Constant Current Charge Research Articles

Temperature-aware charging strategy for lithium-ion batteries with adaptive current sequences in cold environments

This paper proposes a temperature-aware charging strategy with adaptive current sequences for lithium-ion batteries to improve their charging performance in cold environments, trading off between charging speed and the thus-caused capacity degradation. Specifically, a curved surface of the maximal allowed charging currents with different battery temperatures and states of charge (SoCs) is experimentally generated by using an integrated battery model that thoroughly describes temperature’s impacts on battery properties. Then, the commonly used multistage constant current charging scheme is improved by adapting the number of stages and associated transition conditions to battery temperature and SoC. The adaptive charging process enables batteries to self-heat quickly by applying a fast-increasing charging current sequence. Moreover, a balanced contour interval size of the curved surface is selected considering the fitness and computation time of the particle swarm optimization algorithm. Cycle charging tests are conducted with NCR18650B batteries. The results show that the proposed strategy can reduce the average charging time by 207–757 s, slow down the total capacity decay by 63–143 mAh over 20 charging cycles, and reduce the time for batteries to self-heat from −10 to 0 °C by over 500 s when compared with other existing charging methods.

Exploring Optimal Charging Strategies for Off-Grid Solar Photovoltaic Systems: A Comparative Study on Battery Storage Techniques

This paper presents a comparative analysis of different battery charging strategies for off-grid solar PV systems. The strategies evaluated include constant voltage charging, constant current charging, PWM charging, and hybrid charging. The performance of each strategy is evaluated based on factors such as battery capacity, cycle life, DOD, and charging efficiency, as well as the impact of environmental conditions such as temperature and sunlight. The results show that each charging strategy has its advantages and limitations, and the optimal approach will depend on the specific requirements and limitations of the off-grid solar PV system. This study provides valuable insights into the performance and effectiveness of different battery charging strategies, which can be used to inform the design and operation of off-grid solar PV systems. This paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. The main needs for off-grid solar photovoltaic systems include efficient energy storage, reliable battery charging strategies, environmental adaptability, cost-effectiveness, and user-friendly operation, while the primary limitations affecting these systems encompass intermittent energy supply, battery degradation, environmental variability, initial investment costs, fluctuations in energy demand, and maintenance challenges, emphasizing the importance of careful strategy selection and system design to address these factors. It also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.

A fast data-driven battery capacity estimation method under non-constant current charging and variable temperature

Data-driven methods have been widely used in capacity estimation of lithium-ion batteries. Non-constant current charging and variable-temperature operating scenarios are inevitable in real applications. However, existing classical constant current charging based capacity estimation methods may not be applied to such scenarios. To this end, a data-driven method adapted to non-constant current charging is proposed in this work. A single health feature collected within 3 min is combined with Gaussian process regression (GPR) to achieve fast and transferable battery capacity estimation. To validate the effectiveness of the proposed method, ten batteries are performed for aging tests using a non-constant current charging protocol. The validation results show that the proposed method's average capacity estimation error (mean absolute percentage error) is only 0.65 %. The estimation error is reduced by more than 30 % for the new temperature condition. Compared with the three existing classical methods, the capacity estimation accuracy of the proposed method is improved by 69 %, 82 %, and 68 %, respectively. Additional experiments demonstrate the generality of the proposed approach for different battery chemistries and charging protocols. This work opens the door to battery capacity estimation for non-constant current charging scenarios.

A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network

State-of-health (SOH) estimation is critical for the reliable operation of lithium-ion batteries. Existing methods for manually extracting health features from constant-current charging, constant-voltage (CV) charging, and relaxation phases are limited for practical applications. This study proposes a SOH estimation method for lithium-ion batteries based on partial CV charging phase data and a convolutional neural network (CNN). By analysing the current profile trend with battery aging in the CV charging phase, we propose the estimation of the SOH using partial CV charging data. We suggest adding differential current data as input to extract battery aging information from limited data. Additionally, we design a CNN-based SOH estimation framework that can automatically extract features from current and differential current data in the early stage of CV charging phase, thereby avoiding complex feature engineering. In addition, we devise a transfer learning strategy to improve the model's generalization. A battery dataset comprising three materials is used to validate the proposed method. The results show that the proposed method requires only the first 1000 s of data from the CV charging phase to achieve a highly accurate estimation of the SOH, demonstrating the method's practicality and its excellent generalization for batteries composed of different materials.

Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM

The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Fast charging of lithium-ion battery using multistage charging and optimization with Grey relational analysis

Slow charging of batteries is one of the main challenges for the deployment of battery electric vehicles (BEVs) into market. There are multiple concerns with fast charging of lithium-ion batteries, such as rapid rise of surface temperature, accelerated aging, dendrite formation and lower charging efficiency. In order to achieve fast charging without compromising the aging of lithium-ion battery, one of the possible solutions is multistage constant current (MCC) charging. In this study, the charging levels of 5S-CC (five stage- constant current) charging (a type of MCC charging) has been optimized by using a new efficient approach Grey relational analysis (GRA) with Taguchi Design of experiment. It is also the part of the study to investigate CC-CV (Constant current-Constant voltage) charging and provide a comparative analysis with 5S-CC charging for creating an efficient lithium-ion battery charging technique for battery-powered vehicles. Panasonic NCR 18650PF lithium-ion batteries with LiNiCoAlO2 as the cathode and graphite as the anode having nominal capacity of 2750mAh were used in this study. The optimized 5S-CC charging takes 48.11 min compared to 73.48 min for CC-CV_0.05C charging, and the maximum temperature in CC-CV charging is 41.02 °C while in 5S-CC charging is 40.08 °C. The 5S-CC charging, generates significantly lower amount of surface heat up to 93.66 J while CC-CV_0.05C charging, generates 100.11 J, which may address the existing issue of rapid charging while extending battery life.

Design and Control of Two Stage Battery Charger for Low Voltage Electric Vehicles Using High Gain Buck-Boost PFC AC-DC Converter

The design and control of a two-stage onboard battery charger (OBC) for the low-voltage electric vehicle (LVEVs) are presented in this study. The presented charger operates over a wide range of AC input voltage at high power factor. The presented charger utilizes a bridgeless high gain high power factor AC-DC converter with continuous input current characteristics at its front end. At the back end of the charger, an isolated Cuk DC-DC converter is employed to control and implement different battery charging modes, viz. constant current (CC) and constant voltage (CV) charging profiles. The presented AC-DC converter operates under discontinuous current mode (DCM) operation and achieves minimum control complexity at the AC-DC stage. The DCM design and high voltage conversion ratio allow it to function reliably over a comprehensive range of input and intermediate DC link voltage. This study covers the charger's design for a power rating of 650 W and output battery voltage of 24 to 72 V. The overall operational analysis and effectiveness of the design are verified through both simulations and experimental implementation and the performance is compared with other contemporary power factor correction (PFC) topologies on various parameters.

Remaining useful life prediction of lithium-ion batteries based on the Support Vector Regression Optimized and Enhanced Whale Optimization Algorithm

Accurate and reliable predictions of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the proper functioning and longevity of lithium-ion battery systems. Predicting the RUL in its early stages can be challenging and difficult for conventional RUL prediction techniques. To tackle this challenge, this paper proposes a novel approach that integrates five carefully chosen health features with a Support Vector Regression Optimized model and Enhanced Whale Optimization Algorithm (EWOA-SVR) to predict the future capacity trend and subsequently estimate the RUL using XGBoost method according to the predicted capacity. The proposed approach aims to achieve early and accurate capacity estimation and RUL prediction for lithium-ion batteries. To begin, this method extracts some health features during the relaxation process and during the constant-current and constant-voltage charge (CC-CV) process. To demonstrate the strength of the correlation between the health features and capacity, both Pearson and Spearman rank correlation analysis are employed in this study. Then based on the measurable health features and faded capacity data, Ant Lion Optimization (ALO), Whale Optimization Algorithm (WOA) and EWOA are used to select suitable penalty factor and kernel parameter in the SVR algorithm to improve the performance of SVR. With the well-trained model, the future capacity can be predicted with an accuracy of MAPE within 1%, and the RUL prediction error is lower than 11 cycles. The results suggest that the proposed model can achieve a precise and dependable early estimation of the future capacity and RUL prediction.

