SOC Estimation Research Articles

Remote SOC and SOH Estimation of High Energy Lithium Batteries Based on Dual-Mode Extended Kalman Filter Algorithm

Lithium battery, as its main power source, needs to ensure the safety of drivers and passengers not only under some complex external conditions, but also under harsh use conditions, even when damaged. At some point throughout this process, it is required to evaluate the status of the battery itself in order to assure safe usage of the battery and to develop a more effective battery management plan. SOC, SOH, and condition of power are all variables that are commonly used to describe the state of a lithium battery (SOP). The ability of the battery to constantly provide or receive power, the remaining service the life cycle of the battery, and the ability of the battery to output or receive power promptly are all described by these three characteristics. In order to effectively evaluate the health status of batteries, this paper proposes a dual-mode extended Kalman filter (EKF) algorithm for the remote estimation of SOC and SOH of high-energy lithium batteries. In the estimating procedure, the open circuit voltage (OCV) is also included as a state variable in the iterative process, which allows for more accurate results. In this paper, the state space equation is established based on the first-order RC equivalent circuit model, and the battery state estimation and parameter identification are completed by using the double EKF (the dual extended Kalman filtering, DEKF) algorithm, resulting in the realization of the estimation of SOC and SOH.

Optimal estimation of SoC, SoH using machine learning models and design of control algorithm for active cell balancing in lithium ion batteries

Abstract Electric vehicles transform the automotive industry by replacing traditional vehicles powered by fossil fuels with less polluting and efficient vehicles. They are powered by rechargeable Li-ion batteries. While there are drawbacks associated with Li-ion battery technology, it's important to note that these challenges can be addressed or mitigated effectively. A battery management system ensures the tracking of all functions performed by the battery. An advanced battery management system should accurately estimate the battery's state of health and state of charge. This paper aims to develop machine learning models for the estimation of the state of health and state of charge and to implement cell balancing in a battery management system. A dataset of battery charging and discharging profiles was used to train various machine learning models for estimation. These methods are computationally less complex than conventional methods. In addition, a cell balancing algorithm is implemented to control the charging and discharging of individual cells in a battery pack and balance the state of charge of each cell. The machine learning solution is created using several machine learning models, including Gradient Boosting, Random Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models Gradient Boosting and Random Forest provide good MSE and R2 score. The use of machine learning algorithms for the assessment of state of health and charge estimation combined with the design of an efficient control algorithm for active cell balancing offers significant advancements in the battery management system to optimise the performance, reliability, and useful life of Li-ion batteries. The paper also presents a case study utilising a combination of deep learning-based SoC, SoH estimation algorithms in a simulated data set. A well-designed control algorithm for active cell balancing presents a holistic and effective approach to optimise the performance, reliability, and lifespan of Li-ion batteries.

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation

This discovery examines and tests four model-based charge-state (SOC) estimation methods for lithium-ion (Li-ion) batteries. This work evaluates some parts of the SOC estimation, such as error distribution evaluation, rise time estimation, consumption time estimation, etc., instead of the former probing. The battery comparison model is introduced and the state function of the model is inferred. The first step is to study four model-based SOC estimation strategies. The four systems are then tested using simulation and analysis. To mimic the driving conditions of an electric vehicle, the Urban Dynamometer Driving Schedule (UDDS) flow profiles are used. A genetic calculation is then used to find the limits of the model to determine the optimal limits of the Li-ion battery model. The simulations are run continuously and without interruption, and the results are analyzed. To test the device in circle discovery, a battery test bench is developed and uses a Li-ion battery.

SOC estimation of adaptive untraceable Kalman filtered lithium batteries based on fractional order modeling

SOC estimation of adaptive untraceable Kalman filtered lithium batteries based on fractional order modeling

Battery SOC estimation based on UHIF algorithm for third-order circuit models

Battery SOC estimation based on UHIF algorithm for third-order circuit models

LSTM-Based SOC Estimation for Hybrid Energy Pack Combining Li-ion Battery and Supercapacitor

LSTM-Based SOC Estimation for Hybrid Energy Pack Combining Li-ion Battery and Supercapacitor

Multi-output fusion SOC and SOE estimation algorithm based on deep network migration

The burgeoning fields of electric vehicles and renewable energy systems necessitate precise battery state estimation to optimize battery management and enhance both performance and reliability. This study presents a novel MS-SENet-GRU-UKF-TL model which incorporates a multi-scale squeeze-and-excitation networks (MS-SENet), gated recurrent units (GRU), unscented Kalman filter (UKF), and transfer learning (TL) to refine the accuracy of estimating the state of charge (SOC) and state of energy (SOE). SENet networks, featuring varying convolutional kernels, are deployed to extract features effectively across multiple scales. The effective deployment of the multiscale attention mechanism enhances the precision in capturing essential data information. Additionally, the application of the UKF algorithm improves the precision and smoothness of the model's outputs. The framework is verified under four data sets at different temperatures. Experimental results show that compared with other models, the average accuracy of the proposed SOC estimation method is enhanced by at most 37.486 %, and the average accuracy of SOE is enhanced by 35.432 %. Finally, the novel algorithm perform transfer learning on different battery data to validate the adaptive performance of the proposed method under different materials and capacity.

