Lithium-ion (Li-ion) batteries have increasingly been used in diverse applications. Accurate estimation of the state of health (SOH) of the Li-ion batteries is vital for all stakeholders and critical in various applications such as electric vehicles (EVs). The electrical equivalent circuit (EEC) 2-RC model is often used to model the battery operation but has not been used to capture the degradation of battery cells over time. This paper uses the 2-RC model to capture the degradation of the Li-ion battery. The proposed model is not only time-dependent but also captures the effect of temperature on battery degradation. The proposed approach estimates the SOH accurately and is also considerably flexible for diverse cells of different chemistry. We further generalize an N-RC model approach to evaluate the SOH of the battery. We compare the proposed model (2-RC) with the 1-RC model, and through numerical results, we show that the 2-RC model outperforms 1-RC and reduces the computational cost significantly. Similarly, the 2-RC model outperforms 3-RC and higher-order circuits. We also show that the proposed approach can capture the battery dynamics better for specific smaller orders of the polynomial (associated with Arrhenius equation) when compared with the 1-RC approach with considerably reduced (up to 60%) root mean square error (RMSE). Lastly, the average testing RMSE for 2-RC is 52.4%.

The automotive industry is transitioning towards intelligent, connected, and autonomous vehicles to avoid traffic congestion, conflicts, and collisions with increased driver safety. Connected and autonomous vehicles (CAV) must be aware of their surroundings and act as per their environment. Communication infrastructure can be vital in transmitting necessary information to peers and receiving critical information for timely decisions. This article provides a comprehensive review of the topic, covering the aspects of enabling wireless technologies and sensor fusion. The article reviews data acquisition using various sensing devices such as RADAR (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), cameras, and multi-modal sensor fusion of the acquired data after signal processing. Thereafter, it reviews the communication and networking infrastructure for intra- and inter-vehicle communication and related technologies. For each of these themes, research challenges and future directions have been identified.

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi‐modal sensor fusion. A comprehensive review of cyberattacks on intra‐vehicle and inter‐vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining‐aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified.This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Internet of Things

Every year, mining industry sees huge losses in terms of human lives and valuable infrastructure due to accidents and disasters. Besides other measures, effective monitoring and control can greatly reduce the risks of such incidents. Wireless sensor networks (WSNs) are increasingly being used for such applications. This paper proposes a WSN-based system, which is capable of detecting and identifying events of interest (with 90% success rate) and localization of miners (2–4 m) and roof falls (10–12 m). A comprehensive integrated system covering a range of aspects from radio frequency propagation, communication protocol with latency, and energy–efficiency tradeoff and autonomous event detection is presented. The results show a lower path loss for 433 MHz operating frequency compared to 868 MHz. Moreover, a novel energy-efficient hybrid communication protocol using both periodic and aperiodic modes of communication while adhering to low latency requirement for emergency situations is proposed and implemented. Finally, for intelligent processing of gathered data, a spatio-temporal and attribute-correlated event detection mechanism suitable for the highly unreliable mine environment is described.