Xin Zhao

Data-based modeling of a lithium iron phosphate battery as an energy storage and delivery system

Lithium-ion batteries are important for storage and delivery of electrical energy. Monitoring and prediction of the dynamic and time-dependent effects of lithium-ion batteries is crucial in a battery management system (BMS). In this paper, a dynamic model for the battery as an energy storage and delivery system is proposed. The structure and the parameters of the battery models are estimated by monitoring a charge/discharge demand signal and a power storage/delivery signal in real time. The model is combined by individual linear dynamic models, where the parameters can be estimated by a least-squares algorithm and implemented in a recursive fashion. Based on data obtained from the experimental setup, the dynamic model is applied to predict the dynamics of the energy storage and delivery, and validated against real-time measurements. The results show that the model can capture and predict the dynamics of the energy storage and delivery of the battery, which can benefit the control of lithium-ion batteries.

State-of-charge estimation for batteries: A multi-model approach

Monitoring the state-of-charge (SoC) for batteries is challenging, especially when a battery has time-varying parameters. We propose to improve SoC estimation using an adaptive strategy and multiple models in this study, developing a unique algorithm called MM-AdaSoC. Specifically, two submodels in state-space form are generated from a modified Nernst battery model. Both are shown to be locally observable under mild conditions. The iterated extended Kalman filter (IEKF) is then applied to each submodel in parallel, estimating simultaneously the SoC variable and certain unknown parameters. The SoC estimates obtained from the two separately implemented IEKFs are fused to yield the final overall SoC estimates, which tend to have higher accuracy than those obtained from a single-model. Its effectiveness is demonstrated via experiments.

Current Scheduling for Parallel Buck Regulated Battery Modules

Abstract In an electrical energy storage and delivery system, a parallel connection of battery modules can be used to increase the storage capability and power delivery demands. Parallel connection of batteries requires a robust battery management system as batteries may have different operating parameters such as state-of-charge (SoC), open-circuit voltage (OCV), internal resistance or battery chemistry. This paper focuses on current scheduling for a parallel connection of battery modules by utilizing buck regulators in the battery management system (BMS) of each module to improve the system performance via simultaneous, sequential and hybrid discharge scheduling algorithms. The results indicate the feasibility of the scheduling algorithms and motivate the use of parallel connected battery modules despite changes in battery operating parameters.

Disturbance rejection of real power flow by grid-tied inverter

High penetration of distributed and renewable power generation systems challenges the conventional paradigm of stabilizing power systems with the standard rotational inertia used in power generation. To improve the stability of a power system, advanced disturbance rejection control techniques can be used to provide damping and power disturbance mitigation. An approach for three-phase (real) power disturbance mitigation using real-time feedback control is outlined in this paper. The control algorithm for power disturbance rejection is formulated via the internal model principle, computed via minimum variance control and designed to operate with a commercial grid-tied inverter. The end result is a real-time controlled inverter that is able to reduce power disturbances created by unanticipated load changes in an electrical grid. An experimental setup is used to validate the proposed control algorithm through on/off switching of a dynamic load and the results illustrate the feasibility of the proposed controller for real-time mitigation of power flow oscillations.

Agile Bayesian filtering

A novel nonlinear filtering approach, the agile Bayesian filter, is presented in this paper. Its design is directly based on the Bayesian filtering paradigm, a framework particularly useful for development of nonlinear filters. Compared to some existing filters, the agile Bayesian filter is less reliant on the Gaussian distribution approximations, the use of which is common in nonlinear filtering studies but indeed difficult to be justified. The agile Bayesian filtering formulae involve several Gaussian weighted integrals that need to be evaluated for implementation. They are numerically solved by the Monte Carlo integration method and the obtained filter is named the Monte Carlo agile Bayesian filter. The proposed filter is investigated through a simulation based study. Future improvements to this filter can be performed by using more accurate numeric integration rules.