Active state and parameter estimation as part of intelligent battery systems

In recent years, intelligent battery systems came into researchers' focus, which comprise sensors and actuators on cell level. These architectures allow the battery management system to observe and to control the current flow within the battery system, which is particularly promising for battery electric vehicles. Besides, insight into battery cells' states and model parameters is essential for valuable battery management and is often achieved by online state and parameter estimation. Though, during real-world operation the system excitation is often insufficient for an accurate estimate. Within this contribution, we present strategies that utilize the actuators to improve the system's excitation and thereby enhance the observability. Controlling the current flow with the objective of enhanced state and parameter estimation in time domain is a novel approach. The benefit of switching for state and parameter estimation is investigated simulatively and experimentally with NMC/graphite lithium-ion cells. Furthermore, the switching operation's influence on degradation is discussed. Results show that the investigated switching strategies enhance the accuracy of state and parameter estimation by 4% to 30% depending on the inspected parameter, while a negligible impact on cell aging is expected considering the cell heating caused by switching operation is below 1.5%.

Automated summary

This study focuses on intelligent battery systems and the impact of switching operations on state and parameter estimation. By controlling current flow to enhance system excitation and improve observability, switching strategies significantly increase the accuracy of state and parameter estimation.