The global transportation system’s need for electrification is driving research efforts to overcome the drawbacks of battery electric vehicles (BEVs). The accurate and reliable estimation of the states of charge (SOC) and health (SOH) of Li-Ion batteries (LIBs) is crucial for the widespread adoption of BEVs. Transformers, cutting-edge deep learning (DL) models, are demonstrating promising capabilities in addressing various sequence-processing problems. This manuscript presents a thorough survey study of previous research papers that introduced modifications in the development of Transformer-based architectures for the SOC and SOH estimation of LIBs. This study also highlights approximately 15 different real-world datasets that have been utilized for training and testing these models. A comparison is made between the architectures, addressing each state using the root mean square error (RMSE) and mean absolute error (MAE) metrics.

The accurate estimation of Li-ion battery (LIB) states such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP) plays a pivotal role in the efficient operation of Electric Vehicles (EVs). These parameters can impact the battery’s health, driving range, and overall vehicle performance. Transformer-based artificial neural networks have shown impressive results in natural language processing (NLP) and estimation problems of many other domains. This paper presents an intensive study on the capabilities of various Transformer-based models in estimating the SOC and SOH of LIBs, the SOP is obtained based on the estimated SOC. This paper provides the following key original contributions: 1) the application of the Informer and Reformer variants of the Transformer model for the first time for SOH estimation of LIBs in EVs, 2) studying the effect of inverted embedding of iTransformers, a modified architecture of the transformers, on SOC and SOH estimation, inversion is performed on the Informer and Reformer as well; 3) applying a simple feature extraction method using partial discharge cycles for SOH estimation with Transformer-based models; 4) a new robust method is proposed for SOC estimation based on a 2-Encoder-Transformer with a one-dimensional convolutional neural network (1D-CNN) architecture; 5) the various architectures are trained, validated and tested on two real-world datasets comprising various driving scenarios and battery conditions. Comparative analysis with various deep learning architectures show impressive accuracy for estimating the SOC and SOH, leading to better SOP calculation.

Electric vehicles are currently the leading solution in the automotive market to reduce greenhouse gas emissions. However, transitioning from traditional internal combustion engines poses many challenges. One of these challenges is the accurate estimation of the battery’s state of charge (SOC) since its energy cannot be directly measured during transient operation. This paper presents a comparative study that examines the robustness of the Extended Kalman Filter (EKF), the Smooth Variable Structure Filter (SVSF), which is a robust sliding mode state estimator, and the SVSF with a varying boundary layer (SVSF-VBL) in estimating the SOC while introducing uncertainties in voltage, current, temperature, and capacity. A comprehensive dataset is generated at McMaster Automotive Resource Center (MARC) for the Samsung INR21700 30T Li-Ion cells. The dataset includes a set of real-world drive cycles and characterization profiles. A fourth-order equivalent circuit model is used, whose parameters were obtained using measurements of the cycled cells through curve fitting of Hybrid Pulse Power Characterization test data (HPPC). Genetic algorithm-based optimization was used to tune the three filters using the measurements from four drive cycles for a wide range of temperatures. The effect of various sources of uncertainty is studied together with the impact of temperature across a wide range. The general comparison shows that the SVSF-VBL outperformed the EKF and the standard SVSF, achieving an average root mean square error (RMSE) of 1.43% of the SOC for all temperature values and various uncertainties and errors injected.