Search

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles. [ABSTRACT FROM AUTHOR]

Abstract In a Battery Management System (BMS), cell balancing plays an essential role in mitigating inconsistencies of state of charge (SoCs) in lithium-ion (Li-ion) cells in a battery stack. If the cells are not properly balanced, the weakest Li-ion cell will always be the one limiting the usable capacity of battery pack. Different cell balancing strategies have been proposed to balance the non-uniform SoC of cells in serially connected string. However, balancing efficiency and slow SoC convergence remain key issues in cell balancing methods. Aiming to alleviate these challenges, in this paper, a hybrid duty cycle balancing (H-DCB) technique is proposed, which combines the duty cycle balancing (DCB) and cell-to-pack (CTP) balancing methods. The integration of an H-bridge circuit is introduced to bypass the selected cells and enhance the controlling as well as monitoring of individual cell. Subsequently, a DC–DC converter is utilized to perform CTP balancing in the H-DCB topology, efficiently transferring energy from the selected cell to/from the battery pack, resulting in a reduction in balancing time. To verify the effectiveness of the proposed method, the battery pack of 96 series-connected cells evenly distributed in ten modules is designed in MATLAB/Simulink software for both charging and discharging operation, and the results show that the proposed H-DCB method has a faster equalization speed 6.0 h as compared to the conventional DCB method 9.2 h during charging phase. Additionally, a pack of four Li-ion cells connected in series is used in the experiment setup for the validation of the proposed H-DCB method during discharging operation. The results of the hardware experiment indicate that the SoC convergence is achieved at ~ 400 s.

Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices. [ABSTRACT FROM AUTHOR]

Hybrid Electric Vehicles (HEVs) are affected to a high extent by Intelligent Energy Management Systems (IEMS), especially during situations that are challenging and unpredictable including changes in traffic patterns, road gradients, and speed. These uncertainties are not easily solved using the existing energy management systems; therefore, this paper presents the design of an AFLC-IEMS employing Type 1 and Interval Type 2 Fuzzy Logic Controllers for energy distribution improvement. The AFLC-IEMS sustains the combustion of fuel and discharge of battery in a way that promotes efficiency in switching between the internal combustion engine and the electric motor. The simulation results with the one-way analysis of variance test confirm our finding that the proposed system is far superior to the traditional ones. The savings achieved by the AFLC-IEMS are a decrease in fuel consumption from 7.26 Liters/100 km down to 6.69 Liters/100 km, as well as an increase in the battery State of Charge (SoC) from 72.7% to 75.8%. The ANOVA analysis shows that the fuel consumption (p < 0.01), the motor torque (p < 0.01), as well as the SoC of the battery (p < 0.05) in the developed FLC are statistically superior to the Type 1 FLC and Type 2 FLC. These improvements are achieved by adapting the technology to the situation to adjust the control strategy; hence, the efficiency of the energy management system is optimized. Therefore, the AFLC-IEMS is more effective in improving the fuel economy and reducing emissions under various conditions.

This paper presents a fully distributed state-of-charge balance control (DSBC) strategy for a distributed energy storage system (DESS). In this framework, each energy storage unit (ESU) processes the state-of-charge (SoC) information from its neighbors locally and adjusts the virtual impedance of the droop controller in real-time to change the current sharing. It is shown that the SoC balance of all ESUs can be achieved. Due to virtual impedance, voltage deviation of the bus occurs inevitably and increases with load power. Meanwhile, widespread of the constant power load (CPL) in the power system may cause instability. To ensure reliable operation of DESS under the proposed DSBC, the concept of the safe region is put forward. Within the safe region, DESS is stable and voltage deviation is acceptable. The boundary conditions of the safe region are derived from the equivalent model of DESS, in which stability is analyzed in terms of modified Brayton-Moser's criterion. Both simulations and hardware experiments verify the accuracy of the safe region and effectiveness of the proposed DSBC strategy.

