SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm

With the rise in an energy crisis, electric vehicles have become a necessity. An integral part of the electric/hybrid vehicle is batteries. Out of many types, Li-ion batteries are providing features like high power as well as energy density. The features make Li-ion is an excellent choice for multiple applications from electronic appliances to electric vehicles. Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential. The monitoring is dependent on actual physical measurements, which are subject to error contributing factors such as measurement noise, errors etc. With the estimation of SOC and State of Health (SoH) of the battery model, the lifetime of the battery will be calculated out, and along these lines sparing significant cost. In this paper, a study on SoH estimation and Li-ion battery SoC is estimated using a Kalman Filter (KF) algorithm estimation and results are presented to validate the Li-ion operating performanc

Autonomous Characterization of Lithium-Ion Battery Model Parameters utilizing a Mathematical Optimization Methodology

Kalman filtering is commonly used for state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries owing to its simplicity, computational efficiency, and relatively precise results. However, kalman filters depend on the Li-ion battery model. Several laboratory tests such as incremental current and dynamic stress tests are required to determine battery model parameters in model-based SOC estimation. These tests such as incremental current test and dynamic stress test are time-consuming and can take multiple days. A mathematical optimization along with a battery test method, which does not need rest time for battery, are adopted to reduce the battery parameter identification time, drastically. A mathematical optimization stage is embedded prior to Kalman Filter based SOC estimation computing the battery open circuit voltage (OCV) and as well as an initial guess of the RC parameters of the battery equivalent circuit. Therefore, it reduces the required number of tests to one. Extensive numerical studies on a 2 Ah Lithium-ion cell verify the effectiveness of the proposed method by achieving a RMS error less than one percent

Advanced state of charge estimation for lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries have a high theoretical energy density, which could outperform classic Li-ion technology in weight, manufacturing costs, safety and environmental impact. The aim of this study is to extend the research around Li-S through practical applications, specifically to develop a Li-S battery state of charge (SoC) estimation in the environment of electrical vehicles. This thesis is written in paper based form and is organised into three main areas. Part I introduces general topic of vehicle electrification, the framework of the research project REVB, mechanisms of Li-S cells and techniques for SoC estimation. The major scientific contribution is given in Part II within three studies in paper-based form. In Paper 1, a simple and fast running equivalent circuit network discharge model for Li-S cells over different temperature levels is presented. Paper 2 uses the model as an observer for Kalman filter (KF) based SoC estimation, employing and comparing the extended Kalman filter, the unscented Kalman filter and the Particle filter. Generally, a robust Li-S cell SoC estimator could be realized for realistic scenarios. To improve the robustness of the SoC estimation with different current densities, in Paper 3 a fast running online parameter identification method is applied, which could be used to improve the battery model as well as the SoC estimation precision. In Part III, the results are discussed and future directions are given to improve the SoC estimation accuracy for a wider range of applications and conditions. The final conclusion of this work is that a robust Li-S cell SoC estimation can be achieved with Kalman filter types of algorithms. Amongst the approaches of this study, the online parameter identification approach could deliver the best results and also contains most potential for further improvement

A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries.

Lithium-ion battery state-of-charge (SOC) serves as an important battery state parameter monitored by the battery management system (BMS), real-time and accurate estimation of the SOC is vital for safe, reasonable, and efficient use of the battery as well as the development of BMS technology. Taking the ternary lithium battery as the research object, based on the second-order RC equivalent circuit model, a variable forgetting factor least square method (VFFRLS) is used for parameter identification and a combination of the square root of covariance and noise statistics estimation techniques to estimate the SOC, to solve the problem of dispersion of the unscented Kalman filter and the error covariance tends to infinity with iterative calculation, thus ensuring the accuracy of SOC estimation. The feasibility and robustness of the algorithm and the battery state estimation strategy are verified under HPPC and BBDST conditions with maximum errors of 1.41% and 1.53%, respectively. The experimental results show that the combined algorithm of VFFRLS and SRAUKF has good robustness and stability, and has high accuracy in the SOC estimation of Li-ion batteries, which provides a reference for the research of lithium-ion batteries

A Sparse Learning Machine for Real-Time SOC Estimation of Li-ion Batteries

The state of charge (SOC) estimation of Li-ion batteries has attracted substantial interests in recent years. Kalman Filter has been widely used in real-time battery SOC estimation, however, to build a suitable dynamic battery state-space model is a key challenge, and most existing methods still use the off-line modelling approach. This paper tackles the challenge by proposing a novel sparse learning machine for real-time SOC estimation. This is achieved first by developing a new learning machine based on the traditional least squares support vector machine (LS-SVM) to capture the process dynamics of Li-ion batteries in real-time. The least squares support vector machine is the least squares version of the conventional support vector machines (SVMs) which suffers from low model sparseness. The proposed learning machine reduces the dimension of the projected high dimensional feature space with no loss of input information, leading to improved model sparsity and accuracy. To accelerate computation, mapping functions in the high feature space are selected using a fast recursive method. To further improve the model accuracy, a weighted regularization scheme and the differential evolution (DE) method are used to optimize the parameters. Then, an unscented Kalman filter (UKF) is used for real-time SOC estimation based on the proposed sparse learning machine model. Experimental results on the Federal Urban Drive Schedule (FUDS) test data reveal that the performance of the proposed algorithm is significantly enhanced, where the maximum absolute error is only one sixth of that obtained by the conventional LS-SVMs and the mean square error of the SOC estimations reaches to 10 −7 , while the proposed method is executed nearly 10 times faster than the conventional LS-SVMs

Novel Approaches for State of Charge Modeling in Battery Management Systems

One of the key steps of any battery management system design is the representation of the open circuit voltage (OCV) as a function of the state of charge (SOC). The OCV-SOC relationship is very non-linear that is often represented using a polynomial that has log and inverse terms that are not defined around SOC equal to zero or one. The traditional response to this problem was only at the software level. In this thesis, I present a formal scaling approach to the OCV-SOC characterization in Li-ion batteries. I show that, through formal modeling and optimization, the traditional approach to OCV-SOC modeling can be significantly improved by selecting the proper value of $\epsilon$. When the proposed technique is used a decrease in the maximum SOC error of 9\% is reported. The proposed approach is tested on data collected from multiple cells over various temperatures for OCV-SOC characterization and the results are presented. State-space model (SSM) and the Kalman filter have several applications in the emerging areas of automation and data science including in battery SOC estimation. In many such applications, the application of Kalman filtering requires model identification with the help of the observed data. I present the formulas with derivations for linear state-space model parameter estimation using the expectation maximization (EM) algorithm. Particularly, I derive the formulas for different special SSM cases of practical interest, such as the continuous white noise acceleration (CWNA) model. Through simulation, I show the benefits of these derivations for the special models in comparison with the generalized approach

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source