Rapid monitor of states of lithium-ion batteries through non-quasi-static electrochemical impedance spectroscopy and terminal voltage

As lithium-ion batteries are a primary energy source for electric vehicles, their accurate state-of-health (SOH) and state-of-charge (SOC) monitoring is crucial. The two battery states are directly linked to the battery impedances, which can be measured with electrochemical impedance spectroscopy (EIS). However, classical EIS testing is time-consuming due to the broadband frequency measurement and the full relaxation requirement of a battery. A non-quasi-static EIS is carried out in this study by implementing the test immediately after a short relaxation following the end of battery charging/discharging. With the measurement, we observe that the high-frequency and the subsequent partial medium-frequency impedances are almost independent of the relaxation period, while these impedances regularly change with the battery states. This suggests the feasibility of a concurrent estimation of SOH and SOC through utilizing the impedances within these ranges and the terminal voltage as the input to a Gaussian process regression model. We show that the input dimension can be lower than 14 and the measuring time required to acquire the input can be reduced to below 7 s. The root mean square errors of the SOH and SOC estimations are found to be less than 2.66% and 1.57%, respectively. •A non-quasi-static EIS is analyzed under different relaxation times.•Impedances within the frequency of 10 k to 100 Hz and terminal voltages are used.•Time to acquire the impedance data can be reduced to less than 7 s.•Gaussian process regression is most effective in prediction of SOH and SOC.