Skip to main content

Advertisement

Log in

Energy management in the formation of light, starter, and ignition lead-acid batteries

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

This paper discusses energy management in the formation process of lead-acid batteries. Battery production and electricity consumption in during battery formation in a battery plant were analyzed over a 4-year period. The main parameters affecting the energy performance of battery production were identified and different actions to improve it were proposed. Furthermore, an Energy Performance Indicator (EnPI), based on the electricity consumption of battery formation (a difficult and rather expensive parameter to measure), is introduced to assess its energy efficiency. Therefore, a Soft Sensor to measure the electricity consumption in real-time (based on the voltage and current measured during battery formation) and to calculate the EnPI is developed. Moreover, Energy Management (EM), aided by the use of energy baselines and control charts is implemented to assess the energy performance of battery formation, allowing the implementation of rapid corrective actions towards higher efficiency standards. This resulted on the average in a 4.3% reduction of the electricity consumption in battery formation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Block, L., Larsen, A., & Togeby, M. (2006). Empirical analysis of energy management in Danish Industry. Journal of Cleaner Production, 14, 516–526.

    Article  Google Scholar 

  • Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. (2011). Integrating energy efficiency performance in production management e gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19, 667–679.

    Article  Google Scholar 

  • Cabello, J., Santos, V., Gutiérrez, A., Álvarez Guerra, M., Haeseldonckx, D., & Vandecasteele, C. (2016). Tools to improve forecasting and control of the electricity consumption in hotels. Journal of Cleaner Production, 137, 803–812.

    Article  Google Scholar 

  • Chen, H., Wei, Y., Luo, Y., & Duan, S. (1996). Study and application of several-step tank formation of lead/acid battery plates. Journal of power sources, 59, 59–62.

    Article  Google Scholar 

  • Chowdhury, A. (2015). How Soft Sensing Using Simulation Enhances Plant Process Management. Resource document. Cognizant 20-20 Insights. Available in: https://www.cognizant.com/whitepapers/how-soft-sensing-using-simulation-enhances-plant-process-management-codex1186.pdf (10.08.2016).

  • Cope, R. C., Podrazhansky, Y. (1999). The art of battery charging. Battery Conference on Applications and Advances. The Fourteenth Annual. Long Beach, CA, USA, 233–235.

  • Dahodwalla, H., & Herat, S. (2000). Cleaner production options for lead-acid battery manufacturing industry. Journal of Cleaner Production, 8, 133–142.

    Article  Google Scholar 

  • Duarte, M., Braido, B., Duchatsch, H., Rodrigues, M., & Antoniassi, B. (2017). Automation benefits in the formation process of lead-acid batteries. Independent Journal of Management Production, 8, 91–107.

    Article  Google Scholar 

  • Fawkes, S., Oung, K., Thorpe, D. (2016). Best practices and case studies for industrial energy efficiency improvement. An introduction for policy makers. Source of document. Copenhagen Centre on Energy Efficiency and United Nations Environment Programme (UNEP). Copenhagen. Available in: https:// www.unepdtu.org%2F-%2Fmedia%2FSites%2Fenergyefficiencycentre%2FPublications%2FC2E2%2520Publications%2FBest-Practises-for-Industrial-EE_web.ashx%3Fla%3Dda&usg=AOvVaw3Ev0Rdgcum5A9a0Pon89Jl. (10.08.2016).

  • Fortuna, L., Graziani, S., & Xibilia, M. G. (2005). Soft sensors for product quality monitoring in debutanizer distillation columns. Control Engineering Practice., 13, 499–508.

    Article  Google Scholar 

  • Friedman, J., Hastie, T., Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). Springer, Berlin: Springer series in statistics.

  • Giacone, E., & Mancò, S. (2012). Energy efficiency measurement in industrial processes. Energy, 38, 331–345.

    Article  Google Scholar 

  • Gielen, D., & Taylor, P. (2009). Indicators for industrial energy efficiency in India. Energy, 34, 962–969.

    Article  Google Scholar 

  • Goldberg, A., Reinaud, J., Taylor, R. (2011). Promotion Systems and Incentives for Adoption of Energy Management Systems in Industry. Source of document. Institute for Industrial Productivity, Washington, DC, United States. Available in: http://www.iipnetwork.org/IIP-6.%20PromotionSystemsEnMSChina1.pdf. (10.08.2016).

