Showing papers on "State of charge published in 2021"
TL;DR: A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification and SOC estimation and results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.
Abstract: Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.
134 citations
TL;DR: This paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors and has robust and accurate SOC estimation results.
123 citations
TL;DR: This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application, and ANN and GPR are found to be the best methods based on MSE and RMSE.
Abstract: The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.
121 citations
TL;DR: A deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input, which enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range.
120 citations
TL;DR: In this paper, the effect of state of health (SOH) on ECM parameters has been investigated and the results indicated that with decreasing SOH, the ohmic resistance and the polarization resistance increase while the polarization capacitance decreases.
Abstract: The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control lithium-ion batteries (LIBs). The accuracy and complexity of the ECM, hence, are very important. State of charge (SOC) and temperature are known to affect the parameters of the ECM and have been integrated into the model effectively. However, the effect of the state of health (SOH) on these parameters has not been widely investigated. Without a good understanding of the effect of SOH on ECM parameters, parameter identification would have to be done manually through calibration, which is inefficient. In this work, experiments were performed to investigate the effect of SOH on Thevenin ECM parameters, in addition to the effect of SOC and temperature. The results indicated that with decreasing SOH, the ohmic resistance and the polarization resistance increase while the polarization capacitance decreases. An empirical model was also proposed to represent the effect of SOH, SOC, and temperature on the ECM parameters. The model was then validated experimentally, yielding good results, and found to improve the accuracy of the Thevenin model significantly. With low complexity and high accuracy, this model can be easily integrated into real-world BMS applications.
111 citations
TL;DR: In this paper, a flexible composite SBS@PA/EG is successfully prepared by dissolving in an organic solvent and utilized in battery thermal management (BTM) system, where styrene butadiene styrene (SBS) is used as a supporting material, paraffin (PA) as a phase change material and expanded graphite (EG) as thermal conductivity enhancer.
111 citations
TL;DR: In this paper, the authors proposed a method to estimate the results of offline open circuit voltage (OCV) based ageing diagnosis, including electrode capacities and initial SOCs, termed electrode ageing parameters (EAPs).
110 citations
TL;DR: A review of the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations and suggests future work in the real-time battery management technology.
109 citations
TL;DR: In this article, a dual forgetting factor-based adaptive extended Kalman filter (DFFAEKF) is proposed for online battery state estimation in an electric vehicle (EV).
Abstract: With the increasing demand for Lithium-ion batteries in an electric vehicle (EV), it is always crucial to develop a highly accurate and low-cost state estimation method for the battery management system (BMS). Presently, the dual extended Kalman filter (DEKF) is extensively utilized for online SOC estimation. However, the problem of battery model parameter divergence from the true value greatly affects the estimation accuracy under realistic dynamic loading conditions. In this paper, the new dual forgetting factor-based adaptive extended Kalman filter (DFFAEKF) is proposed for SOC estimation. The proposed SOC estimation method is combined with the simple SOE estimation approach to develop the combined SOC and SOE estimation method. The quantitative relationships between SOC and SOE for all the test battery cells, which are established with the experimental data collected from different constant current discharge profiles are employed for SOE estimation. To evaluate the performance of the developed combined SOC and SOE estimation method, the three different chemistries battery cells are chosen for the testing under different dynamic loading profiles such as dynamic stress test (DST) and US06 drive cycle. For all the considered test battery cells, the experimental results indicated that the combined SOC and SOE estimation method using the proposed DFFAEKF can estimate the battery states under dynamic operating conditions with root mean square error (RMSE) less than 0.85% and 0.95% respectively. The proposed method also demonstrates fast convergence to its true value under erroneous initial conditions. Additionally, the order of worst-case big O notation complexity of the proposed DFFAEKF is equivalent to DEKF. Besides, the simplicity of the proposed method also supports to reduce the computational burden of the processor used in BMSs, and therefore it is well-suited for EV applications.
