State Of Charge Range Research Articles

Energy management strategy and operation strategy of hybrid energy storage system to improve AGC performance of thermal power units

With the continuous increase of the renewable energy power generation penetration, the intermittency and fluctuation of renewable energy power generation make the traditional thermal power units (TPU) which play the main role of regulation need to undertake more frequent and more severe regulation tasks. In order to improve the automatic generation control (AGC) command response capability of TPU, an operation strategy of hybrid energy storage system (HESS) is proposed in this paper. While assisting TPU to complete the regulation tasks, it gives full play to the advantages of power-type and energy-type energy storage. Moreover, an energy management strategy of energy storage array (ESA) is proposed to improve the overall operation efficiency of ESA while making the state of charge (SOC) of all energy storage subunits consistent. Finally, the effectiveness of the proposed strategy is verified by simulation experiments. The results show that after using HESS to assist TPU operation, the comprehensive regulation performance index of TPU increased from 4.0198 to 7.8112, which significantly improved the operational flexibility of TPU. Meanwhile, the strategy proposed in this paper makes different types of energy storage systems in HESS operate in a relatively healthy SOC range, and the SOC of the flywheel energy storage system (FESS) reaches the limitation value decrease from 6 times to once, ensuring the overall charge and discharge capacity of HESS.

Optimal hydrogen-battery energy storage system operation in microgrid with zero-carbon emission

To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources, energy storage systems are being deployed in microgrids. Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day. To mitigate this challenge, an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed, with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses. The SoC ranges of the battery energy storage (BES) are determined in the day- ahead stage. Concurrently, the power generated by fuel cells and consumed by electrolysis device are optimized. This is followed by the intraday stage, where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized. To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage, we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG). Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.

Online state of charge estimation of LiFePO4 battery based on EKF-AUKF algorithm with reference compensation for estimation results

The state of charge(SOC) is an important index in Battery Management System(BMS) for smart grids and electric vehicles, whose accuracy is affected by model and sensor errors. In this paper, an online SOC estimation method based on EKF-AUKF algorithm with reference compensation for estimation results is proposed. Firstly, a joint estimation method based on extended Kalman filter(EKF) and adaptive unscented Kalman filter(AUKF) algorithm is adopted to estimate SOC under Beijing bus dynamic stress test(BBDST) condition. The result shows that the estimation error is within 2 %. Secondly, the characteristics of EKF-AUKF algorithm under voltage and current sampling errors are analyzed over the whole SOC range. The results indicate that voltage measurement error increases SOC estimation error. Then, an online reference is built by calculating the sum of the estimation result of ampere-hour counting(AHC) method and constant deviation, and then the difference between the estimation result of EKF-AUKF algorithm and online reference can be obtained. The absolute value of the difference is used to compare with limit deviation value and compensate the SOC of EKF-AUKF method by different compensation coefficients. Finally, the effectiveness of the proposed method is verified under different operating conditions. The experimental results show that the proposed method can greatly improve the SOC estimation accuracy under large voltage error.

Magnetic Field Mapping-Based Battery Management System (BMS) for Lead Acid Application

