Constant Current Charge Research Articles

Robust estimation of lithium-ion battery state of health based on electro-thermal features and machine learning

Accurately estimating the State of Health (SOH) of lithium-ion (Li-ion) batteries is crucial for preventing overcharging and over-discharging, thereby extending battery lifespan. This paper presents a novel method for SOH estimation in lithium-ion batteries by leveraging electro-thermal features, a backpropagation neural network (BPNN), and a particle swarm optimization (PSO) algorithm. First, three health-related features—constant current charging time (CCCT), relative constant current charging time (RCCCT), and maximum temperature during discharge (MTT)—are extracted as indicators of SOH. The correlation between these features and the SOH is validated using Grey Relational Analysis (GRA). Next, a BP neural network is utilized to model the nonlinear relationship between the extracted features and SOH. The PSO algorithm is then employed to optimize the parameters of the BP neural network, enhancing the accuracy of the SOH estimation. The proposed method, which combines electro-thermal features with the optimized BP neural network, is validated through three independent lithium-ion battery aging experiments. Experimental results demonstrate that the proposed approach achieves high estimation accuracy and exhibits strong generalization performance for SOH prediction.

On the Effect of Current Stabilizer on Dynamics of a Small Hybrid Wind Power Generator

The use of small wind power generators remains quite relevant. In particular, they can be efficient for charging batteries in remote locations where there is no centralized power supply (including in the Arctic, Far East, etc.). They can also be used as part of missions to planets with atmospheres. One of the promising design solutions for a small wind power generator with a vertical axis of rotation is a hybrid device. It consists of two wind turbines that have a common axis of rotation: external (Darrieus wind turbine) and internal (Savonius rotor). This scheme represents a compromise between the relatively high power coefficient of the Darrieus turbine and the good startup characteristics the Savonius rotor. It is known that one of the typical battery charging modes is constant current charging. Here we consider a hybrid installation, the generator of which is connected to a current stabilizer. The load is simulated with an active resistance. It is assumed that the generator is a DC generator. A closed mathematical model of the studied system is constructed. The aerodynamic load is described using the quasi-steady approach. It is assumed that the characteristic time of electrical processes is much smaller than the characteristic time of mechanical processes. The influence of load resistance on the behavior of the system is investigated. It is shown that, under certain conditions, several steady modes (up to five) exist in the system. In this case, at least two of them are attracting. Therefore, the hysteresis of the angular speed of the steady mode is possible when the load resistance changes. It should be noted that in a number of situations, an unstable steady mode (which corresponds to a lower angular speed of the turbine than a stable one) may be preferable (for example, to reduce the load on bearings). In this regard, a resistance control strategy has been proposed to ensure stabilization of the unstable stationary regime.

Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Optimized multi-stage constant current fast charging protocol suppressing lithium plating for lithium-ion batteries using reduced order electrochemical-thermal-life model

Multi-stage Constant Current (MCC) is a state-of-the-art fast-charging protocol considering battery aging. It divides the charging process into multiple stages, each with a different current amplitude based on specific transition criteria, significantly influencing battery performance, such as charging time, degradation rate, and thermal effects. A key challenge in designing MCC protocols is addressing the lithium plating (LiP), which can accelerate degradation and pose a severe risk of thermal runaway. Since the LiP onset conditions vary between fresh and aged cells, this paper proposes an optimized MCC (O-MCC) charging protocol suppressing LiP based on the battery's state of health. To accurately simulate LiP conditions, different platforms of reduced-order electrochemical-thermal-life models are designed, compared, and optimized for speed and accuracy using a genetic algorithm, resulting in a 36.4 % reduction in computational time while maintaining the accuracy of the Pseudo Two-Dimensional model. The Nonlinear Model Predictive Control algorithm is then used to optimize the MCC protocol, minimizing charging time while preventing LiP throughout life. Experimental results show that O-MCC reduces charging time by 11.7 % and capacity loss by 59.4 %, enhancing battery safety. Additionally, O-MCCs with varying constraints are developed to meet specific demands and simulated at the battery pack level.

Synthesis of MnMoO4/α-MoO3 composite materials for supercapacitor

In the quest for developing supercapacitors with enhanced energy density, we have successfully synthesized MnMoO4/MoO3 composite materials tailored for supercapacitor applications. The synthesis process utilized (NH4)6Mo7O24·4H2O and MnCl2·4H2O as precursors. The resulting composite materials were thoroughly characterized using a suite of analytical techniques, including scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction analysis, Brunauer-Emmett-Teller surface area measurements, thermogravimetric analysis, and differential scanning calorimetry. Furthermore, the supercapacitive performance of the MnMoO4/α-MoO3 composite materials was evaluated using cyclic voltammetry and constant current charge–discharge methods. The synthesis yielded an ideal composite material, with low-resistance MnMoO4 seamlessly integrated with high ion-conductive MoO3. At a current density of 3 A/g, the specific capacitance was measured at 1878.7 F/g, achieving an energy density of 260.9 Wh kg−1 and a power density of 1.448 kW kg−1. The composite materials demonstrated significant potential for the production of high-density supercapacitors.

