State of health based battery reconfiguration for improved energy efficiency

This paper analyzes the system-level state of health (SOH) and its dependence on the SOHs of its component battery modules. Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth, operating temperature, and environment conditions, capacities of battery modules decay unevenly and randomly. Based on estimated SOHs of battery modules during battery operation, we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities. A rigorous mathematical analysis of system-level capacity utilization is conducted. It is shown that for large battery strings with uniformly distributed capacities, the average string capacity approaches the minimum, implying an asymptotically near worst-case capacity utility without reorganization. It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration. An optimal regrouping algorithm is introduced. Analysis methods, simulation examples, and a case study using real-world battery data are presented.

Automated summary

This paper analyzes the system-level state of health (SOH) and its dependence on the SOHs of its component battery modules. It investigates how the overall SOH of the system deteriorates when battery modules age and become increasingly diverse in their capacities. The study shows that without reorganization, large battery strings with uniformly distributed capacities tend to approach the minimum capacity, resulting in suboptimal capacity utilization. It demonstrates that battery reconfiguration can enhance the efficient utilization of overall battery capacities for extended operational ranges. The paper introduces an optimal regrouping algorithm and includes analysis methods, simulation examples, and a case study using real-world battery data.