Lead Acid Batteries (LAB) are a widely used technology in Energy Storage Systems (ESS) due to their abundant and low-cost materials, non-flammable water-based electrolyte, and high recyclability rate. The main applications for LABs are in mobility, data centers, and telecommunications. According to the Consortium of Battery Innovation (CBI), the market for lead acid batteries is projected to increase 150 GWh between 2025 and 2030. Therefore, it is fundamental to identify emerging failure modes in LABs in ESS systems to reduce battery degradation and to extend the use life.An important failure mode in flooded LABs is acid stratification. This research explores H+ concentration changes in H2SO4 (sulfuric acid) electrolyte as a correlation of acid stratification. The objective is to develop a non-invasive, non-destructive, and accurate real-time battery monitoring system. The Magnetic Field Probing (MFP) technique, popular in the medical imaging field, shows a promising solution for ESS performance evaluation. This technique can measure induced magnetic field changes based on variation in H+ concentration in the cell during cycling. See Figure. 1 (a). MFP provides early onset notification of stratification in flooded LABs with capability to discern other failure modes in non-flooded lead batteries.A set of experimental trials measured pH and magnetic field variation of H2SO4 electrolyte. See Figure. 1 (a). The preliminary results demonstrated changes in electrolyte pH due to possible stratification. See Figure. 1 (b). A correlation between the output voltage resulting from the interaction of the induced magnetic field and the changes in the electrolyte concentration was found. See Figure 1(c) The optimal AC input frequency maximizes the induced magnetic field response. The value found for optimal AC input frequency was 32kHz, which depends on the physical properties of the magnetic field inductors (air core solenoid coils). The experimental results showed and concluded that the induced magnetic field across the tested cell is also inversely proportional to the distance between coils attached to the cell.Further research involves the use of magneto-resistors to establish a magnetic field mapping in lead acid cells as they are being cycled. Magnetic field response is measured while lead acid cells are cycled from 100% to 20% state of charge range. In the future, this study will provide insights and findings to model and predict cell stratification in lead acid cells with electrochemical computational simulations.Keywords: Energy storage systems, lead acid batteries, acid stratification, magnetic field, non-invasive method Figure 1

Optimal Power Model Predictive Control for Electrochemical Energy Storage Power Station

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge (SOC) constraint of the battery, partition the SOC into different states. Using SOC as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery SOC to prevent SOC from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station.

Study on Real‐Time Temperature of a 35 kW Vanadium Stack and Its Influences on the Performance of a Vanadium Redox Flow Battery

AbstractThe temperature is a very important parameter for an operating vanadium redox flow battery (VRFB). During charging and discharging, the temperature of VRFB is constantly changing. In this paper, a self‐made 35 kW vanadium stack was charged & discharged at the current density of 100 and 120 mA cm−2 to investigate the change trend of real‐time operating temperature. The results show that the temperature decreases during charging and increases during discharging. And the average temperature of each cycle increases with continuous charge‐discharge cycles. The influences of real‐time temperature on performance of columbic efficiency (CE), voltage efficient (VE), over‐potential, capacity, energy, state of charge (SOC), and pressure loss were further investigated. The results show that the temperature fluctuation largely affects the electrochemical properties of VRFB. With the real‐time average temperature increased from 16.02 °C to 29.91 °C, the VE, capacity, energy and the SOC range of the battery increased, while the CE, the over‐potential and the average pressure loss decreased. This work not only gives data support to temperature management of VFB, but also provides direction to the engineering application of all flow batteries.

Study on the estimation of the state of charge of lithium-ion battery

Lithium-ion battery (LIB) occupies a major position in rechargeable batteries. However, if the state of charge (SOC) estimation of LIB is not accurate, it will lead to the decline of battery life. SOC is often estimated by the extended Kalman filter (EKF) algorithm, but this method depends on the accuracy of model parameters. The sampling point interval time can affect the accuracy of SOC estimation. In this paper, the first-order RC model parameters are identified by two methods and the SOC is estimated by the EKF algorithm. Using this method, the battery parameters are received more accurate and the SOC estimation error is smaller. When the sampling interval is 0.03 s, the SOC estimation error is the smallest. And the second-order RC model can receive the same result. When the sampling interval is 0.03 s, the SOC estimation error of the 1RC-1 method is less than 1.4 %, the SOC estimation error of the 1RC-2 method is less than 0.017, and the root mean square error (RMSE) is 0.0087 and 0.01084. The conclusion is also used in the second-order RC model and contributes to the improvement of the SOC estimation accuracy. An effective method to estimate the battery SOC range is also proposed. This paper provides an effective method for obtaining more accurate SOC.

