# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 1 Introduction  
The electrification of energy-intensive industries and transportation is crucial in addressing pressing environmental issues. Energy storage systems, particularly batteries, are pivotal in this transition. Batteries are of great importance in the mobility sector due to their cost and limited lifespan, as they are critical for the performance and safe operation of hybrid and electric vehicles (EVs) [1]. The main issue regarding mobility transition is the continuously increasing number of battery systems along with their increasing complexity. This poses a management challenge for type-specific battery estimation, prediction, and assessment [2]. Therefore, it is important to develop tools to understand battery cells and battery packs. Such tools need to be easily adaptable to different batteries. This canbe achieved by using the combined possibilities of long-accumulated battery knowledge and cloud-based data-driven artificial intelligence (AI) technologies [3], which are more adaptable toward the fast-developing pace and complexity of real-world applications when compared to classical methods [4]. This is especially true since the majority of classical methods tend to model battery cells of EVs whilst assuming controlled laboratory settings, which is highly unlikely in real-world applications. The main objective of this work is to enable fast and reliable real EV battery assessment under real working conditions, i.e., using field data. We demonstrate this via an accurate and flexible State of Charge (SoC) prediction framework for Lithium-Ion (Li-ion) batteries using Neural Networks (NNs). This is achieved by conducting comprehensive modeling of SoC estimation and benchmarking state-of-the-art NN architectures for time-series data in this context with data from real working conditions. Multiple methods of SoC estimation for Li-ion batteries have been proposed. There are direct methods such as Coulomb Counting [5; 6; 7]; model-based methods like Equivalent Circuit [5; 8; 9]; observer-based methods akin to the Proportional Integral Observer [5]; filter-based methods that consist of Kalman Filter variations [5; 6]; data-driven methods, which include the use of machine learning such as linear models, trees, ensembles, and kNN enhanced by filters like the Gaussian filter [5; 10]; and other data-driven methods that use NNs [5] to capture the relationships within the data without prior domain knowledge. NNs [11] are mathematical models with a cascade of computational units connected in non-linear manners. They have shown to be capable regressors and classifiers when used in a data-driven manner. The battery of an EV delivers many signals, which makes it a suitable use-case for NN. This is highly relevant in recent years, as suggested by [PERSON] and [PERSON] [12] due to the rise of hybrid methodologies in SoC. Different NN architectures [13] offer different functionalities when it comes to capturing information within the data. For example, Convolutional NNs (CNNs) utilize kernel filters to capture information from images or sequences, while Recurrent NNs (RNNs) utilize gates to capture temporal information from ordered sequences. Related works have deployed basic CNN architectures and RNN variants (LSTM, GRU) to estimate SoC with data from lab tests [14]. Although NNs should be able to learn to estimate SoC via the abundance of signal data from the battery of an EV, signals can have redundancy, and there can also be obfuscated correlations between signals and the SoC. When this is not properly preprocessed, it can be detrimental to a NN's estimation of SoC, where it could prohibit learning of the relationships between the signals and the SoC, thus becoming dependent on an unrelated pattern between signals to make their SoC estimation. Therefore, it is important to identify significance and redundancy within the input data. This is, however, not trivial for NNs, as feature selection is accomplished via their internal mechanism during backpropagation [11].