ANN approach to SOC estimation of Lithium-Ion Battery

ANN approach to SOC estimation of Lithium-Ion Battery

An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%.

An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms.

An efficient buck-boost converter for fast active balancing of lithium-ion battery packs in electric vehicle applications

This article proposes a fast active cell balancing circuit for lithium-ion battery packs. The proposed architecture incorporates a modified non-inverting buck-boost converter to improve balancing efficiency, an equivalent circuit model technique for battery designing, and an extended Kalman Bucy filter for accurate SOC estimation. The proposed balancing technique analyses a six-series and one parallel (6S1P) battery pack combination in static, charging, and discharging modes. With fewer components, the proposed architecture reduces the losses and improves the balancing performance. The design limitations, balancing principle, loss analysis, and control strategies are thoroughly investigated. The proposed topology is modelled in the MATLAB/Simulink platform to perform energy transformation analysis between stronger and weaker cells. The FPGA-based real-time simulator OPAL-RT (OP5700) validates the balancing performance of the proposed topology. For the 22V, 2200mAh battery pack the proposed topology had an overall balancing efficiency of 97.05% and a balancing speed of 1328s. The proposed topology has a 1.3% deviation in balancing efficiency and a 57s deviation in balancing speed compared to MATLAB and real-time simulation. The proposed active cell balancing architecture considerably increases balancing speed and efficiency.

SOC Estimation of Power Lithium Battery Based on RGC and Multi-innovation UKF Joint Algorithm

SOC Estimation of Power Lithium Battery Based on RGC and Multi-innovation UKF Joint Algorithm

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles, portable devices and renewable energy systems. Neural networks have become a powerful tool in the field of SOC estimation, but they have a complex structure, difficult parameter tuning, and high performance requirements. For this reason, this paper proposes a simplified gated recurrent neural network (SGRN) that greatly simplifies the unitary network structure and parameters. A Nadam gradient optimizer is introduced to update the network weight parameters, and the network hyperparameters are optimized using the log-cosine variant slime mold algorithm (LVSM). The algorithm is validated under HPPC, BBDST, and DST operating conditions under 25 °C, 10 °C, and − 5 °C. The results show that the LVSM-SGRN algorithm has higher accuracy, lower performance consumption, and better temperature adaptability than the conventional LSTM algorithm as well as the EKF algorithm. This contributes to the integration and large-scale industrialization of the algorithm, and comprehensively improves the safety of lithium-ion batteries and accuracy of SOC estimation.

SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors

SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors

An Adaptive LSTM Network With Fractional-Order Memory Unit Optimized by Hausdorff Difference for SOC Estimation of Lithium-Ion Batteries

An Adaptive LSTM Network With Fractional-Order Memory Unit Optimized by Hausdorff Difference for SOC Estimation of Lithium-Ion Batteries

A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter

This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm.

An Electrochemical-Thermal Coupling Model Based on Two-Factor Parameter Modification for Lithium-Ion Battery

Abstract The accurate establishment of battery model can improve the design reliability and reduce the design risk, which provides an important basis for the research of battery. First, the key parameters of the Li-ion battery model are identified by the least square method based on the full-battery equivalent circuit model of the single-particle impedance spectrum, and the diffusion coefficient and exchange current density under different temperatures and SOC conditions are calculated. At the same time, the one-dimensional thermal rate model is used as the heat source of the three-dimensional model, and the mean temperature T of the three-dimensional model is calculated by using Fourier's law, and T is fed back to the one-dimensional model as the key parameter to modify the conductivity, diffusion coefficient, and exchange current density, and a semi-empirical electrochemical-thermal coupling model with two-factor parameter modification is established. Finally, the model is verified by the temperature field distribution and discharge voltage curve at different discharge rates. The maximum temperature difference is less than 3.1 °C, and the maximum voltage difference error is less than 0.131 V. The results show that the improved model can accurately reflect the influence of temperature on the model parameters, and has high accuracy in the estimation of battery terminal voltage and SOC.

Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient

Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient