Table 3
The specific allication, descriptions, advantages, and limitations of AI algorithms in the research and development of lithium ion batteries
| Al algorithms | Specific applications | Descriptions | Advantages | Limitations |
| Linear regression | • Battery performance prediction | • Linear regression is a fundamental statistical and machine learning algorithm used to model the relationship between a dependent variable (battery performance metrics) and one or more independent variables (such as charge/discharge cycles, temperature, and current density). | • Simple, interpretable, and effective for identifying key trends. | • Assumes linear relationships; limited for complex, non-linear behaviors. |
| Convolutional neural networks (CNNs) | • Image analysis and defect detection | • Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed for analyzing image data. They excel in detecting patterns, textures, and structural anomalies, making them highly effective for battery defect detection using imaging techniques like Computed Tomography (CT), Scanning Electron Microscopy (SEM), and X-ray imaging. | • Excels in image-based tasks; automated defectdetection. | • Requires large datasets and high computational power. |
| Recurrent neural networks (RNNs) | • Battery behavior prediction | • Recurrent Neural Networks (RNNs) are a type of neural network designed for processing sequential data. They are particularly well-suited for predicting the dynamic behavior of lithium-ion batteries over time, such as charge/discharge cycles, state of charge (SOC), state of health (SOH), and long-term degradation. RNNs have the ability to maintain a “memory” of previous inputs, making them effective at modeling time-dependent battery behavior, which is crucial for battery lifecycle prediction and performance tracking. | • Captures temporal dependencies in sequential data. | • Suffers from vanishing gradients in long sequences; resource-intensive. |
| Long short-term memorys (LSTMs) | • Long-term behavior prediction | • Long short-term memory (LSTM) is a specialized form of recurrent neural networks (RNNs) designed to solve the issues of learning long-term dependencies in sequential data. LSTMs are particularly effective for tasks that require modeling long-term behavior over extended periods, such as battery lifecycle prediction, and for capturing voltage/current dynamics under varying operating conditions. | • Solves vanishing gradient issues; ideal for long-term analysis. | • Computationally demanding; complex to tune. |
| Deep Q-networks (DQNs) |
• Charging strategy optimization | • Deep Q-network (DQN) is a reinforcement learning (RL) algorithm that combines Q-learning (a value-based RL method) with deep neural networks to optimize decision-making in complex environments. In the context of battery charging strategy optimization, DQN is used to determine the most efficient charging protocols that maximize battery lifespan, improve charging speed, and optimize performance under various conditions. | • Real-time decision-making; handles complex, high-dimensional data. | • Requires extensive training data; sensitive to hyperparameters. |
| Support vector machines (SVMs) | • Classification and prediction | • Support vector machine (SVM) is a supervised machine learning algorithm widely used for classification and regression tasks. In the context of battery performance classification and prediction, SVM is leveraged to classify various operational states of a battery (such as healthy, degraded, or faulty) and predict critical metrics like cycle life, capacity retention, and state of charge (SOC) based on high-dimensional data. | • Handles non-linear and small datasets effectively. | • Computationally expensive for large datasets; kernel selection is critical. |
| Physics-informed neural networks (PINNs) | • Optimization of battery design | • PINNs are neural networks that integrate physical laws, typically in the form of partial differential equations (PDEs), into the learning process. This allows the network to respect known physics while learning from data, ensuring that predictions align with fundamental physical principles. | • Physically grounded prediction, reduction data requirements and higher accuracy in complex systems. | • High computational cost, complexity in model formulation and difficulty in incorporating relevant physical laws. |
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