Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries

Abstract

Accurate current measurement is indispensable for the management of lithium-ion battery (LIB), especially for the state-of-charge (SOC) estimation. However, accurate current sensing is challenging in electric vehicles (EVs) due to the electromagnetic interference. Moreover, the currents across the parallel branches of battery pack are even unmeasurable due to the absence of current sensor. Motivated by this, this paper proposes a hierarchical soft measurement framework for the load current and SOC addressing different degrees of current sensor uncertainty. Rooted from a common least squares (LS)-based state optimization problem, a total least square (TLS)-based modification is proposed and solved to compensate for the measurement disturbances, and in accordance to estimate the SOC more accurately. One step further, an input-free optimization method is proposed to co-estimate the SOC and load current without using the current measurements. Simulation and experimental results suggest that the proposed hierarchical framework can realize high-fidelity co-estimation of the SOC and load current, especially in the adverse scenarios of both strong noise corruption and current sensor malfunction/missing. The encouraging results open new paradigms for both the high-robustness current-free SOC estimation and the hardware-free soft current measurement of LIB.