In response to the increasing penetration of volatile and uncertain renewable energy, the regional transmission organizations (RTOs) have been recently focusing on enhancing the models of pump storage hydropower (PSH) plants, which are one of the key flexibility assets in the day-ahead (DA) and real-time (RT) markets, to further boost their flexibility provision potentials. Inspired by the recent research works that explored the potential benefits of excluding PSHs' cost-related terms from the objective functions of the DA market clearing model, this paper completes a rolling RT market scheme that is compatible with the DA market. Then, with the vision that PSHs could be permitted to submit state-of-charge (SOC) headrooms in the DA market and to release them in the RT market, this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms, assisted by the proposed tri-level optimal SOC headroom model. Specifically, in the proposed tri-level model, the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs, and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two markets. Numerical case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

This manuscript proposes a hybrid method for measuring the battery's dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error. The War Strategy Optimization method detects the parameters of the battery method. The Hierarchical Deep Learning Neural Network is used to predict the dynamic-electrical-response of the battery when it deforms during external-force. By using the proposed method, the estimated voltage and measured voltage error are reduced, and identifies the parameter effectively. Finally, the proposed method is done in the MATLAB platform and it is compared with different existing approaches. The error of the proposed method is 4 mV, the Jellyfish Search Optimizer method error is 6 mV, the Heap-based Optimizer method error is 12 mV, and the Grey Wolf Optimizer method error is 14 mV. The proposed method time is 0.7 s The proposed method shows better results in all methods, like Jellyfish Search Optimizer, Heap-based Optimizer, and Grey Wolf Optimizer, The proposed method provides less computation time and error than the existing one is proved from the simulation outcome. • This manuscript proposes a hybrid technique for measuring the battery's dynamic electrical response. • The proposed hybrid technique is the wrapper of WSO and HDLNN. • The main aim of the proposed method is to lessen the battery-voltage error. • WSO is detects the parameters of battery; HDLNN is predict the electrical response of battery. • The estimated voltage and measured voltage error is reduced and identifies the parameter effectively. [ABSTRACT FROM AUTHOR]

Cell State-of-Charge (SoC) balancing is essential to completely utilise the available capacity of a Battery Energy Storage System (BESS). Furthermore, redundant cells within a BESS are a key consideration to achieve high reliability. Contrary to conventional converters, the proposed converter is designed using one branch (rather of three) to take advantage of its idle cells, which are one-third of the overall cells. In this article, experimental validation is performed to establish the effectiveness of the proposed converter and the SoC balancing strategy. The experimental results indicate that during the BESS operation where the grid reference voltage (Vref) is equal to 38 V, at least 3 out of 9 Modules (Ms) (36 cells) are idle, with a 15-level three-phase sinusoidal output voltage (Vout(a,b,c)) obtained in proposed/conventional converters. When Vref is increased from 38 V to 50 V, Vout(a,b,c) is not obtained in the conventional converter (as opposed to the proposed converter). Using the proposed converter, Vout(a,b,c) has increased by 33.3% compared to the conventional converter. Moreover, the SoC balancing among 9 Ms, 36 cells, 4 cells in M with the lowest average SoC, and 4 cells in M with the highest average SoC is achieved in 4400 s, 4600 s, 3100 s, and 3550 s, respectively.

This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM's flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes. [ABSTRACT FROM AUTHOR]

Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to improve battery life, reliability, and utilization efficiency. In this paper, three cascaded fractional-order sliding-mode observers (FOSMOs) are designed for the estimation of SoC by observing the terminal voltage, the polarization voltage, and the open-circuit voltage of a lithium cell, respectively. Furthermore, to calculate the value of the SoH, two FOSMOs are developed to estimate the capacity and internal resistance of the lithium cell. The control signals of the observers are continuous by utilizing fractional-order sliding manifolds without low-pass filters. Compared with the existing sliding-mode observers for SoC and SoH, weaker chattering, faster response, and higher estimation accuracy are obtained in the proposed method. Finally, the experiment tests demonstrate the validity and feasibility of the proposed observer design method. [ABSTRACT FROM AUTHOR]

This article proposes a novel strategy for congestion mitigation in a distribution system by charging coordination strategy of plug-in electric vehicle (PEV) considering grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The G2V mode of a large figure of PEVs creates a congestion scenario in the distribution system. Therefore, a coordinated charging strategy has been considered in this work to mitigate distribution system congestion. Moreover, a precise estimation and prediction of PEVs state-of-charge (SOC) is necessary while formulating PEVs coordinated strategy. The study of the work is two folded. First, for the first time, a combination of machine learning approach like gradient boosting method-Bayesian optimization (GBM-BO) is considered in prediction of PEVs SOC at the finishing of trip. Second, a coordinated charging scheme is established based on particle swarm optimization (PSO) and firefly algorithm (FA) using the PEVs SOC at the finishing of trip. The charging coordination strategy is analyzed on the 38-bus radial distribution system integrated with solar powered charging-cum parking lot (SPCPL). The machine learning based prediction results reveal the significant reduction in errors between predicted and calculated values. The results further reveal the reduction in PEVs charging cost and congestion scenario while considering SPCPL in the distribution system. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert's Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert's Law to better model the rate-capacity effect of the batteries. [ABSTRACT FROM AUTHOR]

Battery energy storage systems are widely used in energy storage microgrids. As the index of stored energy level of a battery, balancing the State-of-Charge (SoC) can effectively restrain the circulating current between battery cells. Compared with passive balance, active balance, as the most popular SoC balance method, maximizes the capacity of the battery cells and reduces heat generation. However, there is no good solution in the battery management system (BMS) to ensure active balance during distributed charging. In view of this, this paper designs two novel distributed charging strategies based on a kind of smart leader, in which a constant static leader is modified by a dynamic leader. The modified leader is in charge of guiding SoC to converge to the target value and repress SoC imbalance. The maximum and weighed error between the state of the leader and its neighbor cells are used in the two methods, respectively, both in an event triggered manner. When the relevant index exceeds the threshold, the two methods are used to regulate the leader's state. Under this modification, the eigenvalue of the followers' error dynamic system is reduced, and SoCs follow the dynamic leader faster, thus repressing SoC imbalance. Compared with a constant leader, the smart leader pays more attention to improving SoC imbalance. Additionally, to facilitate analysis, a reduced method is applied to transform the system with an unified input time delay into a nondelay system. Several cases are designed to verify the effectiveness of the designed strategies and test it under different parameters and different time delays. [ABSTRACT FROM AUTHOR]

Cell State-of-Charge (SoC) balancing is essential to completely utilise the available capacity of a Battery Energy Storage System (BESS). Furthermore, redundant cells within a BESS are a key consideration to achieve high reliability. Contrary to conventional converters, the proposed converter is designed using one branch (rather of three) to take advantage of its idle cells, which are one-third of the overall cells. In this article, experimental validation is performed to establish the effectiveness of the proposed converter and the SoC balancing strategy. The experimental results indicate that during the BESS operation where the grid reference voltage (V ref) is equal to 38 V, at least 3 out of 9 Modules (Ms) (36 cells) are idle, with a 15-level three-phase sinusoidal output voltage (V out(a,b,c)) obtained in proposed/conventional converters. When V ref is increased from 38 V to 50 V, V out(a,b,c) is not obtained in the conventional converter (as opposed to the proposed converter). Using the proposed converter, V out(a,b,c) has increased by 33.3% compared to the conventional converter. Moreover, the SoC balancing among 9 Ms , 36 cells, 4 cells in M with the lowest average SoC, and 4 cells in M with the highest average SoC is achieved in 4400 s, 4600 s, 3100 s, and 3550 s, respectively. [ABSTRACT FROM AUTHOR]

