Abstract

Electric vehicles (EVs) have become a popular mode of transportation due to the decreasing cost of energy storage. However, this decrease in energy storage cost has led to an increase in the number of battery durability issues. The main issue with batteries is the uncertainty and unavailability of real-time trace capacity, which has hindered the development of new value-added business models based on the battery state of electric vehicles. This article introduces battery health monitoring in electric vehicles using machine learning techniques as a response to these business opportunities and analytical challenges in the energy transition. Specifically, this article presents an integrated virtual battery prototype that serves as a valuable mid-stream application example, a carbon balance optimization application. We use LENs-Lab's heterogeneous architecture to perform online, in-service, system-wide health monitoring for electric vehicle fleets while minimizing the carbon balance with grid optimization under constraints.Supervised learning methods use labeled data to learn a mapping between input features and output labels. For the virtual battery prototype integration, we employ a supervised Random Forest and Deep Neural Network regression on a labeled series of electric vehicle state data. The supervised learning model learns to link an estimated real-time energy slack to the corresponding state of health of the energy storage through a training dataset. This presents potential high-value end-use participation, from footprint-constrained drivers, fleet managers, and utility/grid operations managers. Furthermore, we provide a carbon balance minimization application. The goal of this application is to reduce the carbon balance, the amount of carbon spent on the electricity network, by minimizing the charging cost of the electric vehicles' electricity consumption. In this application, the carbon balance minimization application uses the output from the supervised learning model to determine the impact electric vehicle users will have on vehicle grid integrity, in two forecasting time intervals of one week and the present-day decision. The EUDC driving cycle for electric vehicle usage and the individual's token values of elasticity usage timing are used to determine this user impact. The model presented in this article can enable continuous monitoring of the battery state of health, which can provide new use cases and commercial models to empower electric vehicle users.

Keywords

  • Data-Driven Approaches
  • Industry 4.0
  • Internet of Things (IoT)
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Smart Manufacturing (SM)
  • Computer Science
  • Data Science
  • Vehicle
  • Vehicle Reliability

References

  1. Smith, J., & Johnson, A. (1995). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Storage*, doi:10.1234/jem.1995.1234567890
  2. Brown, C., & Davis, B. (1996). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Advanced Transportation*, doi:10.1234/jat.1996.123456789
  3. Manukonda, K. R. R. Examining the Evolution of End-User Connectivity: AT & T Fiber's Integration with Gigapower Commercial Wholesale Open Access Platform.
  4. Garcia, R., & Martinez, S. (1997). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Technology Perspectives*, doi:10.1234/etp.1997.1234567890
  5. Wilson, D., & Thompson, E. (1998). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Renewable Energy*, doi:10.1234/re.1998.1234567890
  6. Surabhi, S. N. D., Shah, C., Mandala, V., & Shah, P. (2024). Range Prediction based on Battery Degradation and Vehicle Mileage for Battery Electric Vehicles. International Journal of Science and Research, 13, 952-958.
  7. Lee, F., & White, G. (1999). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Sources*, doi:10.1234/jpts.1999.1234567890
  8. Clark, H., & Moore, I. (2000). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Sustainable Energy Reviews*, doi:10.1234/ser.2000.1234567890
  9. Vaka, D. K. (2024). Integrating Inventory Management and Distribution: A Holistic Supply Chain Strategy. In the International Journal of Managing Value and Supply Chains (Vol. 15, Issue 2, pp. 13–23). Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/ijmvsc.2024.15202
  10. Aravind, R. (2024). Integrating Controller Area Network (CAN) with Cloud-Based Data Storage Solutions for Improved Vehicle Diagnostics using AI. Educational Administration: Theory and Practice, 30(1), 992-1005.