Multi-Segment State of Health Estimation of Lithium-ion Batteries Considering Short Partial Charging

State of health (SOH) is a critical state parameter of lithium-ion batteries (LIBs). Health indicators (HIs), which are derived from the measured features of LIBs, are used in the current data-driven SOH estimation techniques to determine SOH. However, the common partial charging and discharging make it challenging to derive reliable HIs. In this paper, a SOH estimation approach considering short partial charging is proposed. Unlike other techniques, the constant current charging stage is divided into short segments, the HI, based on the charging capacity and actual initial charging voltage, is extracted within each short segment, and a kernel ridge regression-based estimator is created to characterize the SOH mapping relationship. Subsequently, an estimator fusion frame is established to merge the estimates of the eligible segments, which is decided based on the actual start and end charging voltages of the partial charging. The effectiveness of the proposed approach is validated with two well-known LIBs aging datasets containing real partial charging cycles. The results are satisfactory in terms of accuracy, robustness to partial charging, and good generality to different types of LIBs. Effective SOH value can be deduced whenever the charging voltage range covers at least one short estimation segment.

Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation

Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous complexity, this paper designs a novel residual capacity-based active equalization strategy via data-driven residual charging capacity (RCC) estimation to fully charge all in-pack cells during constant current (CC) charging stage. Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%.

A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging

Efficient battery health indicator (HI) extraction and accurate estimation method are two important issues in the study of battery state of health (SOH) estimation. Although machine learning-based methods have been widely applied to the battery SOH estimation in recent years, the battery HI extraction in most studies is too tedious, the estimation method lacks pertinence, and the aging pattern of the battery aging dataset is simple. To solve the above problems, this paper proposes a novel battery HI based on the charging duration of the equal voltage intervals in the constant current charging process, which can effectively characterize the battery aging characteristics by only 10 continuous charging duration counts directly from the battery management system. Considering the difficulty of collecting battery aging data and the high dimensionality of the extracted HI, the least squares support vector regression (LSSVR), which is suitable for small samples and high dimensional data, is used to build the SOH mapping model and the optimal hyperparameters are found with the help of particle swarm optimization (PSO). The satisfactory SOH estimation accuracy of the proposed method is validated on a public LiFePO4 battery aging dataset containing different temperatures, discharge rates, discharge depths and cycle intervals.

State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining

Accurate state of health (SOH) estimation of lithium-ion batteries is essential to ensure the reliability of power equipment. However, the degradation trajectory of different cells and different types of batteries is not repeatable. At present, there is no unified model or method to effectively predict SOH for all batteries. Therefore, a new SOH estimation method is proposed in the paper. Firstly, two types of new features are proposed in this paper. One is the voltage features extracted from the constant-current charging stage, and the other is the capacity recovery feature. They are used to reflect the nonlinear degradation process of the battery. Secondly, the relationship between features and SOH is established by using the LSTM model, which can prevent the problem of gradient vanishing and gradient explosion during model learning. Finally, for the inconsistencies between the same type or different types of batteries, two different transfer learning strategies (fine-tuning and rebuilding) are proposed in this paper, and the effectiveness of the proposed features and transfer learning strategies is verified on three open-source battery data sets (NASA, Oxford, and CALCE). Experimental results show that the SOH estimation method proposed in the paper has good universality, robustness, and accuracy.

