Creating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic Data

Azkue, Markel; Miguel, Eduardo de; Martinez-Laserna, Egoitz; Perez, Lucia; Iraola, Unai

doi:10.3390/wevj14070197

Cited by 4 publications

(2 citation statements)

References 36 publications

(41 reference statements)

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“…In order to solve the problem of gradient disappearance or explosion of RNN, scholars have proposed a series of improvement algorithms, one of which is the LSTM neural network algorithm [29]. As shown in Figure 6, the biggest difference between LSTM and RNN is the addition of "gate" structure inside LSTM, and LSTM can add or remove certain information through the "gate" structure.…”

Section: Soc Estimation Based On Pso-bp Algorithm 231 Model Buildingmentioning

confidence: 99%

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Zhang,

Xu,

et al. 2023

WEVJ

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With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.

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Section: Soc Estimation Based On Pso-bp Algorithm 231 Model Buildingmentioning

confidence: 99%

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Zhang,

Xu,

et al. 2023

WEVJ

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show abstract

“…The neural network algorithm does not need to deeply understand the electrochemical mechanism inside the battery, nor does it need to establish additional complex physical and chemical models. Instead, it only needs to extract features by using the physical quantities measured during the battery charging and discharging process, and then use the features to train the model and establish a mapping model between the battery data features and SOC [11].…”

Section: Introductionmentioning

confidence: 99%

AdaBoost.Rt-LSTM Based Joint SOC and SOH Estimation Method for Retired Batteries

Liu

et al. 2023

Batteries

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Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the huge number of retired batteries and obvious consistency differences bring bottleneck problems such as long learning time and low model updating efficiency to the traditional data-driven algorithm. In view of this, this paper proposes an integrated learning algorithm based on AdaBoost. Rt-LSTM to realize the joint estimation of SOC and SOH of retired lithium batteries, which relies on the LSTM neural network model and completes the correlation adaption in the spatio-temporal dimension of the whole life cycle sample data. The LSTM model is used as the base learner to construct the AdaBoost. Rt-LSTM strong learning model. The LSTM weak predictor is combined with weights to form a strong predictor, which greatly solves the problem of low accuracy of state estimation due to the large number and variability of retired batteries. Simulation and experimental comparison show that the integrated algorithm proposed in this paper is suitable for improving the SOC and SOH prediction accuracy and the generalization performance of the model.

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A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

Yaghoubi,

Khamees

et al. 2024

Engineering Applications of Artificial Intelligence

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Creating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic Data

Cited by 4 publications

References 36 publications

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

AdaBoost.Rt-LSTM Based Joint SOC and SOH Estimation Method for Retired Batteries

A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

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