Accurately predicting the state of charge of Lithium-ion batteries is essential to the performance of battery management systems of electric vehicles. One of the main reasons for the slow global adoption of electric cars is driving range anxiety. The ability of a battery management system to accurately estimate the state of charge can help alleviate this problem. In this paper, a comparison between data-driven state-of-charge estimation methods is conducted. The paper compares different neural network-based models and common regression models for SOC estimation. These models include several ablated transformer networks, a neural network, a lasso regression model, a linear regression model and a decision tree. Results of various experiments conducted on data obtained from natural driving cycles of the BMW i3 battery show that the decision tree outperformed all other models including the more complex transformer network with self-attention and positional encoding.

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Real-life batteries tend to experience a range of operating conditions, and undergo degradation due to a combination of both calendar and cycling aging. Onboard health estimation models typically use cycling aging data only, and account for at most one operating condition e.g., temperature, which can limit the accuracy of the models for state-of-health (SOH) estimation. In this paper, we utilize electrochemical impedance spectroscopy (EIS) data from 5 calendar-aged and 17 cycling-aged cells to perform SOH estimation under various operating conditions. The EIS curves are deconvoluted using the distribution of relaxation times (DRT) technique to map them onto a function $\textbf{g}$ which consists of distinct timescales representing different resistances inside the cell. These DRT curves, $\textbf{g}$, are then used as inputs to a long short-term memory (LSTM)-based neural network model for SOH estimation. We validate the model performance by testing it on ten different test sets, and achieve an average RMSPE of 1.69% across these sets.

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives.

Carbon emissions are rising at an alarming rate, posing a significant threat to global efforts to mitigate climate change. Electric vehicles have emerged as a promising solution, but their reliance on lithium-ion batteries introduces the critical challenge of battery degradation. Accurate prediction and forecasting of battery degradation over both short and long time spans are essential for optimizing performance, extending battery life, and ensuring effective long-term energy management. This directly influences the reliability, safety, and sustainability of EVs, supporting their widespread adoption and aligning with key UN SDGs. In this paper, we present a novel approach to the prediction and long-term forecasting of battery degradation using Scientific Machine Learning framework which integrates domain knowledge with neural networks, offering more interpretable and scientifically grounded solutions for both predicting short-term battery health and forecasting degradation over extended periods. This hybrid approach captures both known and unknown degradation dynamics, improving predictive accuracy while reducing data requirements. We incorporate ground-truth data to inform our models, ensuring that both the predictions and forecasts reflect practical conditions. The model achieved MSE of 9.90 with the UDE and 11.55 with the NeuralODE, in experimental data, a loss of 1.6986 with the UDE, and a MSE of 2.49 in the NeuralODE, demonstrating the enhanced precision of our approach. This integration of data-driven insights with SciML's strengths in interpretability and scalability allows for robust battery management. By enhancing battery longevity and minimizing waste, our approach contributes to the sustainability of energy systems and accelerates the global transition toward cleaner, more responsible energy solutions, aligning with the UN's SDG agenda.

Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels). Experts' labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted. We introduce the first principled approach that provides two key guarantees. (1) Handover guarantee: similar to a no-regret property, we establish a sublinear bound on the cumulative number of experts' binary labels. Initially, multiple labels per query are needed, but the number of expert labels required asymptotically converges to zero, saving both expert effort and computation time. (2) No-harm guarantee with data-driven trust level adjustment: our adaptive trust level ensures that the convergence rate will not be worse than the one without using advice, even if the advice from experts is adversarial. Unlike existing methods that employ a user-defined function that hand-tunes the trust level adjustment, our approach enables data-driven adjustments. Real-world applications empirically demonstrate that our method not only outperforms existing baselines, but also maintains robustness despite varying labelling accuracy, in tasks of battery design with human experts.

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Accurate lifetime prediction of lithium-ion batteries accelerates battery optimization and improves safety [1-4]. Although this task is challenging due to complicated and convolved degradation mechanisms, various studies have demonstrated the potential in using data-driven approaches [5-13], physics-based approaches [14-18], and hybrid approaches [19-26]. For accurate battery health monitoring, diagnostic techniques such as Differential Voltage Fitting (DVF) [27-30], Incremental Capacity Analysis (ICA) [31, 32], Electrochemical Impedance Spectroscopy (EIS) [10, 33-35], and Hybrid Pulse Power Characterization (HPPC) [36, 37] were developed for physics-based feature extraction during battery operation. Further optimization of these diagnostic techniques includes novel State of Health (SoH) feature development [38-41] and diagnostic time reduction [42, 43]. Compared to the extensive research on lifetime prediction during operation, there have been few studies on lifetime prediction during the manufacturing process (i.e., extreme early cycle life prediction) because of the limited availability of public manufacturing data. In fact, the cycle life can vary greatly based on the protocol used during formation, in which a passivation layer of Solid Electrolyte Interphase (SEI) is rapidly formed on the anode to limit further degradation during use. For example, Weng et al. [44] showed that the Nickel Manganese Cobalt (NMC)/graphite pouch cells with the fast formation protocol proposed by Wood et al. [45, 46] had in average 25% longer cycle lives than the pouch cells with a baseline formation protocol when aging the cells in both room temperature and high-temperature (45

Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development.

Abstract: Remaining Useful Life (RUL) of battery is an important parameter to know the battery's remaining life and need for recharge. The goal of this research project is to develop machine learning-based models for the battery RUL dataset. Different ML models are developed to classify the RUL of the vehicle, and the IoT (Internet of Things) concept is simulated for automating the charging system and managing any faults aligning. The graphs plotted depict the relationship between various vehicle parameters using the Blynk IoT platform. Results show that the catboost, Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and hybrid model developed could classify RUL into three classes with 99% more accuracy. The data is fed using the tkinter GUI for simulating artificial intelligence (AI)-based charging, and with a pyserial backend, data can be entered into the Esp-32 microcontroller for making charge discharge possible with the model's predictions. Also, with an IoT system, the charging can be disconnected, monitored, and analyzed for automation. The results show that an accuracy of 99% can be obtained on models MLP, catboost model and similar accuracy on GRU model can be obtained, and finally relay-based triggering can be made by prediction through the model used for automating the charging and energy-saving mechanism. Keywords: RUL, power management, Internet of Things, catboost, cross-validation 1. Introduction A battery's ability to store and release energy steadily diminishes with use as a result of a number of variables, including temperature changes, chemical deterioration, and charge-discharge cycles. An estimate of how long the battery should continue to function dependably is given by the RUL.