Paul W. C. Northrop

Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications

The current electric grid is an inefficient system that wastes significant amounts of the electricity it produces because there is a disconnect between the amount of energy consumers require and the amount of energy produced from generation sources. Power plants typically produce more power than necessary to ensure adequate power quality. By taking advantage of energy storage within the grid, many of these inefficiencies can be removed. When using battery energy storage systems (BESS) for grid storage, advanced modeling is required to accurately monitor and control the storage system. A battery management system (BMS) controls how the storage system will be used and a BMS that utilizes advanced physics-based models will offer for much more robust operation of the storage system. The paper outlines the current state of the art for modeling in BMS and the advanced models required to fully utilize BMS for both lithium-ion batteries and vanadium redox-flow batteries. In addition, system architecture and how it can be useful in monitoring and control is discussed. A pathway for advancing BMS to better utilize BESS for grid-scale applications is outlined.

Optimal control and state estimation of lithium-ion batteries using reformulated models

First-principles models that incorporate all of the key physics that affect the internal states of a lithium-ion battery are in the form of coupled nonlinear PDEs. While these models are very accurate in terms of prediction capability, the models cannot be employed for on-line control and monitoring purposes due to the huge computational cost. A reformulated model [1] is capable of predicting the internal states of battery with a full simulation running in milliseconds without compromising on accuracy. This paper demonstrates the feasibility of using this reformulated model for control-relevant real-time applications. The reformulated model is used to compute optimal protocols for battery operations to demonstrate that the computational cost of each optimal control calculation is low enough to be completed within the sampling interval in model predictive control (MPC). Observability studies are then presented to confirm that this model can be used for state-estimation-based MPC. A moving horizon estimator (MHE) technique was implemented due to its ability to explicitly address constraints and nonlinear dynamics. The MHE uses the reformulated model to be computationally feasible in real time. The feature of reformulated model to be solved in real time opens up the possibility of incorporating detailed physics-based model in battery management systems (BMS) to design and implement better monitoring and control strategies.

Analytical solution for electrolyte concentration distribution in lithium-ion batteries

In this article, the method of separation of variables (SOV), as illustrated by Subramanian and White (J Power Sources 96:385, 2001), is applied to determine the concentration variations at any point within a three region simplified lithium-ion cell sandwich, undergoing constant current discharge. The primary objective is to obtain an analytical solution that accounts for transient diffusion inside the cell sandwich. The present work involves the application of the SOV method to each region (cathode, separator, and anode) of the lithium-ion cell. This approach can be used as the basis for developing analytical solutions for battery models of greater complexity. This is illustrated here for a case in which non-linear diffusion is considered, but will be extended to full-order nonlinear pseudo-2D models in later work. The analytical expressions are derived in terms of the relevant system parameters. The system considered for this study has LiCoO2–LiC6 battery chemistry.

Kinetic Monte Carlo simulation of surface heterogeneity in graphite anodes for lithium-ion batteries: Passive layer formation

The properties and chemical composition of the solid-electrolyte-interface (SEI) layer have been a subject of intense research due to their importance in the safety, capacity fade, and cycle life of Li-ion secondary batteries. Kinetic Monte Carlo (KMC) simulation is applied to explore the formation of the passive SEI layer in the tangential direction of the lithium- ion intercalation in a graphite anode. The simulations are found to consistent with observations in the literature that the active surface coverage decreases with time slowly in the initial stages of the battery operation, and then decreases rapidly. The effects of operating parameters such as the exchange current density and temperature on the formation of the passive SEI layer are investigated. The active surface coverage at the end of each charging cycle was initially lower at higher temperature, but remained constant for more cycles. The temperature that optimizes the active surface in a lithium-ion battery at Cycle 1 can result in less active surface area for most of the battery life.