Showing papers on "State of charge published in 2022"
TL;DR: In this paper , an improved feedforward-long short-term memory (FF-LSTM) modeling method is proposed to realize an accurate whole-life-cycle state of charge (SOC) prediction by effectively considering the current, voltage, and temperature variations.
105 citations
TL;DR: A novel ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework to mitigate the adverse effect of measurement disturbances and contribute to an unbiased estimation of the ISC resistance.
Abstract: The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this article proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multistate-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well-proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance.
65 citations
TL;DR: In this article , an improved wild horse optimizer with deep learning enabled battery management system (IWHODL-BMS) for IoT-based HEVs is presented, which employs attention based bidirectional long short-term memory (ABiGRU) approach to accurately estimate battery state of charge in HEVs.
65 citations
TL;DR: Li et al. as mentioned in this paper proposed a hybrid method to achieve stable and real-time battery state of charge (SOC) estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF).
55 citations
TL;DR: In this article , a scheme using the reduced-order electrochemical model and dual nonlinear filters is presented for the reliable co-estimations of cell state of charge (SOC) and state of health (SOH) in advanced battery management systems.
Abstract: Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme.
55 citations
TL;DR: In this article , a hybrid method based on the CNN-Bidirectional Weighted Gated Recurrent Unit (BwGRU) was proposed for battery state of charge estimation at low temperatures.
55 citations
TL;DR: An online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults.
Abstract: Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.
53 citations
TL;DR: An RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed, and a battery simulation driving condition is constructed using the real vehicle speed to verify the effectiveness of the proposed method in complex conditions.
46 citations
TL;DR: The multithread dynamic optimization method with fractional-order model and the unscented Kalman filter and the Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOC and SOH.
Abstract: Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.
41 citations
TL;DR: In this article , an RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed, in which the hidden Markov model (HMM) is implemented to predict the battery load and the capacity test at different temperatures is conducted to determine the limited state-of-charge (SOC) in the prediction field.
40 citations
TL;DR: In this article, an Extended Kalman Filter (EKF) algorithm is proposed to estimate the state-of-charge inconsistency of lithium-ion battery, which can be used to describe the battery inconsistency degree and investigate the equalization control strategy.
TL;DR: In this paper , the authors proposed a flexible method using only short pieces of charging data to estimate both maximum and remaining capacities to simultaneously address the state of health and state of charge estimation problems.
TL;DR: In this article , the authors proposed to incorporate two kinds of domain knowledge into the deep learning-based methods, namely voltage and current sequences are decoupled into open circuit voltage (OCV), ohmic response and polarisation voltage to augment the input of deep neural networks (DNNs).
TL;DR: In this article , a low-complexity proportional-integral-differential observer framework incorporating the simplified electrochemical model (SEM) was developed to obtain the physics-based state of charge (SOC) and anode potential.
Abstract: The accurate knowledge of the physics‐based state of charge (SOC) and anode potential for lithium‐ion batteries (LIBs) plays an essential role in the driving range prediction and charge strategy optimization of electric vehicles (EVs). However, the SOC estimation based on empirical equivalent circuit models and the lack of anode potential information makes it challenging in developing advanced battery management systems for EVs. For this reason, this paper proposes a low‐complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)‐based observer under variable load condition. First, based on the Padé approximation and volume average method, a reduced‐order SEM is proposed and verified. Then, a low‐complexity proportional‐integral‐differential observer framework incorporating the SEM is developed to obtain the physics‐based SOC and anode potential. Finally, the effectiveness of the proposed method under variable load conditions is assessed by combining data collected by experiment and COMOSL simulation. The results show that the maximum absolute errors of SOC estimation are basically maintained within 2% under HPPC test profiles and the root mean squared errors of anode potential can be kept at 4.31 mV under US06 test profiles, which achieves a good balance between accuracy and computation cost and provides a strong support on substantially ensuring safe operation of EVs.
TL;DR: In this article , a dual particle filter is used to jointly estimate state of charge (SOC) and state of health (SOH) in a second-order equivalent circuit model.
Abstract: Aiming at the problems of time‐varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second‐order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage.
