Current Charging Mode Research Articles

Research on topology technology of integrated battery energy storage system with reconfigurable battery and converter

In traditional battery energy storage systems (BESS), batteries are usually connected in a simple series or parallel form, and separate converters and balancing modules are typically used for energy exchange between the battery and external sources, as well as for balancing energy between batteries. This paper proposes an integrated battery energy storage system (IBESS) with reconfigurable batteries and DC/DC converters, resulting in a more compact structure. The IBESS can reconfigure the connection of energy storage batteries based on the battery's status and external demand. The DC/DC converters can operate in different modes such as boost, buck, or buck-boost converters. The IBESS has five working modes: discharging mode, balancing mode, battery discharging and balancing mode, constant current and constant voltage (CCCV) charging mode and backup mode. To verify the feasibility of the proposed energy storage system, a model with four battery modules was built using MATLAB/SIMULINK, and the operation of all working modes was demonstrated. The simulation results verified the system's performance and reliability, demonstrating its flexibility and efficiency in different operating modes.

Hybrid Topology with Reconfigurable Rectifier for Enable CC and CV Output Characteristics in Wireless Power Transfer Systems

To achieve simple and reliable conversion from constant current (CC) charging mode to constant voltage (CV) charging mode in wireless power transfer systems, this paper proposes a hybrid topology equipped with a reconfigurable rectifier. An AC switch is adapted to simultaneously realize the change from half-bridge rectification to full-bridge rectification and achieve the shift from a topology with capacitors in the series compensated primary and secondary sides to a topology with capacitors in the series compensated primary side and the inductor-capacitor-inductor (LCL) compensated secondary side. Notably, communication between the transmitter and receiver sides is not necessary in the proposed method. Furthermore, the zero phase angle characteristic is successfully maintained in both charging modes. The experimental results obtained using the 48 V/0.7 A experimental prototype, which was built to validate the relevant theoretical analysis, show that output current/voltage can be achieved in CC/CV charging mode independent of load resistance. Notably, the maximum power transfer efficiency of the system during the charging process reached 95.33%.

Simulation analysis of energy metering error of non-vehicle charger

This paper studies three methods of DC energy measurement for non-vehicle chargers, namely, the average value method, effective value method, and time domain integration method, introduces the definition of ripple, and analyzes the characteristics of constant voltage, constant current charging mode and pulse charging mode of electric vehicles, providing an algorithm basis for the establishment of DC energy metering error model and simulation analysis. This paper studies high-power DC energy measurement algorithms with ripple parameters, analyzes the error characteristics of various algorithms, summarizes the relevant laws, and provides a theoretical basis for the design of a high-precision DC metering module under the influence of ripple.

An improved control for a stand-alone WEC system involving a Vienna rectifier with battery energy storage management

This study focused on efficiency improving, the power flow management and control problem of the standalone wind energy conversion system. Specifically, the system under study consists of a Permanent Magnet Synchronous Generator (PMSM) driving by wind turbine, Vienna rectifier, a Li-ion battery and a DC load. Battery life is vulnerable to fluctuations due to the intermittent nature of the wind, as well as to the demand for energy charging, which reduce considerably battery state of health (SOH) and consequently charging capacity. To overcome these shortcomings, a nonlinear battery charge controller is used to regulate the current supplied to the battery and to the load, and thus guarantee best charging performances. Based on the wind power, the load demand and the battery state of charge (SOC), three operating modes are considered. Specifically, MPPT mode, Constant Current (CC) charging mode and Constant Voltage (CV) charging mode. We also design a new energy management method to protect the energy storage system and increase its lifetime. This algorithm ensure a smooth switching between three controllers designed to provide the three operating modes. The high performances and the efficiency of the proposed controller and management algorithm are highlighted using several MATLAB/SIMULINK simulations.

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.

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.

