State of Health and Lifetime Prediction of Lithium-ion Batteries Using Self-learning Incremental Models

Abstract

Lithium-ion batteries are key energy storage elements in the context of environmental-aware energy systems representing a crucial technology to achieve the goal of zero carbon emission. Therefore, its conditions must be monitored to guarantee the safe and reliable operation of the systems that use these components. Furthermore, lithium-ion batteries’ prognostics and health management policies must cope with the nonlinear and time-varying nature of the complex electrochemical dynamics of battery degradation. This paper proposes an incremental-learning-based algorithm to estimate the State of Health (SoH) and the Remaining Useful Life (RUL) of lithium-ion batteries based on measurement data streams. For this purpose, a two-layer framework is proposed based on incremental modeling of the SoH. In the first layer, a set of representative features are extracted from voltage and current data of partial charging and discharging cycles; these features are then used to train the proposed model in a recursive procedure to estimate the battery’s SoH. The second layer uses the capacity data for incremental learning of an Autoregressive (AR) model for the SoH, which will be used to propagate the battery’s degradation through time to make the RUL prediction. The proposed method was applied to two datasets for experimental evaluation, one from CALCE and another from NASA. The proposed framework was able to estimate the SoH of 8 different lithium-ion cells with an average percentage error below 1.5% for all scenarios, while the lifetime model predicted the cell’s RUL with a maximum average error of 25%.

Investigations of Different Approaches for Controlling the Speed of an Electric Motor with Nonlinear Dynamics Powered by a Li-ion Battery – Case Study

“…The accuracy of Li-ion battery SOC estimation has a significant impact on the efficient operation and EMS of the battery. Many of studies are dedicated to advancing the BMS functions, such as intelligent cell balancing and charging control strategies for lithium-ion battery packs [10], SOC and state of health (SOH) monitoring [11][12][13], and thermal battery control temperature [14].…”

“…Also, its robustness is better when estimating the SOC of different chemistry batteries. To achieve higher accuracy of state estimation, various intelligent algorithms based on Machine Learning (ML) and Deep Learning (DL) Artificial Intelligence (AI) models are applied to the SOC estimation and terminal voltage prediction, as those developed in [7,12,13,17,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] easily to be adapted to all types of batteries and chemistries. The neural networks (NNs) learning techniques have a wide range of applications and are suitable for all types of batteries chemistry.…”