A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter

Tian, Yong; Lai, Rucong; Li, Xiaoyü; Xiang, Lijuan; Tian, Jing

doi:10.1016/j.apenergy.2020.114789

Cited by 249 publications

(73 citation statements)

References 42 publications

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“…The estimation results and error are shown in Fig. (12)(13)(14)(15). Due to external interference and temperature variation, the real rate of SOC presents a nonlinear and unstable trend.…”

Section: Verification With Existing Methodsmentioning

confidence: 99%

“…However, an accurate value of the initial SOC is difficult to obtain, and the degradation of 2017 1 battery performance affects the accurate estimation of SOC [12]. Physical model methods include the Kalman filter [13,14], sliding mode observer [15,16], and particle filter [17,18], among other methods. The Kalman filter is widely used, including, for example, the extended Kalman filter [19], unscented Kalman filter [20], and adaptive Kalman filter [21].…”

Section: Introductionmentioning

confidence: 99%

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State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei

Min

et al. 2020

IEEE Access

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Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this paper presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM INDEX TERMS electric vehicles; state of charge; lithium-ion batteries; NARX; LSTM

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“…The estimation results and error are shown in Fig. (12)(13)(14)(15). Due to external interference and temperature variation, the real rate of SOC presents a nonlinear and unstable trend.…”

Section: Verification With Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei

Min

et al. 2020

IEEE Access

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“…RLS algorithm based on minimum sum-squared error theory is a commonly used model parameter identification method widely applied in tracking of time-varying parameters [31]. To suppress the effect of data saturation in the identification of time-varying parameters with the RLS method, the RLS with forgetting factor (FFRLS) algorithm can be used in battery time-varying parameter identification [32].…”

Section: Model Parameters Identificationmentioning

confidence: 99%

“…Based on the identified ECM parameters, the SOC estimation under the 10 A discharge cycle is presented in Figure 10. As observed, the SOC reference with Arbin EVTS is formed with a black line; the red line and blue line represent the SOC Measurement data and model parameters Initial conditions equations (31) and (32) State prior estimation equation 33Gain matrix update equation 35State error covariance prior estimation equation 34PID coefficient update with gain equation 28State estimation measurement update by PID unit equation 36State error covariance update equation (37) Complexity 7 estimation with single AEKF and AEKF-PID, respectively. From the estimation results of two methods shown in Figure 10(a), these two SOC estimations can both track the SOC reference in a short period of time, no matter AEKF or AEKF-PID.…”

Section: Analysis Of Soc Estimation Under 10 a Dischargementioning

confidence: 99%

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

et al. 2021

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With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.

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“…The challenge is, for example, to combine the difficult task of predicting production from renewable resources with consumption, where, according to Wang et al [24], the stability of the network can be ensured by using the LSTM module of interactive parallel prediction. Tian et al [31] dealt with the successful prediction of the model for optimizing the use of lithium-ion batteries in the field of energy, while Chatterjee and Dethlefs [32] focused on the failures and anomalies in the operation of wind turbines by means of LSTM. Hong et al [33] pointed to the stability and robustness of this method, as verified by extensive cross-validation and comparative analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter

Cited by 249 publications

References 42 publications

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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