Real-Time, Data-Driven Modeling for Enhanced Battery System

Performance and Reliability

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±¨¸æÕªÒª£ºRenewable 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 or modules, whose characteristics are different 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 which are acquired in rea time and individualized.

This talk will present main motivations for and key issues in real-time and individualized modeling. The main issues include: (1) Model structure selection and identifiability; (2) Robustness of SOC estimators; (3) Measurement noises; (4) Joint parameter and state estimation; (5) Reducing real-time identification system complexity; (6) Accurate reliability characterization.

Some recent advancement on adaptive estimation of SOC reali-time modeling and identification algorithms for battery models will be presented that capture individualized characteristics of each battery cell/module and produce updated models in real time. We will show that typical battery models may not be identifiable, observer-based SOC estimators (such as Kalman filters) are not robust against output mapping errors, and standard least-squares methods will encounter identification bias. Modified model structures and identification algorithms are devised to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. Some typical battery model structures are used to illustrate utilities of the methods.