Stage Of Charge Research Articles

Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

A hybrid energy storage system (HESS) can effectively suppress the high and low-frequency power fluctuations generated by wind farms under the intermittency and randomness of wind. However, for the existing power distribution strategies of HESS, power-type and energy-type energy storage have the problem of inconsistent charge–discharge states in the same time sequence, which makes it difficult to achieve optimal operation in terms of charge–discharge coordination and energy flow. To solve this problem, this study firstly adopts adaptive wavelet packet decomposition (WPD) to decompose the original wind power to acquire grid-connected power and HESS initial distribution power, to ensure that the supercapacitor and battery undertake the corresponding high and low-frequency power fluctuations, respectively; Then, for the inconsistent charge–discharge states, a charge–discharge time sequence optimization strategy based on the consistency index is proposed to correct the initial power distribution of HESS for the first time; Finally, aiming at the stage of charge (SOC) over-limit problem, the fuzzy optimization method is adopted to correct the HESS output power for the second time, which can reduce the unnecessary charge–discharge energy effectively. With typical daily output data of a 100 MW wind farm, the proposed control strategy is verified. The results show that it can make different energy storage technologies synchronously suppress wind power fluctuation in the same time sequence; compared with not considering charge–discharge time sequence optimization, the charge–discharge conversion times of the battery obtained by the proposed method are reduced from 71 to 14 times, and the charge–discharge conversion times of supercapacitor are reduced from 390 to 61 times; The cumulative reduction of unnecessary charge–discharge energy by HESS is 12.12 MWh. Besides, the SOC curves of HESS are controlled at a normal level, thus improving the economy and service life of HESS.

The evaluation of battery pack SOH based on Monte Carlo simulation and support vector machine algorithm

ABSTRACT This paper studies the storage life of ICR18650 lithium-ion batteries at different temperatures and different SOC (stage of charge). The author establishes a storage life prediction model. Based on the empirical model, the author analyzed the relationship between temperature and SOC about the influencing factors through experimental data, and used nonlinear regression fitting to determine the model parameters, and the storage life prediction model of irreversible capacity loss with respect to temperature, SOC, and time is finally established. Based on the differences of monomers and measurement errors during test, an error model was established, and a Monte Carlo simulation test was conducted on the battery storage life. Then we build a SVR model based on the obtained data. According to the SVR model, we can analyze the health state of the battery pack based on the given parameters of battery pack in storage. Thus, we can make a prognosis about the SOH of battery pack quickly and accurately. The innovation of the research is that based on the storage test of battery cells combined with data analysis and deductive simulation, we can predict the health state of the whole battery pack. The operation can greatly save experimental resources.

Mechanical behavior analysis of high power commercial lithium-ion batteries

In application, lithium-ion cells undergo expansion during cycling. The mechanical behavior and the impact of external stress on lithium-ion battery are important in vehicle application. In this work, 18 Ah high power commercial cell with LiNi0.5Co0.2Mn0.3O2/graphite electrode were adopted. A commercial compress machine was applied to monitor the mechanical characteristics under different stage of charge (SOC), lifetime and initial external force. The dynamic and steady force was obtained and the results show that the dynamic force increases as the SOC increasing, obviously. During the lifetime with high power driving mode, different external force is shown to have a great impact on the long-term cell performance, with higher stresses result in higher capacity decay rates and faster impedance increases. A proper initial external force (900 N) provides lower impedance increasing. Postmortem analysis of the cells with 2000 N initial force suggests a close correlation between electrochemistry and mechanics in which higher initial force leads to higher direct current internal resistance (DCIR) increase rate. In addition, for the cell with higher external force, deformation of the cathode and thicker solid electrolyte interface (SEI) film on the surface of anode and separator are observed. Porosity reduction and closure was also verified after cycles which is an obstacle to the lithium ion transferring. The largest cause of the observed capacity decline was the loss of active lithium through autopsy analysis. In addition, for the cell with higher external force, deformation of the cathode and thicker SEI film on the surface of anode and separator are observed. Porosity reduction and closure was also verified after cycles which is an obstacle to the lithium ion transferring. The largest cause of the observed capacity decline was the loss of active lithium through autopsy analysis.