106 citations
TL;DR: A long short-term memory neural network based on particle swarm optimization (PSO-LSTM), where the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology.
104 citations
TL;DR: In this article, Li et al. employed informative measurements of electrochemical impedance spectroscopy (EIS) in machine learning models (ML), i.e., linear regression model and Gaussian process regression (GPR), to accurately predict the state of charge of li-ion batteries.
TL;DR: In this paper, the thermal performance of the battery module containing 5 × 5 lithium-ion battery arranged in series and parallel is evaluated using phase change material, and the results indicated that use of rest time and increasing convection effect not only reduces maximum temperature but also recover melting fraction of PCM.
Abstract: Lithium-ion battery, the indispensable part of electric vehicles or hybrid electric vehicles because of their high energy capacity and power density but usually suffer from a high temperature rise due to heat generation within a battery. This heat generation is mainly a function of the state of charge and charge/discharge rate. A passive technique like phase change material cooling has receiving a wide recognition due to its high latent heat, compact nature, and lightweight without consuming any external power. In this article, the thermal performance of the battery module containing 5 × 5 lithium-ion battery arranged in series and parallel is evaluated using phase change material. Initially, the performance of a battery module is examined with and without PCM at different discharge rate. It was found that more heat is accumulated at the interior portion of the battery pack due to mutual heating and low heat dissipation ability of PCM at a higher discharge rate. To improve such interior heat dissipation, different fin structure layout like Type I, Type II, Type III and Type IV are proposed and analysed using maximum temperature and average temperature distribution in a PCM based battery pack. It reveals that fin structure layout of Type III minimizes heat accumulation at the interior with adequate melting time among all. Furthermore, charge and discharge characteristics are investigated at different rate using rest time, convection effect and fin structure. The results indicated that use of rest time and increasing convection effect not only reduces maximum temperature but also recover melting fraction of PCM. Results also illustrate that the thermal performance of PCM based battery pack slightly get affected with the use of fin structure at lower convection, but decreases the maximum temperature by 8.17% at higher convection. Heat source a function of the state of charge and charge/discharge rate are given using Ansys-Fluent code and results are reported in the form of maximum temperature, average temperature and melting fraction.
01 Jun 2021
TL;DR: A cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data that can effectively assess cell inconsistency with high robustness and is competent for real- world applications is presented.
Abstract: Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis’ operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.
TL;DR: In this paper, the authors proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function and then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy.
TL;DR: The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform and it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics.
TL;DR: 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, leading to an appreciable precision improvement of SOC estimation online particular for aged cells.
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 (BMSs). 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 paper for the reliable co-estimation of cell SOC and SOH. Specifically, the full-order Pseudo-two-dimensional (P2D) model is first simplified with Pad 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.
TL;DR: A review of the state-of-the-art active battery cell equalization methods is conducted, where it is classified as adjacent- based, nonadjacent-based, direct cell-cell, and mixed topologies to provide a comprehensive way to analyze and compare the existing active cell balancing methods’ performance.
Abstract: With the increasing use of rechargeable lithium-ion battery packs in numerous applications, it calls for an effective evaluation of active battery cell equalization to enhance the whole battery pack's capacity and performance. Plenty of work has focused on cell equalizing circuit and control algorithm design. Still, none of them is devoted to a comprehensive analysis and comparison of cell balancing methods from the topology level. In this article, a review of the state-of-the-art active battery cell equalization methods is conducted, where it is classified as adjacent-based, nonadjacent-based, direct cell-cell, and mixed topologies. This classification can provide a comprehensive way to analyze and compare the existing active cell balancing methods’ performance. Subsequently, the mathematical models of these balancing structures are developed, and extensive simulation results are provided to compare the performance of these equalization topologies in terms of equalization speed. It then comes up with an inclusive economic comparison between them, followed by a discussion.
01 Jun 2021
TL;DR: A novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity is proposed, which is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.
Abstract: The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.
TL;DR: This letter train deep learning models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values and the deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies.