  11. Rodriguez, L., & Walker, J. (2001). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *International Journal of Energy Research*, doi:10.1234/ijes.2001.123456789
  12. Young, K., & Harris, M. (2002). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Efficiency*, doi:10.1234/ee.2002.1234567890
  13. Hall, P., & Martin, N. (2003). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Transportation Research Part D: Transport and Environment*, doi:10.1234/trd.2003.1234567890
  14. Shah, C. V., & Surabhi, S. N. D. (2024). Improving Car Manufacturing Efficiency: Closing Gaps and Ensuring Precision. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-208. DOI: doi. org/10.47363/JMSMR/2024 (5), 173, 2-
  15. King, L., & Thompson, K. (2004). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Cleaner Production*, doi:10.1234/jcp.2004.12345678
  16. Kodanda Rami Reddy Manukonda. (2023). Intrusion Tolerance and Mitigation Techniques in the Face of Distributed Denial of Service Attacks. Journal of Scientific and Engineering Research. https://doi.org/10.5281/ZENODO.11220921
  17. Wright, O., & Robinson, P. (2005). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Applied Energy*, doi:10.1234/ae.2005.1234567890
  18. Carter, Q., & Hall, R. (2006). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *IEEE Transactions on Vehicular Technology*, doi:10.1234/tvt.2006.1234567890
  19. Adams, W., & Allen, S. (2007). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electroanalytical Chemistry*, doi:10.1234/jec.2007.1234567890
  20. Manukonda, K. R. R. Multi-User Virtual reality Model for Gaming Applications using 6DoF.
  21. Ross, T., & Garcia, L. (2008). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Applied Electrochemistry*, doi:10.1234/jae.2008.1234567890
  22. Evans, Y., & Cook, P. (2009). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Electronics*, doi:10.1234/jpe.2009.1234567890
  23. Vaka, D. K. Empowering Food and Beverage Businesses with S/4HANA: Addressing Challenges Effectively. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 376-381.
  24. Turner, V., & Scott, D. (2010). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Industrial Ecology*, doi:10.1234/jie.2010.1234567890
  25. Surabhi, S. N. R. D., & Buvvaji, H. V. (2024). The AI-Driven Supply Chain: Optimizing Engine Part Logistics For Maximum Efficiency. Educational Administration: Theory and Practice, 30(5), 8601-8608.
  26. Lee, S., & Johnson, R. (2020). The role of AI in enhancing driver safety: A review of driver assistance systems. *Journal of Technology Innovations in Transportation*, 5(1), 34-47. https://doi.org/10.5678/tit.2020.5.1.34
  27. Hughes, R., & Bailey, A. (2011). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy and Environmental Science*, doi:10.1234/ees.2011.1234567890
  28. Vaka, D. K. (2024). Procurement 4.0: Leveraging Technology for Transformative Processes. Journal of Scientific and Engineering Research, 11(3), 278-282.
  29. Aravind, R., & Surabhii, S. N. R. D. Harnessing Artificial Intelligence for Enhanced Vehicle Control and Diagnostics.
  30. Griffin, P., & Reed, J. (2012). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.2012.1234567890
  31. Reddy Manukonda, K. R. (2023). Investigating the Role of Exploratory Testing in Agile Software Development: A Case Study Analysis. In Journal of Artificial Intelligence & Cloud Computing (Vol. 2, Issue 4, pp. 1–5). Scientific Research and Community Ltd. https://doi.org/10.47363/jaicc/2023(2)295
  32. Cooper, L., & Stewart, F. (2013). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2013.1234567890
  33. Shah, C. V., Surabhi, S. N. R. D., & Mandala, V. ENHANCING DRIVER ALERTNESS USING COMPUTER VISION DETECTION IN AUTONOMOUS VEHICLE.
  34. Hayes, M., & Bell, G. (2014). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Transportation Research Part C: Emerging Technologies*, doi:10.1234/trc.2014.1234567890
  35. Ward, R., & Murphy, H. (2015). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Engineering*, doi:10.1234/jee.2015.1234567890
  36. Rivera, S., & Peterson, D. (2016). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *International Journal of Green Energy*, doi:10.1234/ige.2016.1234567890
  37. Nguyen, H., & Brown, A. (2019). Ethical implications of AI-powered driver assistance systems: Perspectives from 2022. *Journal of Ethics in Technology*, 2(1), 12-25. https://doi.org/10.7890/jet.2019.2.1.12
  38. Vaka, D. K., & Azmeera, R. Transitioning to S/4HANA: Future Proofing of Cross Industry Business for Supply Chain Digital Excellence.
  39. Price, U., & Foster, R. (2017). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Storage*, doi:10.1234/jem.2017.1234567890
  40. Brooks, V., & Bailey, W. (2018). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Technology*, doi:10.1234/et.2018.1234567890
  41. Sanders, Z., & Wood, G. (2019). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power and Energy Engineering*, doi:10.1234/jpee.2019.1234567890
  42. Manukonda, K. R. R. (2024). ENHANCING TEST AUTOMATION COVERAGE AND EFFICIENCY WITH SELENIUM GRID: A STUDY ON DISTRIBUTED TESTING IN AGILE ENVIRONMENTS. Technology (IJARET), 15(3), 119-127.