Online Estimation of Internal Short Circuit Resistance for Large-Format Lithium-Ion Batteries Combining a Reconstruction Method of Model-Predicted Voltage

The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery mainly presents abnormal stage of charge (SOC) depletion behaviors, the SOC estimation processes based on state observers and battery models will act an important basis of the ISC resistance estimation problem. However, as it will be exhibited in this paper, when directly using the measured voltage of the ISC battery as the output variable of the state observer, the battery model error will limit the SOC estimation accuracy and further lead to very inaccurate or even divergent ISC resistance estimation results for large-format batteries, which present quite slight SOC depletion behaviors at the ISC state. To this end, this paper proposes a novel SOC and ISC resistance co-estimation method which combines a reconstruction method of the model-predicted voltage of the ISC battery. Experimental validations are carried out with a 37 Ah battery, results show that the proposed method which uses the reconstructed model-predicted voltage (RMPV) as the output variable of the state observer only present maximum estimation errors of 39.96 Ω and 2.00 Ω for the ISC resistances of 100 Ω and 10 Ω, respectively.

DEVELOPMENT OF COST-EFFECTIVE ENERGY MANAGEMENT STRATEGY FOR STAND-ALONE HYBRID SYSTEM

Deployment of Photovoltaic (PV) and Wind Turbine (WT) as a stand-alone system are the most affordable solution to the electrification problem in the rural areas. However, the main challenge that hinder the sustainability of PV and WT is the output fluctuation. Thus, the battery and the non- Renewable Energy (RE) sources are required to make sure the continuous supply to the load is met. Therefore, the purpose of this paper is to propose the rule-based Energy Management Strategy (EMS) for the stand-alone hybrid system in order to ensure the continuity of supply and the minimum utilization of non-RE can be achieved. The continuous monitoring is designed to avoid the degradation of battery performance while minimizing the non-RE cost. The efficient Stage of Charge (SoC) limit of battery is investigated and applied in this study to store the excess RES power effectively. In this paper, the diesel fired Microturbine (MT) is selected as a non-RE source to integrate into the stand-alone hybrid system that consists of PV, WT and battery. The result shows that the proposed rule-based EMS for the in the stand-alone hybrid system is able to reduce the cost of MT operation to a minimum. Restricting the SoC limits effects the use of MT.

Lithium-ion battery capacity estimation based on open circuit voltage identification using the iteratively reweighted least squares at different aging levels

In traditional incremental SOC (stage of charge)-capacity method, errors of the selected SOC points inevitably lead to inaccurate capacity estimation. In this paper, a capacity estimation method based on OCV (open circuit voltage) identification and IRLS (iteratively reweighted least squares) algorithm method is proposed. Firstly, the accuracy and complexity of three parameter identification schemes of first-order RC model at different aging levels are studied with the forgetting factor recursive least squares algorithm. Through the online identified OCV by the optimal scheme, the full interval SOC estimation can be obtained indirectly. Finally, the capacity is estimated using the full interval SOC estimation with IRLS algorithm, which avoids the randomness of the SOC point selection, and solves the problem of possible outliers in the SOC estimation or the unknown variance of the random error of the SOC gain. Through the designed aging experiment, the capacity estimation accuracy is verified under different life conditions. The results show that the estimation accuracy of this method is high and the error does not exceed 2.5%.

Experimental study on thermal runaway of fully charged and overcharged lithium-ion batteries under adiabatic and side-heating test

In this work, the thermal runaway (TR) hazards of a 4.5 Ah 21,700 cylindrical lithium-ion battery (LIB) are investigated at 100%, 110% and 120% stage of charge (SOC). The TR behavior of the sample at elevated temperature is characterized using an extended volume accelerating rate calorimetry and a customized battery test canister. The results show that the time interval from the LIB voltage drop to the initial of TR increases with the increase of SOC. The time interval for the LIB with 120% SOC is significantly greater than 100% and 110% SOCs, which is meaningful for the early warning of TR. In adiabatic or side-heating test, the initial temperature of TR of LIBs at different SOCs is almost the same, but the temperature at voltage drop shows a downward trend as the adding SOC. The separator thermal stability of slightly overcharged LIB is lower than fully charged LIB according to the SEM and DSC tests. Besides, the TR hazards of the LIB are recommended to be determined by multiple factors, including pressure, ambient temperature and battery surface temperature.

