# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 2 Related Work
### Explainable Artificial Intelligence  
NNs are often difficult to interpret due to obscured internal mechanisms. Being able to understand which features amongst the input are contributing to a model's output is important, as it can lead to better decision making and redundancy reduction. In the case of EV batteries, where features are signals that are collected from the battery, being able to understand what signals NNs are focusing on can reduce redundancy in SoC estimation.  
There are many feature importance approaches, such as the use of model gradients [28], information backpropagation [27], embedded feature selection methods [29; 30], etc. These can be grouped into global [31; 32] or local approaches [33]. Global approaches estimate general importance across samples, whereas local approaches estimate importance per individual sample.  
Global approaches focus on the predictive power of feature subsets. They include methods such as Triple-Hierarchy Feature Enhancement (THFE [34]), which utilizes multiple layers of a Deep NN to extract different feature spaces. Such spaces are then used in conjunction to provide information regarding feature importance captured by the model. Shapley Additive Global Importance (SAGE [35]) is another global approach, which quantifies a feature's importance based on how much a model depends on a feature and its interaction with other features in its predictive power.  
Local approaches include model interpretability methods that focus on attributing importance. This can create attribution maps, which can be aggregated to reveal data properties picked up by the model [36]. This is also the preferred approach when it comes to understanding models [37]. Examples include InputXGradient [27], which attributes importance by combining the input and the respective gradient; and DeepLIFT [27], which attributes importance based on activation differences during propagation.  
In the previous section, we discussed the difference between existing works and our work in terms of the field data nature of our dataset and our data-driven modeling approach using NNs for SoC estimation. Table 1 further shows another notable difference, which is the incorporation of xAI within our work. We utilized xAI to gain a better understanding of what signals are important from an EV battery when it comes to estimating its SoC. We utilized both global and local xAI approaches to compare and verify the information derived. This provides additional justification for the feature choices during the NN modeling process.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Publication** & **Data** & **NN** & **xAI** \\ \hline [PERSON] et al. [19] & Laboratory simulated & FNN variant & – \\ \hline [PERSON] et al. [16] & NASA battery dataset [23] & FNN, CNN, LSTM & – \\ \hline [PERSON] et al. [14] & Laboratory simulated & RNN, LSTM, GRU & – \\ \hline [PERSON] et al. [20] & Panasonic Li-ion dataset [24; 25] & CNN & – \\ \hline [PERSON] et al. [22] & Laboratory simulated & Self-supervised transformer & – \\ \hline
**Our work and contribution** & Deployed EV (VW e-Golf) battery pack & LSTM, GRU, CNN-LSTM, Informer, ResNet, etc. & PIEE [26], InputXGradient [27] \\ \hline \hline \end{tabular}
\end{table}
Table 1: NN architectures and datasets used in existing works related to battery capacity estimation.