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As the automotive industry undergoes a major shift to electric propulsion, reliable assessment of battery health and potential safety issues is critical. This review covers advances in sensor technology, from mechanical and gas sensors to ultrasonic imaging techniques that provide insight into the complex structures and dynamics of lithium-ion batteries. In addition, we explore the integration of physics-guided machine learning methods with multi-sensor systems to improve the accuracy of battery modeling and monitoring. Challenges and opportunities in prototyping and scaling these multi-sensor systems are discussed, highlighting both current limitations and future potential. The purpose of this study is to provide a comprehensive overview of the current status, challenges, and future directions of combining sensors with physically guided methods for future vehicle battery management systems. To help the industry move towards effective, reliable, and safe battery management solutions, Xia Zeng and Maitane Berecibar present an overview of emerging sensor technologies and physics-guided methods for automotive battery management systems.

This review paper aims to provide the background and literature review of a hybrid energy storage system (ESS) called a lithium-ion capacitor (LiC). Since the LiC structure is formed based on the anode of lithium-ion batteries (LiB) and cathode of electric double-layer capacitors (EDLCs), a short overview of LiBs and EDLCs is presented following the motivation of hybrid ESSs. Then, the used materials in LiC technology are elaborated. Later, a discussion regarding the current knowledge and recent development related to electro-thermal and lifetime modeling for the LiCs is given. As the performance and lifetime of LiCs highly depends on the operating temperature, heat transfer modeling and heat generation mechanisms of the LiC technology have been introduced, and the published papers considering the thermal management of LiCs have been listed and discussed. In the last section, the applications of LiCs have been elaborated.

Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.

Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime. Models of lithium-ion battery life produced using data from batteries early in their lifetime.

Accurate state-of-health (SoH) prediction is vital for safe and efficient battery management, enabling extended lifespan and improving technologies such as electric vehicles and stationary energy storage systems. In this work, a graph neural network-based deep learning framework is proposed to predict the SoH of a commercial nickel–manganese–cobalt oxide (NMC) lithium-ion technology. Health indicators obtained from the in-house-generated experimental aging dataset are used to train and validate the model across batteries subjected to diverse operating conditions. The hybrid architecture combines graph neural networks (GraphSAGE) with convolutional neural networks (CNN) and long short-term memory (LSTM) blocks, capturing both local structural relationships and temporal patterns in the battery data. Evaluation results show strong predictive performance, achieving an R2 of 0.989 and a mean squared error of 3.23 × 10−6. These findings suggest that the proposed methodology could be deployed as a useful diagnostic tool.

Lithium-ion (Li-ion) batteries have emerged as a promising energy source for electric vehicle (EV) applications owing to the solution offered by their high power, high specific energy, no memory effect, and their excellent durability. However, they generate a large amount of heat, particularly during the fast discharge process. Therefore, a suitable thermal management system (TMS) is necessary to guarantee their performance, efficiency, capacity, safety, and lifetime. This study investigates the thermal performance of different passive cooling systems for the LTO Li-ion battery cell/module with the application of natural convection, aluminum (Al) mesh, copper (Cu) mesh, phase change material (PCM), and PCM-graphite. Experimental results show the average temperature of the cell, due to natural convection, Al mesh, Cu mesh, PCM, and PCM-graphite compared with the lack of natural convection decrease by 6.4%, 7.4%, 8.8%, 30%, and 39.3%, respectively. In addition, some numerical simulations and investigations are solved by COMSOL Multiphysics®, for the battery module consisting of 30 cells, which is cooled by PCM and PCM-graphite. The maximum temperature of the battery module compared with the natural convection case study is reduced by 15.1% and 17.3%, respectively. Moreover, increasing the cell spacing in the battery module has a direct effect on temperature reduction.

Untethered robots carry their own power supply in the form of a battery pack, which has a crucial impact on the robot’s performance. Although battery technologies are richly studied and optimized for applications such as electric vehicles, computers and smartphones, they are often a mere afterthought in the design process of a robot system. This tutorial paper proposes criteria to evaluate the suitability of different battery technologies for robotic applications. Taking into consideration the requirements of different applications, the capabilities of relevant battery technologies are evaluated and compared. The tutorial also discusses current limitations and new technological developments, pointing out opportunities for interdisciplinary research between the battery technology and robotics communities.

Electric vehicles are becoming more common daily because countries are moving towards net-zero emissions. Different generations of NMC battery cells are used for EV applications. This work investigates the degradation behavior of high-energy 75 Ah prismatic NMC631 lithium-ion cells using a combined incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) framework under different conditions. Cells are cycled at an identical C-rates and depths of discharge (DoD), and at different temperatures to systematically evaluate the impact of temperature on electrochemical aging. ICA results revealed that cells cycled at low temperatures maintain stable peaks and a high SoH (>90%) after completing 1600 full equivalent cycles (FECs). EIS analysis confirms the distinct impedance evolution patterns. Degradation mode analysis is performed using the ICA, and EIS highlights the combined evolution of conductivity loss, loss of lithium inventory, and loss of active material. It also highlights different degradation path trajectories under identical operating conditions stem from the progressive amplification of internal cell heterogeneities during aging. The results demonstrate that combining ICA and EIS provides complementary insights into degradation evolution and enables clear differentiation between gradual aging and sudden failure pathways in high-energy NMC cells.

The second-life application of vehicle batteries is getting attention as millions of battery systems, modules, or cells are going to enter the market in the coming decade. The performance uncertainty with or without historical knowledge of the batteries’ vehicle usage is a concern. Moreover, detailed studies on second-life battery cell behavior is sparse and an improved understanding is required for reuse/repurpose. In this work, two second-life battery packs are dismantled, and the extracted prismatic and pouch Nickel–Manganese–Cobalt (NMC) cells with 141 Ah and 65 Ah, respectively, are extensively investigated to understand the second-life degradation behavior. The one-and-a-half-year-long test campaign has followed dedicated suitable stationary test matrices, generating a valuable dataset. The aging dataset is then filtered with the most correlated features via Pearson correlation analysis (PCA) and used to train different machine learning algorithms, resulting in a root-mean-square-error (RMSE) of 0.065 and 0.109 for prismatic and pouch cells, respectively, with the best-performing ElasticNet model validated against real-life stationary profiles. The developed framework is suitable for edge computation where the SoH could be evaluated online, facilitating state-based performance and lifetime extension.