As Lithium-ion batteries (LiBs) emerge as pivotal energy storage solutions for automotive applications, maintaining their performance and longevity presents challenges due to power and capacity fade influenced by environmental and usage conditions. Thus, to estimate battery degradation, estimating the state of health (SOH) or predicting remaining useful life (RUL) without considering future operational loads, can limit accurate SOH forecasting. Meanwhile, machine learning (ML) models including deep neural networks (DNNs), have become effective techniques for SOH forecasting of LiBs due to their capability to handle various regression problems without relying on physics-based models. The methodology used in this study, helps battery developers link different operational strategies to battery aging. We use inputs such as temperature (T), current (I), and state of charge (SOC) and utilize a feature transformation technique which generates histogram-based stressor features representing the time that the battery cells spend under operational conditions, then investigate the performance of DNN models along with explainable machine learning (XML) techniques (e.g., SHapley Additive exPlanations) in predicting LiB SOH. The comparative analysis leverages an extensive open-source dataset to evaluate the performance of deep learning models such as LSTM, GRU, and FNN. The forecasting is executed in two distinct modes: one capping the forecasted cycles at 520, and another extending the predictions to the end of the battery’s first life (SOH=80%).Furthermore, this study explores the practicality of a lightweight model, e.g., support vector regression (SVR) model, to compare against DNN models for scenarios with constrained computational and memory resources. The results show that utilizing a feature refinement to ensure the coverage of critical features can lead to performance comparable with the DNN (e.g., LSTM) for the SVR model.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-67618 |
| Date | January 2024 |
| Creators | Sheikhani, Arman, Agic, Ervin |
| Publisher | Mälardalens universitet, Akademin för ekonomi, samhälle och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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