The perception and acquisition of navigation-related information are essential for the safety of intelligent ship. To address the effectiveness of navigational information monitoring system designed for the intelligent ship, in the present study, a comprehensive evaluation methodology is proposed. The various functions designed for obtaining corresponding navigational information are re-organized to develop the structure of intelligent ship navigational information monitoring system, which is subjected to evaluate the effectiveness by analyzing the information behavior processes in this proposed system and the available information monitoring technologies applied on intelligent ships. The orthogonal exploratory analysis technique was applied to establish the functional relationship between system effectiveness and contributing subsystems, as a result, the contribution degree distribution of contributing subsystems identified for system effectiveness is obtained. Finally, an application of the proposed methodology verifies its feasibility in calculating system effectiveness and its potential to extend to intelligent ships at different levels. The present study provides a new perspective to understand the safety of intelligent ships, and the comprehensive evaluation methodology provides a new path for the effectiveness analysis of intelligent ship navigational information monitoring system.

With the widespread adoption of lithium iron phosphate (LFP) batteries in energy storage and electric vehicles, overcharge‐induced thermal runaway (TR) is recognized as a critical threat to battery safety. However, conventional voltage/temperature threshold‐based detection methods suffer from delayed responses, proving inadequate for early‐warning applications. To address these shortcomings, this research employs a common prismatic LFP battery, collecting multimodal data (gas emissions—H ₂ /CO/HF, voltage, and temperature) through five stepwise overcurrent overcharging experiments. With the only consistent quantitative association among multimodal data, the time stamp was chosen to synchronize temporal alignment to enable both representing multimodal measurements at any given time point as a unified numeric array suitable for deep learning and quantitatively benchmarking algorithmic superiority. Based on this, a hybrid deep learning framework is developed to extract spatiotemporal features from multimodal electrochemical time‐series data, using integrated transformer layers and multiscale convolutional layers. Compared with experimental measurements, the proposed multimodal early‐warning model is demonstrated to surpass traditional voltage/temperature change rate threshold methods. Validation results indicate that the warning time was advanced by an average of 289 s across all overcharging conditions—ranging from 188 s (1.5 times current overcharge) to 492 s (0.5 times current overcharge)—which represents a 37.2% improvement. These results provide an intelligent safety monitoring strategy beyond single‐parameter thresholds and a time‐aligned multimodal learning paradigm that can be generalized to broader electrochemical safety diagnostics.

Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging rates and ambient temperatures in overcharge-induced thermal runaway, filling the knowledge gaps associated with multi-indicator thermal management approaches. Through experiments on prismatic LFP cells across five operational conditions (1C/35 °C, 1.5C/5 °C, 1.5C/15 °C, 1.5C/25 °C, and 1.5C/35 °C), synchronized infrared thermography and electrochemical monitoring quantitatively characterize the thermal–electric coupling dynamics throughout overcharge-to-runaway transitions. The experimental findings reveal three key observations: (1) Charge rate and temperature have synergistic amplification effects on triggering thermal runaway. (2) Contrary to intuition, while low-current/high-temperature charging enhances safety versus high-current/high-temperature conditions, low-temperature/high-current charging triggers thermal runaway faster than high-temperature/high-current scenarios. (3) Staged multi-indicator lithium battery thermal runaway warning signals would be more accurate (first peaks > 0.5 °C/s temperature rise rate + >10 V/s voltage drop rate). These findings collectively demonstrate the imperative for next-generation battery management systems integrating real-time ambient temperature compensation with adaptive C-rate control, fundamentally advancing beyond conventional single-variable thermal regulation strategies. Intelligent adaptation is critical for mitigating thermal runaway risks in LFP battery operations.

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle.

Accurate evaluation of the risk mitigation status of navigating ships is essential for guaranteeing navigational safety. This research mainly focuses on the feasibility and accuracy of evaluating the real effectiveness of a risk mitigation system for navigating ships, including addressing the problem of immeasurableness for risk mitigation capability and determining the degradation regulation of risk mitigation capability over time. The proposed method to solve the problem is an effectiveness evaluation model based on the capability perspective, composed of a capability measurement algorithm based on entropy theory and capability degradation regulation analysis based on numerical process fitting. First, combined with the theoretical framework of a comprehensive defence system, the risk mitigation system designed for navigating ships is reconstructed based on capability building. Second, using a numerical fitting method, the degradation regulation of risk mitigation capability with time is obtained to improve the accuracy of the dynamic evaluation. Finally, referring to entropy theory, the uncertainty of capability is calculated, and then the model is constructed based on this uncertainty to realize the effectiveness evaluation from a capability perspective. The results obtained in an application test of the proposed model indicate that using the entropy of capability can realize an accurate effectiveness evaluation of a risk mitigation system for navigating ships, with a 9% improvement in accuracy, and the Weibull curve fitting is more consistent with capability degradation regulation, with a signification level of 2.5%. The proposed model provides a new path for evaluating the effectiveness of a risk mitigation system for navigating ships from the entropy of capability, and compared with the traditional probabilistic method, the model is more realistic and accurate in actual applications.