State of Health Estimation using a Temporal Convolutional Network for an Efficient Use of Retired Electric Vehicle Batteries within Second-Life Applications

This paper presents an accurate state of health (SOH) estimation algorithm using a temporal convolutional neural network (TCN) for lithium-ion batteries (LIB). With its self-learning ability, this data-driven approach can model the highly non-linear behaviour of LIB due to changes of environment and working conditions all along the battery lifetime. The precise SOH predictions of the TCN are especially needed to ensure a safe and efficient usage of retired electric vehicle batteries within second-life applications. The provided network is trained and tested with data gathered from commercial industry applications in the domain of energy storage. It is shown, that even for dynamic load profiles, the TCN achieves a mean squared error (MSE) of less than 1.0 %. Using this approach, the uncertainty of the heterogeneous performances and characteristics of retired electric vehicle batteries can be drastically reduced.