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Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems

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Artificial Intelligence Techniques for a Scalable Energy Transition

Abstract

Keeping the power supply of autonomous and electrical vehicles working even in case of faults is of uttermost importance in order to maintain the desired behavior during operation. Especially in case of increased autonomy faults occurring in the power supply when driving should not require the vehicle to stop operation immediately. Instead the autonomous vehicle should still be able to reach a safe state like an emergency lane or a parking space. In this chapter, we introduce a method that enables the development of battery systems that react on internal or external faults in a smart way. In particular, we discuss model-based reasoning for this purpose and show its application for configuring and diagnosing systems. Besides discussing the foundations behind model-based reasoning, we make use of a smart battery system as a case study. In addition, we describe how to use the corresponding physical model for fault detection and a logical model for computing the root cause of the observed failure. The intention behind the chapter is to provide all necessary details of the methods allowing to adapt the methods to implement similar smart adaptive systems.

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Notes

  1. 1.

    The function ⌊⌋ rounds to the nearest smaller integer value.

References

  1. M. Andoni, W. Tang, V. Robu, D. Flynn: Data analysis of battery storage systems. CIRED - Open Access Proc. J. 2017(1), 96–99 (2017). https://doi.org/10.1049/oap-cired.2017.0657

    Google Scholar 

  2. A. Beschta, O. Dressler, H. Freitag, M. Montag, P. Struss, A model-based approach to fault localization in power transmission networks. Intell. Syst. Eng. 2, 3–14 (1992)

    Google Scholar 

  3. G. Biswas, K. Krishnamurthy, P. Basu, Applying qualitative reasoning techniques for analysis and evaluation in structural design, in Proceedings of the Seventh IEEE Conference on Artificial Intelligence Application (1991), pp. 265–268

    Google Scholar 

  4. F. Cascio, L. Console, M. Guagliumi, M. Osella, A. Panati, S. Sottano, D.T. Dupré, Generating on-board diagnostics of dynamic automotive systems based on qualitative models. AI Commun. 12(1/2), 33–43 (1999)

    Google Scholar 

  5. C.L. Chang, R.C.T. Lee, Symbolic Logic and Mechanical Theorem Proving (Academic Press, Cambridge, 1973)

    MATH  Google Scholar 

  6. C. Chen, M. Pecht, Prognostics of lithium-ion batteries using model-based and data-driven methods, in Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) (2012), pp. 1–6. https://doi.org/10.1109/PHM.2012.6228850

  7. J. Crow, J. Rushby, Model-based reconfiguration: toward an integration with diagnosis. In: Proceedings of the National Conference on Artificial Intelligence (AAAI) (Morgan Kaufmann, Los Angeles, 1991), pp. 836–841

    Google Scholar 

  8. R. Davis, Diagnostic reasoning based on structure and behavior. Artif. Intell. 24, 347–410 (1984)

    Google Scholar 

  9. R. Davis, H. Shrobe, W. Hamscher, K. Wieckert, M. Shirley, S. Polit, Diagnosis based on structure and function, in Proceedings of the National Conference on Artificial Intelligence (AAAI), Pittsburgh (1982), pp. 137–142

    Google Scholar 

  10. R. Dechter, Constraint Processing (Morgan Kaufmann, Los Angeles, 2003)

    MATH  Google Scholar 

  11. J. de Kleer, A.K. Mackworth, R. Reiter, Characterizing diagnosis and systems. Artif. Intell. 56, 197–222 (1992)

    MATH  Google Scholar 

  12. J. de Kleer, B.C. Williams, Diagnosing multiple faults. Artif. Intell. 32(1), 97–130 (1987)

    MATH  Google Scholar 

  13. A. Felfernig, G. Friedrich, D. Jannach, M. Stumptner, Consistency based diagnosis of configuration knowledge bases, in Proceedings of the European Conference on Artificial Intelligence (ECAI), Berlin (2000)

    Google Scholar 

  14. A. Felfernig, G. Friedrich, D. Jannach, M. Stumptner, An integrated development environment for the design and maintenance of large configuration knowledge bases, in Proceedings Artificial Intelligence in Design (Kluwer Academic Publishers, Worcester, 2000)

    Google Scholar 

  15. G. Friedrich, M. Stumptner, Consistency-based configuration. Tech. Rep., Technische Universität Wien (1992)

    MATH  Google Scholar 

  16. G. Friedrich, G. Gottlob, W. Nejdl, Physical impossibility instead of fault models, in Proceedings of the National Conference on Artificial Intelligence (AAAI), Boston (1990), pp. 331–336. Also appears in Readings in Model-Based Diagnosis (Morgan Kaufmann, 1992)

    Google Scholar 

  17. D. Gao, M. Huang, J. Xie, A novel indirect health indicator extraction based on charging data for lithium-ion batteries remaining useful life prognostics. SAE Int. J. Alternative Powertrains 6(2), 183–193 (2017). https://www.jstor.org/stable/26169183

    Google Scholar 

  18. E. Gelle, R. Weigel, Interactive configuration with constraint satisfaction, in Proceedings of the 2nd International Conference on Practical Applications of Constraint Technology (PACT) (1996)

    Google Scholar 

  19. R. Greiner, B.A. Smith, R.W. Wilkerson, A correction to the algorithm in Reiter’s theory of diagnosis. Artif. Intell. 41(1), 79–88 (1989)

    MathSciNet  MATH  Google Scholar 

  20. M. Heinrich, E. Jüngst, A resource-based paradigm for the configuring of technical systems from modular components, in Proceedings of the IEEE Conference on Artificial Intelligence Applications (CAIA), pp. 257–264 (1991)

    Google Scholar 

  21. M.W. Hofbaur, J. Köb, G. Steinbauer, F. Wotawa, Improving robustness of mobile robots using model-based reasoning. J. Intell. Robot. Syst. 48(1), 37–54 (2007)

    Google Scholar 

  22. M.R. Jongerden, B.R. Haverkort, Which battery model to use? IET Softw. 3(6), 445–457 (2009). https://doi.org/10.1049/iet-sen.2009.0001

    Google Scholar 

  23. U. Junker, QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems, in Proceedings of the 19th national conference on Artifical intelligence (AAAI) (AAAI Press/The MIT Press, Menlo Park/Cambridge, 2004), pp. 167–172

    Google Scholar 

  24. R. Klein, Model representation and taxonomic reasoning in configuration problem solving, in Proceedings of the German Workshop on Artificial Intelligence (GWAI), Informatik-Fachberichte, vol. 285 (Springer, Berlin, 1991), pp. 182–194

    Google Scholar 

  25. R. Klein, M. Buchheit, W. Nutt, Configuration as model construction: the constructive problem solving approach, in Proceedings of Artificial Intelligence in Design ’94 (Kluwer, Dordecht, 1994), pp. 201–218

    Google Scholar 

  26. M.E. Klenk, J. de Kleer, D.G. Bobrow, B. Janssen, Qualitative reasoning with Modelica models, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI). (AAAI Press, Menlo Park, 2014), pp. 1084–1090

    Google Scholar 

  27. A. Malik, P. Struss, M. Sachenbacher, Case studies in model-based diagnosis and fault analysis of car-subsystems, in Proceedings of the European Conference on Artificial Intelligence (ECAI) (1996)

    Google Scholar 

  28. I. Matei, A. Ganguli, T. Honda, J. de Kleer, The case for a hybrid approach to diagnosis: a railway switch, in DX@Safeprocess, CEUR Workshop Proceedings, vol. 1507, (2015), pp. 225–234. https://www.CEUR-WS.org

    Google Scholar 

  29. J.McDermott, R1: a rule-based configurer of computer systems. Artif. Intell. 19, 39–88 (1982)

    Google Scholar 

  30. S. Mittal, F. Frayman, Towards a generic model of configuration tasks, in Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI) (1989), pp. 1395–1401

    Google Scholar 

  31. I. Nica, F. Wotawa, Condiag – computing minimal diagnoses using a constraint solver, in Proceedings of the 23rd International Workshop on Principles of Diagnosis (2012)

    Google Scholar 

  32. I. Nica, I. Pill, T. Quaritsch, F. Wotawa, The route to success - a performance comparison of diagnosis algorithms, in International Joint Conference on Artificial Intelligence (IJCAI), Beijing (2013)

    Google Scholar 

  33. B. Peischl, I. Pill, F. Wotawa, Using Modelica programs for deriving propositional horn clause abduction problems, in Proceedings of the 39th German Conference on Artificial Intelligence (KI), Lecture Notes in Artificial Intelligence, vol. 9904. Springer, Klagenfurt (2016)

    Google Scholar 

  34. B. Pell, D. Bernard, S. Chien, E. Gat, N. Muscettola, P. Nayak, M. Wagner, B. Williams, A remote-agent prototype for spacecraft autonomy, in Proceedings of the SPIE Conference on Optical Science, Engineering, and Instrumentation. Volume on Space Sciencecraft Control and Tracking in the New Millennium, Bellingham, Washington (Society of Professional Image Engineers, Albany, 1996)

    Google Scholar 

  35. C. Picardi, R. Bray, F. Cascio, L. Console, P. Dague, O. Dressler, D. Millet, B. Rehfus, P. Struss, C. Vallée, Idd: integrating diagnosis in the design of automotive systems, in Proceedings of the European Conference on Artificial Intelligence (ECAI) (IOS Press, Lyon, 2002), pp. 628–632

    Google Scholar 

  36. I. Pill, T. Quaritsch, Rc-tree: a variant avoiding all the redundancy in Reiter’s minimal hitting set algorithm, in ISSRE Workshops. IEEE Computer Society, Washington, 2015), pp. 78–84

    Google Scholar 

  37. K. Rajan, D. Bernard, G. Dorais, E. Gamble, B. Kanefsky, J. Kurien, W. Millar, N. Muscettola, P. Nayak, N. Rouquette, B. Smith, W. Taylor, Y. Tung, Remote agent: an autonomous control system for the new millennium, in Proceedings of the 14th European Conference on Artificial Intelligence (ECAI), Berlin (2000)

    Google Scholar 

  38. R. Reiter, A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    MathSciNet  MATH  Google Scholar 

  39. M. Sachenbacher, P. Struss, Task-dependent qualitative domain abstraction. Artif. Intell. 162(1–2), 121–143 (2005)

    MathSciNet  MATH  Google Scholar 

  40. M. Sachenbacher, P. Struss, C.M. Carlén, A prototype for model-based on-board diagnosis of automotive systems. AI Commun. 13 (2000). Special Issue on Industrial Applications of Model-Based Reasoning, pp. 83–97

    Google Scholar 

  41. R.M. Smullyan, First Order Logic (Springer, Berlin, 1968)

    MATH  Google Scholar 

  42. G. Steinbauer, F. Wotawa, Model-based reasoning for self-adaptive systems - theory and practice, in Assurances for Self-Adaptive Systems. Lecture Notes in Computer Science, vol. 7740 (Springer, Berlin, 2013), pp. 187–213

    Google Scholar 

  43. P. Struss, O. Dressler, Physical negation — integrating fault models into the general diagnostic engine, in Proceedings 11th International Joint Conference on Artificial Intelligence, Detroit (1989), pp. 1318–1323

    Google Scholar 

  44. M. Stumptner, A. Haselböck, A generative constraint formalism for configuration problems, in 3rd Congress Italian Association for Artificial Intelligence. Lecture Notes in Artificial Intelligence, vol. 729 (Springer, Torino, 1993)

    Google Scholar 

  45. M. Stumptner, F. Wotawa, Model-based reconfiguration, in Proceedings Artificial Intelligence in Design, Lisbon (1998)

    Google Scholar 

  46. M. Stumptner, A. Haselböck, G. Friedrich, COCOS - a tool for constraint-based, dynamic configuration, in Proceedings of the 10th IEEE Conference on AI Applications (CAIA), San Antonio (1994)

    Google Scholar 

  47. T. Tomiyama, From general design theory to knowledge-intensive engineering. Artif. Intell. Eng. Des. Anal. Manuf. 8(4), 319–333 (1994)

    Google Scholar 

  48. M. Uta, A. Felfernig, Towards knowledge infrastructure for highly variant voltage transmission systems, in ConfWS, CEUR Workshop Proceedings, vol. 2220, (2018), pp. 109–118. https://www.CEUR-WS.org

  49. T. Voigt, S. Flad, P. Struss, Model-based fault localization in bottling plants. Adv. Eng. Inform. 29(1), 101–114 (2015)

    Google Scholar 

  50. R. Walter, A. Felfernig, W. Küchlin, Inverse QuickXplain vs. MaxSAT - a comparison in theory and practice, in Configuration Workshop CEUR,Workshop Proceedings, vol. 1453, CEUR-WS.org (2015), pp. 97–104

    Google Scholar 

  51. J. Weber, F. Wotawa, Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot. Appl. Intell. 36(4), 511–528 (2012)

    Google Scholar 

  52. B.J. Wielinga, A.T. Schreiber, KADS: a modeling approach to knowledge engineering. Knowl. Acquis. 4(1), 5–53 (1992)

    Google Scholar 

  53. F. Wotawa, A variant of Reiter’s hitting-set algorithm. Inf. Process. Lett. 79(1), 45–51 (2001)

    MathSciNet  MATH  Google Scholar 

  54. F. Wotawa, Reasoning from first principles for self-adaptive and autonomous systems, in Predictive Maintenance in Dynamic Systems – Advanced Methods, Decision Support Tools and Real-World Applications, ed. by E. Lughofer, M. Sayed-Mouchaweh (Springer, Berlin, 2019). https://doi.org/10.1007/978-3-030-05645-2

    Google Scholar 

  55. J.R. Wright, E.S. Weixelbaum, K. Brown, G.T. Vesonder, S.R. Palmer, J.I. Berman, H.G. Moore, A knowledge-based configurator that supports sales, engineering, and manufacturing at AT&T network systems, in Proceedings of the 5th Conference on Innovative Applications of AI (AAAI Press, Menlo Park, 1993)

    Google Scholar 

  56. G. Yost, T. Rothenfluh, Configuring elevator systems. Int. J. Hum.-Comput. Stud. 44, 521–568 (1996)

    Google Scholar 

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Acknowledgements

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged.

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  1. CD Lab for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems, Institute for Software Technology, Graz, Austria

    Franz Wotawa

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  1. Franz Wotawa
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Correspondence to Franz Wotawa .

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  1. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Wotawa, F. (2020). Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems. In: Sayed-Mouchaweh, M. (eds) Artificial Intelligence Techniques for a Scalable Energy Transition. Springer, Cham. https://doi.org/10.1007/978-3-030-42726-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-42726-9_11

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