Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review
Abstract
:1. Introduction
2. Methods Analyzed
- Feedforward Neural Networks (FNNs);
- Recurrent Neural Networks (RNNs).
- Consistent Quality Metrics: Utilize identical or comparable quality indices across studies to ensure that the evaluation criteria are uniform and allow for objective comparisons. These metrics could include Mean Absolute Error (MAE), root mean squared error (RMSE), or accuracy percentages, as referenced in the literature;
- Dataset Transparency: Clearly state the dataset used for training and testing, specifying whether it is a standard publicly available dataset or a custom dataset obtained from specific battery configurations. This helps in understanding the results;
- Algorithm Complexity: Analyze the computational complexity of the proposed methods, highlighting their advantages and drawbacks. This includes evaluating processing time, memory requirements, and the feasibility of real-time implementation on embedded systems;
- Different Approach: we chose articles that presented a particular method that differed in the interesting way in which they addressed the problem of SOC estimation.
3. Feedforward Neural Networks (FNNs) for Battery SOC Estimation
4. Recurrent Neural Networks (RNNs) for Battery SOC Estimation
4.1. LSTM Neural Network
- Forget Gate : Controls how much information from the previous memory cell should be retained.
- Input Gate : Regulates how much of the new information will be added to the memory cell.
- Output Gate : Determines how much of the information from the memory cell should be used in the output .
4.2. Bidirectional LSTM Neural Networks
- Forward LSTM: Processes the input sequence from the first time step to the last , generating a sequence of hidden states.Here, represents the hidden state at time t for the forward LSTM;
- Backward LSTM: Processes the same input sequence in reverse, from back to , generating another sequence of hidden states.is the hidden state at time t for the backward LSTM;
- Combined Output: The output of the BiLSTM at each time step t is a combination of the hidden states from the forward and backward LSTMs. The combination is typically achieved using concatenation, summation, or averaging:
- -
- Concatenation:
- -
- Summation:
- Forward LSTM Hidden State:
- Backward LSTM Hidden State:
- Output Combination:Depending on the task, the choice of f (concatenation, summation, or averaging) can vary.
4.3. Gated Recurrent Unit Neural Networks
- Reset Gate (): Controls how much of the past information to forget;
- Update Gate (): Determines how much of the previous state to retain and how much to update with new information.
- Update Gate:
- Reset Gate:Here, , , and are the corresponding weight matrices and bias for the reset gate;
- Candidate Hidden State: The reset gate determines how much of the past hidden state contributes to the candidate hidden state.
- Current Hidden State: The update gate determines the final hidden state as a combination of the previous hidden state and the candidate hidden state.
5. Discussion
5.1. Challenges in Machine Learning-Based SOC Estimation
- Inconsistencies in Model Evaluation: Comparing different models remains challenging due to variability in datasets, hyperparameter settings, optimization algorithms, and computational resources used across studies [107]. These inconsistencies complicate cross-study benchmarking and make it difficult to assess the relative effectiveness of different approaches. Furthermore, the use of complex composite algorithms, which often combine serial or parallel structures, increases model complexity [108]. As the number of modules grows, the model’s overall controllability diminishes, making it more vulnerable to disturbances and raising concerns about its robustness in practical applications [109];
- Dataset Limitations: Large variations in datasets are caused by differences in experimental conditions and the mismatch between training data and real-world electric vehicle operation [110]. This discrepancy impacts even the most sophisticated models, reducing their accuracy and applicability in real-world scenarios [111]. The lack of standardized, comprehensive datasets exacerbates this issue;
- Computational Complexity: Many advanced SOC estimation algorithms require significant computational power due to their intricate structures and optimization techniques. This increases the demands on hardware resources, potentially hindering the real-time performance of SOC estimation systems [112]. Balancing computational efficiency with accuracy is critical for enabling online SOC estimation;
- Lack of Open-Source Tools: Few researchers publish their code, and there is often little transparency regarding preprocessing techniques, input parameter choices, and hyperparameter tuning. These gaps hinder reproducibility and prevent effective collaboration within the research community. Without standardized benchmarks, it is difficult to attribute performance improvements to either the model or preprocessing techniques [113];
- Generalization to Diverse Conditions: We must adapt SOC estimation models to perform reliably across a wide range of driving conditions and environmental factors [116].