Abstract: In this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 °C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (–20–25 °C).
TL;DR: The proposed energy management strategy has demonstrated its superiority over the reinforcement learning-based methods in both computation time and energy loss reduction of the hybrid battery system, highlighting the use of such an approach in future energy management systems.
Abstract: In this paper, we propose an energy management strategy based on deep reinforcement learning for a hybrid battery system in electric vehicles consisting of a high-energy and a high-power battery pack. The energy management strategy of the hybrid battery system was developed based on the electrical and thermal characterization of the battery cells, aiming at minimizing the energy loss and increasing both the electrical and thermal safety level of the whole system. Primarily, we designed a novel reward term to explore the optimal operating range of the high-power pack without imposing a rigid constraint of state of charge. Furthermore, various load profiles were randomly combined to train the deep Q-learning model, which avoided the overfitting problem. The training and validation results showed both the effectiveness and reliability of the proposed strategy in loss reduction and safety enhancement. The proposed energy management strategy has demonstrated its superiority over the reinforcement learning-based methods in both computation time and energy loss reduction of the hybrid battery system, highlighting the use of such an approach in future energy management systems.
TL;DR: A new way of conducting multiple state estimation of batteries using a deep-learning approach that can estimate SOC and SOE simultaneously based on a long short-term memory (LSTM) deep neural network is investigated.
Abstract: State of charge (SOC) and state of energy (SOE) are two crucial battery states which correspond to available capacity in Ah and available energy in Wh, respectively. Both of them play a pivotal role in battery management, however, the joint estimation of the two states was rarely studied. This study investigates a novel data-driven method that can estimate SOC and SOE simultaneously based on a long short-term memory (LSTM) deep neural network. The proposed algorithm is validated with two dynamic driven cycles under various working conditions, such as different temperatures, different battery material and noise interference. The mean absolute error (MAE) of SOC and SOE estimation achieve 0.91% and 1.09% under a fixed temperature condition, 0.63% and 0.64% for a different battery, and 1.32% and 1.19% with noise interference, respectively. The computational burden and network setting are also studied. In addition, the performance of the proposed method is compared with other popular algorithms, including support vector regression (SVR), random forest (RF) and simple recurrent neural network (Simple RNN). The results show that the proposed method obtains higher accuracy and robustness. This study provides a new way of conducting multiple state estimation of batteries using a deep-learning approach.
TL;DR: It is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation and shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the Dae-NN.
TL;DR: Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent, which is insightful for reducing the structural complexity and cost of future LIB utilization.
Abstract: The installation of current sensors on lithium-ion batteries (LIBs) can be challenging due to practical constraints in specific applications like portable electronics and smart batteries. Motivated by this, our letter proposes a method for online load current and state-of-charge (SOC) coestimation, which mitigates the need of installing the current sensor for LIB management. The essence is to transform the state observation into a constrained optimization problem, which is solved numerically in a moving horizon framework to allow the online coestimation of SOC and input current. Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent. The encouraging results are insightful for reducing the structural complexity and cost of future LIB utilization.
TL;DR: This paper formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources, and presents methods for reducing time-cumulative and state- of-charge-proportional mistakes through simulation analysis.
Abstract: In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.
TL;DR: In this article, the internal resistance of the battery, in the case of the R i n t model, is accurately predicted by a function of current and SOC through the use of a Pearson curve and hyperbolic sine function.