TL;DR: In this article , a real-time energy management strategy for plug-in hybrid electric vehicles (PHEVs) is proposed based on the adaptive regulation of multiple parameters including the driving cycle, driving distance and battery state of charge (SOC).
TL;DR: In this paper , a comprehensive review of different techniques for SoC estimation of batteries is presented, followed by a review of Li-ion battery model parameter estimation methods. But, the precise estimation of battery SoC using the Kalman filters largely relies on accurate battery modeling and its online model parameters estimation.
Abstract: The state of charge (SoC) is the most commonly used performance indicator of battery used in various applications. A chronic erroneous estimation of battery SoC may result in constant over charging and discharging, which in turn causes permanent damage to the internal structure of the battery cells along with system disruptions. This paper presents a comprehensive review of different techniques for SoC estimation of batteries, followed by a review of Li-ion battery model parameter estimation methods. Then this paper classifies the Kalman filters (KFs) in a systematic manner and conducts a detailed literature review on the linear Kalman filter (LKF) and non-linear Kalman filters (NLKFs). In recent literature, the NLKFs such as extended Kalman filter (EKF), adaptive EKF (AEKF), unscented Kalman filter (UKF), and adaptive UKF (AUKF) are the most extensively established techniques for an accurate and reliable SoC estimation of batteries. However, the precise estimation of battery SoC using the Kalman filters largely relies on accurate battery modeling and its online model parameter estimation. According to the literature, the recursive least square (RLS) and the polynomial regression-based battery model (PRBM) are the most often used techniques for estimating real-time model parameters of Li-ion batteries. Therefore, this paper performs an experimental comparative performance evaluation of the most popularly used NLKFS and battery modeling techniques in terms of SoC estimation accuracy at constant and varying operating conditions. The EKF, AEKF, UKF, and AUKF techniques augmented with the popularly used RLS or PRBM are first developed and tested with offline measured data in the MATLAB platform. Then they are implemented on the LabVIEW based battery testing platform using the Math-Script feature of MATLAB for real-time parameters and SoC estimation. Rigorous experimental studies have been carried out for comparative performance evaluation of the PRBM-EKF, PRBM-AEKF, PRBM-UKF, PRBM-AUKF, RLS-EKF, RLS-AEKF, RLS-UKF, and RLS-AUKF techniques under the standard room temperature (25 °C) and a wide temperature range (−5 °C to 45 °C). Overall, the PRBM-AUKF and RLS-AUKF surpassed other approaches in terms of SoC estimation accuracy.
TL;DR: In this article , a fractional-order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters.
Abstract: Supercapacitors are characterized by a long service lifetime and high power density, which can meet the instantaneous high‐power demand during the acceleration of electric vehicles. In this study, a fractional‐order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters. The accurate prediction of the state of charge (SOC) can improve efficiency, prolong the service lifetime, and ensure the safety of supercapacitors. This study proposes a multi‐innovation unscented Kalman filter algorithm based on the fractional‐order model to improve the SOC estimation accuracy. The proposed algorithm is compared with other algorithms and analyzed under different temperatures and operating conditions to verify the accuracy and effectiveness of the proposed algorithm in estimating the SOC and tracking the terminal voltage. Experimental results show that the root mean squared error and mean absolute error of the proposed algorithm are less than those of the other algorithms. The proposed algorithm accurately estimates the SOC and tracks the terminal voltage. The maximum root mean squared error and mean absolute error of SOC estimation error are 1.8% and 1.78%, respectively.
TL;DR: In this paper , the multithread dynamic optimization method is proposed to solve the problem of state-of-charge (SOC) and state of health (SOH) estimation.
Abstract: Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.
TL;DR: In this article , an improved gated recurrent unit (GRU)-based transfer learning method was proposed for small target sample sets to estimate the state-of-charge (SOC) of lithium-ion batteries.
TL;DR: In this paper , the authors proposed a controllable deep transfer learning (CDTL) network for short and long-term state-of-charge (SOC) estimation at early stages of degradation.
TL;DR: In this article , a review of the current widely used equivalent circuit and electrochemical models for battery state predictions is presented, compared, and summarized, and future key challenges and opportunities are discussed.
