Onboard Health Estimation using Distribution of Relaxation Times for Lithium-ion Batteries
This work addresses the limited accuracy of onboard health estimation models for lithium-ion batteries, which is crucial for applications like electric vehicles and energy storage, but it is incremental as it combines existing techniques (DRT and LSTM) on new data.
The paper tackled the problem of accurately estimating state-of-health (SOH) for lithium-ion batteries under varied operating conditions by using electrochemical impedance spectroscopy data deconvoluted with the distribution of relaxation times technique and an LSTM neural network, achieving an average RMSPE of 1.69% across ten test sets.
Real-life batteries tend to experience a range of operating conditions, and undergo degradation due to a combination of both calendar and cycling aging. Onboard health estimation models typically use cycling aging data only, and account for at most one operating condition e.g., temperature, which can limit the accuracy of the models for state-of-health (SOH) estimation. In this paper, we utilize electrochemical impedance spectroscopy (EIS) data from 5 calendar-aged and 17 cycling-aged cells to perform SOH estimation under various operating conditions. The EIS curves are deconvoluted using the distribution of relaxation times (DRT) technique to map them onto a function $\textbf{g}$ which consists of distinct timescales representing different resistances inside the cell. These DRT curves, $\textbf{g}$, are then used as inputs to a long short-term memory (LSTM)-based neural network model for SOH estimation. We validate the model performance by testing it on ten different test sets, and achieve an average RMSPE of 1.69% across these sets.