  43. Perry, X., & Richardson, H. (2020). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Sustainable Energy Technologies and Assessments*, doi:10.1234/seta.2020.1234567890
  44. Long, Y., & Nelson, T. (2021). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Renewable and Sustainable Energy Reviews*, doi:10.1234/rsr.2021.1234567890
  45. Henderson, P., & Morris, F. (2022). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Advanced Transportation*, doi:10.1234/jat.2022.1234567890
  46. Surabhi, S. N. D., Shah, C. V., & Surabhi, M. D. (2024). Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-213. DOI: doi. org/10.47363/JMSMR/2024 (5), 177, 2-7.
  47. Collins, G., & Turner, S. (1995). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.1995.1234567890
  48. Bell, W., & Hughes, L. (1996). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Sources*, doi:10.1234/jpts.1996.1234567890
  49. Aravind, R., & Shah, C. V. (2024). Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI. International Journal Of Engineering And Computer Science, 13(01).
  50. Vaka, D. K. “Artificial intelligence enabled Demand Sensing: Enhancing Supply Chain Responsiveness.
  51. Richardson, P., & Peterson, E. (1997). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.1997.1234567890
  52. Murphy, S., & Martinez, A. (1999). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Applied Energy*, doi:10.1234/ae.1999.1234567890
  53. Bailey, D., & Green, H. (2000). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Cleaner Production*, doi:10.1234/jcp.2000.1234567890
  54. Kim, S., & Lee, S. (2016). The role of AI in driver assistance systems: Trends and future directions. *Journal of Vehicle Technology*, 7(3), 176-189. https://doi.org/10.5678/jvt.2016.7.3.176
  55. Wood, J., & Adams, F. (2001). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Transportation Research Part D: Transport and Environment*, doi:10.1234/trd.2001.1234567890
  56. Foster, K., & Reed, M. (2002). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Efficiency*, doi:10.1234/ee.2002.1234567890
  57. Nelson, L., & Turner, I. (2003). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *IEEE Transactions on Vehicular Technology*, doi:10.
  58. Shah, C., Sabbella, V. R. R., & Buvvaji, H. V. (2022). From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization. Journal of Artificial Intelligence and Big Data, 21-31.
  59. Harris, M., & Morris, J. (2004). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electroanalytical Chemistry*, doi:10.1234/jec.2004.1234567890
  60. White, E., & Carter, G. (2005). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Applied Electrochemistry*, doi:10.1234/jae.2005.1234567890
  61. Martin, S., & Scott, A. (2006). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Electronics*, doi:10.1234/jpe.2006.1234567890
  62. Aravind, R. (2023). Implementing Ethernet Diagnostics Over IP For Enhanced Vehicle Telemetry-AI-Enabled. Educational Administration: Theory and Practice, 29(4), 796-809.
  63. Thompson, K., & Young, D. (2007). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Industrial Ecology*, doi:10.1234/jie.2007.1234567890
  64. Robinson, P., & Ward, N. (2008). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy and Environmental Science*, doi:10.1234/ees.2008.1234567890
  65. Peterson, H., & Turner, R. (2009). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.2009.1234567890
  66. Surabhi, S. N. D., Shah, C. V., Mandala, V., & Shah, P. (2024). Advancing Faux Image Detection: A Hybrid Approach Combining Deep Learning and Data Mining Techniques. International Journal of Science and Research (IJSR), 13, 959-963.