Novel battery power capability assessment for improved eVTOL aircraft landing

A key aspect in the deployment of electric vertical take-off and landing (eVTOL) aircraft is the safety and performance capabilities of the batteries used. A component of the safety requirements is the need for reserve energy, which would only be utilised in case of emergencies. In the literature, it has been observed that the lower bound of the reserve region of battery energy should be limited to avoid the area where a precipitous drop in voltage would occur. Here, a method for defining the lower bound is proposed. This seeks to extend the amount of time the aircraft can cruise before landing can no longer be completed. A novel power capability testing procedure is used to measure the lowest state-of-charge (SoC) at which a constant power pulse can be completed. This differs from existing power capability tests that perform pulses at predetermined SoC points. The goal of the proposed method is to replicate the conditions of landing to understand the power capability performance at low SoC. The test is performed for a variety of environmental conditions and use cases, which includes temperature and power pulses, as well as two sets of differently aged cells. For the defined test conditions, the lowest SoC values for the calendar aged cells ranges from 6% to 14% SoC, whereas the range for cycle aged cells is 8% to 27% SoC. The outcome of this test is a characteristic map which correlates temperature, pulse power and pulse duration to the lowest SoC the pulse can be completed. The characteristic map indicates the lowest SoC value allowed for the battery before landing needs to be performed. The accuracy of the characteristic map is compared to a battery equivalent circuit model, parametrised from the test data. The defined approaches are experimentally validated based on a set of previously unmeasured experimental conditions. Overall, the characteristic map provides good accuracy compared to measured values, with both map and model methods having an average maximum absolute percentage error of at most 7.5%. Additionally, the test results show that if the worst case landing scenario is used as the lower bound of the reserve region, the useable SoC range for nominal flight would be greatly affected if cell degradation is not considered.

Analysis of Battery Testing Protocols in the Transition from the Lab to the Field: A Test Case Using Advanced Zn-MnO2 Batteries in Off-Grid Solar Microgrids

Understanding cycling performance of batteries under varying conditions is crucial for continued development of emerging chemistries. Currently much of this work is focused on cycling single cells in well-controlled lab experiments. There are few module cycling studies in the open literature. Studies of full-sized fielded systems are even rarer. As a result of this data gap, there is limited understanding of how single cell cycling can be used to predict performance of fielded systems prior to their deployment.Sandia National Laboratories (SNL) recently deployed an advanced Zn-MnO2 secondary battery energy storage system in an off-grid solar plus storage system. Using this deployment as a test case, we will detail how performance and degradation for batteries can vary between the lab and the field. We will also discuss ways that single cell and module lab cycling can be improved to better predict operation in fielded systems.In preparation for this deployment, SNL conducted single cell cycling experiments in the lab to simulate operation in the field and determine the expected life of the systems. The first test program utilized elements of the IEC 61427-1 standard for photovoltaic off-grid application to simulate the temperature and predicted state of charge (SOC) range the cells will be cycled during field operation. The simulated field cycling targeted SOC ranges of 10 to 40% and 80-100% over 150 cycles with temperature varied between -4 and 35oC to match the ambient conditions in the field during each season. The second type of cycling test was a temperature dependence study to determine how temperature impacted cell degradation. Cells were cycled at C/10 at their full SOC range and at a single temperature until they reached 40% capacity retention. Both cycling studies predicted that low temperature operation would increase the rate of degradation.The system was deployed in May of 2022, and since that time the SNL team has collected a significant amount of performance data to help analyze system level behaviors as compared to observations from cell level testing. These include battery interactions with the solar charge controller, limitations on power inverter settings, and variance between the average system level state of charge and the test protocol. Additionally, the team is analyzing conditions found in the field, with regards to ambient temperature, solar insolation and battery charging rates, and customer usage patterns for comparison with cell-level testing protocol.Observations of the deployed system’s performance indicate there is a need for more nuanced testing protocols for lab testing that can better approximate field conditions. These may include dynamic temperature ranges that vary throughout a charge/discharge cycle, and solar charging assumptions that account for reduced insolation due to cloud cover. This can help further inform the development of charge control algorithms, system hardware and software that can help improve the overall performance of solar microgrid energy storage systems.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2023-02502A