5.2. Areas for Improvement and Key Observations
- Standardization of Datasets and Data Quality: Machine learning models often depend on specific datasets that limit generalization. We must develop high-quality, standardized datasets with precise measurements and diverse data types. Researchers should also improve data quality by applying advanced data preprocessing techniques [117], sensor fusion methods, and machine learning-based denoising algorithms to ensure accurate and reliable input data [118];
- Open-Source Benchmarks: Creating open-source initiatives and establishing standardized benchmarks for data preprocessing, model architectures, and hyperparameter tuning may lead to significant improvements [119]. Adopting uniform techniques to clean, normalize, and prepare data establishes a consistent foundation for experiments. Designing model architectures with comparable features and systematically tuning hyperparameters—by adjusting factors like learning rates and network layers—minimizes biases from arbitrary decisions. This approach promises enhanced reproducibility, fair comparisons among methods, and increased collaboration, ultimately accelerating progress in ANN-based SOC estimation for electric vehicle batteries [120];
- Balanced Algorithm Complexity: In pursuit of increased accuracy, SOC estimation models must deliver computational efficiency and meet real-world constraints [121]. Evaluating these models from diverse perspectives—including robustness, adaptability, and efficiency—ensures practicality for deployment;
- Generalization and Transfer Learning: One of the challenges in achieving accurate State of Charge estimation is that vehicles are subjected to a wide range of driving conditions, many of which are not fully represented during the training phase. Ensuring that these models generalize well across such diverse conditions remains a significant concern [122]. Transfer learning is emerging as a promising approach to adapt these models to varying scenarios, with researchers actively exploring how to leverage knowledge from one domain to enhance performance in another;
- AI Hardware Acceleration: The advent of specialized hardware for AI and machine learning is expected to accelerate the deployment of complex models in real-time applications [123]. In particular, hardware accelerators such as GPUs and TPUs drastically reduce training and inference times, paving the way for the development and integration of increasingly sophisticated state-of-charge estimation models in electric vehicles;
- Big Data and Cloud Computing: Integrating big data analytics with cloud computing platforms can optimization SOC management. Cloud-based solutions provide scalability and allow remote monitoring and optimization by analyzing data from large fleets, leading to more robust and adaptive management strategies [124];
- Advanced Machine Learning Techniques: Advanced models—including Convolutional Neural Networks (CNNs) and Transformer-based architectures—promise significant improvements [125]. These techniques excel at feature extraction and capture complex, non-linear relationships in battery data. For example, models using attention mechanisms (such as AMBiLSTM) and multi-head self-attention in Transformers effectively identify intricate patterns and dynamic operating conditions [126];
- Hybrid Models and Optimization Strategies: Combining multiple architectures (e.g., GRU–ASG, GLA–CNN–BiLSTM) in hybrid models can improve accuracy and robustness [127]. Researchers must optimize these models to manage increased computational complexity. Likewise, advanced optimization strategies (as seen in RS-LSTM) can boost performance while requiring more computational resources [128].