Investigating the effects of variable pulse charging on temperature during charging of battery electric vehicles

This study investigates the efficacy of variable pulse charging (VPC) on charging 18,650 secondary battery packs (12 V, 20 Ah) with NMC chemistry. VPC, a modern technique applied to secondary battery charging, aims to mitigate effects like a thermal runaway and thermal propagation caused by increased charging temperature. VPC involves varied duty factors (10 % to 90 %), charging rates (0.5C, 1C, 1.5C), and an optimal switching frequency determined through frequency response analysis. Its digital model is based on in situ electrochemical impedance spectroscopy measurements and MATLAB/Simulink simulations. At charging rates of 0.5C (10 A), 1C (20 A), and 1.5C (30 A), the temperature of the battery pack reaches 42.46 °C, 57.87 °C, and 70.37 °C, respectively. However, implementing VPC at a 50 % duty factor yields temperature reductions of 3.66 °C, 5.06 °C, and 5.42 °C, respectively. Similarly, employing VPC at a 10 % duty cycle results in temperature reductions of 11.2 °C, 17.7 °C, and 19.1 °C, respectively. The results indicate a significant reduction in charging temperature compared to constant current charging. Furthermore, the 1C condition is validated using a custom-made dual active bridge DC-DC variable pulse charger. In conclusion, applying optimal frequency-based VPC with specific duty factors demonstrates the potential to reduce temperature elevation during battery pack charging significantly.

Multi‐objective optimization of inductive power transfer system with reconfigurable topology for misalignment tolerance

AbstractAt present, most of the analyses or studies about inductive power transfer (IPT) with constant current (CC) output and constant voltage (CV) output are carried out without considering misalignment conditions or different gaps. An IPT system satisfying battery charging demand and anti‐misalignment requirements simultaneously is infrequent. This paper proposes a multi‐objective particle swarm optimization method of IPT reconfigurable topology to realize CC and CV modes at varying resistance conditions and wide coupling ranges. The output characteristics of an inductor–capacitor–capacitor (LCC)–LCC compensation circuit have been explored, and it is found that the secondary‐side compensated capacitors have a greater impact on the output power, which can be used to improve power regulation ability accompanied by coupling varying. Eight optimization compensated parameters of the reconfigurable topology are obtained from the Pareto front to achieve the required CC and CV charging outputs. By switching the compensated capacitors, the selected parameters can make the current and voltage fluctuation less than 9.3% and 7.9%, respectively, during the coupling charging range from 0.3 to 0.22. Moreover, primary zero voltage switching operation is achieved to enable high efficiency. The simulation and the experimental verification are carried out to verify the validity of the proposed method.

The Control Strategies for Charging and Discharging of Electric Vehicles in the Vehicle–Grid Interaction Modes

In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid.

Methods for faster estimation of the entropy profile of a lithium-ion battery: A comparison of accelerated potentiometry and the estimation of entropy through thermal signatures

The operating temperature of a lithium-ion battery (LIB) has strong effects on its electrochemical performance, safety, and lifetime. Reliable predictions of cell temperature and heat generation rate are required for the design and thermal management of battery systems. The entropy variation of a LIB has a significant influence on the heat generation and the cell temperature variation, especially at a lower C-rate. Since the entropy is a function of State-of-Charge (SoC), and may change during ageing, the entropy profile may as well be used as an indicator for battery State-of-Health diagnostics. The present experimental methods, such as potentiometric or calorimetric methods, for determining the entropy variation throughout a battery's full SoC window are time-consuming and require specialized equipment with good thermal insulation and instrumentation. Hence, faster and more convenient methods are needed. In this paper, we compare an accelerated potentiometric method with a new entropy characterization method where the entropy profile is extracted from the thermal signature of a LIB during charge and discharge at low C-rates. The accelerated potentiometric method aims to obtain the entropy from thermal cycling while the cell is still not completely relaxed to its stable OCV. The thermal signature method monitors the variations of a cell's outer surface temperature during a low constant current charge or discharge, from which the continuous variation of entropy with the SoC can be extracted. The obtained entropy profile was validated through a modeling approach that combines a P2D-electrochemical model for battery operation and a 3D-finite-element model for the thermal behavior during battery operation. Good agreement between the model predictions and experiments was achieved on the temporal variation of the cell's outer surface temperature.

Ultrasound-enabled adaptive protocol for fast charging of lithium-ion batteries

Abstract This paper introduces an ultrasound-assisted multi-stage constant current (UA-MSCC) charging protocol to enhance the charging performance of lithium-ion batteries. In this approach, ultrasound is applied during the final portion of each multi-stage constant current (MSCC) charging phase. Experimental results demonstrate that ultrasound decreases the internal resistance of pouch cells by up to 7%, leading to significant increase in charging capacity during each MSCC stage. The overall charging time is reduced by 26% compared to the conventional constant current constant voltage (CCCV) protocol. The performance improvement delivered by this ultrasound-assisted charging approach is especially large when the battery is charged at low temperatures and to a partial capacity. Notably, the application of ultrasound improves the coulombic efficiency to levels comparable to that at the room temperature when charging in cold environments (0°C). This approach can be applied to commercial batteries to immediately improve their charging performance, and can be seamlessly integrated into battery management systems. Unlike approaches that necessitate electrode material modifications or electrolyte additives, which require a long development time, this UA-MSCC charging protocol offers a practical and easily applicable solution for improving the battery charging performance.

Evolution of aging mechanisms and performance degradation of lithium-ion battery from moderate to severe capacity loss scenarios

The aging mechanisms of Nickel-Manganese-Cobalt-Oxide (NMC)/Graphite lithium-ion batteries are divided into stages from the beginning-of-life (BOL) to the end-of-life (EOL) of the battery. The corresponding changes in the battery performance across these stages have been analyzed, and a digital twin model is established to quantify the primary parameters that influence these aging mechanisms. Post-mortem analysis is applied to validate the results. This paper compares the aging mechanisms from BOL to EOL using two charging protocols: a multi-step fast charge protocol and a common constant-current fast charge protocol that applies the average current of multi-step currents. Notably, a transition from a linear to a non-linear degradation trend in capacity fade is observed, beginning from a 10% capacity reduction to EOL. From BOL to 10% degradation, the resistance of solid electrolyte interphase (SEI) grows steadily. The graphite anode’s crack depth exhibits a significant increase, accompanied by an evident collapse of cathode materials in all the test cases following a 10% degradation until EOL. This phenomenon can be attributed to one primary reason—the expansion of the corresponding simulated resistance of charge transfer (Rct). Post-mortem analysis revealed the change in morphology, structure, and composition of various degradation conditions. This analysis proved that the primary driver of the linear aging stage is the SEI growth. Furthermore, it is evident that the transition to a non-linear aging degradation is dominated by electrode defects resulting from continuous mechanical stress during long-term aging.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

Modeling the Dynamics of Supercapacitors by Means of Riemann–Liouville Integral Definition

The application of fractional calculus to obtain dynamic models for supercapacitors represents an alternative approach to obtaining simpler and more accurate models. This paper presents a model for the supercapacitor in the time domain, based on the use of the fractional or non-integer order integral. This fractional model is compared with the conventional simple model, which is typically used in industrial applications. This fractional integral-based model provides satisfactory fits in relation to the number of parameters used in the model. Furthermore, an interpretation of the effect of the application of fractional integration is presented for constant current charging and discharging processes at constant current, using the Riemann–Liouville definition for the non-integer order integral. Supercapacitors are devices that exhibit non-linear behavior, with a distinct charging and discharging operation. There are several methods of dynamic analysis for the characterization of supercapacitors. The information extracted from these methods is essential for understanding the behavior of supercapacitors and, thus, ensuring that processes involving supercapacitors are as efficient as possible. This paper presents a dynamic analysis based on charge and discharge operations with constant currents. The conclusion is that the fractional model provides fairly accurate fits.