Open circuit voltage - state of charge curve calibration by redefining max–min bounds for lithium-ion batteries

The relationship between open circuit voltage (OCV) and state of charge (SoC) is essential for SoC estimation of lithium-ion batteries, which can be secured by either low-current OCV test or incremental OCV test, with incremental OCV test demonstrating better results. Nevertheless, low current always leads to a discharge capacity that is inconsistent with maximum available capacity, which is a key parameter in SoC estimation. Therefore, the resultant OCV-SoC curve fails to provide a consistent SoC range and thus affects SoC estimation. In this paper, OCV-SoC curves obtained from low-current OCV tests are calibrated by redefining max–min bounds to improve SoC estimation accuracy. Max–min bounds of SoC are redefined by measuring, calculating and resetting upper and lower cut-off voltages of the OCV-SoC curve. Based on second-order RC model, model parameters are identified online and adaptive square-root unscented Kalman filter (ASRUKF) algorithm is introduced to perform SoC estimation, which is proved to be precise and adaptive. The calibrated OCV-SoC curve achieves an overall better SoC estimation performance than that obtained from the low-current OCV test and incremental OCV test, which is validated by experiments of LiNiMnCoO2 (NMC) batteries at 0°C and 25°C.

Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel

This paper proposes a multiport energy management system (EMS) and its rule-based expert control strategy for a 150 kW range-extended towing vessel (RETV). The system integrates a diesel generator system, a permanent magnet synchronous motor, a lithium battery, and supercapacitors. To verify its feasibility and effectiveness, the proposed multiport EMS was modelled and tested through MATLAB/Simulink. Simulation results demonstrate that the designed multiport EMS works efficiently under the five typical operating conditions of the 150 kW RETV. In addition, two case studies were conducted and compared to investigate the impact of the battery’s initial state of charge (SoC) on the system’s energy efficiency. It was found that an overall 85% energy efficiency can be achieved for the RETV when the initial SoC is either 75% or 15%. The battery consistently operates within the optimal SoC range of 20% to 80%, and the supercapacitors effectively meet the instantaneous high-power demand.

Optimize the operating range for improving the cycle life of battery energy storage systems under uncertainty by managing the depth of discharge

Globally, renewable energy penetration is being actively promoted by renewable energy 100% (RE100) policies. BESS operators using time-of-use pricing in the electrical grid need to operate the BESS effectively to maximize revenue while responding to demand fluctuations. Battery energy storage (BESS) is needed to overcome supply and demand uncertainties in the electrical grid due to increased renewable energy resources. BESS operators using time-of-use pricing in the electrical grid need to operate the BESS effectively to maximize revenue while responding to demand fluctuations. However, excessive discharge depth and frequent changes in operating conditions can accelerate battery aging. Deep discharge depth increases BESS energy consumption, which can ensure immediate revenue, but accelerates battery aging and increases battery aging costs. The proposed BESS management system considers time-of-use tariffs, supply deviations, and demand variability to minimize the total cost while preventing battery aging. In this study, we investigated a BESS management strategy based on deep reinforcement learning that considers depth of discharge and state of charge range while reducing the total operating cost. In the proposed BESS management system, the agent takes actions to minimize the total operating cost while avoiding excessive discharge depth and low state of charge. A series of experiments using a real BESS demonstrated that the proposed BESS management system has improved performance compared to the existing methods.

Cycle Characterization of SiO-Based Lithium-Ion-Batteries Using Real Load Profiles

Understanding the cyclic behavior of Lithium-Ion Batteries (LIBs) is crucial for optimizing their performance and extending their operational lifespan. This work presents a study on the cycle characterization of silicon-oxide-based (SiOx) cells, focusing on the impact of real load profiles and state-of-charge (SoC) ranges while varying the SiOx content in the cells. Various load profiles representing real usage patterns obtained from an industrial partner were applied to SiO-based pouch cells. These load profiles are represented over different SoC ranges to explore the effect of varying levels of charge/discharge on battery aging. The aging characteristics of the batteries are evaluated by monitoring capacity fade, state-of-health (SoH), and capacity end-point-slippage. The experimental results demonstrate that the different SiOx content of the investigated cells and the SoC range significantly influence the cycle behavior of the cells. The resulting capacity loss was affected especially by the anode overhang effect. Cycling under high SoC conditions accelerates capacity fade and leads to higher SoH loss. The findings also indicate that SiO-based cells exhibited higher aging than traditional graphite-based cells. The capacity fade rate increased at higher SiOx content.

