The accurate estimation of the remaining charge time (RCT) is essential in a battery management system (BMS), because it guarantees the safety and dependability of the power battery systems of new energy vehicles. However, the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process, which complicates the estimation of RCT in actual scenarios. Hence, this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times. Through an in-depth analysis of multi-scenario charging data, the RCT of the charging process is estimated using the temporal convolutional network (TCN) model, which has a strong generalization ability. Additionally, a dynamic learning rate (DLR) mechanism and an early stopping strategy (ES) are designed in the TCN model (DLR-ES TCN) for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy. Finally, compared with the traditional TCN model and four common deep learning models under three different scenarios, the experimental results show the mean absolute percentage error (MAPE) of the proposed method is less than 2%, indicating better accuracy and stability. This research can improve the safety monitoring of power batteries when applied to various target domains.

The emergence of electric vehicles (EVs) is critical for reducing environmental impacts and advancing sustainable transportation. Despite the known benefits, the complexities of EV integration with urban systems, power infrastructure, and environmental policies remain in-sufficiently explored. This literature review seeks to fill these gaps by synthesizing recent research across three key domains: environmental impact and sustainability, pattern analysis and system science, and methodology and operation of EV systems. The focus is on how EV adoption impacts climate change, net-zero emissions, air pollution, power grid dynamics, and mobility patterns, along with evaluating the effectiveness of AI-driven methodologies in enhancing EV operation. We conducted a systematic literature review of top-tier publications from journals such as Nature, Science, and PNAS, covering environmental science, urban systems engineering, and operational research related to EVs. Our findings highlighted that while EVs have the potential to reduce greenhouse gas emissions and improve urban air quality significantly, their actual environmental benefits depend heavily on factors such as electricity generation sources and the lifecycle impacts of battery production. Furthermore, we identified patterns in EV integration that can inform infrastructure planning and revealed the efficacy of advanced computational techniques, particularly reinforcement learning, in optimizing EV operations. Our major contributions include a comprehensive overview of the environmental impacts of EVs, insights into the challenges and opportunities of integrating EVs into urban systems, and showcasing the potential of AI-driven methodologies to enhance EV operations, with significant implications for policymakers, urban planners, and researchers.

The increasing reliance on batteries in transportation and energy storage sectors plays a pivotal role in addressing the challenges of energy security and grid power instability. However, the recurrent occurrence of battery safety incidents has emerged as the primary obstacle to their more extensive deployment. This necessitates the implementation of precise and efficient battery safety management technology to enhance the safety of batteries throughout their lifecycle, safeguarding the users’ assets and optimizing energy utilization. This article explores battery safety management technologies for power and energy batteries, starting with an overview of battery technology and then reviewing battery applications, failure mechanisms, and the analysis of existing intelligent safety management technologies. Finally, the paper consolidates current advancements, pinpoints gaps, and projects future trends in intelligent safety management technologies for power and energy-storage batteries. The insights presented will serve as a valuable reference and guideline for future research and development of battery safety management technology.

Digital twin technology used to realize the interactive mapping between digital model and physical entity in virtual space plays a crucial role in promoting the transformation of battery management to digitalization and intelligence. The key to achieving a digital twin is developing a virtual model that can accurately reflect the physical object. However, the intricate time-varying and nonlinear reaction characteristics within the battery often pose challenges in simulating complex operational conditions and maintaining high accuracy throughout the full life cycle. Incremental learning algorithms are suitable for online streaming data processing and can adapt to concept drift of the data stream by receiving new data online without retraining the entire model from scratch. This paper employs a simplified electrochemical model of the battery and integrates an Aggregated Mondrian Forest with incremental learning capabilities to construct a hybrid mechanism-data battery model. The developed hybrid model can acquire battery data and train in real time during battery operation to realize co-evolution with the physical battery to ensure the voltage prediction accuracy during the full life cycle of the battery.