Revisiting Pseudo-OCV Pulse-Based Incremental Capacity Analysis

Li-ion batteries are more prone to safety incidents than other battery technologies. This is due to the inherently higher energy density as well as the flammability of especially anode materials, separator, and electrolyte solvents. During charging, metallic Lithium may form on the anode surface and could in the worst case short the battery from within and cause a fire and or explosion [1]. It is therefore vital to be able to monitor the battery’s state-of-health (SoH) and state-of-safety (SoS) [2], to avoid further use of Li-ion batteries that may have aged in a detrimental fashion and can have an increased safety risk. Especially when a battery has aged considerably (SoH < 80%) the path of ageing can significantly affect the safety properties of the battery and cause dramatic differences in the severity of a possible safety incident.Incremental capacity analysis (ICA, dQ/dV) has emerged as a very powerful diagnostic technique for Li-ion batteries. We recently used ICA to identify different degradation mechanisms in commercial Li-ion cells through classification and tracking of selected dQ/dV features [3]. This allowed us to identify safety critical ageing at an early stage. ICA requires constant charge and discharge at slow currents (C-rate < C/10) [4, 5]. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to battery systems without access to high precision battery pack testers.In this work we will revisit applying ICA on the Open-Circuit-Voltage (OCV) curve in the capacity space [6]. The OCV curve can be obtained from any sequence of current or power pulses followed by a rest period to allow the cell to reach a pseudo-OCV after each pulse. By pulsing through the entire state-of-charge window an OCV vs capacity curve can be obtained with sufficient accuracy to perform ICA.A direct comparison of conventional constant current ICA (cc-ICA) and high-resolution-OCV ICA (ocv-ICA) is presented. A strong correlation between ageing patterns is observed providing a first proof-of-concept for the method.

(Battery Division Postdoctoral Associate Research Award Sponsored by MTI Corporation and the Jiang Family Foundation) Science of Multiphysics Behavior of Si/C Composite Anodes in High-Energy-Density Lithium-Ion Batteries

The rapidly growing demands on energy storage technologies over the last decade have imposed further requirements for the high energy/power density, safety, and durability of lithium-ion batteries (LIBs). Si/C composite materials have attracted enormous research interest as the most promising candidates for the anodes of next-generation lithium-ion batteries, owing to their high energy density and mechanical buffering property. However, the major disadvantage of materials with ultra-high capacities, such as Si-based materials, is the significant volume change during cycling, which further leads to mechanical and electrochemical degradation. However, a sophisticated and quantitative understanding of the highly electrochemical-mechanical coupling behaviors is still lacking.A comprehensive computational model is indispensable in the developing process of the excellent performance of anode material due to the low-realizability, inconvenience, and high-cost of experiments, which also provides powerful tools for fabrication guidance of novel Si/C composites designs. Hence, a multiphysics modeling framework is established with a detailed geometric description to quantitatively reveal the underlying governing mechanisms of Si/C composite anode behaviors. We studied the effects of the Si weight percentage, the Si-related particle distribution, the Si-related particle size, the mechanical constraint, and the binder domain gradient on the battery performance regarding the potential behavior, capacity delivery, mechanical stress/strain, Li plating, and polarization evolution. In our study, we used silicon monoxide (SiO) as the Si element source and graphite (Gr) as the C element source. Results discover that an 8-10 wt% of SiO would be an optimal choice regarding capacity delivery and minimizing Li plating under 1C constant current charging condition. Positioning SiO particles near the separator and reducing the sizes of SiO particles are also demonstrated to be beneficial for electrochemical performance with trivial influence on mechanical mismatch. In addition, the mechanical constraint demonstrates a balanced effect on the overall performance of cells and the local behaviors of particles. Our findings also indicate that reducing the proportion of carbon-binder domain (CBD) in the upper domain (near the anode surface) compared to the lower domain (near the current collector) positively influences electrochemical performance, particularly in terms of capacity and Li plating. However, such an arrangement introduces potential risks of mechanical failures and we recommend to incorporate a higher proportion of CBD alongside the SiO particles. Finally, an anode design with a lower CBD proportion in the upper domain exhibits superior rate performance.This study explores the multiphysics behavior of Si/C anodes material using a comprehensive methodology combining experimental and FEA techniques, systematically revealing the coupling mechanism among various physical fields, as well as providing efficient and powerful tools in the design, development, and evaluation of high energy density lithium-ion batteries.

An optimized AC side startup strategy of E‐STATCOM for ITER pulsed power electrical network

AbstractDuring the operation of the ITER machine, hundreds of MW/Var of active and reactive power will be exchanged with the grid. The E‐STATCOM scheme composed of the Modular Multilevel Converter (MMC) and split supercapacitor energy storage has been proposed to improve the power compensation performance of the existing reactive power compensation system in the previous study. However, one of the main technical challenges which is lack of research is to precharge all submodule capacitors and supercapacitors from zero to their nominal voltage values efficiently during a startup process. As the capacitance and operating voltage of supercapacitors are much different from capacitors in each submodule, the startup of E‐STATCOM is a more complicated process. To coordinate the energy exchange between submodule capacitors and supercapacitors, submodule capacitors and the grid, this paper presents an optimized four‐stage AC side startup strategy for the E‐STATCOM. The proposed method minimizes the use of current‐limiting resistors while suppressing the surge current in the zero‐voltage startup process of supercapacitors, in addition to optimizing the energy consumption. The pulse current charging, constant current charging and constant power charging strategies of supercapacitors are adopted in different charging stages, and the detailed coordinated control scheme between submodule capacitors and supercapacitors are described and analyzed. The effectiveness and performance of the proposed method are verified by simulation results and hardware‐in‐the‐loop (HIL) experiments.