Investigation of Performance Difference between Photo‐Charging and Conventional Constant Current Charging for Energy Storage Batteries

AbstractSolar cells offer clean and abundant power sources for directly photo‐charging rechargeable batteries, which shows great potential for the development of integrated power supply. In order to deepen the understanding of the novel type of charging process, this research takes silicon solar cells and lithium cobalt oxide batteries as examples to compare the performance difference between photo‐charging and conventional constant current charging in detail. Surprisingly, the photo‐charging turns out to be superior in charging efficiency, polarization mitigation, and cyclic performance even with the intrinsic instability of the solar cells when compared with constant current charging mode. Finally, this research optimizes the calculation method of energy storage efficiency in the integrated power supply by calculating the actual power during the photo‐charging process by recording the voltage and current change with time. Besides, the power matching degree is quantified by the ratio of to η1, providing new ideas for the matching method of solar cells and rechargeable batteries in integrated power supplies.

Single-Stage Active Rectifier With Wide Impedance Conversion Ratio Range for Inductive Power Transfer System Delivering Constant Power

Compared with the constant current (CC) charging mode, the constant power (CP) charging mode can speed up the battery charging rate and free the charger from excessive thermal design problems. However, the range of the battery's equivalent resistance in CP mode is wider than that in CC mode, which makes it difficult to track the optimal resistance of the inductive power transfer (IPT) system for efficiency enhancement. To solve this problem, this paper proposes a single-stage active rectifier (SSAR) integrating an interleaved buck converter with a full-bridge active rectifier (FBAR). Compared with the traditional FBAR, the resistance conversion ratio range of the SSAR is extended from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[0, 8/\pi ^{2}]$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[0, 8]$</tex-math></inline-formula> , and the output current ripple is reduced due to the interleaved operation. A novel optimal phase angle control strategy is correspondingly proposed for the IPT system with the SSAR, which features advantages of wide impedance conversion ratio range, zero voltage switching (ZVS) turn-on of all MOSFETs, and no communication between the primary and the secondary sides. A 150 W experimental prototype is provided to verify the effectiveness of the proposed rectifier.

Lithium-ion battery charging optimization based on electrical, thermal and aging mechanism models

The development of power batteries has driven the popularity of electric vehicles (EVs). For EV, charging management directly affects battery pack performance and vehicle portability. In this paper, a multi-stage constant current charging mode considering the temperature rise, health loss, and charging time is proposed. Based on the equivalent circuit model, thermal model and aging empirical model of the battery, the objective function of charging optimization is constructed. The solution is solved by with different optimization algorithms. A comparative analysis of the different solutions on the Pareto front is performed, and the optimization results show that the non-dominated sorting genetic algorithm-II (NSGA-II) results are significantly better than the non-dominated sorting moth flame optimization (NSMFO). Finally, comparing the optimization results with the conventional constant current (CC) charging method shows that the charging optimization algorithm proposed in this work is able to reduce health loss and temperature rise. Although, there is a mutually binding relationship between charging time, temperature rise and health loss. However, the best charging optimization can be obtained on the Pareto front according to different charging requirements.

Cyclic aging monitoring of lithium-ion battery based on acoustic emission

ABSTRACT A timely and accurate understanding of the state of health (SOH) of lithium-ion batteries is essential for early detection and prediction of their potential safety risks. Acoustic emission (AE) can perform dynamic non-destructive testing of lithium-ion batteries under normal charge-discharge cycles, and detect the deformation of internal electrodes of the battery behavior. In this paper, firstly, the whole aging process of lithium-ion batteries and recorded AE signals are monitored. Secondly, the waveform and signal time interval from AE signals during battery aging in the time domain is extracted. Thirdly, the spectrum of AE signals is analyzed. The experimental results show that the total number of AE signals will change with the decline of battery capacity and SOH. The turning point in the time interval waveform of the battery AE signal can be used to identify the constant current or voltage charging mode. The AE signals of batteries mainly exist in two frequency bands, where the content of frequency band 1 is about 10kHz-120kHz, and the range of frequency band 2 is about 130kHz-180kHz. The maximum amplitude of frequency bands 1 and 2 shows their characteristics during the charging and discharging process.

