Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Arif I. Sarwat

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Malek Adjouadi

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Jean Andrian

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Mohammad Ashiqur Rahman

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Ravindra Singh

Fifth Advisor's Committee Title

Committee Member

Keywords

Battery, Battery management systems, Grid-connected microgrid, Lifecycle assessment, Charge control, Battery degradation

Date of Defense

2-22-2022

Abstract

Energy storage systems are the critical components enabling an electrified future ranging from electric vehicles (EVs) to microgrid (MG) applications. With the advancements in these applications, the operational dynamics of electrochemical energy storage such as Lithium-ion batteries need to be analyzed from the lifecycle impact, cybersecurity, balancing requirements, and prognostics perspectives. These operations enable the interconnected systems to be secure, efficient, and self-adaptive while reducing greenhouse emissions.

This research aims to develop threefold solutions. The first objective of this dissertation is to analyze lifecycle impact, quantify the interconnection requirements of battery energy storage systems, and evaluate cybersecurity frameworks for battery management systems (BMSs). The KJ/kWh lifecycle impact of various sized battery packs/banks is analyzed using environmental protection agency approved drive cycles for EVs. Additionally, load profiles of critical infrastructures in Miami for renewable energy storage are also analyzed. The battery with the lowest KJ/kWh is then analyzed from an interconnection requirements standpoint, quantified, and validated with FIU’s MG’s specifications and power quality data. Finally, a cybersecurity framework for the interconnected BMSs is proposed, describing the system’s vulnerability not accounted for in well-known standards. A primal brief on the next-generation storage systems is also covered in this objective.

The second objective of this dissertation is to build multiple machine learning models to estimate the operational, degradation parameters and for attack identification in a BMS. These models, both standalone and unified in nature, were subjected to an actual dataset obtained from a 18650 battery cycled under decrementing C-rates, indicating long-duration operations. The proposed minimized Akaike information criterion-based unified nonlinear autoregressive exogenous model reduced the resulting mean squared error (MSE) to 0.0175% when estimating state of charge (SOC) at C/10 rate. Similar wavelet-based recurrent models are analyzed to identify the capacity fade phenomenon occurring in fast-charging batteries, subjected to incrementing C-rates. Proposed unified empirical mode decomposition-recurrent wavelet neural network (RWNN) model outperforms other wavelet neural network (WNN)-based models when the number of hidden layers and wavelet type is varied. In addition, a hybrid principal component analysis based k-means clustering approach implemented on a simulated active BMS with a close-to-second life battery identifies cyber-attacks (for voltage buffer (∆V) ≤ 0.2) taking place on the ∆V parameter within the BMS, with reasonable accuracy.

As a continuation, fast time-to-balance algorithms for the same BMS with varying equalization circuits and battery pack configurations are developed for the last objective. The cell-to-stack-to-cell charge transfer algorithm implemented on a distributed converter topology connected across non-interleaved battery pack leads the balancing performance. In addition, a passive BMS hardware-in-the-loop testbed is implemented to validate a commercial BMS, results of which suggest an MSE reduction of 0.18% for voltage and 3.61% for SOC for a scaled-up battery.

Identifier

FIDC010492

ORCID

0000-0003-1769-868X

Previously Published In

  1. A. Khalid, and A. I. Sarwat, “Unified univariate-neural Network Models for Lithium-ion Battery State-of-charge Forecasting using Minimized Akaike Information Criterion Algorithm.” IEEE Access 9 (2021): 39154-39170.
  2. A. Khalid, A. Stevenson, and A. I. Sarwat, “Overview of Technical Specifications for Grid-Connected Microgrid Battery Energy Storage Systems.” IEEE Access 9 (2021): 163554-163593.
  3. A. Khalid, A. Stevenson, and A. I. Sarwat. “Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed.” Energies 14.23 (2021): 8037.
  4. A. Khalid, A. Hernandez, A. Sundararajan, and A. I. Sarwat, "Simulation-based Analysis of Equalization Algorithms on Active Balancing Battery Topologies for Electric Vehicles.“ Advances in Intelligent Systems and Computing, Springer, Cham, 2019.
  5. A. Sarwat, A. Khalid, A. H. Jalal, and S. Bhansali. "Sizing and Lifecycle Assessment of Electrochemical Batteries for Electric Vehicles and Renewable Energy Storage Systems. "Smart Mobility - Recent Advances, New Perspectives and Applications, IntechOpen (accepted).
  6. A. Sarwat, A. Khalid, and A. Sundararajan. "Systems and methods for forecasting battery state of charge." U.S. Patent No. 10,969,436. 6 Apr. 2021.
  7. A. Khalid, M. Khan, M. Stevenson, S. Batool, A. Sarwat. "Investigation of cell voltage buffer manipulation attack in a battery management system using unsupervised learning technique." 2021 IEEE Design Methodologies Conference (DMC). IEEE, 2021.
  8. A. Khalid, and A. Sarwat. "Fast charging li-ion battery capacity fade prognostic modeling using correlated parameters' decomposition and recurrent wavelet neural network." 2021 IEEE Transportation Electrification Conference & Expo (ITEC). IEEE, 2021.
  9. A. Khalid, S. Tufail, and A. Sarwat. "A review on quantum computing approach for next-generation energy storage solution." SoutheastCon 2021. IEEE, 2021.
  10. J. Sanfiel, A. Khalid, I. Parvez, A. Sarwat. "Simulation-based sizing and impact study of microgrid on a university campus." SoutheastCon 2021. IEEE, 2021.
  11. A. Khalid, A. Sundararajan, and A. Sarwat. "An ARIMA-NARX model to predict li-ion state of charge for unknown charge/discharge rates." 2019 IEEE Transportation Electrification Conference (ITEC-India). IEEE, 2019.
  12. A. Khalid, and A. Sarwat. "Battery module performance analysis under varying interconnection topology for electric vehicles." 2019 IEEE Transportation Electrification Conference (ITEC-India). IEEE, 2019.
  13. A. Khalid, A. Sundararajan, and A. Sarwat. "A statistical out-of-sample forecast to estimate lithium-ion parameters that determine state of charge." ECS Meeting Abstracts. No. 4. IOP Publishing, 2019.
  14. A. Khalid, A. Sundararajan, and A. Sarwat. "A multi-step predictive model to estimate li-ion state of charge for higher c-rates." 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). IEEE, 2019.
  15. A. Khalid, A. Sundararajan, and A. Sarwat. "FACTS approach to address cybersecurity issues in electric vehicle battery systems." 2019 IEEE Technology & Engineering Management Conference (TEMSCON). IEEE, 2019.
  16. A. Khalid, A. Sundararajan, I. Acharya, and A. Sarwat. "Prediction of li-ion battery state of charge using multilayer perceptron and long short-term memory models." 2019 IEEE Transportation Electrification Conference and Expo (ITEC). IEEE, 2019.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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