Sustainable City: Energy Usage Prediction Method for Electrified Refuse Collection Vehicles

With the initiative of sustainable smart city space, services and structures (3S), progress towards zero-emission municipal services has advanced the deployment of electric refuse collection vehicles (eRCVs). However, eRCVs are commonly equipped with oversized batteries which not only contribute to the majority of the weight of the vehicles but also remain a consistent weight, independent of the stage of charge (SoC), thus crucially jeopardising the significance of eRCVs in sustainability and economic strategies. Hence, customising the battery capacity in such a way that minimises its weight while storing ample energy for stalwart serviceability could significantly enhance its sustainability. In this study, taking only addresses as input, through an emergent two-stage data analysis, the energy required to collect refuse from a group of addresses was predicted. Therefore, predictions of the battery capacity requirement for the target location are possible. The theories and techniques presented in this paper were evaluated using real-life data from eRCV trials. For the same group of addresses, predicted results show an averaged error rate of 8.44%, which successfully demonstrates that using the proposed address-driven energy prediction approach, the energy required to collect refuse from a set of addresses can be predicted, which can provide a means to optimise the vehicle’s battery requirement.

The investigation of thermal runaway propagation of lithium-ion batteries under different vertical distances

The time of safety valve cracks and thermal runaway propagation are influenced seriously by vertical distance and the stage of charge (SOC). In this study, a series of experiments with four lithium-ion batteries has been finished to evaluate the fire hazards by in situ calorimeter. The temperature of safety valve crack is inversely proportional to the SOC. For upper batteries of 0, 50 and 100% SOC, the safety valve was not cracked at 3, 5 and 8 cm, respectively. The safety valve crack time is an exponential function of distance for 50% and 100% SOC. The SO2 can be obviously found under a high temperature for the fully charged batteries.

Power Distribution Strategy of Microgrid Hybrid Energy Storage System Based on Improved Hierarchical Control

Traditional hierarchical control of the microgrid does not consider the energy storage status of a distributed hybrid energy storage system. This leads to the inconsistency of the remaining capacity of the energy storage system in the process of system operation, which is not conducive to the safe and stable operation of the system. In this paper, an improved hierarchical control strategy is proposed: the first allocation layer completes the allocation between the distribution energy storage systems considering the state of hybrid energy storage systems, and the second allocation layer realizes the allocation within the hybrid energy storage systems based on variable time constant low-pass filtering. Considering the extreme conditions of energy storage systems, the transfer current is introduced in the second allocation process. The SOC (stage of charge) of the supercapacitor is between 40% and 60%, which ensures that the supercapacitor has enough margin to respond to the power demand. An example of a 300 MW photovoltaic microgrid system in a certain area is analyzed. Compared with the traditional hierarchical control, the proposed control strategy can reduce the SOC change of a hybrid energy storage system by 9% under the same conditions, and make the supercapacitor active after power stabilization, which is helpful to the stable operation of the microgrid.

Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm

Multiple battery energy storage systems (BESSs) are used to compensate for the fluctuation in wind generations effectively. The stage of charge (SOC) of BESSs might be unbalanced due to the difference of wind speed, initial SOCs, line impedances and capabilities of BESSs, which have a negative impact on the operation of the wind farm. This paper proposes a distributed control of the wind energy conversion system (WECS) based on dynamic average consensus algorithm to balance the SOC of the BESSs in a wind farm. There are three controllers in the WECS with integrated BESS, including a machine-side controller (MSC), the grid-side controller (GSC) and battery-side controller (BSC). The MSC regulates the generator speed to capture maximum wind power. Since the BSC maintains the DC link voltage of the back-to-back (BTB) converter that is used in the WECS, an improved virtual synchronous generator (VSG) based on consensus algorithm is used for the GSC to control the output power of the WECS. The functionalities of the improved VSG are designed to compensate for the wind power fluctuation and imbalance of SOC among BESSs. The average value of SOCs obtained by the dynamic consensus algorithm is used to adjust the wind power output for balancing the SOC of batteries. With the proposed controller, the fluctuation in the output power of wind generation is reduced, and the SOCs of BESSs are maintained equally. The effectiveness of the proposed control strategy is validated through the simulation by using a MATLAB/Simulink environment.

Progressive intelligence estimation of SOC based on multiple models

In order to accurately estimate stage of charge(SOC) of the electric car lithium-ion battery, the paper used a variety of equivalent circuit model and built space state equation, through real-time online with forgetting factor recursive least squares identification battery model parameters, dynamic real-time update battery model state equation, the experimental condition to make use of the Matlab simulation, based on the battery circuit model of joint (FFRLS - EKF) algorithm, and joined the battery stage of health(SOH), The average error of the obtained SOC estimate is less than 1.8i% and the maximum error is less than 3%.Finally, the accuracy of FFRL-SEKF-SOC is verified, and the error accumulation problem is solved.