The state-of-charge (SOC) estimation and remaining-dischargeable-time (RDT) prediction are critical and challenging to safe operation of Li-ion batteries. The main challenges are the limited accuracy of traditional equivalent circuit model and computation-inefficiency of electrochemical battery models. To address this problem, this article proposes a Lebesgue-sampling-based extended Kalman filter (LS-EKF) approach that integrates the high fidelity of a simplified first principle (SFP) model with the low computation of Lebesgue sampling (LS) in a Bayesian estimation framework. In this framework, the SFP model is first introduced along with its design and validation. The LS-EKF is employed with the SFP model to estimate the SOC and predict the RDT. The proposed method is verified with a series of experiments under different operating conditions. The results and comparisons demonstrate the effectiveness of the proposed method in terms of the accuracy of estimation and prediction, as well as the computational efficiency. [ABSTRACT FROM AUTHOR]

For power batteries used in the electric vehicle, accurate state-of-charge (SOC) and state-of-health (SOH) are important. Meanwhile, the current rate in actual working conditions often varies dramatically. However, the influence of current rates on voltage and battery states estimation has been neglected for a long time. To solve this problem, in this article, a hierarchical estimation model considering the current rate is proposed. The fractional-order model is used in the battery modeling, the data-driven method is used to identify parameters, and a multiscale dual extended Kalman filter (DEKF) is used in estimation of battery states. For SOC estimation, the proposed method improves the accuracy by 35.8% and 36.5% compared with traditional DEKF under two conditions. For SOH estimation, the proposed method improves the accuracy by 34.8% and 43.1% under two current conditions. [ABSTRACT FROM AUTHOR]

Although several state-of-charge (SOC) estimation methods have been proposed at the battery cell level, limited work has been done to identify the effect of cell aging on SOC estimations. To address this challenge, this article proposes a novel method for estimating the SOC of Lithium-ion (Li-ion) battery cells by accurately modeling the cell aging and degradation information. The proposed method, termed as “NNGP,” is a deep neural network with Gaussian process feedback. The novel Gaussian process feedback helps the NNGP correlate SOC trends over consecutive charge–discharge cycles. Instead of time, the NNGP leverages available energy to correlate these SOC trends. The deep neural network within the NNGP then utilizes this information and other measured inputs to capture long-term cell aging and degradation trends. The NNGP leverages the most recent cell information to adapt its weights and estimate the SOC by employing an adaptive weighted training strategy. In our experiments on four Li-ion battery cells from three publicly available accelerated aging datasets, the NNGP clearly outperforms other benchmarked methods. The NNGP is also shown to be a useful prognostic tool capable of accurately estimating the SOC for up to 25 cycles in the future with an MAE below 3.5%. When tested under dynamic driving conditions and unknown initial SOC, the NNGP is shown to offer considerable improvements over other state-of-art methods. The derivation of the model followed by experimental evaluation is presented. [ABSTRACT FROM AUTHOR]

For power batteries used in the electric vehicle, accurate state-of-charge estimation is important. However, as one of the most commonly used estimation method, the least square support vector regression is hard to balance the accuracy and efficiency. To solve this problem, the fuzzy information granulation based on asymmetric Gaussian membership function is proposed to improve the utilization efficiency of the effective data and enhance the prediction accuracy of battery states. In addition, the performance of the proposed method is compared with that of other commonly used membership functions. In experiments, the dynamic stress test condition and the urban dynamometer driving schedule condition are used to verify the effectiveness. Compared with the most commonly used triangle membership function, the proposed method improves the accuracy of estimation by 6.47% and 2.18% under two current conditions, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM’s flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes.

Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to improve battery life, reliability, and utilization efficiency. In this paper, three cascaded fractional-order sliding-mode observers (FOSMOs) are designed for the estimation of SoC by observing the terminal voltage, the polarization voltage, and the open-circuit voltage of a lithium cell, respectively. Furthermore, to calculate the value of the SoH, two FOSMOs are developed to estimate the capacity and internal resistance of the lithium cell. The control signals of the observers are continuous by utilizing fractional-order sliding manifolds without low-pass filters. Compared with the existing sliding-mode observers for SoC and SoH, weaker chattering, faster response, and higher estimation accuracy are obtained in the proposed method. Finally, the experiment tests demonstrate the validity and feasibility of the proposed observer design method.

