# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Artificial Intelligence and Applications in Marine Vessels
#### 2.5.3 ML-Based Approaches  
Machine learning (ML)-based approaches introduce dramatic advancements to EMSs and BMSs in naval-based electric vehicles (EVs) and hybrid electric vehicles (HEVs). By utilizing data-driven techniques, these approaches provide adaptive, predictive, and highly efficient energy and battery management solutions that can dynamically respond to changing conditions and complex operational demands on naval environments. These types of approaches can be further divided into three main branches:  
Figure 6: Taxonomy diagram for AI, ML, and DL-based approaches in EMSs and BMSs. Prediction-Based Approaches  
These approaches are particularly deployed for constructing informed decisions about energy usage, maintenance schedules, and operational strategies based on predictive models. Generally, specific-sized historical data are given as input into these models, and consequently, the models are optimized to properly capture underlying patterns of the input samples collected from the specific naval environment and create a prediction based on learned patterns from the historical data. Specific methods in these types of approaches are decision trees, artificial neural networks (ANNs), support vector machines (SVMs), random forests, and ensemble-based ML [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88]. Additionally, specially designed neural network (NN) structures such as convolutional neural networks (CNNs) [87] for handling spatial and temporal information and recurrent neural networks (RNNs) [89], gated recurrent units (GRUs) [90], and long short-term memory (LSTM) [91] are particularly utilized for handling multidimensional sequential data within BMSs and for leveraging battery state estimation, determining optimal charging and discharging plans, and proper error detection in naval/ship-based EV batteries depending on SoE, SoC, and RUL factors. Learning-based approaches, especially reinforcement learning (RL) and deep reinforcement learning (DRL) approaches, are highly potent for obtaining adaptive strategies in complex and dynamic environments similar to the EMS and BMS for naval-based environments. These approaches focus on training algorithms to make a sequence of decisions to maximize a cumulative reward over time. This makes these approaches well-suited for optimizing long-term operational objectives. Unlike other approaches, samples to be used as an input to the model are not obtained from a fixed-size observation but as constant feedback from the dynamic and changing environment itself (in our case, a naval environment). More specifically, for the model's every choice of action from an action set, the environment provides the model with the current state for itself (observation) and reward value for the optimization process. Approaches such as deep Q-network (DQN), proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and double-delayed Q-learning (DDQL) are specific examples of these types of approaches [91; 92; 93; 94; 95; 96; 97]. Linear Programming Approaches  
Recent advancements have integrated artificial intelligence (AI) and machine learning (ML) with linear programming (LP) to enhance the optimization capabilities of energy management systems (EMSs) and battery management systems (BMSs) in naval environments. AI algorithms are now employed to predict changing conditions, such as variations in weather, sea currents, and power demands, enabling these systems to dynamically adjust optimization models in real time. This integration leads to more adaptive and responsive energy distribution strategies, improving efficiency and performance under challenging maritime conditions [98]. In complex and dynamic naval power systems, ML models analyze historical data to fine-tune EMSs and BMSs, predicting optimal operational parameters for energy storage and consumption. This optimization reduces the need for manual calibration, enhancing system autonomy, and reducing reliance on constant human intervention [99].