Multisource Coordination Energy Management Strategy Based on SOC Consensus for a PEMFC–Battery–Supercapacitor Hybrid Tramway

For the sake of coordinating multiple energy sources appropriately from power demand and guarantee stage of charge (SOC) consensus of the energy storage systems in different operation conditions, a multisource coordination energy management strategy based on self-convergence droop control is proposed for a large-scale and high-power hybrid tramway. A hybrid powertrain configuration that includes multiple proton exchange membrane fuel cell systems, batteries, and supercapacitors is designed for a 100% low-floor light rail vehicle (LF-LRV) tramway. According to the hybrid system model of LF-LRV tramway developed with commercial equipment, this proposed multisource coordination energy management strategy is assessed with a real driving cycle of tramway. The results obtained from RT-LAB platform testify that the proposed strategy is capable of coordinating multiple energy sources, guaranteeing the SOC consensus and improving the efficiency of overall tramway.

The Impact of Control Strategies on the Performance and Profitability of Li-Ion Home Storage Systems

An increase in electricity prices along with a decrease in the price of storage systems has led to rapid expansion of the PV-battery home storage system market in Germany. In order to be economically viable PV-storage systems must fulfill certain performance criteria, and in this context the system control strategy has a large impact on the overall system performance. In a nutshell, the control software should regulate the system in such a way as to maximize the self-supply ratio as well as the battery lifetime. An intelligent control strategy also has added benefits for the grid operator.At KIT 20 commercially available PV-battery systems have been analyzed with respect to specific performance criteria: of those the following were used to quantify the level of intelligence of the storage system control software: (a) whether the software contains a predictive module at all, and if so whether it depends on external weather data; (b) the success of delayed charging in reducing the time spent at high stage of charge (SOC) levels and (c) whether prediction errors lead to the battery not being fully charged so that the user’s self-sufficiency is unnecessarily reduced. Since the different effects are not independent the goal is to quantify the effects on system performance and profitability in each case. This shows the effect of software on the overall economics of energy storage systems and complements other studies based on simulation or those that look at different aspects like cell aging in an isolated context.Roughly one quarter of the systems tested have an intelligent algorithm that controls the battery charging in such a way as to minimize calendar aging. In addition, differences of up to two years in battery lifetime are shown using voltage measurements with realistic household profiles and measured PV data. In this way, the present work outlines how control software can influence the performance and in particular the calendar aging of PV storage systems.

Adaptive Battery Equalization Algorithm for Capacitor-based Battery Management System

In recent years, as energy-saving and environmental protection were widely concerned around the world, the lithium iron phosphate (LFP) battery has been widely used in hybrid electric vehicle (HEV) and electric vehicle (EV). Generally the battery pack for HEV is composition of the number of cells in series. In this case, imbalance among cells due to the difference of degradation and temperature will be accelerated by the cycles of charge and discharge without an appropriate battery equalization management system. However, the balancing system only intended to reduce the difference of cell voltage or Stage of Charge (SoC) before. Adaptive Battery Equalization Algorithm for Capacitor-based Battery Management System this thesis proposed re-explains the meaning of battery equalization. Instead of long-term using battery equalizer on standby, this thesis equalizes the battery when charging to save much spending time. It could not only adjust the balancing mechanism automatically in order to keep the available charge but also raise the average SoC to prevent the effect of battery aging and improve the cycle number of battery. Experiment results indicate that the available charge increases 1.5% and the battery capacity efficiently improves 7.3%.

Study on the correlation between state of charge and coulombic efficiency for commercial lithium ion batteries

Coulombic efficiency (CE) is an important parameter for battery cells. Unfortunately, precise measurement of CE is extremely difficult, so CE is seldom focused during cell performance study. Nevertheless, correlation between stage of charge (SOC) and CE has significant impact on battery pack performance, while the influences of different correlations between SOC and CE on battery packs have never been explored as far as we know. We present a novel method which implements two series-connected cells that can determine the correlation between SOC and CE. The experimental result shows that CE is almost invariant with SOC changes for the experimental commercial LiFePO4/C cells. This paper further investigates series-connected cells with three typical correlations between SOC and CE in simulations. The simulation results indicate that cells with negative correlations between CE and SOC are preferred for series-connected battery packs, as they tend to diminish SOC difference and increase the pack capacity which can be considered as a self-balancing mechanism.