State of charge and power rating gains in industrial-scale vanadium redox flow batteries through thermal activation of electrodes

Vanadium redox flow batteries (VRFB) are typically envisaged for application in large-scale energy storage systems. High-power operation of VRFBs reduces the cost of such installations but also significantly affects the energy storage capacity owing to markdown in the operable range of the state of charge (SoC). This situation can be remedied to some extent by minimizing the area-specific resistance (ASR) of the VRFB cells. In the present study, the application of thermal activation of the electrodes was studied on an industrial-scale VRFB cell with an active electrode area of 936 cm2. The carbon felt electrode was heat treated at 350, 400, and 450 °C for 30 h. Following physical and electrochemical characterization of the heat-treated carbon felt, electrochemical testing of the full cell was carried out through charge-discharge cycling studies conducted at current densities of 60, 90, 120 and 150 mA/cm2. Comparative studies were also conducted with an unactivated carbon felt. Significant reduction in ASR was observed with the optimally heat-treated carbon felt, leading to nearly 20 % expansion in the operating SoC range at a current density of 120 mA/cm2 and 34 % enhancement in the rated power of the cell. Long-term durability studies over 200 cycles and post-test inspection and physical characterization confirmed the stability of thermal activation.

Ageing of High Energy Density Automotive Li-Ion Batteries: The Effect of Temperature and State-of-Charge

Lithium ion batteries (LIB) have become a cornerstone of the shift to electric transportation. In an attempt to decrease the production load and prolong battery life, understanding different degradation mechanisms in state-of-the-art LIBs is essential. Here, we analyze how operational temperature and state-of-charge (SoC) range in cycling influence the ageing of automotive grade 21700 batteries, extracted from a Tesla 3 long Range 2018 battery pack with positive electrode containing LiNixCoyAlzO2 (NCA) and negative electrode containing SiOx-C. In the given study we use a combination of electrochemical and material analysis to understand degradation sources in the cell. Herein we show that loss of lithium inventory is the main degradation mode in the cells, with loss of material on the negative electrode as there is a significant contributor when cycled in the low SoC range. Degradation of NCA dominates at elevated temperatures with combination of cycling to high SoC (beyond 50%).

A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC estimation accuracy within 1%. The results clearly demonstrate that the developed approach achieves twice the accuracy of the existing approach, highlighting its superior effectiveness.

Syllogism of Li-FePO4 Battery Cell Voltage Parameter Guess Under Aperiodic Dynamic Current Profile by Some Data-Driven Techniques: A Error-Based Statistical Comparison Between Decision Tree, Support Vector, Bee Colony, and Neural Network

The various procedures are used in the literature for defining battery parameter change such as direct measurement methods, model-based methods, and data-driven methods, which contain the algorithms used in this paper also. The main aim of this study is to present a powerful and highly correct way of parameter forecasting of the A123 Systems 26650 cylindrical type Li-FePO4 battery cell. A few of the goal of this paper is to show the guessing performance of the artificial bee colony algorithm, which has a very limited number of applications on the battery parameter of literature, under the non-periodic dynamic charge/ discharge current profile. Then, a comparison has been made between artificial bee colony, artificial neural networks, support vector machine, and decision tree algorithms used in the paper. The load-connected terminal voltage is defined by considering the 100%-60% state of charge range in the primary usage areas of the batteries. A statistical comparison has been made by considering the absolute errors, squared errors, and the regression values information regarding the results presented by the methods. Consequently, the regression values that give information about the consistency of the confidence interval and results, of the bee colony, neural network, support vector, and decision tree methods have been determined as 99.92%, 99.75%, 96.00% and 95.79%, respectively. Moreover, mean squared errors of the methods has been calculated as 0.00202%, 0.00648%, 0.00998%, and 0.11%, respectively. As a new generation algorithm, artificial bee colony, which gave the most successful results according to the results obtained in the study, has been compared with two different methods selected from the existing literature, eXtreme Gradient Boosting and Smoothed eXtreme Gradient Boosting.

