Le Yi Wang

System identification using binary sensors

System identification is investigated for plants that are equipped with only binary-valued sensors. Optimal identification errors, time complexity, optimal input design, and impact of disturbances and unmodeled dynamics on identification accuracy and complexity are examined in both stochastic and deterministic information frameworks. It is revealed that binary sensors impose fundamental limitations on identification accuracy and time complexity, and carry distinct features beyond identification with regular sensors. Comparisons between the stochastic and deterministic frameworks indicate a complementary nature in their utility in binary-sensor identification.

System identification with quantized observations

Overview.- System Settings.- Stochastic Methods for Linear Systems.- Empirical-Measure-Based Identification: Binary-Valued Observations.- Estimation Error Bounds: Including Unmodeled Dynamics.- Rational Systems.- Quantized Identification and Asymptotic Efficiency.- Input Design for Identification in Connected Systems.- Identification of Sensor Thresholds and Noise Distribution Functions.- Deterministic Methods for Linear Systems.- Worst-Case Identification under Binary-Valued Observations.- Worst-Case Identification Using Quantized Observations.- Identification of Nonlinear and Switching Systems.- Identification of Wiener Systems with Binary-Valued Observations.- Identification of Hammerstein Systems with Quantized Observations.- Systems with Markovian Parameters.- Complexity Analysis.- Space and Time Complexities, Threshold Selection, Adaptation.- Impact of Communication Channels on System Identification.

Asymptotically efficient parameter estimation using quantized output observations

This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramer-Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank.

Electric Vehicle Battery Technologies

This chapter aims at bridging the gap between chemistry scientists and electrical engineers on electric vehicle (EV) batteries. The power and energy of electric propulsion are first reviewed in Sect. 2.2. Commonly used terms to describe battery performance and characterization are then introduced in Sect. 2.3, followed by the review of various battery charging methods and EV charging schemes in Sect. 2.4. The fundamentals of EV battery technologies are addressed in Sect. 2.5. Two currently most common EV battery technologies, namely, nickel metal hydride (NiMH) and lithium-ion (Li-ion), are covered. It is targeted for giving power engineers a basic understanding of battery chemistry. The EV battery modeling is introduced in Sect. 2.6. It is important for power engineers to appreciate the fundamentals of battery chemistry and battery modeling and use it for power electronic interfacing converter design, battery management, and system level studies. Section 2.7 covers the topic on battery characterization including battery model parameter estimation, state of charge (SOC), and state of health (SOH) estimation. The battery aggregation for power grid applications is discussed in Sect. 2.8. The concept of virtual power plant (VPP) for battery aggregation is introduced to support EV’s participation in power markets.

Joint identification of plant rational models and noise distribution functions using binary-valued observations

System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed.

Integrated System Identification and State-of-Charge Estimation of Battery Systems

Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles.

Fast identification n-widths and uncertainty principles for LTI and slowly varying systems

The optimal worst-case uncertainty that can be achieved by identification depends on the observation time. In the first part of the paper, this dependence is evaluated for selected linear time invariant systems in the l/sup 1/ and H/sup /spl infin// norms and shown to be derivable from a monotonicity principle. The minimal time required is shown to depend on the metric complexity of the a priori information set. Two notions of n-width (or metric dimension) are introduced to characterize this complexity. In the second part of the paper, the results are applied to systems in which the law governing the evolution of the uncertain elements is not time invariant. Such systems cannot be identified accurately. The inherent uncertainty is bounded in the case of slow time variation. The n-widths and related optimal inputs provide benchmarks for the evaluation of actual inputs occurring in adaptive feedback systems. >

Local-global double algebras for slow H/sup infinity / adaptation. I. Inversion and stability

For Pt.II see ibid., vol.36, no.2, p.143-51 (1991). In this two-part work, a common algebraic framework is introduced for the frozen-time analysis of stability and H/sup infinity / optimization in slowly time-varying systems, based on the notion of a normed algebra on which local and global products are defined. Relations between local stability, local (near) optimality, local coprime factorization, and global versions of these properties are sought. The framework is valid for time-domain disturbances in l/sup infinity /. H/sup infinity / behavior is related to l/sup infinity / input-output behavior via the device of an approximate isometry between frequency and time-domain norms. The authors presently elaborate on the double-algebra concept for Volterra operators which approximately commute with the shift, and summarize the main algebraic properties and norm inequalities. Local conditions for global invertibility are also obtained. Classical frozen-time stability conditions are incorporated in relations between local and global spectra. >

Enhanced Identification of Battery Models for Real-Time Battery Management

Renewable energy generation, vehicle electrification, and smart grids rely critically on energy storage devices for enhancement of operations, reliability, and efficiency. Battery systems consist of many battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to a variety of factors such as aging, operational conditions, and chemical property variations. Their effective management requires high fidelity models. This paper aims to develop identification algorithms that capture individualized characteristics of each battery cell and produce updated models in real time. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares methods will encounter identification bias. This paper devises modified model structures and identification algorithms to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. A typical battery model structure is used to illustrate utilities of the methods.

Robust disturbance attenuation with stability for linear systems with norm-bounded nonlinear uncertainties

In this paper, the problem of robust stabilization and robust disturbance attenuation is investigated for systems with linear nominal parts and norm-bounded nonlinear uncertainties on both states and control inputs. It is shown that the type of nonlinear uncertainty sets considered in this paper has an equivalent representation by linear uncertainty sets. Based on this key result and some standard Riccati inequality approaches for robust control of linear uncertain systems, a constructive design procedure is developed.