A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review

“…2) Support Vector Regression (SVR): Support Vector Regression (SVR) is a non-linear regression model that leverages support vector machines to capture complex relationships in the data (Figure 2 and equation 3). The kernel function allows SVR to map the input features into a higher-dimensional space, facilitating the modeling of intricate SOC patterns [41].…”

“…This accuracy is crucial for reliable SOC estimation in HEVs. SVR tends to generalize well to unseen data, making it robust in scenarios where the model needs to perform accurately on new, previously unseen data points [41].…”

“…SVR is designed for continuous data and may not handle categorical variables naturally. Preprocessing steps, such as one-hot encoding, may be necessary for datasets containing categorical features [41]. In cases where the kernel is too complex or the regularization parameter is too small, SVR may be prone to overfitting, capturing noise in the training data and leading to poor generalization on new data [42].…”

“…Advantages Disadvantages [26] Nonlinearity Handling Computational Intensity [27] High-Dimensional Spaces Model Complexity [28] Kernel Trick Interpretability [29] Robustness to Outliers Memory Usage [30] Global Optimization Sensitivity to Noise [31] Flexibility in Kernel Selection Black-Box Nature [32] Tuning Parameters Data Scaling Importance [33] Effective in Small Sample Sizes Large Parameter Search Space [34] Prediction Accuracy Limited Handling of Categorical Data [35] Generalization Capability Overfitting Risk [36] Resource Efficiency Resource Requirements [37] Ease of Model Comparison Limited Interpretability [38] Adaptability to Various Distributions Data Preprocessing Challenges [39] Robust to Irrelevant Features Limited Handling of Time-Series Data [40] Facilitates Hypothesis Testing Assumption of Homoscedasticity [41] Versatility in Problem Types Dependency on Kernel Choice [42] Ease of Hyperparameter Tuning Limited Handling of Missing Data [43] Robustness to Nonlinearities Difficulty with Non-Continuous Variables NNR offers several advantages for estimating SOC in HEVs. Neural Networks (NNs) can model complex, nonlinear relationships between input variables (such as current, voltage, and temperature) and SOC [36].…”

“…NNs can take advantage of parallel processing capabilities, which can significantly speed up training and prediction processes. This is beneficial in applications where computational efficiency is essential [41]. NNs can handle noisy data and outliers to some extent.…”