Power curves of megawatt-scale battery storage technologies for frequency regulation and energy trading

Large-scale stationary battery energy storage systems (BESS) continue to increase in number and size. Most systems have been put into operation for grid services because of their technical capabilities. With increasing and more dynamic energy prices, their use in short-term energy trading such as day-ahead and intraday trading has also been gaining importance. In current technical and economic simulations and trading models, batteries are often used as an energy reservoir that can charge and discharge a constant power specified by the energy over a certain time. However, this simplification can lead to wrong results and makes economic assessments difficult. In order to successfully use BESS in energy trading, their real operating ranges and limits must be investigated, since batteries respectively BESS cannot deliver the same power over the entire state of charge (SOC) range. With a performance test of our hybrid BESS M5BAT, we show the characteristic performance curves for different battery technologies and consequently suitable operating ranges in a large-scale system configuration. The results show the wide range of challenges such as battery aging and balancing states that occur in the real-world implementation of BESS. The lithium-ion batteries of the system under test have a remaining usable energy between 75 % and 90 %, depending on the type of lithium-ion battery, while the usable energy of the lead acid batteries is only 60 %. The lithium-ion batteries were able to deliver a constant power output in the SOC range between 10 % and 80 %, which is a necessary requirement in short-term energy trading. The lead-acid batteries could only be discharged at full power in the range of 100 %–50 % SOC and charged at full power between 0 % and 50 %. In the performance test, balancing was a limiting factor for lithium-ion batteries, while aging was the limiting factor for lead-acid batteries. Based on our findings, estimates for other existing BESS can be made to determine feasible operating ranges of these batteries for short-term energy trading. This also provides a guideline for individual tests that should be carried out on other BESS for verification.

A novel pseudo-open-circuit voltage modeling method for accurate state-of-charge estimation of LiFePO4 batteries

LiFePO4 batteries are widely used in electric vehicles and energy storage due to their high safety. However, their flat voltage characteristics in the middle state of charge (SOC) range make it difficult to correct SOC estimation errors with a feedback mechanism. This study proposes a pseudo-open circuit voltage (OCV) modeling method to improve the performance of a closed-loop feedback correction. First, the relationship between the derivative of OCV and SOC or the slope of OCV, SOC range, and estimation error is analyzed to select the SOC interval for OCV curve construction. Second, a pseudo-OCV curve is established. An optimization algorithm is developed to identify the parameters of the OCV model based on the selected SOC interval and OCV slope, which establishes a pseudo-OCV curve. Third, the pseudo-OCV is compared with the real OCV to adjust the selected interval and further determine the optimal construction interval, then the curves of the pseudo-OCV and real OCV are stitched together to obtain the optimal OCV curve for global SOC estimation. Finally, SOC estimation is carried out using the constructed full pseudo-OCV and compared with the reference SOC obtained from experiments. The results show that the SOC estimation error at the full SOC range is less than 3 %.

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and DC/DC converters. This characteristic enables isolation of faulty cells from the system and allows fine power control for individual cells toward optimal system-level performance. An optimal power management approach is developed to extensively exploit the merits of the proposed design. Based on receding-horizon convex optimization, this approach aims to minimize the total power losses in charging/discharging while allocating the power in line with each cell's condition to achieve state-of-charge (SoC) and temperature balancing. By appropriate design, the approach manages to regulate the power of a cell across its full SoC range and guarantees the feasibility of the optimization problem. We perform extensive simulations and further develop a lab-scale prototype to validate the proposed system design and power management approach.