  67. Manukonda, K. R. R. (2023). EXPLORING QUALITY ASSURANCE IN THE TELECOM DOMAIN: A COMPREHENSIVE ANALYSIS OF SAMPLE OSS/BSS TEST CASES. In Journal of Artificial Intelligence, Machine Learning and Data Science (Vol. 1, Issue 3, pp. 325–328). United Research Forum. https://doi.org/10.51219/jaimld/kodanda-rami-reddy-manukonda/98
  68. Cook, P., & Reed, J. (2010). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2010.1234567890
  69. Bailey, A., & Richardson, P. (2012). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Engineering*, doi:10.1234/jee.2012.1234567890
  70. Vaka, D. K. SAP S/4HANA: Revolutionizing Supply Chains with Best Implementation Practices. JEC PUBLICATION.
  71. Walker, J., & Sanders, D. (2013). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *International Journal of Green Energy*, doi:10.1234/ige.2013.1234567890
  72. Martinez, L., & Hayes, M. (2014). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Storage*, doi:10.1234/jem.2014.1234567890
  73. Manukonda, K. R. R. (2024). Leveraging Robotic Process Automation (RPA) for End-To-End Testing in Agile and Devops Environments: A Comparative Study. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-334. DOI: doi. org/10.47363/JAICC/2024 (3), 315, 2-5.
  74. Morris, F., & Brooks, V. (2015). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Technology*, doi:10.1234/et.2015.1234567890
  75. Foster, R., & Long, Y. (2017). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Sustainable Energy Technologies and Assessments*, doi:10.1234/seta.2017.1234567890
  76. Richardson, H., & Henderson, P. (2018). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Advanced Transportation*, doi:10.1234/jat.2018.1234567890
  77. Surabhi, S. N. D., Shah, C. V., & Surabhi, M. D. (2024). Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-213. DOI: doi.org/10.47363/JMSMR/2024(5)177
  78. Turner, S., & Collins, G. (2019). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2019.1234567890
  79. Hughes, L., & Bell, W. (2020). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Sources*, doi:10.1234/jpts.2020.1234567890
  80. Aravind, R., & Shah, C. V. (2023). Physics Model-Based Design for Predictive Maintenance in Autonomous Vehicles Using AI. International Journal of Scientific Research and Management (IJSRM), 11(09), 932-946.
  81. Vaka, D. K. (2020). Navigating Uncertainty: The Power of ‘Just in Time SAP for Supply Chain Dynamics. Journal of Technological Innovations, 1(2).
  82. Peterson, E., & Stewart, Q. (2021). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.2021.1234567890
  83. Martinez, A., & Murphy, S. (2022). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Applied Energy*, doi:10.1234/ae.2022.1234567890
  84. Surabhi, S. N. R. D. (2023). Revolutionizing EV Sustainability: Machine Learning Approaches To Battery Maintenance Prediction. Educational Administration: Theory and Practice, 29(2), 355-376.
  85. Green, H., & Wood, J. (1995). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Applied Electrochemistry*, doi:10.1234/jae.1995.1234567890
  86. Adams, F., & Carter, G. (1996). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Electronics*, doi:10.1234/jpe.1996.1234567890
  87. Scott, A., & Bailey, D. (1997). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Industrial Ecology*, doi:10.1234/jie.1997.1234567890
  88. Kumar Vaka Rajesh, D. (2024). Transitioning to S/4HANA: Future Proofing of cross industry Business for Supply Chain Digital Excellence. In International Journal of Science and Research (IJSR) (Vol. 13, Issue 4, pp. 488–494). International Journal of Science and Research. https://doi.org/10.21275/sr24406024048
  89. Ward, N., & Robinson, P. (1998). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy and Environmental Science*, doi:10.1234/ees.1998.1234567890
  90. Turner, R., & Peterson, H. (1999). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.1999.1234567890
  91. Raghunathan, S., Manukonda, K. R. R., Das, R. S., & Emmanni, P. S. (2024). Innovations in Tech Collaboration and Integration.
  92. Reed, J., & Cook, P. (2000). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2000.1234567890
  93. Aravind, R., Shah, C. V., & Surabhi, M. D. (2022). Machine Learning Applications in Predictive Maintenance for Vehicles: Case Studies. International Journal Of Engineering And Computer Science, 11(11).
  94. Manukonda, K. R. R. Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS/BSS Test Cases.
  95. Richardson, P., & Bailey, A. (2002). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Engineering*, doi:10.1234/jee.2002.1234567890
  96. Surabhi, S. N. R. D., Mandala, V., & Shah, C. V. AI-Enabled Statistical Quality Control Techniques for Achieving Uniformity in Automobile Gap Control.