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Article | Innovations | Dataset & EV Tests | Performance | Advantages | Disadvantages | Computational Cost |
---|---|---|---|---|---|---|
UKF for SOC Estimation | OCV–SOC-T table with temperature (0–50 °C); UKF for nonlinearity handling; Simplified Rint model. | Lab tests on LiFePO4 (18650) with DST, OCV–SOC-T, FUDS; No direct EV tests. | RMS error < 5% (40 °C), up to 16.4% (0 °C). | Accurate SOC estimation with temperature integration; Real-time capable. | Poor performance at low temperatures; Sensitive to flat OCV–SOC regions. | Low; optimized for BMS real-time use. |
Neural-Network-Embedded ECM [66] | ECM with 3 feedforward NNs for residual error correction; Physics-informed loss in training. | Panasonic 18650PF; HPPC, US06, HWFET at 10 °C to −20 °C; Lab-tested under EV conditions. | MSE reduced by 82.5% (US06); RMSE improved 33–64% (US06), 5–29% (HWFET). | High accuracy, especially in extreme conditions; Adaptive to uncertainties. | Initial/final phase errors; Complex offline training, requires retraining. | Offline: 264–450 s; Online: 1.3–2.2 s (real-time suitable). |
Physics-Informed NN (PINN) [63] | Deep FNN with physics-constrained loss (data + charge conservation). | McMaster Univ. (LG HG2, 3Ah); UDDS, US06, LA92, HWFET, Mix; −10 °C to 25 °C; Climatic chamber tests. | RMSE < 2.85%; Outperforms NN-only and AKF methods. | High accuracy and robustness with noisy/sparse data; Physically consistent solutions. | Demanding offline training; Complex design. | Offline: demanding; Online: efficient, real-time capable. |
NN + UKF for Error Cancellation [45] | Hybrid NN for SOC estimation + UKF for noise/error reduction; No OCV–SOC table needed. | Dynamic tests; NN trained on DST, validated on US06/FUDS; Li-ion (mostly LiFePO4) under EV conditions. | RMS error (US06, 0 °C) reduced from 4.1% (NN only) to 2.4% (NN + UKF). | Higher robustness via UKF filtering; No need for OCV–SOC table. | Risk of NN overfitting; Complex NN–UKF integration. | Offline: structural optimization; Online: real-time with filtered errors. |
Article | Innovations | Dataset & EV Tests | Performance | Advantages | Disadvantages | Computational Cost |
---|---|---|---|---|---|---|
Hybrid FNNs for SOC [51] | Hybrid FNN model for SOC estimation; NN optimization to improve generalization and reduce errors. | Lab data on Li-ion (LiFePO4) batteries; DST, US06, FUDS at variable temperatures. | RMS error < 3–4% under optimal conditions. | Robust to nonlinearities; No need for OCV–SOC tables. | Complex offline training; Requires careful optimization. | Offline: demanding; Online: real-time capable. |
FNN + EKF [48] | Hybrid approach: FNN + EKF with parameter for filter update; Direct SOC estimation without OCV–SOC table. | Lab data on Li-ion (LiFePO4); DST, US06, FUDS under various temperatures. | Reduced RMS and max errors; Stable SOC estimation. | Accurate and robust; EKF ensures real-time applicability. | EKF less accurate in strong nonlinearities; Requires tuning and sensor calibration. | Offline: demanding; Online: real-time suitable. |
BPNN + BSA Model [62] | BPNN with BSA model for direct SOC estimation; Improved data fusion (voltage, current, temperature). | Lab data on Li-ion (LiFePO4); DST, US06, FUDS at various temperatures. | RMS and MAE errors typically < 5%. | Strong nonlinear learning; No OCV–SOC table needed. | Complex training; Requires high-quality data. | Offline: computationally heavy; Online: real-time feasible. |
LSTM–RNN for SOC [85] | EI-LSTM-CO model: extended input (avg. voltage via sliding window), output constrained by AhI strategy for stability. | Public dataset (CALCE, LiFePO4); DST for training, US06 and FUDS for validation (7 temperatures, 1s sampling). | RMSE < 1.3%, MAXE < 3.2%; Improved stability vs. standard LSTM–RNN. | Smooth SOC estimation; No need for accurate initial SOC; High accuracy for BMS. | Requires careful tuning (epochs, window size, constraints); More complex than conventional NNs. | Offline: 150 epochs, window size 50; Online: fast, real-time capable. |
