Flooded Lead Acid Battery SOC Estimation for Energy Conscious LVDC Building: Warm and Humid Climate
DOI:
https://doi.org/10.48048/tis.2022.5765Keywords:
State of Charge (SOC), Battery thevenin model, Battery internal characteristics, Battery Energy Storage System (BESS), Lead acid battery, Home Energy Management System (HEMS)Abstract
Microgrids make it easier to integrate Renewable Energy Sources (RESs) and Energy-Storage Systems (ESSs) at the consumer level, with the intent of enhancing power quality, reliability, and efficiency. This microgrid concept at the nano grid level is championed by a low voltage DC (LVDC) grid, facilitating the direct integration of several distributed generators, storage and loads that are almost DC source/load. Since the State of Charge (SOC) of the battery is an essential parameter for building energy management systems, careful monitoring of SOC is essential. The SOC of a battery directly reflects its performance; thus, straightforward, reasonably accurate, and timely estimation of SOC protects against overcharging or discharging issues, with fewer processor requirements and computations. A novel SOC estimation method is proposed based on the literature assessment of different existing SOC estimation methods. In regions with a tropical climate, the temperature range over the year is small, and the sunshine is relatively intense and fairly even throughout the year. Also, for all practical purposes, it is a fact that the model that takes inputs at the point of use gives accurate results. Considering the 2 facts mentioned above, the current model uses a less complicated algorithm, within acceptable accuracy for the purpose and relatively user-friendly.
HIGHLIGHTS
- State Of Charge (SOC) estimation of 48V Lead Acid battery for Low Voltage DC (LVDC) building energy management systems
- Straightforward, reasonably accurate, and timely estimation of SOC which protects against overcharging or discharging issues, with fewer processor requirements and computations
- For the SOC estimation, the open-circuit voltage is obtained by measuring the terminal voltage, current and first-order model of the battery developed under similar climatic condition
- Development and testing of an algorithm incorporating temperature-independent first-order battery model for the tropical climate with lesser temperature variation
GRAPHICAL ABSTRACT
Downloads
References
V Janamala. Solar PV tree: Shade-free design and cost analysis considering Indian scenario. Walailak J. Sci. Tech. 2021; 18, 8995.
D Groppi, A Pfeifer, DA Garcia, G Krajačić and N Duić. A review on energy storage and demand side management solutions in smart energy islands. Renew. Sustain. Energ. Rev. 2020; 135, 110183.
H Khorramdel, B Khorramdel and M Poshtyafteh. Programming of energy storage system in an island microgrid with photovoltaic and fuel cell. Walailak J. Sci. Tech. 2015; 12, 361-71.
International Electrotechnical Commission. LVDC: Electricity for the 21st century. International Electrotechnical Commission, Geneva, Switzerland, 2017.
GJ May, A Davidson and B Monahov. Lead batteries for utility energy storage: A review. J. Energ. Storage 2018; 15, 145-57.
V Esfahanian, AA Shahbazi and F Torabi. A real-time battery engine simulation tool (BEST) based on lumped model and reduced-order modes: Application to lead-acid battery. J. Energ. Storage 2019; 24, 100780.
RK Chauhan, C Phurailatpam, BS Rajpurohit, FM Gonzalez-Longatt and SN Singh. Demand-side management system for autonomous DC microgrid for building. Tech. Econ. Smart Grids Sustain. Energ. 2017; 2, 4.
V Pop, HJ Bergveld, PHL Notten and PPL Regtien. State of the art of battery state of charge determination. Meas. Sci. Tech. 2005; 16, R93.
J Kim and BH Cho. Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation. Energy 2013; 57, 581-99.
V Nandhini, K Bharathi, S Giri, S Sowvav and A Suyampulingam. Energy management and smart control of home appliances. J. Phys. Conf. Ser. 2020; 1706, 012087.
S Piller, M Perrin and A Jossen. Methods for state-of-charge determination and their applications. J. Power Sourc. 2001; 96, 113-20.
S Boulmrharj, R Ouladsine R, Y NaitMalek, M Bakhouya, K Zine-dine, M Khaidar and M Siniti. Online battery state-of-charge estimation methods in micro-grid systems. J. Energ. Storage 2020; 30, 101518.
CT Ma. Design and implementation of a hybrid real-time state of charge estimation scheme for battery energy storage systems. Processes 2020; 8, 2.
HS Ramadan, M Becherif and F Claude. Extended kalman filter for accurate state of charge estimation of lithium-based batteries: A comparative analysis. Int. J. Hydrogen Energ. 2017; 42, 29033-46.
K Soon, CS Moo, YP Chen and YC Hsieh. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy. 2009; 86, 1506-11.
W Chang. The state of charge estimating methods for battery: A review. Int. Sch. Res. Not. Appl. Math. 2013; 1, 953792.
K Petr. Methods of SoC determination of lead acid battery. J. Energ. Storage 2018; 15, 191-5.
AH Anbuky and PE Pascoe. VRLA battery state-of-charge estimation in telecommunication power systems. IEEE Trans. Ind. Electron. 2000; 47, 565-73.
K Bundy, M Karlsson, G Lindbergh and A Lundqvist. An electrochemical impedance spectroscopy method for prediction of the state of charge of a nickel-metal hydride battery at open circuit and during discharge. J. Power Sources 1998; 72, 118-25.
CW Zhang, SR Chen, HB Gao, KJ Xu and MY Yang. State of charge estimation of power battery using improved back propagation neural network. Batteries 2018; 4, 69.
J Chen, Q Ouyang, C Xu and H Su. Neural network-based state of charge observer design for lithium-ion batteries. IEEE Trans. Contr. Syst. Tech. 2018; 26, 313-20.
Y Guo, Z Yang, K Liu, Y Zhang and W Feng. A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system. Energy 2021; 219, 119529.
L Hu, X Hu, Y Che, F Feng, X Lin and Z Zhang. Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering. Appl. Energ. 2020; 262, 114569.
D Saji, PS Babu and K Ilango. SoC estimation of lithium-ion battery using combined coulomb counting and fuzzy logic method. In: Proceedings of the 4th International Conference on Recent Trends in Electronics, Information, Communication & Technology, Bangalore, India. 2019, p. 948-52.
V Chandran, CK Patil, A Karthick, D Ganeshaperumal, R Rahim and A Ghosh. State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms. World Electr. Veh. J. 2021; 12, 38.
J Li, M Ye, S Jiao, D Shi and X Xu. State estimation of lithium battery based on least squares support vector machine. DEStech Transactions on Environment Energy and Earth Science. Lancaster PA, United States, 2019, p. 248-51.
N Watrin, B Blunier and A Miraoui. Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation. In: Proceedings of the IEEE Transportation Electrification Conference and Expo, Dearborn, Michigan, United States. 2012, p. 1-6.
RMS Santos, CLGDS Alves, ECT Macedo, JMM Villanueva, LV Hartmann and SYC Catunda. Lead acid battery soc estimation based on extended kalman filter method considering different temperature conditions. In: Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Turin, Italy. 2017, p. 1-6.
Bureau of Indian Standards. Guidelines for 48 V ELVDC distribution system - IS 16711. Bureau of Indian Standards, Delhi, India, 2017.
SK Korkua and K Thinsurat. A load prioritization model for a smart demand responsive energy management system in the residential sector. Walailak J. Sci. Tech. 2014; 11, 7-18.
RK Chauhan, BS Rajpurohit, FM Gonzalez-Longatt, SN Singh. Intelligent Energy Management System for PV-Battery-based Microgrids in Future DC Homes. Int. J. Emerg. Electr. Power Syst 2016; 17, 339-50.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