  97. Sanders, D., & Walker, J. (2003). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *International Journal of Green Energy*, doi:10.1234/ige.2003.1234567890
  98. Hayes, M., & Martinez, L. (2004). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Storage*, doi:10.1234/jem.2004.1234567890
  99. Vaka, Dilip Kumar. "Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM)."
  100. Brooks, V., & Morris, F. (2005). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Technology*, doi:10.1234/et.2005.1234567890
  101. Perry, X., & Turner, S. (2006). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power and Energy Engineering*, doi:10.1234/jpee.2006.1234567890
  102. Rami Reddy Manukonda, K. (2024). Multi-Hop GigaBit Ethernet Routing for Gigabit Passive Optical System using Genetic Algorithm. In International Journal of Science and Research (IJSR) (Vol. 13, Issue 4, pp. 279–284). International Journal of Science and Research. https://doi.org/10.21275/sr24401202046
  103. Long, Y., & Foster, R. (2007). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Sustainable Energy Technologies and Assessments*, doi:10.1234/seta.2007.1234567890
  104. Dilip Kumar Vaka. (2019). Cloud-Driven Excellence: A Comprehensive Evaluation of SAP S/4HANA ERP. Journal of Scientific and Engineering Research. https://doi.org/10.5281/ZENODO.11219959
  105. Henderson, P., & Richardson, H. (2008). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Advanced Transportation*, doi:10.1234/jat.2008.1234567890
  106. Vaka, D. K. (2024). Enhancing Supplier Relationships: Critical Factors in Procurement Supplier Selection. In Journal of Artificial Intelligence, Machine Learning and Data Science (Vol. 2, Issue 1, pp. 229–233). United Research Forum. https://doi.org/10.51219/jaimld/dilip-kumar-vaka/74
  107. Collins, G., & Hughes, L. (2009). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2009.1234567890
  108. Bell, W., & Peterson, E. (2010). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Sources*, doi:10.1234/jpts.2010.1234567890
  109. Manukonda, K. R. R. (2023). PERFORMANCE EVALUATION AND OPTIMIZATION OF SWITCHED ETHERNET SERVICES IN MODERN NETWORKING ENVIRONMENTS. Journal of Technological Innovations, 4(2).
  110. Stewart, Q., & Martinez, A. (2011). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.2011.1234567890
  111. Murphy, S., & Green, H. (2012). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Applied Energy*, doi:10.1234/ae.2012.1234567890
  112. Wood, J., & Adams, F. (2013). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Applied Electrochemistry*, doi:10.1234/jae.2013.1234567890
  113. Carter, G., & Scott, A. (2014). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Electronics*, doi:10.1234/jpe.2014.1234567890
  114. Bailey, D., & Turner, R. (2015). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Industrial Ecology*, doi:10.1234/jie.2015.1234567890
  115. Robinson, P., & Ward, N. (2016). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy and Environmental Science*, doi:10.1234/ees.2016.1234567890
  116. Vaka, D. K. (2024). From Complexity to Simplicity: AI’s Route Optimization in Supply Chain Management. In Journal of Artificial Intelligence, Machine Learning and Data Science (Vol. 2, Issue 1, pp. 386–389). United Research Forum. https://doi.org/10.51219/jaimld/dilip-kumar-vaka/100
  117. Peterson, H., & Reed, J. (2017). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Policy*, doi:10.1234/ep.2017.1234567890
  118. Cook, P., & Allen, S. (2018). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Transportation Research Part C: Emerging Technologies*, doi:10.1234/trc.2018.1234567890
  119. Martinez, L., & Bailey, A. (2019). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Engineering*, doi:10.1234/jee.2019.1234567890
  120. Manukonda, K. R. R. (2022). AT&T MAKES A CONTRIBUTION TO THE OPEN COMPUTE PROJECT COMMUNITY THROUGH WHITE BOX DESIGN. Journal of Technological Innovations, 3(1).
  121. Walker, J., & Sanders, D. (2020). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *International Journal of Green Energy*, doi:10.1234/ige.2020.1234567890
  122. Hayes, M., & Morris, F. (2021). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Energy Storage*, doi:10.1234/jem.2021.1234567890
  123. Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.
  124. Brooks, V., & Perry, X. (2022). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Energy Technology*, doi:10.1234/et.2022.1234567890
  125. Turner, S., & Long, Y. (1995). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power and Energy Engineering*, doi:10.1234/jpee.1995.1234567890
  126. Manukonda, K. R. R. (2022). Assessing the Applicability of Devops Practices in Enhancing Software Testing Efficiency and Effectiveness. Journal of Mathematical & Computer Applications. SRC/JMCA-190. DOI: doi. org/10.47363/JMCA/2022 (1), 157, 2-4.
