Degradation-Conscious Multiobjective Optimal Control of Reconfigurable Li-Ion Battery Energy Storage Systems

Abstract

Lithium-ion battery energy storage systems are made from sets of battery packs that are connected in series and parallel combinations depending on the application’s needs for power. To achieve optimal control, advanced battery management systems (ABMSs) with health-conscious optimal control are required for highly dynamic applications where safe operation, extended battery life, and maximum performance are critical requirements. The majority of earlier research assumed that the battery cells in these energy storage systems were identical and would vary uniformly over time in terms of cell characteristics. However, in real-world situations, the battery cells might behave differently for a number of reasons. Overcharging and over-discharging are caused by an electrical imbalance that results from the cells’ differences in properties and capacity. Therefore in this study, a stratified real-time control scheme was developed for the dual purposes of minimizing the capacity fade and the energy losses of a battery pack. Each of the cells in the pack is represented by a degradation-conscious physics-based reduced-order equivalent circuit model. In view of the inconsistencies between cells, the proposed control scheme uses a state estimator such that the parametric values of the circuit elements in the cell model are determined and updated in a decentralized manner. The minimization of the capacity fade and energy losses is then formulated as a multiobjective optimization problem, from which the resulting optimal control strategy is realized through the switching actions of a modular multilevel series-parallel converter which interconnects the battery pack to an external AC system. A centralized controller ensures optimal switching sequence of the converter leading to the maximum utilization of the capacity of the battery pack. Both simulation and experimental results are used to verify the proposed methodologies which aim at minimizing the battery degradation by reconfiguring the battery cells dynamically in accordance with the state of health (SOH) of the pack.

A Novel Differentiated Control Strategy for Energy Storage System That Minimizes Battery Aging Cost Based on Multiple Health Features

“…Yan et al [10] primarily concentrated on investigating the impact of DOD, establishing a control strategy for frequency regulation scenarios aimed at enhancing battery lifetimes. Furthermore, Karunathilakeet al [11] developed a multi-objective optimization control strategy that takes into account aging and is based on multiple features reflecting energy and power performance. However, They focused on multiple independent cells as the subjects of research.…”

A Novel Differentiated Control Strategy for an Energy Storage System That Minimizes Battery Aging Cost Based on Multiple Health Features

“…Yan et al [12] primarily concentrated on investigating the impact of DOD, establishing a control strategy for frequency regulation scenarios aimed at enhancing battery lifetimes. Furthermore, Karunathilake et al [13] developed a multi-objective optimization control strategy that takes into account aging and is based on multiple features reflecting energy and power performance. However, they focused on multiple independent cells as the subjects of research.…”

Improved Battery Balancing Control Strategy for Reconfigurable Converter Systems

“…Currently, reconfigurable battery energy storage systems have attracted increasing attention due to their ability to dynamically reconfigure the battery topology in real time to adapt to specific application requirements [15][16][17][18][19]. This can more effectively utilize battery resources, isolate corresponding batteries according to their current state of charge and health status without affecting the charge and discharge processes of other batteries, and extend the battery's service life while reducing the possibility of module failure [20,21].…”