scholarly journals Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Donghoon Shin ◽  
Beomjin Yoon ◽  
Seungryeol Yoo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Estimation Method ◽  
State Of Charge ◽  
Estimation Methods ◽  
Short Term ◽  
Term Memory ◽  
Soc Estimation ◽  
Long Short Term Memory

Many battery state of charge (SOC) estimation methods have been studied for decades; however, it is still difficult to precisely estimate SOC because it is nonlinear and affected by many factors, including the battery state and charge–discharge conditions. The extended Kalman filter (EKF) is generally used for SOC estimation, however its accuracy can decrease owing to the uncertain and inaccurate parameters of battery models and various factors with different time scales affecting the SOC. Herein, a SOC estimation method based on the EKF is proposed to obtain robust accuracy, in which the errors are compensated by a long short-term memory (LSTM) network. The proposed approach trains the errors of the EKF results, and the accurate SOC is estimated by applying calibration values corresponding to the condition of the battery and its load profiles with the help of LSTM. Furthermore, a multi-LSTM structure is implemented, and it adopts the ensemble average to guarantee estimation accuracy. SOC estimation with a root mean square error of less than 1% was found to be close to the actual SOC calculated by coulomb counting. Moreover, once the EKF model was established and the network trained, it was possible to predict the SOC online.

scholarly journals Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 758
Author(s):  
Gelareh Javid ◽  
Djaffar Ould Abdeslam ◽  
Michel Basset
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Short Term ◽  
Term Memory ◽  
Soc Estimation ◽  
Li Ion ◽  
Long Short Term Memory

The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.

scholarly journals Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning

2020 ◽  
Vol 10 (12) ◽  
pp. 4335 ◽  
Author(s):  
Truong-Ngoc Tan ◽  
Ali Khenchaf ◽  
Fabrice Comblet ◽  
Pierre Franck ◽  
Jean-Marc Champeyroux ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Single Point ◽  
Base Station ◽  
Base Stations ◽  
Short Term ◽  
Position Accuracy ◽  
Long Short Term Memory ◽  
Position Precision

In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.

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