A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning

State-of-charge (SoC) estimation is of great importance for electric vehicles (EVs) optimum operation, while highly dynamic operation environment makes this task extremely thorny. By far, considerable researches have been done in modeling and approaches to accurately estimate SoC for lithium-ion batteries (LiBs) used in EVs. Nevertheless, existing reviews are either ambiguous in classifications or incomplete in methodologies, especially lack of detailed evaluation. To solve these issues, this work undertakes a comprehensive survey on various models and methods utilized in LiBs SoC estimation, which aims to more specifically guide automobile engineers and researchers utilize these techniques in an effective manner and facilitate future research works. Enumeration method is used for review screening to select the most representative papers in an application-oriented manner. Firstly, five modeling techniques are outlined, which are significant in improving estimation precision. Then, six categories along with twenty-one evaluation criteria are summarized for quantitative performance verification. Moreover, various SoC estimation methods in six categories are thoroughly discussed, among which ninety-three methods are carefully elaborated in table for a systematic evaluation and fair comparison. Meanwhile, this work gives insight to SoC estimation methods with emphasis on their major benefits/limitations and application conditions. Lastly, important recommendations of future researches are disclosed in the hope of providing some inspirations to propel EVs applications. All in all, this work provides a state-of-the-art one-stop handbook of LiBs SoC estimation used in EVs.

Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey

Lithium-ion batteries have gained significant prominence in various industries due to their high energy density compared to other battery technologies. This has led to their widespread use in energy storage systems, electric vehicles, and portable electronic devices. However, lithium-ion batteries still face limitations, particularly concerning safety issues such as overheating and aging. BMS play a crucial role in ensuring safe and effective operation by providing control and monitoring functions. Among the key challenges in BMS is the accurate prediction of SOH, SOC, and RUL of the battery. Additionally, fault detection of lithium-ion batteries is an essential function of BMS. Given the complex electrochemical characteristics of lithium-ion batteries, there is a growing interest in developing advanced BMS that can accurately estimate the battery state. This review article focuses on the increasing popularity of ANN based methods for predicting the state of lithium-ion batteries. The literature review encompasses a wide range of studies on ANN-based battery management systems. The BMS applications and prediction methods for SOH, SOC, and RUL are thoroughly classified. The review covers state-of-the-art ANN methods, including feedforward neural network, deep neural network, convolutional neural network, and recurrent neural network, and provides a comparative analysis. The article also highlights current trends and identifies gaps in BMS applications. Furthermore, it offers insights and directions for future research and development, aiming to upgrade existing BMS or create advanced BMS systems. The comprehensive analysis presented in this review article serves as a valuable resource for research and studies seeking to enhance BMS capabilities and improve battery management strategies in the context of lithium-ion batteries. 

Applications of artificial neural network based battery management systems: A literature review

The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long short-term memory (LSTM) neural network model can overcome the problems associated with the nonlinear battery model and adapt to the complexity and uncertainty of the estimation process. The accuracy of the developed model was compared with results obtained from Feed-Forward Neural Network (FFNN) topology and Deep-Feed-Forward Neural Network (DFFNN) topology under three different time series. The system was trained using real data from the Al-Manara PV power plant. The LSTM with learn-and-adapt-to-train-date properties, as well as the idea of “forget gate,” shows exceptional ability to determine the SoC under various ID data. The LSTM properly calculated the SoC for all three-time models with a maximum standard error (MSE) of less than 0.62%, while the FFNN and DFFNN provided a fair estimate for the SoC with MSEs of 5.37 to 9.22% and 4.03 to 7.37%, respectively. The promising results can lead to excellent monitoring and control of battery management systems. • Real-time estimation of the state of charge (SoC) of battery storage systems is crucial. • SoC of large-capacity grid-scale batteries was estimated during charging/discharging process. • LSTM captures nonlinear relationship between SoC and ID and achaive excellent tracking accuracy. • LSTM avoids long-term dependency problems and accurately estimates SoC with error < 0.62%. • The purposed NN model supports excellent monitoring and control of battery systems. 

State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network

In this paper combines the artificial neural network (ANN) method and the internal resistance measuring method, which is designed for the lithium-ion battery state of charge (SOC) estimation. It is different from the general neural network research that only uses voltage and current as parameters. The input layer adds important parameters: the battery voltage and current, and the internal resistance of the battery as external inputs. We design a recurrent neural networks (RNN) model with non-linear autoregressive with exogenous input (NARX). The network compares the difference between the simulation results under the same benchmark with the back-propagation neural networks (BPNN). Experiments show that this architecture not only improves the convergence speed of the neural network, but also shortens its average execution time, and the mean-square error is improved. It is a good indicator of the accuracy of the measurement. This paper also discusses the application of this architecture to the difference between DC internal resistance and AC internal resistance.

State of Charge Estimation for Lithium-ion Battery using Recurrent Neural Network

In order to pursue small size, high efficiency and low cost, this paper proposes a method of balancing the battery pack using an isolated bidirectional converter. The proposed converter adopts active clamping technology and synchronous rectification technology, combined with switch control, and can achieve zero voltage switching (ZVS) effect and improve efficiency. In order to improve the accuracy of the state of charge (SOC) estimation, the average SOC is calculated by a lookup table method and a Coulomb integration method to determine the equilibrium state of the battery pack.

New Lithium Battery Balancing Circuit Design Using Isolated Converter

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State of Charge Estimation for Lithium-ion Battery using Recurrent Neural Network

New Lithium Battery Balancing Circuit Design Using Isolated Converter