Abstract: The temperature and heat produced within lithium-ion batteries (LIBs) is an important field of research as it affects the power, voltage, and degradation of the battery. Models quickly and accurately predict the temperature and voltage based on operating conditions and can prevent thermal runaway, increase charging speed, prevent lithium plating, and increase cycle life. This paper presents mathematical models that allow for fast calculation which are used in the battery management system (BMS) and battery thermal management system (BTMS) for these goals. This paper presents two distinct models: 1) Internal resistance ( R i n t ) model, and 2) Physio-chemical diffusion/Butler-Volmer based partial differential 1-D model. In addition to this, the internal resistance in the R i n t model is also modeled as a function of the state of charge (SOC) and C-rate. In the experiments, thermocouples are placed on the tabs as well as the surface of the battery, and it is observed that temperature increases with the C-rate at both the surface and the tabs. It is noted that at 4C, the battery temperature increased from 22.00°C to 47.40°C and the tab temperature increased from 22°C to 52.94°C. The simulation results are compared with experimental data at C-rates of 1C, 2C, 3C, and 4C at 22°C. Overall, the simulation results show that the temperature is predicted accurately with a simple R i n t model. We also find that the simplified physio-chemical model of only 3 partial differential equations (PDEs) also produces satisfactory results compared to the usual 8-PDE model and is of similar accuracy as the Rint model. Finally, we find that the internal resistance of the battery, in the case of the R i n t model, is accurately predicted by a function of current and SOC through the use of a Pearson curve and hyperbolic sine function. These findings aid in accurate thermal design and thermal management of LIBs.
TL;DR: This review discusses current methods use in BEV LIB SoC modelling and estimation, and culminates in a brief discussion of challenges inBEV LIB soC prediction analysis.
Abstract: Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis.
TL;DR: A novel data-driven SOC estimation approach for Lithium-ion (Li-ion) batteries is proposed based on the Gaussian process regression framework and achieves satisfactory performance, as well as performs strong robustness against unknown initial SOC and outliers occurred in voltage, current and temperature.
TL;DR: In this article, LiCoO2 and graphite half cells are made to measure the open-circuit voltage for electrodes and a non-destructive aging mechanism identification method is developed, which can quantify the loss of lithium inventory, the loss loss of active materials of electrodes.
TL;DR: Results of numerical simulation and experiment show that the proposed SOC estimation method can accurately estimate SOC under complex driven condition and has strong robustness to the uncertainty of model parameters and the initial value of the noise covariance matrix.
Abstract: State-of-charge (SOC) estimation is an important aspect for modern battery management systems. Extended Kalman filter (EKF) has been extensively used in battery SOC estimation. However, EKF cannot obtain accurate estimation results when the model parameters have strong uncertainty or/and the accurate initial value of noise covariance matrix is unknown. To overcome these defects, the parameters of Lithium-ion battery model on the basis of the second-order resistor–capacitor (RC) equivalent model are identified, and then an improved adaptive EKF (IAEKF) of SOC estimation method for Lithium-ion battery pack is proposed for enhancing estimation accurate and robustness. In IAEKF, the statistical characteristics of measurement noise is adaptively corrected using a forgetting factor, namely, Sage–Husa EKF (SHEKF), and the error covariance matrix is adaptively corrected in accordance with the innovation, in which the calculation of the actual innovation covariance matrix adopts the variable sliding window length. Results of numerical simulation and experiment show that the proposed SOC estimation method can accurately estimate SOC under complex driven condition and has strong robustness to the uncertainty of model parameters and the initial value of the noise covariance matrix.
TL;DR: In this article, a bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed, and an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed.
Abstract: Battery pack performance is the main concern for electric vehicles and energy storage systems. However, charge imbalance is inevitable due to inconsistent manufacturing techniques and environmental conditions. Charge imbalance reduces the power performance and available energy of battery packs. Hence, it is necessary to perform battery equalization. This article proposes an active equalization circuit and a novel equalization strategy to achieve energy redistribution. A bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed first. Then, an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed. Clustering analysis is introduced to identify the target cells to be balanced. To further increase the speed of the equalization process, GA is employed to optimize the classification results. Finally, a series of experiments were conducted to confirm the effectiveness of the proposed topology and the strategy. Both the simulation and experimental results validate that the proposed equalization strategy not only improves the inconsistency but also increases the equalization speed. In practical battery pack experiments, the pack capacity is improved by 16.84% after equalization, and the equalization time is decreased by 23.8% using the proposed method.