Robust Tracking Control of a Three-Phase Bidirectional Charger for Electric Vehicle

This paper presents a robust control strategy for an electric vehicle's three-phase off-board bidirectional AC-DC battery charger. The conventional constant current (CC) and constant voltage (CV) charging mode are considered to provide a fast-charging performance for the batteries. The bidirectional charger also allows using of the vehicle as an energy storage system for the grid i.e., charging during the peak-off times and delivering the energy back to the grid during peak times of electrical consumption. In discharging mode, the bidirectional charger maintains constant active power flow to the grid with a given reference. For both cases, user of a robust state feedback controller with integral action is made in the DQ-synchronous frame. The set of stabilizing gains of this controller are determined by a linear matrix inequality (LMI)-based optimization so that the convergence time to steady stead is minimized in the occurrence of the parametric uncertainties of the L-filter. The efficacy of the proposed controller is verified through simulation and experimental results on 102.4 V Lithium iron phosphate (LiFePO4) batteries.

Energy Consumption in Capacitive Deionization for Desalination: A Review.

Capacitive deionization (CDI) is an emerging eco-friendly desalination technology with mild operation conditions. However, the energy consumption of CDI has not yet been comprehensively summarized, which is closely related to the economic cost. Hence, this study aims to review the energy consumption performances and mechanisms in the literature of CDI, and to reveal a future direction for optimizing the consumed energy. The energy consumption of CDI could be influenced by a variety of internal and external factors. Ion-exchange membrane incorporation, flow-by configuration, constant current charging mode, lower electric field intensity and flowrate, electrode material with a semi-selective surface or high wettability, and redox electrolyte are the preferred elements for low energy consumption. In addition, the consumed energy in CDI could be reduced to be even lower by energy regeneration. By combining the favorable factors, the optimization of energy consumption (down to 0.0089 Wh·gNaCl−1) could be achieved. As redox flow desalination has the benefits of a high energy efficiency and long lifespan (~20,000 cycles), together with the incorporation of energy recovery (over 80%), a robust future tendency of energy-efficient CDI desalination is expected.

A new high speed charge and high efficiency Li-Ion battery charger interface using pulse control technique

A new Li-Ion battery charger interface (BCI) using pulse control (PC) technique is designed and analyzed in this paper. Thanks to the use of PC technique, the main standards of the Li-Ion battery charger, i.e. fast charge, small surface area and high efficiency, are achieved. The proposed charger achieves full charge in forty-one minutes passing by the constant current (CC) charging mode which also included the start-up and the constant voltage mode (CV) charging mode. It designed, simulated and layouted which occupies a small size area 0.1 mm2 by using Taiwan Semiconductor Manufacturing Company 180 nm complementary metal oxide semi-conductor technology (TSMC 180 nm CMOS) technology in Cadence Virtuoso software. The battery voltage VBAT varies between 2.9 V to 4.35 V and the maximum battery current IBAT is 2.1 A in CC charging mode, according to a maximum input voltage VIN equal 5 V. The maximum charging efficiency reaches 98%.

Multi-mode control strategy for a stand-alone wind energy conversion system with battery energy storage

This work addresses the problem of controlling a stand-alone wind energy conversion system with battery energy storage. The study target consists of a series association of a permanent magnet synchronous aero-generator, an uncontrolled rectifier, a Zeta converter, a Li-ion battery, and a DC load. It is well-known that the intermittent nature of wind power, as well as the frequent variations in load demand, decreases battery lifetime and reduces its charging performance. To overcome these drawbacks, a battery charge controller is required to instantaneously balance the wind turbine power flow delivered to the DC load and the battery, so that the wind turbine power is properly utilized and the battery is suitably charged. Precisely, depending on the available wind power, the battery state of charge (SOC), and DC load demand, three charging modes are adapted, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Afterward, to ensure the safety of the battery a new battery energy management algorithm is designed to operate the system in one of the three aforementioned modes, taking into account the weather conditions and load demand variations. Interestingly, no wind speed or battery soc. sensors are required for the implementation of the control system. Therefore, a nonlinear adaptive is also developed to provide the online estimation of the battery SOC required for the management algorithm. The performances of the studied system are tested through a semi-experimental platform involving a Processor-in-the-Loop (PIL) under the embedded board eZdsp TMS320F28335. The obtained simulation results prove that the proposed controller meets its objectives with high performances.

Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery

Abstract Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R 2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.

A power balance control strategy for stand alone wind energy conversion systems

Supplying off-grid areas with stand-alone wind energy conversion systems required a battery charge controller for energy storage. Furthermore, to cope with the stochastic wind nature, the frequent variations of the load demand, together with ensuring a proper battery charging process, based on the available wind power, the battery status, and the load power demand, a multi-mode control strategy is proposed, which can instantly balance the wind power flow delivered to the DC load and the battery. The control system entails three operating modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, constant voltage (CV) charging mode. Thereafter, a new battery energy management algorithm is also developed, to ensure a smooth switching from one operating mode to another. The controller performances are theoretically analyzed, then are tested through MATLAB/SIMULINK/SIMPOWER environment. The obtained simulation results prove that the proposed controller meets its objectives under various weather conditions and load demand.

Single-Stage Wireless Battery Charging Circuit with Coupling Coefficient Prediction

This paper proposes a single-stage wireless battery charging circuit with a coupling coefficient prediction method. The proposed circuit consists of only two stages: full bridge inverter with transmitter coil in the first stage and full bridge rectifier with receiver coil in the second stage. This circuit implements the constant current (CC) charging mode at the resonant frequency of two coils and the constant voltage (CV) charging mode at a specific frequency that is dependent on the coupling coefficient of two coils. The operation at a specific frequency guarantees the CV operation regardless of load condition and reduces the switching losses than the operation at the resonant frequency owing to a zero-voltage switching (ZVS) operation. In CC-CV modes, the phase-shift technique is additionally applied to improve the output voltage/current regulation. Unlike other approaches, the proposed single-stage wireless battery charging circuit does not require multiple stages of power conversion, or additional components, a pre-measured coupling coefficient or a complex control algorithm for CC-CV charging operation. The prototype proposed circuit was tested under various coil alignment conditions, and successfully implemented the CC-CV charging operation for a 36 V battery pack. The predicted coupling coefficient had an error of ≤0.62% in the coil alignment condition, and the circuit had errors of ≤0.32%, ≤0.1% in the output current and voltage regulation, respectively.

Design of a Novel Current Mode Charge Pump for Very-Low-Voltage Applications in 130 nm SOI-BCD Technology

A novel charge pump with current mode control suitable to work under a very-low-voltage supply is proposed in this paper. The proposed charge pump consists of two sections. The first section is a power switches stage which consists of seven cascaded DEPMOS power switches. The second section is a low voltage stage which consists of a Low Voltage Level Shifter, Current Mode control, Follower Amplifier, Error Amplifier, Soft Start Comparator, and Skip mode &amp; Over Voltage Comparator. The charge pump has been designed, simulated, and layout in Cadence using TSMC 130 nm SOI technology with LDMOS transistors, which have very low on-resistance. The input range of the charge pump is 2.7– 4.4 V, and it can supply up to 100 mA load current. The maximum efficiency is 90%, and the chip area is only 0.597 mm².

An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods

This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model's discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%.

Adaptive Output Feedback Nonlinear Control of a Wireless Power Transfer Charger for Battery Electric Vehicle

This paper deals with the problem of controlling a series—series compensated wireless power transfer (WPT) charger for battery electric vehicle operating in constant current charging mode. The difficulty is threefold: i) The WPT charger model involves highly nonlinear terms, ii) some state variables are not accessible to measurements, and iii) the equivalent model of the battery is nonlinear; moreover, its parameters are unknown and subject to change due to several factors such as the battery state of charge (SOC), the aging and the operating temperature. To cope with all these difficulties, a nonlinear adaptive output feedback controller is designed using the backstepping control method combined with an adaptive observer-based Kalman-like and coulomb counting estimation method. The control objectives are the following: i) tight regulation of the battery charging current, ii) asymptotic stability of the closed-loop system, iii) estimation of the WPT charger’s state variables and iv) estimation of the battery internal parameters such as the SOC, the open-circuit voltage and the equivalent series resistance. The effectiveness of the proposed control strategy is highlighted by various simulation scenarios showing that all control objectives are achieved.