An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management

Abstract

As a critically important power source for modern electric vehicles, lithium-ion batteries need to operate safely, reliably, and efficiently, which entails effective battery management systems (BMSs). How to accurately estimate internal battery states constitutes a key functionality in a BMS and needs to be designed meticulously. In this paper, a novel co-estimation hierarchy for state of charge (SOC), state of health (SOH) and state of power (SOP) in lithium-ion batteries is devised and validated experimentally. Considering the underlying coupling and characteristics among these states, a multi-time-scale estimation framework is developed for accurate estimates and moderate computational cost. First, the online, model-based SOC estimation is fulfilled by modified moving horizon estimation (mMHE) for better convergence and fault tolerance. Second, the model parameters are periodically updated by virtue of the mMHE-type optimization with a relatively long horizon. Third, the ampere-hour integral and the estimated SOC are employed to realize the capacity estimation offline. Given updated states and parameters, the model-based real-time SOP estimation reliably predicts the battery peak power respecting multiple operational constraints. Finally, the effectiveness and resilience of the joint SOC/SOH/SOP estimation is demonstrated through a number of experiments. Experimental results show that the proposed co-estimation hierarchy presents remarkable benefits, compared to separate estimation solutions. The estimation errors of SOC, voltage and capacity are less than 3%, 25 mV and 3%, respectively, for both fresh and aged cells.