Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

AbstractAccurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Download Full-text

Towards Accurate State of Charge Estimation for Lithium-ion Batteries using Self-supervised Transformer Model: A Deep Learning Approach

10.21203/rs.3.rs-687515/v1 ◽

2021 ◽

Author(s):

M Hannan ◽

Dickson How ◽

M. S. Hossain Lipu ◽

M Mansor ◽

Pin Ker ◽

...

Keyword(s):

Deep Learning ◽

Lithium Ion ◽

Absolute Error ◽

State Of Charge ◽

Training Data ◽

Ambient Temperatures ◽

Soc Estimation ◽

Proposed Model ◽

Li Ion ◽

Transformer Model

Abstract Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning-enabled transformer model achieves the lowest root-mean-square-error (RMSE) of 1.2% and a mean-absolute-error (MAE) of 0.7% on the test dataset at various ambient temperatures. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Download Full-text

Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations

Energies ◽

10.3390/en13102653 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2653 ◽

Cited By ~ 1

Author(s):

Miriam A. Figueroa-Santos ◽

Jason B. Siegel ◽

Anna G. Stefanopoulou

Keyword(s):

Stress Test ◽

Lithium Ion ◽

Absolute Error ◽

Iron Phosphate ◽

State Of Charge ◽

Linear Quadratic ◽

Estimation Algorithms ◽

Soc Estimation ◽

Wide Range ◽

Measured Force

Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ± 5 % absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates.

Download Full-text

An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

Energies ◽

10.3390/en12071366 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1366 ◽

Cited By ~ 2

Author(s):

Jinqing Linghu ◽

Longyun Kang ◽

Ming Liu ◽

Bihua Hu ◽

Zefeng Wang

Keyword(s):

Computational Efficiency ◽

Model Equation ◽

Gaussian Function ◽

Lithium Ion ◽

State Of Charge ◽

Estimation Accuracy ◽

Soc Estimation ◽

Proposed Model ◽

Model Equations ◽

The Impact

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

Download Full-text

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Complexity ◽

10.1155/2020/8231243 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Ming Zhang ◽

Kai Wang ◽

Yan-ting Zhou

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Unscented Kalman Filter ◽

Estimation Error ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

State Of Charge ◽

Soc Estimation ◽

Hybrid Filtering ◽

Li Ion

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

Download Full-text

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽

10.3390/en12030446 ◽

2019 ◽

Vol 12 (3) ◽

pp. 446 ◽

Cited By ~ 45

Author(s):

Muhammad Umair Ali ◽

Amad Zafar ◽

Sarvar Hussain Nengroo ◽

Sadam Hussain ◽

Muhammad Junaid Alvi ◽

...

Keyword(s):

Electric Vehicle ◽

Management System ◽

Lithium Ion ◽

State Of Charge ◽

Estimation Methods ◽

Battery Management System ◽

Battery Management ◽

Li Ion Battery ◽

Soc Estimation ◽

Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Download Full-text

Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study

Energies ◽

10.3390/en13112749 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2749 ◽

Cited By ~ 3

Author(s):

Nicolae Tudoroiu ◽

Mohammed Zaheeruddin ◽

Roxana-Elena Tudoroiu

Keyword(s):

Comparative Study ◽

Real Time ◽

Lithium Ion ◽

The State ◽

State Of Charge ◽

Li Ion Battery ◽

Matlab Simulation ◽

Soc Estimation ◽

Battery Model ◽

Li Ion

Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis.

Download Full-text

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

Complexity ◽

10.1155/2021/6665509 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Zheng Liu ◽

Yuan Qiu ◽

Chunshan Yang ◽

Jianbo Ji ◽

Zhenhua Zhao

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Estimation Method ◽

Lithium Ion ◽

Absolute Error ◽

State Of Charge ◽

Soc Estimation ◽

Terminal Voltage ◽

The Common ◽

Adaptive Extended Kalman Filter

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.

Download Full-text

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

Energies ◽

10.3390/en14030758 ◽

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.

Download Full-text

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211018038 ◽

2021 ◽

pp. 095440702110180

Author(s):

Meng Wei ◽

Min Ye ◽

Jia Bo Li ◽

Qiao Wang ◽

Xin Xin Xu

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

State Of Charge ◽

Support Vector ◽

Battery Management System ◽

Dynamic Neural Network ◽

Soc Estimation ◽

Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

Download Full-text

Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽

10.3390/en14041054 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1054

Author(s):

Kuo Yang ◽

Yugui Tang ◽

Zhen Zhang

Keyword(s):

Kalman Filter ◽

Parameter Identification ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Recursive Least Squares ◽

Model Parameters ◽

Soc Estimation ◽

Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

Download Full-text