Description
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
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
- 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
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.