Article | Innovations | Dataset & EV Tests | Performance | Advantages | Disadvantages | Computational Cost |
---|---|---|---|---|---|---|
CNN–LSTM for SOC [82] | Hybrid CNN–LSTM for feature extraction and temporal modeling; Robust to unknown initial SOC and temperature variations. | A123 18650 LiFePO4; DST, FUDS, US06; Climatic chamber tests. | MAE < 1%, RMSE < 2% (unknown initial SOC); RMSE < 2%, MAE < 1.5% (temperature variations). | Stable and accurate SOC estimation; Rapid convergence; Good adaptation to environmental variations. | Complex architecture; Long offline training and parameter tuning. | Offline: 10,000 epochs (161 min on GPU); Online: 0.098 ms per time step (real-time suitable). |
RS-LSTM (Random Search LSTM) [86] | LSTM with Random Search optimization; Random Forest for feature selection; Auto-tuned hyperparameters. | CALCE dataset (INR 18650-20R); DST, FUDS, US06; Lab and real vehicle data at different temperatures. | Optimal settings: Look back = 45, Epochs = 177, Batch = 64, LR = 0.0026; MAE = 0.221%, RMSE = 0.262%. | Reduces noise via Random Forest; Auto-optimized hyperparameters; High accuracy and stability. | High offline computation due to hyperparameter search; Complex model tuning. | Offline: intensive training; Online: fast, real-time capable. |
GLA–CNN–BiLSTM for SOC [92] | CNN for spatial and BiLSTM for temporal features; GLA for automatic hyperparameter tuning. | Six EV discharge datasets (HWFET, US06, BJDST, DST, FUDS, UDDS); Lab tests on Li-ion. | MAE < 1%, RMSE < 1%, Max error < 2%. | Combines CNN (features) + BiLSTM (time); GLA ensures fast convergence and adaptability. | Intensive offline training; Complex hyperparameter tuning. | Offline: high due to GLA; Online: fast, real-time suitable. |
Article | Innovations | Dataset & EV Tests | Performance | Advantages | Disadvantages | Computational Cost |
---|---|---|---|---|---|---|
AMBiLSTM with Attention [94] | BiLSTM with spatial/temporal attention; Temperature compensation in OCV and capacity; SOC and SOH co-estimation. | Lab tests on LiNiCoAlO2 (2.6 Ah); 0–40 °C; DST, UDDS; 1 s/10 s sampling. | DST (25 °C): RMSE ↓9.39%; UDDS (25 °C): RMSE ↓22.36%; SOH ↑21.45%. | Improved feature extraction; Robust to temperature variations; Simultaneous SOC/SOH estimation. | Complex model; Requires large dataset and intensive training; Potential short-sequence errors. | Offline: GPU-intensive (RTX 3090); Online: fast, real-time feasible. |
GRU–RNN with Momentum [105] | GRU–RNN with momentum gradient algorithm; Noise injection to prevent overfitting. | Lab tests on BTcap 21700 (2.2 Ah); Charge-discharge cycles; Voltage-current measurements. | Sigma = 0.03: RMSE = 0.0092, MAE = 0.0041, R2 = 0.9990. | Fast convergence; Reduced weight oscillations; High accuracy and generalization. | Requires precise tuning of momentum (beta) and hyperparameters. | Offline: intensive; Online: extremely fast, real-time capable. |
GRU–ASG for SOC [106] | GRU with adaptive Savitzky–Golay (ASG) filter; Spearman coefficient for dynamic window selection. | Real data from LiFePO4 (280 Ah, 3.2 V); Energy storage plant; Six discharge datasets. | MSE < 0.15%, MAE < 3%; Outperforms standard GRU and filters. | Robust in varying conditions; Adaptive filter eliminates manual tuning; Effective memory structure. | Complex integration of NN with adaptive filtering; Additional online computation. | Offline: GPU training (GTX 1060, TensorFlow); Online: fast, real-time suitable. |
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Dini, P.; Paolini, D. Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review. Batteries 2025, 11, 107. https://doi.org/10.3390/batteries11030107
Dini P, Paolini D. Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review. Batteries. 2025; 11(3):107. https://doi.org/10.3390/batteries11030107
Chicago/Turabian StyleDini, Pierpaolo, and Davide Paolini. 2025. "Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review" Batteries 11, no. 3: 107. https://doi.org/10.3390/batteries11030107
APA StyleDini, P., & Paolini, D. (2025). Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review. Batteries, 11(3), 107. https://doi.org/10.3390/batteries11030107