Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation

OAPA In this paper, the robustness of model-based state observers including extended Kalman filter (EKF) and unscented Kalman filter (UKF) for state of charge (SOC) estimation of a lithium-ion battery against unknown initial SOC, current noise, and temperature effects is investigated. To more comprehensively evaluate the performance of EKF and UKF, two battery models including the first-order resistor-capacitor (RC) equivalent circuit and combined model are considered. A novel method is proposed to identify the parameters of the equivalent circuit model. The performance of SOC estimation is evaluated by employing measurement data from a commercial lithium-ion battery cell. The experiment results show that UKF generally outperforms EKF in terms of estimation accuracy and convergence rate for each battery model. However, the advantages of UKF over EKF with the combined model is not as significant as with the equivalent circuit model. Both EKF and UKF demonstrate strong robustness against current noise. The updates of model parameters corresponding to operational temperatures generally improve the estimation accuracy of EKF and UKF for both models

Reduce state of charge estimation errors with an extended Kalman filter algorithm

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications

State of charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium‐ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time‐varying temperature from −20°C to 60°C. Then, the genetic algorithm is leveraged to identify the temperature‐dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time‐varying temperature range with the maximum error of less than 2%

An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack

A Critical Evaluation of Eco-Driving Strategies for Connected Autonomous Electric Vehicles at Signalized Intersections

Signalized intersections are significant spots of energy consumption because of frequent stop-and-go behavior. Eco-driving aims to reduce energy usage by optimizing driving behavior. Researchers have reviewed optimization-based method while lack of them reviewed the learning-based approaches. This work critically reviewed two different types of approach. In addition, one well-known rule-based car-following model and two state-of-the-art optimization-based and learning-based methods are selected to test in a signalized intersections environment with the metrics of energy consumption, travelling time and algorithm execution time. The experiment results show that the travelling time of three algorithms are similar, while the energy consumption of the learning-based method and optimization-based method are 30.72% and 51.82% less than that of the rule-based method respectively. However, due to algorithm execution time, the optimization-based method is not suitable to be used in real-time

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation

Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future

An improved packing equivalent circuit modeling method with the cell-to-cell consistency state evaluation of the internal connected lithium-ion batteries.

The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium-ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium-ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real-time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real-time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance-capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium-ion battery pack in the power supply application

Advanced battery modeling for interfacial phenomena and optimal charging

Lithium-ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium-ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium-ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack -Abstract, p. i

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid