In order to meet the increasing requirements for better utilization of renewable energy technologies, lithium-ion battery energy storage system have currently been developed to power an ever-increasing electrical applications in renewable energy industry and automotive industry. Energy storage capabilities are determined by the battery size, typically expressed in the units from watt hour to megawatt hour. Battery capacity is often fixed and amount of charge is specified in state of charge. If battery is used to plan for long term energy provision, battery size has to be chosen large, making battery energy storage system cost prohibitive. It is necessary to design battery smaller and in modular, but allow them to be exchanged and make sure different modular keep same for state of charge via balancing.
In this dissertation, some state-of-the-art modeling and control approaches for battery energy storage system are thoroughly proposed and validated in detail. Firstly, a fractional differential model method is presented for modeling the dynamics of a lithium-ion battery system over a large operating range, which is combination of conventional equivalent circuit model and electrochemical impedance spectroscopy experimental data. The proposed model includes a constant phase element term to approximate the non-linear dynamical behavior of the lithium-ion battery through broad operating range. The continuous-time system identification methods are introduced to estimate model parameters of the proposed fractional differential model. Validated on experimental data obtained from a lithium-ion battery, the estimated model provides better model accuracy and model performance than traditional integer equivalent circuit model methods.
Secondly, to better utilize a battery energy storage system, a fractional differential battery modeling approach is proposed to characterize power delivery dynamics, given charge and discharge demand as an input, not only in normal operating range, but also in extreme cases, such as battery over-charging and over-discharging. In particular, the proposed model is combined by individual voltage and current models to predict the dynamics of the energy storage and delivery of a lithium-ion battery system. The continuous-time parameterization and estimation methods are fully described and validated on the experimental data from a lithium iron phosphate battery.
Finally, some current scheduling strategies are proposed to solve battery heterogeneity and further improve the performance, lifespan and safety of a battery energy storage system with parallel connected battery modules. The scheduling algorithms are formulated in both open-loop and closed-loop implementation. The open-loop algorithm is formulated by solving a typical linear programming problem with detailed knowledge of the battery system. The closed-loop method is computed autonomously by recursive control algorithm without detailed battery knowledge, even when the characteristic parameters change as the battery pack ages. The experimental results indicate the feasibility and flexibility of the proposed current scheduling method in a battery pack system with parallel placed buck regulated battery modules.
