Study and integration of these techniques in the proposed solution is another research axis. The suggested solution is adaptable for a variety of applications by parameterizing different system modules. Employing the proposed solution to study the considered Li-Ion cell behaviour for other load profiles such as electric vehicles, hybrid vehicles, drones, etc. is a future work. Other research axis is to explore and integrate other SoH estimation and calibration approaches such as Kalman filtering, Dual Extended Kalman Filter (DEKF), data driven approaches and statistical model-based tactics. Additionally, studying the performance of proposed approaches for a variety of recharge able batteries and particularly for different types of Li-Ion battery technologies is another research point to explore.
88
REFERENCES
[1] M. Li, J. Lu, Z. Chen, and K. Amine, ‘30 years of lithium‐ion batteries’, Adv. Mater., vol. 30, no. 33, p. 1800561, 2018.
[2] M. H. Amrollahi and S. M. T. Bathaee, ‘Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response’, Appl. Energy, vol. 202, pp. 66–77, 2017.
[3] R. Xiong, L. Li, and J. Tian, ‘Towards a smarter battery management system: A critical review on battery state of health monitoring methods’, J. Power Sources, vol. 405, pp.
18–29, 2018.
[4] S. M. Qaisar, ‘Efficient mobile systems based on adaptive rate signal processing’, Comput. Electr. Eng., vol. 79, p. 106462, 2019.
[5] M. A. Roscher, J. Assfalg, and O. S. Bohlen, ‘Detection of utilizable capacity
deterioration in battery systems’, IEEE Trans. Veh. Technol., vol. 60, no. 1, pp. 98–103, 2010.
[6] M. H. Lipu et al., ‘A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations’, J. Clean. Prod., vol. 205, pp. 115–133, 2018.
[7] D. Yang, Y. Wang, R. Pan, R. Chen, and Z. Chen, ‘State-of-health estimation for the lithium-ion battery based on support vector regression’, Appl. Energy, vol. 227, pp.
273–283, 2018.
[8] M. A. Roscher, J. Assfalg, and O. S. Bohlen, ‘Detection of utilizable capacity
deterioration in battery systems’, IEEE Trans. Veh. Technol., vol. 60, no. 1, pp. 98–103, 2010.
[9] S. M. Qaisar, Electronic management system for rechargeable battery has measuring circuit measuring parameter determining variation of parameter transmitting data to electronic processing unit if variation is higher than predetermined threshold. 2011.
[10] Y. Al-Saleh, ‘Renewable energy scenarios for major oil-producing nations: The case of Saudi Arabia’, Futures, vol. 41, no. 9, pp. 650–662, 2009.
[11] F. Rahman, S. Rehman, and M. A. Abdul-Majeed, ‘Overview of energy storage systems for storing electricity from renewable energy sources in Saudi Arabia’, Renew. Sustain.
Energy Rev., vol. 16, no. 1, pp. 274–283, 2012.
[12] Y. Al-Saleh, ‘Renewable energy scenarios for major oil-producing nations: The case of Saudi Arabia’, Futures, vol. 41, no. 9, pp. 650–662, 2009.
[13] M. Murnane and A. Ghazel, ‘A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries’, Internet Httpwww Analog Co
Mmediaentechnical-Doc.-Artic.-Closer-Look--State--Charge--State-Health-Estim.- Tech.- Pdf, 2017.
[14] M. A. Salam and S. A. Khan, ‘Transition towards sustainable energy production–A review of the progress for solar energy in Saudi Arabia’, Energy Explor. Exploit., vol.
36, no. 1, pp. 3–27, 2018.
[15] O. Hafez, ‘Time of use prices considering smart meters and their future implementation in Saudi Arabia smart grid’, in 2017 Saudi Arabia Smart Grid (SASG), 2017, pp. 1–5.