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the multioutput convolved Gaussian process (MCGP), a machine learning framework for multitask and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells into latent functions. The latent functions are then convolved with optimized kernel smoothers to reconstruct and forecast the capacity trends. The latent functions capture nontrivial cross correlations between the capacity trends of the available battery cells. The MCGP also provides uncertainty quantification for its predictions. These two merits make the proposed MCGP a very reliable and practical solution for applications that use battery cell packs. The MCGP is derived and compared to benchmark methods on two experimental lithium-ion battery cells datasets. The results show the effectiveness of the proposed MCGP for long-term capacity forecasting. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation, this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model.

State-of-charge (SOC) prediction is an important part of the battery management system (BMS) in electric vehicles. Since external factors (voltage, current, temperature, arrangement of the battery, etc.) impact SOC prediction differently, the SOC is difficult to model. In this article, we apply principal component analysis (PCA) to analyze the contribution of various external factors and propose a new SOC prediction method based on an improved support vector machine for regression (SVR) with data classification and training set size optimization. Three groups of simulation experiments with different inputs based on the original SVR algorithm are conducted in the software ADVISOR, and the simulation results show that the input of three features of the battery (current, voltage and temperature) can satisfy the SOC prediction accuracy. The improved SVR algorithm is then applied to the simulation experiment of the three input features. The proposed method is demonstrated to be faster and more accurate than the original SVR algorithm through a comparison of the simulation results.

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.

Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter(AUKF) and least-squares support vector machine(LSSVM) is proposed. This article improves the accuracy of SOC estimation from four aspects. Firstly, the nonlinear relationship between SOC, current, and voltage is established by LSSVM. Secondly, a novel voltage estimation method based on improved LSSVM is proposed. Thirdly, the measurement equation of the novel AUKF is created by the improved LSSVM. Finally, the effectiveness of the proposed model is verified under different driving conditions. The comparison results show that the model can improve the accuracy of voltage and SOC estimation, and the SOC estimation error is controlled within 2%.

The successful transfer toward green renewable energy depends heavily on good, reliable Energy Storage Systems (ESS). Lithium-ion batteries are the preferred choice for many applications; however, they need careful management, especially an accurate State-Of-Charge (SOC) estimation. Hence, in this paper an overview of some SOC estimation methods is briefly described; then, an automated battery cell test system prototype that will enable further improvement is designed and implemented. Some tests are conducted on an aged lithium-ion cell and the obtained results are satisfactory and accurate with an error of around 0.5 × 10−3 (V or A), thus validating the proposed prototype.

Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert’s Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert’s Law to better model the rate-capacity effect of the batteries.

The growing popularity of plug-in electric vehicle (PEV) around the world makes complexity in power sector. The distribution system is subjected to overload due to the random penetration of PEVs in charging depending on their level of state-of-charge (SOC). The accurate calculation and prediction of SOC considering their travel distance makes significant impact on the level of SOC. Therefore, the accurate SOC prediction of PEVs is need of the hour in transportation sector. However, the prediction of SOC allows the PEVs owners to decide the charging/discharging mode or priority based charging. Recently, machine learning techniques are gaining popularity in prediction analysis of different parameters. This article proposes machine learning approaches in combination with Bayesian optimization (BO) for prediction analysis of PEVs SOC. The gradient boosting method (GBM) and random forest method (RFM) are used as machine learning approaches in this work. The energy consumption pattern, different battery capacities and total trip distance of PEVs are included in calculation for the estimation of accurate SOC. A satisfactory result of SOC prediction has been observed using both GBM-BO and RFM-BO. The comparative study of results reveals the performance and efficacy of GBM-BO against RFM-BO in the PEVs SOC prediction analysis. Moreover, the hybrid machine learning techniques with BO performs better than individual machine learning techniques in the prediction analysis of PEVs SOC. [ABSTRACT FROM AUTHOR]