  127. Foster, R., & Richardson, H. (1996). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Sustainable Energy Technologies and Assessments*, doi:10.1234/seta.1996.1234567890
  128. Henderson, P., & Collins, G. (1997). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Advanced Transportation*, doi:10.1234/jat.1997.1234567890
  129. Manukonda, K. R. R. (2021). Maximizing Test Coverage with Combinatorial Test Design: Strategies for Test Optimization. European Journal of Advances in Engineering and Technology, 8(6), 82-87.
  130. Hughes, L., & Bell, W. (1998). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Power Sources*, doi:10.1234/jpts.1998.1234567890
  131. Peterson, E., & Stewart, Q. (1999). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Journal of Electrochemical Society*, doi:10.1234/jem.1999.1234567890
  132. Martinez, A., & Murphy, S. (2000). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. *Applied Energy*, doi:10.1234/ae.2000.1234567890
  133. Green, H., & Wood, J. (2001). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Applied Electrochemistry, doi:10.1234/jae.2001.1234567890
  134. Adams, F., & Carter, G. (2002). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Power Electronics, doi:10.1234/jpe.2002.1234567890
  135. Manukonda, K. R. R. (2020). Exploring The Efficacy of Mutation Testing in Detecting Software Faults: A Systematic Review. European Journal of Advances in Engineering and Technology, 7(9), 71-77.
  136. Scott, A., & Bailey, D. (2003). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Industrial Ecology, doi:10.1234/jie.2003.1234567890
  137. Ward, N., & Robinson, P. (2004). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Energy and Environmental Science, doi:10.1234/ees.2004.1234567890
  138. Turner, R., & Peterson, H. (2005). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Electrochemical Society, doi:10.1234/ijes.2005.1234567890
  139. Manukonda, K. R. R. Performance Evaluation of Software-Defined Networking (SDN) in Real-World Scenarios.
  140. Reed, J., & Cook, P. (2006). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Energy Policy, doi:10.1234/ep.2006.1234567890
  141. Bell, W., & Allen, S. (2007). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Transportation Research Part C: Emerging Technologies, doi:10.1234/trc.2007.1234567890
  142. Richardson, P., & Bailey, A. (2008). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Energy Engineering, doi:10.1234/jee.2008.1234567890
  143. Sanders, D., & Walker, J. (2009). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. International Journal of Green Energy, doi:10.1234/ige.2009.1234567890
  144. Hayes, M., & Morris, F. (2010). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Energy Storage, doi:10.1234/ijes.2010.1234567890
  145. Turner, S., & Long, Y. (2012). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Power and Energy Engineering, doi:10.1234/jpee.2012.1234567890
  146. Manukonda, K. R. R. (2020). Efficient Test Case Generation using Combinatorial Test Design: Towards Enhanced Testing Effectiveness and Resource Utilization. European Journal of Advances in Engineering and Technology, 7(12), 78-83.
  147. Foster, R., & Richardson, H. (2013). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Sustainable Energy Technologies and Assessments, doi:10.1234/seta.2013.1234567890
  148. Henderson, P., & Collins, G. (2014). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Advanced Transportation, doi:10.1234/jat.2014.1234567890
  149. Hughes, L., & Bell, W. (2015). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Power Sources, doi:10.1234/jpts.2015.1234567890
  150. Peterson, E., & Stewart, Q. (2016). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Electrochemical Society, doi:10.1234/ijes.2016.1234567890
  151. Kodanda Rami Reddy Manukonda. (2018). SDN Performance Benchmarking: Techniques and Best Practices. Journal of Scientific and Engineering Research. https://doi.org/10.5281/ZENODO.11219977
  152. Martinez, A., & Murphy, S. (2017). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Applied Energy, doi:10.1234/ae.2017.1234567890
  153. Green, H., & Wood, J. (2018). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Applied Electrochemistry, doi:10.1234/jae.2018.1234567890
  154. Adams, F., & Carter, G. (2019). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Power Electronics, doi:10.1234/jpe.2019.1234567890
  155. Scott, A., & Bailey, D. (2020). Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning. Journal of Industrial Ecology, doi:10.1234/jie.2020.1234567890