Robustness analysis for battery-supported cyber-physical systems

This article establishes a novel analytical approach to quantify robustness of scheduling and battery management for battery supported cyber-physical systems. A dynamic schedulability test is introduced to determine whether tasks are schedulable within a finite time window. The test is used to measure robustness of a real-time scheduling algorithm by evaluating the strength of computing time perturbations that break schedulability at runtime. Robustness of battery management is quantified analytically by an adaptive threshold on the state of charge. The adaptive threshold significantly reduces the false alarm rate for battery management algorithms to decide when a battery needs to be replaced.

[1]  Chenyang Lu,et al.  A utilization bound for aperiodic tasks and priority driven scheduling , 2004, IEEE Transactions on Computers.

[2]  Giorgio C. Buttazzo,et al.  Rate Monotonic Analysis: The Hyperbolic Bound , 2003, IEEE Trans. Computers.

[3]  Michael B. Jones,et al.  Operating System Support for Multimedia: The Programming Model Matters , 2000 .

[4]  Luigi Glielmo,et al.  State of charge Kalman filter estimator for automotive batteries , 2004 .

[5]  Chung Laung Liu,et al.  Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment , 1989, JACM.

[6]  Iain Bate,et al.  Minimising Task Migration and Priority Changes in Mode Transitions , 2007, 13th IEEE Real Time and Embedded Technology and Applications Symposium (RTAS'07).

[7]  Sarma B. K. Vrudhula,et al.  Battery Modeling for Energy-Aware System Design , 2003, Computer.

[8]  Giuseppe Lipari,et al.  Elastic Scheduling for Flexible Workload Management , 2002, IEEE Trans. Computers.

[9]  Sarma Vrudhula,et al.  A model for battery lifetime analysis for organizing applications on a pocket computer , 2003, IEEE Trans. Very Large Scale Integr. Syst..

[10]  John P. Lehoczky,et al.  Fixed priority scheduling of periodic task sets with arbitrary deadlines , 1990, [1990] Proceedings 11th Real-Time Systems Symposium.

[11]  Alan Burns,et al.  Schedulability Analysis for Real-Time Systems with EDF Scheduling , 2009, IEEE Transactions on Computers.

[12]  Suleiman Abu-Sharkh,et al.  Rapid test and non-linear model characterisation of solid-state lithium-ion batteries , 2004 .

[13]  Alan Burns,et al.  Schedulability analysis of fixed priority real-time systems with offsets , 1997, Proceedings Ninth Euromicro Workshop on Real Time Systems.

[14]  Alan Burns,et al.  Hard Real-Time Scheduling: The Deadline-Monotonic Approach , 1991 .

[15]  Björn Andersson,et al.  Exact admission-control for integrated aperiodic and periodic tasks , 2005, 11th IEEE Real Time and Embedded Technology and Applications Symposium.

[16]  Philip S. Yu,et al.  Machine Learning in Cyber Trust: Security, Privacy, and Reliability , 2009 .

[17]  Iain Bate,et al.  Incorporating Scenarios And Heuristics To Improve Flexibility In Real-Time Embedded Systems , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[18]  Fumin Zhang,et al.  Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems , 2008, 2008 Real-Time Systems Symposium.

[19]  Björn Andersson,et al.  Exact Admission-Control for Integrated Aperiodic and Periodic Tasks , 2005, IEEE Real-Time and Embedded Technology and Applications Symposium.

[20]  Kang G. Shin,et al.  On Dynamic Reconfiguration of a Large-Scale Battery System , 2009, 2009 15th IEEE Real-Time and Embedded Technology and Applications Symposium.

[21]  Tei-Wei Kuo,et al.  Load adjustment in adaptive real-time systems , 1991, [1991] Proceedings Twelfth Real-Time Systems Symposium.

[22]  D. Niebur,et al.  Simulink Model for Hybrid Power System Test-bed , 2007, 2007 IEEE Electric Ship Technologies Symposium.

[23]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[24]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[25]  Lui Sha,et al.  Cyber-Physical Systems: A New Frontier , 2008, 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008).

[26]  Georg Brasseur,et al.  Modeling of high power automotive batteries by the use of an automated test system , 2003, IEEE Trans. Instrum. Meas..

[27]  Henk Jan Bergveld,et al.  Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications , 2008 .

[28]  Wayne H. Wolf,et al.  Cyber-physical Systems , 2009, Computer.

[29]  Sarma B. K. Vrudhula,et al.  Energy management for battery-powered embedded systems , 2003, TECS.

[30]  Edward A. Lee.  Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[31]  W. G. Hurley,et al.  An Improved Battery Characterization Method Using a Two-Pulse Load Test , 2008, IEEE Transactions on Energy Conversion.

[32]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[33]  John Regehr,et al.  Scheduling tasks with mixed preemption relations for robustness to timing faults , 2002, 23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002..

[34]  Mathai Joseph,et al.  Finding Response Times in a Real-Time System , 1986, Comput. J..

[35]  K.-E. Arzen,et al.  How does control timing affect performance? Analysis and simulation of timing using Jitterbug and TrueTime , 2003, IEEE Control Systems.

[36]  John P. Lehoczky,et al.  The rate monotonic scheduling algorithm: exact characterization and average case behavior , 1989, [1989] Proceedings. Real-Time Systems Symposium.

[37]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[38]  Michael D. Lemmon,et al.  Generalized Elastic Scheduling , 2006, 2006 27th IEEE International Real-Time Systems Symposium (RTSS'06).

[39]  Zdzislaw Bubnicki,et al.  Modern Control Theory , 2005 .