  • Gomnam, E., & Jazayeri-rad, H. (2013). Development of an adaptive soft sensor based on FCMILSSVR. International Journal of Scientific & Technology Research., 2, 199–203.

    Google Scholar 

  • Hadid, B., Ouvrad, R., Le Brusquet, L., Poinot, T., Etien, E., Sicard, F., & Grau, A. (2014). Design low cost sensors for industrial process energy consumption measurement. Application to the gas flow consumed by a boiler. In B. Hadid (Ed.), Sensing technology: current status and future trends IV (pp. 24–46). New York: Springer-Verlag.

    Google Scholar 

  • Hong, S. J., Jung, J. H., & Han, C. (1999). A design methodology of a soft sensor based on local models. Computers & Chemical Engineering., 23, S351–S354.

    Article  Google Scholar 

  • IEC 60095–1 (2000). Lead-acid starter batteries—part l: General requirements and methods of test.

  • ISO. 2011. 50001. (2011).Energy management systems--requirements with guidance for use. International Organization for Standardization.

  • ISO. 2012. 50004. (2012). Energy management systems — Guidance for the implementation, maintenance and improvement of an energy management system. International Organization for Standardization.

  • ISO. 2014. 50006. (2014). Energy management systems. Measuring energy performance using energy baselines (EnB) and energy performance indicators (EnPI). General principles and guidance. International Organization for Standardization.

  • Järvisalo, M., Ahonen, T., Ahola, J., Kosonen, A., & Niemelä, M. (2016). Soft-sensor-based flow rate and specific energy estimation of industrial variable-speed-driven twin rotary screw compressor. IEEE Transactions on Industrial Electronics, 63, 3282–3289.

    Article  Google Scholar 

  • Jung, J., Zhang, L., Zhang, J. (2016). Lead-acid battery technologies. Fundamentals, materials, and applications. CRC Press. Taylor & Francis Group. New York.

  • Kadlec, P., Gabrys, B., & Strandt, S. (2009). Data-driven soft sensors in the process industry. Computers & Chemical Engineering, 33, 795–814.

    Article  Google Scholar 

  • Kalos, A., Kordon, A., Smits, G., Werkmeister, S. (2003). Hybrid model development methodology for industrial soft sensors. In American Control Conference, 2003. Proceedings of the 2003 (Vol. 6, pp. 5417-5422). IEEE. Denver, United States.

  • Kiessling, R. (1992). Lead acid battery formation techniques. Source of document. Digatron Firing Circuits. Available in: http://www.digatron.com/fileadmin/pdf/lead_acid.pdf (10.08.2016).

  • Kortela, J., & Jämsä-Jounela, S. L. (2012). Fuel-quality soft sensor using the dynamic superheater model for control strategy improvement of the BioPower 5 CHP plant. International Journal of Electrical Power & Energy Systems, 42, 38–48.

    Article  Google Scholar 

  • Leonow, S., & Mönnigmann, M. (2014). Automatic controller tuning for soft sensor based flow rate control. The International Federation of Automatic Control Proceedings Volumes., 47, 5229–5234.

    Google Scholar 

  • Li, Z., Luan, X., Liu, T., Jin, B., & Zhang, Y. (2014). Room cooling load calculation based on soft sensing. In International conference on life system modeling and simulation and international conference on intelligent computing for sustainable energy and environment (pp. 331–341). Berlin: Springer Berlin Heidelberg.

    Google Scholar 

  • Lin, B., Recke, B., Knudsen, J. K., & Jørgensen, S. B. (2007). A systematic approach for soft sensor development. Computers & chemical engineering, 31, 419–425.

    Article  Google Scholar 

  • Madrigal, J. A., Cabello Eras, J. J., Hernández Herrera, H., Sousa Santos, V., & Balbis Morejón, M. (2018). Planificación energética para el ahorro de fueloil en una lavandería industrial. Ingeniare. Revista chilena de ingeniería, 26(1), 86–96.

    Article  Google Scholar 

  • McKane, A., Scheihing, P., Williams, R. (2008). Certifying industrial energy efficiency performance: aligning management, measurement, and practice to create market value. Source of document. Lawrence Berkeley National Laboratory. Available in: http://aceee.org/files/proceedings/2007/data/papers/56_5_049.pdf. (10.08.2016).

  • Miloloža, I. (2013). Tendencies of development of global battery market with emphasis on republic of Croatia. Interdisciplinary Description of Complex Systems., 11, 318–333.

    Article  Google Scholar 

  • Najar, S., Tissier, J., Etien, E., Cauet, E. (2015). Soft sensor for distribution transformers: thermal and electrical models. Sorce of document. 23rd International Conference on Electricity Distribution. CIRED 2015. Lyon, France. Available in: http://cired.net/publications/cired2015/papers/CIRED2015_0419_final.pdf . (10.08.2016).

  • Palamutcu, S. (2010). Electric energy consumption in the cotton textile processing stages. Energy, 35, 2945–2952.

    Article  Google Scholar 

  • Pavlov, D. (2011). Lead-acid batteries: Science and technology. A handbook of lead-acid battery technology and its influence on the product (1st ed.). Amsterdam: Elsevier.

    Google Scholar 

  • Pavlov, D., Petkova, G., Dimitrov, M., Shiomi, M., & Tsubota, M. (2000). Influence of fast charge on the life cycle of positive lead–acid battery plates. Journal of power sources, 87, 39–56.

    Article  Google Scholar 

  • Petkova, G., & Pavlov, D. (2003). Influence of charge mode on the capacity and cycle life of lead–acid battery negative plates. Journal of power sources, 113, 355–362.

    Article  Google Scholar 

  • Ploennigs, J., Ahmed, A., Hensel, B., Stack, P., & Menzel, K. (2011). Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating. Advanced Engineering Informatics, 25, 688–698.

    Article  Google Scholar 

  • Ponce, M., & Moreno, E. (2015). Alternative definitions of energy for power meters in non-sinusoidal systems. International Journal of Electrical Power & Energy Systems, 64, 1206–1213.

    Article  Google Scholar 

  • Poscha, A., Brudermann, T., Braschela, N., & Gabriel, M. (2015). Strategic energy management in energy-intensive enterprises: A quantitative analysis of relevant factors in the Austrian paper and pulp industry. Journal of Cleaner Production, 90, 291–299.

    Article  Google Scholar 

  • Prout, L. (1993). Aspects of lead/acid battery technology 4. Plate formation. Journal of power sources, 41, 195–219.

    Article  Google Scholar 

  • Qi, F., Shukeir, E., & Kadali, R. (2015). Model predictive control of once through steam generator steam quality. IFAC-Papers on Line, 48, 716–721.

    Article  Google Scholar 

  • Rantik, M. (1999). Life cycle assessment of five batteries for electric vehicles under different charging regimes. Stockholm: KFB – Kommunikations forsknings-beredningen.

    Google Scholar 

  • Report Buyer Ltd. (2015). Global and China Lead-acid Battery Industry Report, 2015–2018. Source of document. Battery Industry Report. Available in: https://www.reportbuyer.com/product/3548160/global-and-china-lead-acid-battery-industry-report-2015-2018.html . (10.08.2016).

  • Rudberg, M., Waldemarsson, M., & Lidestam, H. (2013). Strategic perspectives on energy management: A case study in the process industry. Applied Energy, 104, 487–496.

    Article  Google Scholar 

  • Rydh, C. J., & Sandén, B. A. (2005). Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Conversion and Management, 46, 1957–1979.

    Article  Google Scholar 

  • Rydh, C. J. (1999). Environmental assessment of vanadium redox and lead-acid batteries for stationary energy storage. Journal of power sources, 80, 21–29.

    Article  Google Scholar 

  • Sagastume, A., Cabello, J., Sousa, V., Hernandez, H., Hens, L., & Vandecasteele, C. (2018). Electricity management in the production of lead-acid batteries: the industrial case of a production plant in Colombia. Journal of Cleaner Production, 198, 1443–1458.

    Article  Google Scholar 

  • Gómez, J., Viego, P., Torres, Y., Alvarez, M., Santos, V., & Haeseldonckx, D. (2018). A new energy performance Indicator for energy management system of a wheat mill plant. International Journal of Energy Economics and Policy, 8(4), 324–330.

    Google Scholar 

  • Schluchter, M.D. (2014). Mean square error. Wiley StatsRef: Statistics reference online. https://doi.org/10.1002/9781118445112.stat05906.

  • Sullivan, J. L., & Gaines, L. (2012). Status of life cycle inventories for batteries. Energy Conversion and Management, 58, 134–148.

    Article  Google Scholar 

  • Sullivan, J.L., Gaines, L. (2010). A review of battery life-cycle analysis: state of knowledge and critical needs (No. ANL/ESD/10-7). Argonne National Laboratory (ANL). Available in: https://greet.es.anl.gov/publication-batteries_lca (10.08.2016).

  • Thanayankizil, L. V., Ghai, S. K., Chakraborty, D., Seetharam, D.P. (2012). Softgreen: Towards energy management of green office buildings with soft sensors. Sorce of document. Fourth International Conference on Communication Systems and Networks (COMSNETS 2012). IEEE. Bangalore, India. Available in: https://pdfs.semanticscholar.org/ 3665/f7955f5cab8d65bc1d11be81a6b1969d9bfa.pdf (10.08.2016).

  • Thi Minh, N. (2009). Lead acid batteries in extreme conditions: accelerated charge, maintaining the charge with imposed low current, polarity inversions introducing non-conventional charge methods. Doctoral dissertation. Source of document. Université Montpellier II-Sciences et Techniques du Languedoc. France. Available in: https://tel.archives-ouvertes.fr/tel-00443615/document (10.08.2016).

  • Velázquez, D., Gonzalez, R., Perez, L., Gallego, L., Monedero, I., & Biscarri, F. (2013). Development of an energy management system for a naphtha reforming plant: A data mining approach. Energy Conversion and Management, 67, 217–225.

    Article  Google Scholar 

  • Vesma, V. (2009). Energy Management Principles and Practice. British Standards Institution. Available in: http://group.skanska.com/globalassets/sustainability/environmental-responsibility/energy/energy-management-bip2187.pdf (29.07.2018).

  • Wang, D., Liu, J., & Srinivasan, R. (2010). Data-driven soft sensor approach for quality prediction in a refining process. IEEE Transactions on Industrial Informatics, 6, 11–17.

    Article  Google Scholar 

  • Wang, T., Chen, Z., Xu, J.Y., Wang, F.Y., Liu, H.M. (2017). Energy-saving management modelling and optimization for lead-acid battery formation process. In IOP Conference Series: Earth and Environmental Science, 93, 1–9.

  • Wang, Y., Wu, J., Long, C., & Zhou, M. (2015). Economical manufacturing from optimal control perspective: simplification, methods and analysis. IFAC-PapersOnLine., 48, 231–237.

    Article  Google Scholar 

  • Warne, K., Prasad, G., Rezvani, S., & Maguire, L. (2004). Statistical and computational intelligence techniques for inferential model development: A comparative evaluation and a novel proposition for fusion. Engineering Applications of Artificial Intelligence, 17, 871–885.

    Article  Google Scholar 

  • Weighall, M. J. (2003). Techniques for jar formation of valve-regulated lead–acid batteries. Journal of power sources, 116, 219–231.

    Article  Google Scholar 

  • Weinert, N., Chiotellis, S., & Seliger, G. (2011). Methodology for planning and operating energy-efficient production systems. CIRP Annals - Manufacturing Technology, 60, 41–44.

    Article  Google Scholar 

  • Wong, Y. S., Hurley, W. G., & Wölfle, W. H. (2008). Charge regimes for valve-regulated lead-acid batteries: Performance overview inclusive of temperature compensation. Journal of Power Sources, 183, 783–791.

    Article  Google Scholar 

  • Worrell, E., 2011. Barriers to energy efficiency: International case studies on successful barrier removal. Development policy, statistics and research branch. Source of document. United Nations Industrial Development Organization, 1–19. Available in: http://dspace.library.uu.nl/handle/1874/250419 (10.08.2016).

  • Zhang, K., Dai, X., Zhang, G., Ma, C. (2008). Left-inversion soft-sensing method for a class of nonlinear DAE sub-systems. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on (pp. 5651-5656). IEEE. Chongqing, China.

  • Zhao, Z., Zeng, D., Hub, Y., & Gaob, S. (2015). Soft sensing of coal quality. Thermal Science, 19, 231–242.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juan J. Cabello Eras.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cabello Eras, J.J., Gutiérrez, A.S., Santos, V.S. et al. Energy management in the formation of light, starter, and ignition lead-acid batteries. Energy Efficiency 12, 1219–1236 (2019). https://doi.org/10.1007/s12053-018-9741-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-018-9741-6

Keywords

Navigation