Revisiting Pulse-Based OCV Incremental Capacity Analysis for Diagnostics of Li-Ion Batteries

This paper presents the concept of applying incremental capacity analysis (ICA) on the OCV curve in the SoC space. The OCV curve can be obtained from any sequence of discharge or charge current or power pulse with a necessary rest period to allow the cell to reach a pseudo-OCV after each pulse. With a high resolution (&gt;100 pulses) in the full SoC window, an OCV-vs.-SoC curve can be obtained with sufficient accuracy to perform an ICA on the obtained OCV curve. ICA as a diagnostic technique has commonly been applied on Li-ion cells with constant charge and discharge at slow currents. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to a battery in an application. Here, we revisit pulse-based ICA to supplement the conventional constant-current-based technique. Based on actual ageing data, we show that ICA performed on a selection of high-resolution OCV curves is comparable or better than conventional ICA with constant current. The main advantage of OCV-ICA is that it can be applied to most cells and systems without a significant interruption of normal cell operation. OCV-ICA can provide valuable insights into ageing mechanisms as well as, e.g., detailed information on changes in internal resistance.

Target decomposition-led light-weighted offline training strategy-aided data-driven state-of-charge online estimation during constant current charging conditions over battery entire lifespan

This paper develops a novel target decomposition-led light-weighted offline training strategy-aided data-driven state-of-charge (SOC) online estimation method during constant current (CC) charging conditions over battery entire lifespan. Firstly, real SOC is conceptually decomposed into base SOC and SOC error. Subsequently, taking voltage and real SOC of initial CC charging cycle as input and output, machine learning algorithm is adopted to offline establish base SOC acquisition model without considering battery aging. Thirdly, the errors between real SOC and acquired base SOC are calculated, where the extremely similar distribution of SOC error against different battery degradation with two aging-dependent peaks can be clearly observed. Following this precious characteristic, a SOC error calculation model is further built only via several typical CC charging cycles with base SOC and battery capacity as input. Finally, the acquired base SOC is compensated by the computed SOC error for real SOC calculation. The validation results demonstrate that the proposed target decomposition-led method has overwhelming advantages in light-weighted offline training and accurate SOC online estimation during CC charging conditions, where maximum mean absolute error and maximum root mean squared error of SOC estimation results over the same type of batteries’ entire lifespan are only 0.98 % and 1.2 %, respectively.

Three-Coil Wireless Charging System Based on S-PS Topology

To protect the battery, radio energy transmission charging typically uses constant current (CC) charging before switching to constant voltage (CV) charging to enhance battery durability. This paper proposes adding an auxiliary clamp coil to the original circuit topology. The IPT battery charger designed with the auxiliary clamp coil can achieve both constant current (CC) and constant voltage (CV) outputs. The mutual inductance between the auxiliary clamp coil and the primary side coil greatly influences the output performance of the entire IPT system, so the auxiliary clamp coil should not be too large. To solve this problem, an S-S-PS circuit with secondary compensation topology in the secondary coil is proposed. This circuit topology reduces the size of the auxiliary clamp coil, allowing it to be placed in an optimal position. When the constant voltage output critical position is reached, the IPT system can still automatically, continuously, and smoothly switch between CC and CV modes. Consequently, this approach avoids increased cost consumption associated with detecting CC-CV switching thresholds, adding wireless transmission communication modules, real-time control of the power transmitter, and active protection of the circuit during constant current charging. Finally, a 48 V/2.5 A prototype was built to verify that the IPT system has CC-CV conversion functionality.

Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages for feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable capacity estimation approach utilising short-duration random charging voltages during the constant-current charging stage and leveraging convolutional neural networks (CNNs). Based on the user-friendly “Vstart−tend” strategy, two health features including charging voltage and its increment are firstly extracted from random charging segments. Secondly, a feature evolution pattern analysis over the battery's lifespan is proposed to divide the charging voltage range for robust model development. An optimal combination of both the sampling interval and data length is determined for the feature extraction. Then, a two-dimensional CNN model is developed to effectively learn ageing-related knowledge from various random charging segments in a specific charging voltage range. The effectiveness of the proposed approach is ultimately verified using two distinct types of batteries across three operational temperatures. The results demonstrate that the proposed approach show much potential as a promising capacity estimation technique utilising a 600 s random charging segment sampled at a 20 s interval.