This article presents a hierarchical state-of-charge (SOC) balancing control method for a battery energy storage system. In the presented system, multiple battery cells are connected in-parallel at the inputs of a single-inductor multiinput single output (SI-MISO) power converter to form a battery module and multiple battery modules are connected in series at the output to form the complete battery system or pack. The presented hierarchical controller is able to control the SOC (achieve SOC balancing) at both the cell level and the module level (between all cells that are effectively connected in series and in-parallel) while simultaneously maintaining bus voltage regulation at the battery system or pack output. This is achieved by using the presented modulation scheme for the duty cycles of the power converter switches. Theoretical analysis and results from a proof-of-concept experimental prototype are presented and discussed in this article in order to evaluate and validate the operation of hierarchical SOC balancing controller and battery system. [ABSTRACT FROM AUTHOR]

Battery energy storage systems are widely used in energy storage microgrids. As the index of stored energy level of a battery, balancing the State-of-Charge (SoC) can effectively restrain the circulating current between battery cells. Compared with passive balance, active balance, as the most popular SoC balance method, maximizes the capacity of the battery cells and reduces heat generation. However, there is no good solution in the battery management system (BMS) to ensure active balance during distributed charging. In view of this, this paper designs two novel distributed charging strategies based on a kind of smart leader, in which a constant static leader is modified by a dynamic leader. The modified leader is in charge of guiding SoC to converge to the target value and repress SoC imbalance. The maximum and weighed error between the state of the leader and its neighbor cells are used in the two methods, respectively, both in an event triggered manner. When the relevant index exceeds the threshold, the two methods are used to regulate the leader’s state. Under this modification, the eigenvalue of the followers’ error dynamic system is reduced, and SoCs follow the dynamic leader faster, thus repressing SoC imbalance. Compared with a constant leader, the smart leader pays more attention to improving SoC imbalance. Additionally, to facilitate analysis, a reduced method is applied to transform the system with an unified input time delay into a nondelay system. Several cases are designed to verify the effectiveness of the designed strategies and test it under different parameters and different time delays.

This article presents the fuzzy-based charging–discharging control technique of lithium-ion battery storage in microgrid application. Considering available power, load demand, and battery state-of-charge (SOC), the proposed fuzzy-based scheme enables the storage to charge or discharge within the safe operating region. Various controlling techniques have been implemented to evaluate and control the battery performance, which has the limitation of controlling overcharging or overdischarging, complexity in control, and longer charging time. Besides, a fuzzy controller is less complex and faster, as it obviates the extra sensing components, requires no additional deep discharging and overcharging protection, and easy to implement due to the absence of mathematical calculation. The numerical simulations with the load demand and the generations demonstrate the effectiveness of the proposed charging–discharging controller strategy. The efficacy of the proposed controller is tested under certain load variations for real-time application. The obtained experimental result shows that the developed model can control the battery charging–discharging efficiently. Moreover, it is also seen from the output that the battery SOC does not go beyond the limit of the respective safe battery operating region (20%–80%). Thus, the main contribution of this research is to develop an improved fuzzy model and, thus, implement the system for real-time application to control the charging–discharging of the battery. [ABSTRACT FROM AUTHOR]

For lithium-ion batteries used in the electric vehicles, accurate prediction of capacity and remaining useful life online is extremely important. However, most of the research works focus on the prediction accuracy but neglect the complexity of the test environment, which makes many methods show poor robustness in application. To solve the problem, in this article, we first introduce the simultaneous input and state estimation algorithm into the online prediction of state-of-charge and capacity, and combine the Gauss–Hermite extended particle filter to predict the remaining useful life. By setting different gradients of state noises in experiments, the proposed algorithm demonstrates the best accuracy and robustness in comparison with other algorithms. Through the two-factor authentication in simulations, the maximum error of capacity estimation is 35 mAh. For the prediction of remaining useful life, the minimum relative error of the proposed method is 0.4%. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

