# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Artificial Intelligence and Applications in Marine Vessels  
AI, ML, and deep learning (DL)-based approaches can significantly leverage and automatze EMS and BMS platforms for maritime platforms and minimize human staff supervision for monitoring, maintenance, and repair process costs. More specifically, AI applications in maritime energy systems introduce significant practical benefits, especially in fuel optimization and predictive maintenance. For instance, AI-powered route optimization systems can use real-time weather data, ocean current patterns, and vessel load information to suggest fuel-efficient navigation paths. A specific example is Wartsila's Navi-Planner,which employs AI approaches for dynamically adjusting speed and route, consequently reducing fuel consumption by noticeable levels on long-haul voyages. Similarly, predictive maintenance in battery systems can be further leveraged by machine learning models that analyze voltage fluctuations, temperature variations, and historical degradation patterns. As a particular instance, the usage of recurrent neural network (RNN) models in BMS platforms enables early detection of internal short circuits or abnormal SoC drops, which provides timely intervention before a potential system failure. Consequently, these real-world applications indicate how AI not only enhances energy efficiency but also ensures the reliability and safety of hybrid and electric marine vessels, which points to a critical step toward autonomous maritime operations [55; 56]. Generally, within the following main factors, these approaches drastically contribute to EMSs and BMSs [51; 52; 44]. Optimization of energy consumption: AI, ML, and DL-based approaches can estimate and supervise a ship's or naval platform's energy requirement more efficiently. These approaches can analyze patterns from past data to estimate future energy requirements and optimize power distribution across different systems on naval platforms. Consequently, this process provides better energy management, diminishes waste, and concurrently increases the efficiency of operations within EMSs and BMSs [57; 58]. Predictive maintenance: AI, ML, and DL-based methods can foresee potential failures and errors in energy systems before they arise. Especially for BMSs, these methods can estimate battery life degradation or predict the cells that might fail beforehand. Additionally, for EMSs, these approaches can heuristically detect machinery failures that potentially give rise to escalated energy consumption. Consequently, this predictive feature greatly contributes to planning maintenance and management tasks proactively, hence minimizing downtime and increasing the life of equipment dramatically [58]. Real-time decision making: Learning (a specific domain of ML) and AI-based approaches provide real-time decision making by quickly processing and analyzing great volumes of data. Especially in environments such as naval operations and other maritime-based domains including multiple variable and complex factors, DL models can make immediate decisions about power allocation and system configurations for adapting to changing conditions, such as different operational modes based on naval environments, weather conditions, or critical system malfunctions [44]. For example, modern maritime systems utilize real-time AI techniques to continuously monitor ship performance, fuel consumption, and weather conditions and consequently adjust operational parameters to increase energy efficiency and safety. Additionally, in specific maritime systems, reinforcement-learning-based systems are deployed for real-time autonomous navigation and power allocation under dynamic marine conditions. These techniques combine real-time inputs from radar, GPS, battery sensors, and engine telemetry to make quick decisions about optimal energy distribution and propulsion settings.