# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 6 Conclusions  
Within this work, we utilized data-driven methods to estimate the SoC of a deployed EV battery. The data used were field data from a real-world scenario as opposed to simulated data from a laboratory setting. We benchmarked time-series-suitable NN architectures on the estimation of SoC. As a result, we established a framework for the training and testing of the deployed EV battery that enables the evaluation of SoC estimation based on concrete metrics. Furthermore, we demonstrated the use of two xAI methods to investigate the relationship between the signals within an EV battery. This paper documents our complete approach with regards to the data, NN modeling, and signal analysis. We formulated three experiments to achieve the outlined outcomes, described in Sections 4.1-4.3. In E1, NN architectures in the context of time-series data were benchmarked for SoC estimation. It was found that LSTM, GRU, and CNN-LSTM performed better than the others based on visual inspection and metrics standards. In E2, we investigated the fusion of dynamic and static features by constructing the Fusion Hybrid Model. The model outperformed the other NN architectures from E1 in some cases based on visual inspection and metric standards. In E3, we demonstrated the application of the xAI method InputXGradient [27] and an importance estimate method PIEE [26] to investigate the signals from the battery. We showcased how these methods were used to determine signals that the model over-relied on for the estimation of SoC. Through xAI, we discovered the signals from the battery that are essential for the NNs conducting SoC estimation. Lastly, we were able to reduce the input window size whilst retaining performance. The process of determining the reduced window size was not trivial. After evaluating our results, we discussed potential future research directions. Specifically, we discussed how the current framework could be expanded to estimate the SoC of other car models' batteries with the aid of transfer learning and how an ensemble could be incorporated to improve the reliability and robustness of current SoC estimations. We hope that this work is useful to other researchers and to relevant communities. Methodology, everyone contributed; Development of Experiments 1 and 2, A.S., A.M. and K.C. ; Development of Experiment 3, H.T.J.C., I.S. and K.C. ; Writing: Draft Preparation, everyone contributed; Writing: Editing, H.T.J.C. and E.V. ; Supervision, J.R.-Z., F.P. and E.V. ; Administration, J.R.-Z. All authors have read and agreed to this version of the manuscript. This project, grant number FO999888165, was funded by the program \"BRIDGE\" of the Austrian Federal Ministry for Climate Action (BMK). This publication was written at Virtual Vehicle Research GmbH in Graz and partially funded within the COMET K2 Competence Centers for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK), the Austrian Federal Ministry for Labour and Economy (BMAW), the Province of Styria (Dept. 12), and the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion Agency (FFG) authorized the program management. The APC of this manuscript was supported by the TU Graz Open Access Publishing Fund. The dataset used for the experiments cannot be shared due to data confidentiality of the owner, AVILOO. The NN architectures used for the experiments and their parameters are available in Appendix B The code for the explainability methods, PIEE and DeepLIFT, is available. Details of PIEE can be found in [PERSON] and [PERSON]'s work [26], and the code for implementation is available on Github [56]. Details of DeepLIFT can be found in [PERSON]'s work [27], and an implementation of it is available in the Captum [55] framework. Open Access Funding by the Graz University of Technology. The authors [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company \"Know-Center Research GmbH\". [PERSON] was also employed by Graz University of Technology.