This article proposes an electric vehicle (EV) onboard microgrid for battery module balancing and vehicle-to-grid (V2G) applications. The proposed microgrid is formed by an onboard photovoltaic (PV) system, a bidirectional charger, an auxiliary power module (APM), and selection switches. The system is designed to use solar energy if available for battery balancing by supporting the battery modules with low state-of-charge (SOC) during driving or charging. During charging, when the battery pack is fully charged, the PV system is disconnected from the battery and delivers the solar power to the grid. When the number of these PV-assisted EVs is big enough, they can work together as an aggregated virtual solar farm with energy storage. When there is no solar energy available, the battery management system is able to use the APM for battery management. Simulation-based case studies with recorded solar irradiance data in Detroit, Michigan, show that the virtual solar farm with 10,000 EVs can generate 0.7 MW peak power and 2,953 kWh energy on a sunny winter day, and 1 MW peak power and 5,190 kWh energy on a sunny summer day. The actual experiments verify that the proposed system can perform the designed functions and automatically switch among the required operating modes. [ABSTRACT FROM AUTHOR]

As batteries become more prevalent in grid energy storage applications, the controllers that decide when to charge and discharge become critical to maximizing their utilization. Controller design for these applications is based on models that mathematically represent the physical dynamics and constraints of batteries. Unrepresented dynamics in these models can lead to suboptimal control. Our goal is to examine the state-of-the-art with respect to the models used in optimal control of battery energy storage systems (BESSs). This review helps engineers navigate the range of available design choices and helps researchers by identifying gaps in the state-of-the-art. BESS models can be classified by physical domain: state-of-charge (SoC), temperature, and degradation. SoC models can be further classified by the units they use to define capacity: electrical energy, electrical charge, and chemical concentration. Most energy based SoC models are linear, with variations in ways of representing efficiency and the limits on power. The charge based SoC models include many variations of equivalent circuits for predicting battery string voltage. SoC models based on chemical concentrations use material properties and physical parameters in the cell design to predict battery voltage and charge capacity. Temperature is modeled through a combination of heat generation and heat transfer. Heat is generated through changes in entropy, overpotential losses, and resistive heating. Heat is transferred through conduction, radiation, and convection. Variations in thermal models are based on which generation and transfer mechanisms are represented and the number and physical significance of finite elements in the model. Modeling battery degradation can be done empirically or based on underlying physical mechanisms. Empirical stress factor models isolate the impacts of time, current, SoC, temperature, and depth-of-discharge (DoD) on battery state-of-health (SoH). Through a few simplifying assumptions, these stress factors can be represented using regularization norms. Physical degradation models can further be classified into models of side-reactions and those of material fatigue. This article demonstrates the importance of model selection to optimal control by providing several example controller designs. Simpler models may overestimate or underestimate the capabilities of the battery system. Adding details can improve accuracy at the expense of model complexity, and computation time. Our analysis identifies six gaps: deficiency of real-world data in control literature, lack of understanding in how to balance modeling detail with the number of representative cells, underdeveloped model uncertainty based risk-averse and robust control of BESS, underdevelopment of nonlinear energy based SoC models, lack of hysteresis in voltage models used for control, lack of entropy heating and cooling in thermal modeling, and deficiency of knowledge in what combination of empirical degradation stress factors is most accurate. These gaps are opportunities for future research.

