Thermal management is pivotal for ensuring the safe and efficient operation of LIBs under dynamic conditions. Accurate core temperature monitoring remains a key BTMS challenge for predicting thermal distributions and mitigating TR risks. This study proposes a real-time core temperature estimation framework integrating joint EKF with an electro-thermal-aging model (ECM-1D). Using only surface temperature and voltage measurements, it simultaneously estimates core temperature, SOC, and capacity with bidirectional electro-thermal coupling. The hybrid approach pre-calibrates temperature/SOC/SOH-dependent parameters offline while updating capacity online. Validation under extreme conditions (high-rate cycling, aging, and ISCs) demonstrates 60% lower core temperature RMSE during high-rate cycling, a maximum estimation error below 0.9 K, and 58.9% reduction in SOC estimation error under aging conditions versus existing methods. The framework reliably tracks core temperature trends despite parasitic heat and signal noise, enabling earlier critical temperature warnings. This provides a foundation for TR prevention, advancing battery safety for EV and grid storage applications. Future extensions could integrate physical aging mechanisms and enhance fault detection capabilities.

The estimation of battery aging plays a crucial role in electric vehicle applications. Currently, battery health status, including capacity estimation, has attracted extensive research and innovation. Gaussian process regression (GPR) is a prominent nonparametric modeling approach that can be used to evaluate battery degradation mechanisms while quantifying uncertainty. The kernel functions of GPR and their related length-scale parameters are relatively sensitive to the input distribution. However, they are often fixed during GPR applications, which may not be suitable for the battery’s dynamic circumstances. In this study, an automatic relevance determination (ARD)-based GPR with a constructed kernel function is proposed to ensure the estimation performs as promised, where the ARD is applied to capture five health indicators (HIs) during the battery charging–discharging process. Through Bayesian optimization (BO) for the parameter identifications for varying time windows, the estimated multiple-dimensional length scales of ARD-GPR show the dependence of the degradation process during the various phases. According to the cross-validation results under a complex environment, the proposed multikernel ARD-GPR can provide a precise estimation for experimental battery cells with an average root-mean-square error (RMSE) of 0.0195, which is 30.4% lower than the conventional methods. Moreover, the proposed multikernel ARD-GPR locates the degradation level by attributing the variation of ARD length scales under various battery degradation circumstances, which can offer an insight into the battery degradation mechanism.

To guarantee the safety of electric vehicles (EVs), it is crucial to gain a comprehensive understanding of internal short circuits (ISCs) and to develop effective early detection methods within battery management systems (BMSs). This study presents a detailed analysis of ISC detection in cylindrical lithium-ion battery (LIB) cells, utilizing a mechanistic coupled electrochemical–thermal model to replicate various ISC scenarios. The proposed approach integrates an extended Kalman filter (EKF) with a coupled lumped-parameter electrothermal model, enabling real-time ISC detection leveraging both electrical and thermal measurements. Extensive evaluations demonstrate that the coupled model significantly improves detection accuracy and timeliness, reducing detection times by 36% and errors by 44% on average compared to uncoupled models in different ISC scenarios. These findings are particularly significant for preventing with thermal runaway (TR), especially in the presence of severe ISC. Robustness analysis further highlights the impact of ISC location on detection accuracy. The study concludes that the proposed ISC detection framework offers an efficient, accurate, and reliable solution to detect ISC faults in cylindrical battery cells, contributing to safer operation. By mitigating TR risks and extending battery lifespan, this work supports the development of sustainable energy systems and improves the reliability of energy storage technologies.

Concerns over fuel scarcity and environmental degradation largely drive the increasing popularity of electric vehicles (EVs). Lithium-ion batteries (LIBs), known for their high energy and power densities, are the favored power source for EVs. Over the past few decades, research has been concentrated on ensuring these batteries operate efficiently, safely, and reliably. A key issue impacting the safety of Li-ion batteries is thermal runaway (TR), which can lead to hazardous battery fires. Internal short circuits (ISCs) are often the primary cause of these TR incidents, making the early detection of spontaneous ISC formation a pivotal diagnostic task. This research introduces an innovative ISC detection technique for cylindrical Li-ion battery cells. This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. This framework enables real-time estimation of the internal ISC state while maintaining computational efficiency. The proposed method is tested numerically considering a high-fidelity numerical plant cycled using charge-depleting tests that mimic a practical battery cell working cycle at various C rates and at different ambient temperatures to account for both load and environmental uncertainties. The results demonstrate the robustness and effectiveness of the method. In addition, the method has been proven to be computationally efficient, demonstrating the feasibility of its real-time implementation.

Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.