reliability inductions. Educational and Psychological Measurement 60(4): 509–522.
Webb N, Shavelson R and Haertel E (2007) Reliability coefficient and generalizability theory. In: Rao CR and Sinharay S (eds) Handbooks of Statistics 26: Psychometrics. Amsterdam: Elsevier, pp. 81–124.

 

---

CHAPTER TWO: The chapter one of this work has been displayed above. The complete chapter two of "reliability assessment of batteries" is also available. Order full work to download. Chapter two of "reliability assessment of batteries" consists of the literature review. In this chapter all the related work on "reliability assessment of batteries" was reviewed.

CHAPTER THREE: The complete chapter three of "reliability assessment of batteries" is available. Order full work to download. Chapter three of "reliability assessment of batteries" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.

CHAPTER FOUR: The complete chapter four of "reliability assessment of batteries" is available. Order full work to download. Chapter four of "reliability assessment of batteries" consists of all the test conducted during the work and the result gotten after the whole work

CHAPTER FIVE: The complete chapter five of "reliability assessment of batteries" is available. Order full work to download. Chapter five of "reliability assessment of batteries" consist of conclusion, recommendation and references.

 

---

To "DOWNLOAD" the complete material on this particular topic above click "HERE"

Do you want our Bank Accounts? please click HERE

To view other related topics click HERE

To "SUMMIT" new topic(s), develop a new topic OR you did not see your topic on our site but want to confirm the availiability of your topic click HERE

Do you want us to research your new topic? if yes, click "HERE"

Do you have any question concerning our post/services? click HERE for answers to your questions

---

For more information contact us through any of the following means:

Mobile No :+2348146561114 or +2347015391124 [Mr. Innocent]

Email address :engr4project@gmail.com

Watsapp No :+2348146561114

---

COUNTRIES THAT FOUND OUR SERVICES USEFUL

Australia, Botswana, Canada, Europe, Ghana, Ireland, India, Kenya, Liberia, Malaysia, Namibia, New Zealand, Nigeria, Pakistan, Philippines, Singapore, Sierra Leone, South Africa, Uganda, United States, United Kindom, Zambia, Zimbabwe, etc
Support: +234 8146561114 or +2347015391124

Watsapp No :+2348146561114

Email Address :engr4project@gmail.com

---

FOLLOW / VISIT US VIA: