Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Conseque...Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares(LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation.Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.展开更多
This work concerns Lotka–Volterra models that are formulated using stochastic differential equations with regime-switching.Distinct from the existing formulations,the Markov chain that models random environments is u...This work concerns Lotka–Volterra models that are formulated using stochastic differential equations with regime-switching.Distinct from the existing formulations,the Markov chain that models random environments is unobservable.For such partially observed systems,we use Wonham’s filter to estimate the Markov chain from the observable evolution of the population,and convert the original system to a completely observable one.We then show that the positive solution of our model does not explode in finite time with probability 1.Several properties including stochastic boundedness,finite moments,sample path continuity and large-time asymptotic behaviour are also obtained.Moreover,stochastic permanence,extinction and feedback controls are also investigated.展开更多
This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge ra...This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and randomly.Based on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is conducted.It is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without reorganization.It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration.An optimal regrouping algorithm is introduced.Analysis methods,simulation examples,and a case study using real-world battery data are presented.展开更多
基金The National Natural Science Fund Projeet(51575293,51622504)National Key R&D Program of China(2016YFB0100906)International Sci&Tech Cooperation Program of China(2016YFE0102200)~~
文摘Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares(LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation.Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.
基金This work was supported in part by the National Science Foundation under DMS-1207667.
文摘This work concerns Lotka–Volterra models that are formulated using stochastic differential equations with regime-switching.Distinct from the existing formulations,the Markov chain that models random environments is unobservable.For such partially observed systems,we use Wonham’s filter to estimate the Markov chain from the observable evolution of the population,and convert the original system to a completely observable one.We then show that the positive solution of our model does not explode in finite time with probability 1.Several properties including stochastic boundedness,finite moments,sample path continuity and large-time asymptotic behaviour are also obtained.Moreover,stochastic permanence,extinction and feedback controls are also investigated.
基金The research of Z. Jin was supported by the Faculty Research Grant of University of Melbourne, and the research of G. Yin was partially supported by the National Science Foundation （No. DMS-1207667）.
基金supported in part by the Army Research Office(W911NF-19-1-0176).
文摘This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and randomly.Based on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is conducted.It is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without reorganization.It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration.An optimal regrouping algorithm is introduced.Analysis methods,simulation examples,and a case study using real-world battery data are presented.