A Bayesian deep learning pipeline for lithium‐ion battery SOH estimation with uncertainty quantification

AbstractIn recent years, deep learning (DL) methods for state of health (SOH) estimation of lithium‐ion (Li‐ion) batteries have attracted great attention. However, most existing DL‐based methods for SOH estimation were designed using specific battery dataset. To gain better performance, the procedure can be labor‐intensive, involving designing intricate features and fine‐tuning complex DL model, which significantly limits the applications of these methods. Moreover, uncertainty quantification, that is how much uncertainty these DL‐based methods can have on their SOH estimation, cannot be addressed with only point estimation. To this end, this paper proposes a Bayesian DL pipeline for Li‐ion battery SOH estimation targeting at automatic feature extraction and uncertainty‐aware DL‐model developing for general application to batteries under different working conditions. The proposed pipeline is composed of three modules accounting for common feature extraction, automatic feature selection, and Bayesian neural network (BNN) model construction. Specifically, a frequently‐used feature set is designed using the voltage, current and time data of constant current and constant voltage (CC‐CV) charging mode, while the recursive feature elimination (RFE) with cross validation (RFECV) algorithm is used to automatically select features based on relevance. Based on the automatically selected features, DL model is automatically constructed without too much human intervention. The uncertainty of SOH estimation is quantified by BNN model using Monte Carlo (MC) dropout method, which is subsequently used to calculate the confidence of the estimation results. Four battery datasets, covering regular charging, fast charging, and second‐use applications, are used to demonstrate the performance of the proposed method. The proposed pipeline shows superior performance to state‐of‐art DL‐based methods in terms of application capability to different working conditions and uncertainty quantification for SOH estimation.

A MOF-derived Zn-TiO2 electrochromic supercapacitor

Electrochromic supercapacitors caused widespread concern due to their ability to exhibit color changes to monitor the charging and discharging states. However, the unsatisfactory energy storage and electrochemical durability seriously hinder their practical applications, especially for titanium dioxide (TiO2) electrochromic supercapacitors. To address the above issues, a strategy for designing the novel bi-functional Zn-TiO2 film derived from the metal-organic framework (MOF)-based material is proposed for the first time. The ZnO@zeolitic imidazolate framework-8 (ZIF-8) acts as the self-sacrificial template, beneficial from the diverse porous structure of ZIF-8 and the core-shell structured of ZnO@ZIF-8, the obtained Zn-TiO2 film inherits the large pore volume and specific surface area, facilitating ion/electron intercalation/deintercalation and transport, presenting good electrochemical performance, large specific capacitance, and good constant current charging and discharging stability. In addition, the persistent deintercalation of the embedded Zn2+ in the TiO2 lattice continuously generates new available sites for Li+ intercalation, and the highly porous structure of Zn-TiO2 provides sufficient space to cope with the volume expansion during the electrochemical cycling, leading to the superior durability of the film. Finally, an asymmetric electrochromic supercapacitor using the Zn-TiO2 electrode is constructed and investigated. This study promotes the exploration for designing and fabricating the high-performance electrochromic supercapacitors towards intelligent technology applications.

Supercapacitor’s Parameter Calculation based on Three Branch Equivalent Circuit Model under Different Constant Charging Current

Supercapacitor is a type of energy storage with higher capacitance value compared to the normal capacitor. But it has lower voltage level compared to normal capacitor. However, it can be charged with high current and provide higher current to the load when needed when compared to the same size energy storage such as battery. The Three Branch Equivalent Circuit is one of the simple yet accurate model that has been proposed. The parameter of the Three Branch model included the capacitance, resistance and a unit of voltage dependent capacitance. The Parameter needed to be calculated accurately as it depends heavily on the terminal voltage of the supercapacitor at respective time which obtained through a charging and self-discharging experiment under constant current charging. The different constant charging current give effect toward the parameter calculation as it manipulates the rate of charging and self-discharging. This paper will calculate the parameter of supercapacitor based on the Three Branch model under different constant charging current and compared the result using the simulation to show the accuracy of the model. The parameter obtained throughout the study shows a high accuracy especially the parameters obtained using higher charging current.

Effect of protonic acid concentration on electrical properties of fly ash magnetic beads/polyaniline composites

AbstractMagnetic polyaniline nanofibers were synthesized by oxidative polymerization using protonic acid and fly ash magnetic beads as dopants and ammonium persulfate as oxidant. The synthesized magnetic polyaniline was characterized by FT‐IR, UV, XRD and SEM, and its morphology and structure were revealed. Polyaniline magnetic electrodes were prepared by pressing method, and their electrochemical properties were analyzed by cyclic voltammetry and constant current charge–discharge methods. The capacities of polyaniline powders doped with optimal concentration of hydrochloric acid and p‐toluene sulfonic acid at current density of 1 A/g were 250.75 and 410.5 F/g, respectively. The magnetic polyaniline doped with optimal acid concentration has good conductivity through impedance measurement analysis. A smooth and uniform film was prepared by pressing magnetic polyaniline powder and its photosensitivity was observed under different monochromatic illumination. The results show that the current and voltage have a linear relationship and that different monochromatic light energies can be distinguished significantly with the increase of voltage.

One-step microwave synthesis of FeSe2@CNT as high-performance supercapacitor anode material

In this work, FeSe2@CNT nanocomposite anode materials are fabricated using a convenient and efficient one-step microwave method. The FeSe2@CNT electrode exhibits superior electrochemical performance. The carbon nanotube structure effectively mitigates the severe volume change in the FeSe2@CNT electrode during continuous charge and discharge. Thus, the capacitance value of 573 F g−1 and capacitance retention of 81.9% for 3000 constant current charge and discharge cycles are achieved. In addition, the asymmetric device assembled with FeSe2@CNT as the anode and NiSe2@CNT as the cathode exhibits high Energy density (E can reach 41.32 Wh kg−1 when P is 800 W kg−1), and the device achieves 82.6% capacity retention after 3000 cycles.

Aging mechanism analysis and capacity estimation of lithium - ion battery pack based on electric vehicle charging data

Due to the incompleteness of charging data, the voltage step caused by fast charging conditions and sampling accuracy of the battery management system, the conventional mechanism model is not applicable to the aging mechanism analysis and capacity estimation of electric vehicle batteries. Therefore, this study applies support vector regression to achieve the actual charging condition equivalence based on the variable operating conditions charging data of electric vehicles. The aging parameters and open circuit voltage reconstruction based on the dual-tank model are applied to obtaining the aging state and the capacity of cells. The capacity of the battery pack is calculated by the pack formation theory. The maximum error of the aging parameters obtained by the multiple stage constant current is 5.572% compared with the 1/20 C (C is the charge/discharge current rate unit) constant current charging of the experimental battery. As to the maximum relative error of cell capacity estimation based on vehicle data is 0.99%, and battery pack capacity estimation is 0.86%. The method proposed in this paper is not only able to quantitatively analyze the dominant factors of battery capacity decay, but also achieves high accuracy capacity estimation of the vehicle battery pack and its individual cells.

Mitigation of Binder Migration Behavior during the Drying Process by Applying an Electric Field for Fast‐Charging in Lithium‐Ion Batteries

AbstractThe binder migration due to the capillary force‐driven solvent evaporation during the drying process in the electrode manufacturing process induces inhomogeneous binder distribution in the electrode, which deteriorates Li‐ion kinetics and corresponding poor fast‐charging properties in lithium‐ion batteries (LIBs). Here, we report an effective strategy to mitigate the binder migration behavior by applying an electric field during the drying process. As the employed carboxymethyl cellulose (CMC) and styrene‐butadiene rubber (SBR) binders have a negative charge in an aqueous anode slurry system (pH 7), the binder migration behavior could be mitigated by generating an electrical attraction force into the bottom direction by positive electrification of the current collector. The anode prepared with electric field exhibits homogeneous binder distribution in the longitudinal direction, which enhances Li‐ion kinetics, corresponding constant current charging capacity, and cycling stability compared to those of the anode prepared without electric field.