A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

Mei, Peng; Karimi, Hamid Reza; Chen, Fei; Yang, Shichun; Huang, Cong; Qiu, Song

doi:10.3390/s22239474

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…For non-real-time estimated states, such as SOE and SOH, the method of vehicle-cloud collaboration approach is still promising. Mei et al [131] proposed a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration, given in Figure 10. The SOH correction in SOE estimation achieves the joint estimation with different time scales.…”

Section: Joint Estimation Based On Vehicle-cloud Collaborationmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

Self Cite

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Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.

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Section: Joint Estimation Based On Vehicle-cloud Collaborationmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

Self Cite

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“…In this method, a learning-based capacity estimation model is first created using a gated convolutional and bi-directional long short-term memory hybrid neural network (GateCNN-BiLSTM). The CNN is used to capture the advanced spatial features of the battery sequence [29], and the bi-directional long short-term memory (BiLSTM) neural network is used to extract the positive and negative context information of the battery sequence [30]. The gating mechanism can suppress noise information and enhance the sensitivity of the network to important features, thereby improving the generalization ability of the network.…”

Section: Introductionmentioning

confidence: 99%

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun,

Tian,

et al. 2024

Batteries

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Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.

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“…The error is kept within 3%. 32 However, the single traditional SOC and SOE estimation methods cannot get more accurate and effective estimates. With the progress of science and technology, the emergence of machine learning has continuously solved this problem.…”

Section: Introductionmentioning

confidence: 99%

“…This method can not only use the historical battery data of the cloud platform to predict SOH but also correct SOE according to the predicted value of SOH. The error is kept within 3% 32 …”

Section: Introductionmentioning

confidence: 99%

An improved long short‐term memory based on global optimization square root extended Kalman smoothing algorithm for collaborative state of charge and state of energy estimation of lithium‐ion batteries

Wang

Cao

et al. 2023

Circuit Theory & Apps

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SummaryState of charge and state of energy are essential performance indicators of the battery management system and the key to reflecting the remaining capacity of batteries. Aiming at the problems of low precision, long time, and strongly nonlinear system estimation of state of charge and state of energy of lithium‐ion batteries based on traditional algorithm under complex working conditions, this paper proposes a hybrid method consisting of the long short‐term memory neural network and square root extended Kalman smoothing. The long short‐term memory neural network can enhance the memory ability of the previous time data. The sliding window technology is introduced into the network to improve the correlation between the last time and the subsequent time estimation. Based on the traditional Kalman filtering algorithm, the square root and reverse smoothing algorithms are introduced to solve the risk of the negative covariance matrix and the problems of slow convergence and significant estimation deviation caused by a strongly nonlinear system. According to experiments, under the hybrid pulse power characterization working condition at 25°C, the maximum absolute errors of state of charge and state of energy are 1.779% and 1.487%, and the mean absolute errors are 0.352% and 0.894%, respectively. Under the Beijing bus dynamic stress test working condition at 25°C, the maximum absolute errors of state of charge and state of energy are 2.703% and 2.369%, and the mean absolute errors are 0.462% and 0.621%, respectively. The experimental results show that this algorithm can obtain reliable state of charge and state of energy under different complex working conditions with high accuracy, convergence, and robustness.

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A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

Cited by 10 publications

References 37 publications

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

An improved long short‐term memory based on global optimization square root extended Kalman smoothing algorithm for collaborative state of charge and state of energy estimation of lithium‐ion batteries

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