TL;DR: In this article , an improved Coulomb-Counting (iCC) algorithm and uncertainty evaluation over a ten-year period was used to estimate the state-of-charge (SoC) of a 12V100Ah lithium ion battery.
Abstract: An accurate estimation of the State-of-Charge (SoC) for a battery is the key to designing an efficient Battery Management System (BMS). This is due to the fact that SoC cannot be accessed directly. There are many factors leading to inaccurate estimation of SoC including battery model inaccuracies, parametric uncertainties, the nonlinearity of the battery system, battery capacity fade due to charge/discharge cycles, and temperature- and time-dependent characteristics. This paper presents a mathematical model to precisely estimate the SoC of a Lithium-ion battery based on an improved Coulomb-Counting (iCC) algorithm and uncertainty evaluation over a ten-year period. Experimental measurements using a 12V100Ah Lithium-ion battery are conducted to evaluate the performance and effectiveness of the proposed model. The obtained results indicate that the maximum estimation error using the proposed method is 0.3%, which verifies the high accuracy of SoC estimation compared to other analytical and heuristic approaches.
01 Dec 2022
TL;DR: In this article , a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and battery state of charge (SOC), and the estimation error can reach 1.29% under certain conditions (e.g. 80% SOC at 30 °C).
Abstract: State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS) based technique has been proved to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (SOC), leading to the limited flexibility of these methods under dynamic environments. In this work, a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and SOC. An equivalent circuit model (ECM) is first introduced, in which the solid electrolyte interface (SEI) resistance and charge transfer resistance are employed to map their relationship with SOH under variant temperature and SOC. Subsequently, a probabilistic model, taking charge transfer resistance, temperature and SOC as input variables, is developed for LIB SOH estimation. Experimental study indicates that the estimation error of the proposed method is around 4% when simultaneously considering the temperature and SOC effects. Moreover, the estimation error can reach 1.29% under certain conditions (e.g. 80% SOC at 30 °C). Both results of estimation error are better than the existing EIS-based methods, which indicates that the proposed method is more flexible for SOH estimation with higher precision.
TL;DR: In this article , a joint state-of-charge (SOC) and SOAP estimation method based on online battery model parameter identification was proposed. But the estimation results show that the improved ABSE achieves higher accuracy than the original ABSE at different battery aging states.
Abstract: This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. First, the SOAP of lithium-ion batteries is analyzed thoroughly, and a safe operating area border-based (SOAB-based) SOAP estimation is proposed. Second, based on the adaptive battery-state estimator (ABSE) and improved ABSE, a joint SOC and SOAB-based SOAP estimation method is proposed. The joint estimation results show that the improved ABSE achieves higher accuracy than the ABSE at different battery aging states. The open-loop accuracy evaluation results show that the improved ABSE identifies the battery model parameters more accurately, and the ABSE algorithm error source lies in its identified Rp being much higher than the actual value when the battery is charged/discharged at a high current. The ABSE does not consider the influence of load current on the equivalent circuit model parameters, so it is not suitable for SOAP estimation in theory. The improved ABSE proposed by our team can eliminate this modeling error, identify the battery model parameters, and estimate the SOC and SOAB-based SOAP more accurately. This improved ABSE is an effective algorithm for estimating the battery state when the battery is charged/discharged with a high current.
TL;DR: In this article , the authors examined the recent literature on estimating the state-of-charge (SOC) of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF).
Abstract: With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.
TL;DR: Based on the computation simplification of central difference algorithm, an adaptive central difference Kalman filter by fractional order model is designed to estimate the state of charge in this article , and the designed approach is modelled by simulink and translated into C code.
TL;DR: In this article , the recursive least square with forgetting factor (FFRLS) is applied to identify the parameters including the open-circuit voltage (OCV) values based on the Thevenin model.
TL;DR: In this paper , a hierarchical soft measurement framework for the load current and state-of-charge (SOC) estimation of electric vehicles is proposed, where a total least square (TLS)-based modification is proposed and solved to compensate for measurement disturbances, and in accordance to estimate the SOC more accurately.
01 Jan 2022
TL;DR: In this paper , the authors proposed an adaptive extended Kalman filter (AEKF) for the estimation of the state of charge (SOC) of the lithium-ion battery.
Abstract: The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.