Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network

Li, Renzheng; Wang, Hui; Dai, Haifeng; Hong, Jichao; Tong, Guangyao; Chen, Xinbo

doi:10.1016/j.energy.2022.123853

Cited by 26 publications

(7 citation statements)

References 43 publications

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“…For a rigorous assessment of the efficacy of the proposed MSFGRU model, we benchmark it against the well-established baseline models such as linear regression (LR), LSTM [32], GRU with dropout [33], Transformer [42], and PatchTST [40]. As Table IV illustrates, LR exhibits the least favorable performance in the prediction tasks, followed by LSTM.…”

Section: E Comparison With Test Setmentioning

confidence: 99%

“…Reference [32] proposed an LSTM-based multi-step SOC prediction method that utilized actual vehicle data while accounting for weather and driver behaviors as influential factors [32]. Similarly, [33] proposed a novel multi-step SOC prediction method for real-world BESS by employing a gated recurrent unit (GRU) [33]. However, when deep learning techniques such as LSTM and GRU are employed for direct prediction, the predictive performance may exhibit limitations in capturing long-term dependency relationships.…”

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confidence: 99%

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Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems

Liu,

Liang,

et al. 2024

Journal of Modern Power Systems and Clean Energy

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Accurate prediction of the state-of-charge (SOC) of battery energy storage system (BESS) is critical for its safety and lifespan in electric vehicles. To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction, this paper introduces a novel multi-scale fusion (MSF) model based on gated recurrent unit (GRU), which is specifically designed for complex multi-step SOC prediction in practical BESSs. Pearson correlation analysis is first employed to identify SOC-related parameters. These parameters are then input into a multi-layer GRU for point-wise feature extraction. Concurrently, the parameters undergo patching before entering a dual-stage multi-layer GRU, thus enabling the model to capture nuanced information across varying time intervals. Ultimately, by means of adaptive weight fusion and a fully connected network, multi-step SOC predictions are rendered. Following extensive validation over multiple days, it is illustrated that the proposed model achieves an absolute error of less than 1.5% in real-time SOC prediction. Index Terms--Electric vehicle, battery energy storage system (BESS), state-of-charge (SOC) prediction, gated recurrent unit (GRU), multi-scale fusion (MSF). X i  Y iValues of the i th datapoint X ˉ Y ˉSample means of variables X and Y z tUpdate gate of a GRU

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Section: E Comparison With Test Setmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems

Liu,

Liang,

et al. 2024

Journal of Modern Power Systems and Clean Energy

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“…The use of neural networks to estimate the state of charge of batteries has achieved excellent performance. Li et al, proposed a new multi-step prediction method for battery SOC (State of Charge) systems based on gated recurrent neural networks to more accurately test the battery state of charge of electric vehicles and verified the effectiveness of their proposed method through experiments [4]. Oyewole et al, proposed a controlled transfer learning network for SOC prediction in the initial stages of degradation to solve the problem of deep learning models ignoring the dynamic changes of batteries, and experimentally proved the effectiveness of the algorithm [5].…”

Section: Related Workmentioning

confidence: 99%

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Zhang,

Ren

2023

WEVJ

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Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction.

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“…Huang et al [27] used real-world vehicle data from the National Big Data Alliance of New Energy Vehicles (NDANEV) but equalized battery degradation effects with the vehicle driving mileage. Li et al [28] integrated weather and vehicle data and used a novel dual-dropout-based neural network to predict the SOC. Despite these efforts to include vehicle-related data, both studies suffered from significant data loss and sampling limitations.…”

Section: Related Workmentioning

confidence: 99%

DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches

Wang,

Chen,

Fan

et al. 2023

Mathematics

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Accurate mathematical modeling of state of charge (SOC) prediction is essential for battery management systems (BMSs) to improve battery utilization efficiency and ensure a good safety performance. The current SOC prediction framework only considers battery-related features but ignores vehicle information. Additionally, in light of the emergence of time-series Transformers (TSTs) that harness the power of multi-head attention, developing a SOC prediction model remains a significant challenge. Therefore, we introduce a new framework that integrates laboratory battery data with mathematical vehicle model features to improve the accuracy of the SOC and propose a prediction model named DLPformer, which can effectively capture variations in the SOC attributed to both trend and seasonal patterns. First, we apply Matlab/Simulink to simulate a mathematical model of electric vehicles and process the generated vehicle data with Spearman correlation analysis to identify the most relevant features, such as the mechanical losses of the electric motor, differential, and aerodynamic drag. Then, we employ a data fusion method to synchronize the heterogeneous datasets with different frequencies to capture the sudden changes in electric vehicles. Subsequently, the fused features are input into our prediction model, DLPformer, which incorporates a linear model for trend prediction and patch-input attention for seasonal component prediction. Finally, in order to effectively evaluate the extrapolation and adaptability of our model, we utilize different driving cycles and heterogeneous battery datasets for training and testing. The experimental results show that our prediction model significantly improves the accuracy and robustness of SOC prediction under the proposed framework, achieving MAE values of 0.18% and 0.10% across distinct driving cycles and battery types.

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Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network

Cited by 26 publications

References 43 publications

Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems

Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches

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