[16] F. Alfawzan, J. E. Alleman, and C. R. Rehmann, ‘Wind energy assessment for NEOM city, Saudi Arabia’, Energy Sci. Eng., 2019.
89 [17] M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. Van Mierlo, and P. Van den
Bossche, ‘Critical review of state of health estimation methods of Li-ion batteries for real applications’, Renew. Sustain. Energy Rev., vol. 56, pp. 572–587, 2016.
[18] T. Huria, M. Ceraolo, J. Gazzarri, and R. Jackey, ‘High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells’, in 2012 IEEE International Electric Vehicle Conference, 2012, pp. 1–8.
[19] I. Buchmann, Batteries in a portable world: a handbook on rechargeable batteries for non-engineers. Cadex Electronics Richmond, 2001.
[20] J. B. Goodenough and K.-S. Park, ‘The Li-ion rechargeable battery: a perspective’, J.
Am. Chem. Soc., vol. 135, no. 4, pp. 1167–1176, 2013.
[21] M. A. Hannan, M. H. Lipu, A. Hussain, and A. Mohamed, ‘A review of lithium-ion battery state of charge estimation and management system in electric vehicle
applications: Challenges and recommendations’, Renew. Sustain. Energy Rev., vol. 78, pp. 834–854, 2017.
[22] M. A. Hannan, M. H. Lipu, A. Hussain, and A. Mohamed, ‘A review of lithium-ion battery state of charge estimation and management system in electric vehicle
applications: Challenges and recommendations’, Renew. Sustain. Energy Rev., vol. 78, pp. 834–854, 2017.
[23] B. Kumar, N. Khare, and P. K. Chaturvedi, ‘Advanced battery management system using MATLAB/Simulink’, in 2015 IEEE International Telecommunications Energy Conference (INTELEC), 2015, pp. 1–6.
[24] S. M. Qaisar, L. Fesquet, and M. Renaudin, ‘Adaptive rate filtering a computationally efficient signal processing approach’, Signal Process., vol. 94, pp. 620–630, 2014.
[25] S. M. Qaisar, L. Fesquet, and M. Renaudin, ‘Spectral analysis of a signal driven sampling scheme’, in 2006 14th European Signal Processing Conference, 2006, pp. 1–
5.
[26] S. M. Qaisar, L. Fesquet, and M. Renaudin, ‘An adaptive resolution computationally efficient short-time Fourier transform’, J. Electr. Comput. Eng., vol. 2008, 2008.
[27] M. Gholizadeh and F. R. Salmasi, ‘Estimation of state of charge, unknown
nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model’, IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1335–1344, 2013.
[28] R. Yang, R. Xiong, H. He, H. Mu, and C. Wang, ‘A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles’, Appl. Energy, vol. 207, pp. 336–345, 2017.
[29] B. Nykvist and M. Nilsson, ‘Rapidly falling costs of battery packs for electric vehicles’, Nat. Clim. Change, vol. 5, no. 4, p. 329, 2015.
[30] P. Ellis, ‘Extension of phase plane analysis to quantized systems’, IRE Trans. Autom.
Control, vol. 4, no. 2, pp. 43–54, 1959.
[31] C. Weltin-Wu and Y. Tsividis, ‘An event-driven clockless level-crossing ADC with signal-dependent adaptive resolution’, IEEE J. Solid-State Circuits, vol. 48, no. 9, pp.
2180–2190, 2013.
[32] K. W. E. Cheng, B. P. Divakar, H. Wu, K. Ding, and H. F. Ho, ‘Battery-management system (BMS) and SOC development for electrical vehicles’, IEEE Trans. Veh.
Technol., vol. 60, no. 1, pp. 76–88, 2010.
[33] P. Venugopal, ‘State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition’, Energies, vol. 12, no. 22, p. 4338, 2019.
90 [34] N. Watrin, B. Blunier, and A. Miraoui, ‘Review of adaptive systems for lithium
batteries state-of-charge and state-of-health estimation’, in 2012 IEEE Transportation Electrification Conference and Expo (ITEC), 2012, pp. 1–6.
[35] Y. Zou, X. Hu, H. Ma, and S. E. Li, ‘Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles’, J. Power Sources, vol. 273, pp. 793–803, 2015.
[36] Y. Zou, X. Hu, H. Ma, and S. E. Li, ‘Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles’, J. Power Sources, vol. 273, pp. 793–803, 2015.
[37] M. H. Lipu et al., ‘A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations’, J. Clean. Prod., vol. 205, pp. 115–133, 2018.
[38] P. Guo, Z. Cheng, and L. Yang, ‘A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction’, J. Power Sources, vol. 412, pp. 442–450, 2019.
[39] S. M. Qaisar, D. Dallet, S. Benjamin, P. Desprez, and R. Yahiaoui, ‘Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and
measurement’, in 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2013, pp. 1522–1525.
[40] M. H. Lipu et al., ‘A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations’, J. Clean. Prod., vol. 205, pp. 115–133, 2018.
[41] X. Hu, S. Li, and H. Peng, ‘A comparative study of equivalent circuit models for Li-ion batteries’, J. Power Sources, vol. 198, pp. 359–367, 2012.
[42] S. M. Qaisar, S. Benjamin, P. Desprez, G. Barrailh, and D. Dallet, Système et procédé de gestion électronique d’une batterie rechargeable. 2010.
[43] T. Huria, M. Ceraolo, J. Gazzarri, and R. Jackey, ‘High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells’, in 2012 IEEE International Electric Vehicle Conference, 2012, pp. 1–8.
[44] S. Benjamin et al., ‘Lifemit’, in 2010 IEEE Vehicle Power and Propulsion Conference, 2010, pp. 1–5.
[45] D. Yang, Y. Wang, R. Pan, R. Chen, and Z. Chen, ‘State-of-health estimation for the lithium-ion battery based on support vector regression’, Appl. Energy, vol. 227, pp.
273–283, 2018.
[46] S. M. Qaisar, R. Yahiaoui, and D. Dominique, ‘A smart power management system monitoring and measurement approach based on a signal driven data acquisition’, in 2015 Saudi Arabia Smart Grid (SASG), 2015, pp. 1–4.
[47] R. Xiong, L. Li, and J. Tian, ‘Towards a smarter battery management system: A critical review on battery state of health monitoring methods’, J. Power Sources, vol. 405, pp.
18–29, 2018.
[48] M. Zeng, P. Zhang, Y. Yang, C. Xie, and Y. Shi, ‘SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm’, Energies, vol. 12, no. 16, p. 3122, 2019.
[49] M. H. Lipu et al., ‘A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations’, J. Clean. Prod., vol. 205, pp. 115–133, 2018.
91 [50] S. M. Qaisar and M. AlQathami, ‘Level-Crossing Sampling for Li-Ion Batteries
Effective State of Health Estimation’, in 2020 19th International Conference on Harmonics and Quality Power (ICHQP2020), 2020, pp. 1–5.
[51] R. S. Esfandiari and B. Lu, Modeling and analysis of dynamic systems. CRC press, 2018.
[52] S. M. Qaisar and M. AlQathami, ‘A Proficient Li-Ion Batteries State of Health
Assessment Based on Event-Driven Processing’, in 2019 3rd International Conference on Energy Conservation and Efficiency (ICECE), 2019, pp. 1–5.
[53] S. M. Qaisar and M. AlQathami, ‘Event-Driven Sampling Based Li-Ion Batteries SoH Estimation in the 5G Framework’, in The 17th International Learning and Technology Conference, 2019, pp. 1–5.
[54] C. N. Truong, M. Naumann, R. C. Karl, M. Müller, A. Jossen, and H. C. Hesse,
‘Economics of residential photovoltaic battery systems in Germany: The case of Tesla’s Powerwall’, Batteries, vol. 2, no. 2, p. 14, 2016.
[55] S. Teleke, M. E. Baran, S. Bhattacharya, and A. Q. Huang, ‘Rule-based control of battery energy storage for dispatching intermittent renewable sources’, IEEE Trans.
Sustain. Energy, vol. 1, no. 3, pp. 117–124, 2010.
[56] B. Diouf and R. Pode, ‘Potential of lithium-ion batteries in renewable energy’, Renew.
Energy, vol. 76, pp. 375–380, 2015.
[57] K. W. E. Cheng, B. P. Divakar, H. Wu, K. Ding, and H. F. Ho, ‘Battery-management system (BMS) and SOC development for electrical vehicles’, IEEE Trans. Veh.
Technol., vol. 60, no. 1, pp. 76–88, 2010.
[58] A. B. Awan, ‘Performance analysis and optimization of a hybrid renewable energy system for sustainable NEOM city in Saudi Arabia’, J. Renew. Sustain. Energy, vol. 11, no. 2, p. 025905, 2019.
[59] L. Tan and J. Jiang, Digital signal processing: fundamentals and applications.
Academic Press, 2018.
[60] C. Weng, J. Sun, and H. Peng, ‘A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring’, J. Power Sources, vol. 258, pp. 228–237, 2014.
[61] S. Sepasi, R. Ghorbani, and B. Y. Liaw, ‘Inline state of health estimation of lithium-ion batteries using state of charge calculation’, J. Power Sources, vol. 299, pp. 246–254, 2015.
[62] M. Ye, H. Guo, R. Xiong, and Q. Yu, ‘A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries’, Energy, vol. 144, pp. 789–799, 2018.
[63] Z. Lao, B. Xia, W. Wang, W. Sun, Y. Lai, and M. Wang, ‘A novel method for lithium- ion battery online parameter identification based on variable forgetting factor recursive least squares’, Energies, vol. 11, no. 6, p. 1358, 2018.
[64] X. Chen, W. Shen, M. Dai, Z. Cao, J. Jin, and A. Kapoor, ‘Robust adaptive sliding- mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles’, IEEE Trans. Veh. Technol., vol. 65, no. 4, pp. 1936–
1947, 2015.
92 [65] C. Zhang, ‘A Fuzzy Logic Inference System for Testing Lithium-ion Battery State of
Charge’, 2018.
[66] M. Kim et al., ‘Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search’, Appl. Energy, vol. 254, p. 113644, 2019.
93
APPENDICES
Appendix-I:A paper that got accepted in the 2019 International Conference on Energy Conservation and Efficiency” (ICECE 2019) under the title of “A Proficient Li-Ion Batteries State of Health Assessment Based on Event-Driven Processing”. The conference took a place between October23rd and 24th, 2019 at University of Engineering& Technology. The paper has been indexed in IEEE Xplore and Scopus. The following is the citation of the paper:
S. M. Qaisar and M. AlQathami, ‘A Proficient Li-Ion Batteries State of Health Assessment Based on Event-Driven Processing’, in 2019 3rd International Conference on Energy Conservation and Efficiency (ICECE), 2019, pp. 1–5.
94 Appendix-II:
This paper was accepted in the 17th International Learning and Technology Conference (L
& T 2020) under the title of “Event-Driven Sampling Based Li-Ion Batteries SoH Estimation in the 5G Framework”. The conference was held in Jeddah, at Effat University and took a place on the 30th of January 2020. The accepted papers will be published in Procedia of Computer Science.
95 Appendix-III:
This poster was participated in the 9th Saudi Arabia Smart Grid Conference (SASG 2019) which was held in Jeddah between the 10thand12thof December 2019. The poster was under the title of “Li-Ion Batteries State of Health Assessment Based on Event- Driven Processing and Analysis”. This poster was won the second place in Smart Grid Conference (SASG 2019).