Cell state-of-charge (SoC) balancing within a battery energy-storage system (BESS) is the key to optimizing capacity utilization of a BESS. Many cell SoC balancing strategies have been proposed; however, control complexity and slow SoC convergence remain as key issues. This paper presents two strategies to achieve SoC balancing among cells: main balancing strategy (MBS) using a cascaded hybrid modular multi-level converter (CHMMC) and a supplementary balancing strategy (SBS) using a cascaded parallel modular dual L-bridge (CPMDLB). The control and monitoring of individual cells with a reduction in the component count and the losses of BESS are achieved by integrating each individual cell into an L-bridge instead of an H-bridge. The simulation results demonstrate a satisfactory performance of the proposed SoC balancing strategy. In this result, SoC balancing convergence point for the cells/modules is achieved at 1000 min when cell-prioritized MBS-CHMMC works without SBS-CPMDLB and at 216.7 min when CPMBS-CHMMC works together with SBS-CPMDLB and when the duration required reduces by 78.33 %. Similarly, a substantial improvement in SoC balancing convergence point for the cells/modules is achieved when module-prioritized MBS-CHMMC works together with SBS-CPMDLB; the duration needed to reach the SoC balancing convergence point for the cells/modules is achieved after 333.3 and 183.3 min.

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

A state-of-charge (SOC) balancing method which accounts for state-of-health (SOH) status of battery cells is presented in this article. The data collected from aging experiments conducted in the laboratory indicates that there is correlation between the minimum impedance of the battery and capacity fading which can be used to predict capacity capability of the battery. However, this relationship is complex and nonlinear. In this article, artificial neural network (ANN) is utilized to learn this relationship and an ANN-based capacity estimator is developed to predict available capacity of battery cells. An online impedance measurement method with improved measurement resolution is presented to obtain a more accurate minimum impedance for the ANN-based capacity estimator. The estimated capacity from the ANN capacity estimator is fed to an SOC balancing controller to calculate SOC values for the battery cells. A battery cell with worse health has lower available capacity than a battery cell with better health, and, therefore, its SOC value is adjusted to a smaller value when SOH or capacity estimation functionality is activated. Because of this mechanism and the control principle of the presented SOC balancing controller, the system draws energy at a slower rate from the battery cell(s) with lower SOH and draws energy at a faster rate from the battery cell(s) with higher SOH such that all battery cells reach their end of discharge at the same time. This results in extending operation time of the system, makes best use of energy from every battery cell, and avoids over-discharging battery cells with lower SOH. The presented method is evaluated using results obtained from a laboratory experimental setup. [ABSTRACT FROM AUTHOR]

This work investigates the real-time estimation of the state-of-charge (SoC) of Lithium-ion (Li-ion) cells for reliable, safe and efficient utilization. A novel attractive ellipsoid based sliding-mode observer (AESMO) algorithm is designed to estimate the SoC in real-time. The algorithm utilizes standard equivalent circuit model (ECM) of a Li-ion cell and provides reliable and efficient SoC estimate in the presence of bounded uncertainties in the battery parameters as well as exogenous disturbances. The theoretical framework of the observer design is not limited to the SoC estimation problem of Li-ion cell but applicable to a wider class of nonlinear systems with both matched and mismatched uncertainties. The main advantage of the proposed observer is to provide a fast and optimal SoC estimate based on minimization over the uncertainty bound. The proposed method is experimentally tested and evaluated for a range of temperatures using the hybrid pulse power characterization test (HPPC), EPA's Federal Test Procedure (FTP75) and Supplemental Test Procedure (US06) data, which demonstrate its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

It is preferable for the retired batteries to balance their states-of-health (SOH) in the battery energy storage system (BESS) since it can prolong the system lifetime and reduce the maintenance burden. So far, the corresponding balancing techniques mainly focus on either the SOH balancing among packs or the SOH balancing of cells inside a pack. This article further proposes the multilayer SOH equalization scheme to equalize all cells’ SOHs of large-scale BESS by comprehensively combining the pack SOH balancing strategy and the commercial cell equalization techniques. It is noted that the balancing schemes for pack and cell cannot be simply superimposed since packs and cells have totally different connection modes and are equipped with different types of balancing circuits. In specific, the modular multilevel converter (MMC) is assumed to coordinate the pack and cell SOH equalization schemes, where the charging/discharging power per submodule is properly adjusted according to the extent of cell SOH deviation and the extent of pack SOH deviation together. MATLAB simulation and experimental results of a 10-kWh MMC-BESS prototype verified the performance of the proposed method. [ABSTRACT FROM AUTHOR]