Battery Charging Research Articles

Energy transition in Portugal: The harnessing of solar photovoltaics in electric mobility and its impact on the carbon footprint

The ambition to achieve carbon neutrality by 2050, reinforced by international agreements, requires both the reduction of gas emissions in the transport sector and the acceleration of the realization of the energy transition. There is therefore growth potential for electric mobility and photovoltaic technology. In this context, this study explores the integration of electric vehicles into the Portuguese car fleet using photovoltaic solar energy for battery charging, with the aim of reducing greenhouse gas (GHG) emissions and reducing dependence on fossil fuels for mobility. A multi-phase approach was adopted: i) a sample of electric vehicles representative of the Portuguese car fleet and their energy needs was sized; ii) the equipment to be incorporated into the Self-Consumption Production Units was selected and sized, using PVsyst software version 7.3.4, and based on the energy needs resulting from three scenarios of weekly use of the electric vehicle [50 km (scenario 1), 300 km (scenario 2) and 750 km (scenario 3)]; iii) the reductions in GHG emissions were quantified by analysing: emissions from the Public Service Electricity Grid (RESP) and the Photovoltaic Integration System for Self-Consumption and Grid (SIFAR), and also emissions associated with the fossil fuels under analysis (petrol and diesel). In scenario 1, the difference was 57.5%. In scenario 2, it was −24.3%, and in scenario 3, it was −55.9%. These results demonstrate that SIFAR is an attractive and more sustainable alternative to RESP in the most energy-demanding scenarios, in this case, scenarios 2 and 3. By contributing to a considerable reduction in GHG emissions into the atmosphere, SIFAR stands out for its role in building sustainability. Presenting a new methodological perspective and vision on the integration of renewable energies in the transport sector in Portugal, this study closes a knowledge gap and provides relevant information for planning and public policy.

Boosting OER activity of Fe-N Moieties via Ni induced d-Orbital Tailoring for durable rechargeable Zn-Air batteries

Fe-N-C-based catalysts are considered the most promising Pt candidates due to their high oxygen reduction reaction (ORR) activity and low cost, whereas their oxygen evolution reaction (OER) performance is extremely poor due to the sluggish O-O coupling process, which hampers the practical application of rechargeable zinc-air batteries. Here, we in situ infiltrate Ni and Fe ions into metal triazolidine (MET)-derived N-doped porous carbon via a microporous confinement-pyrolysis strategy to enhance the OER activity of the Fe-N sites. The introduction of Ni induced the electronic delocalization of Fe atoms at the Fe-N center, which lowered the adsorption energy barriers for the decisive velocity step (O*→OOH*), thus accelerating the O-O bond coupling and OER kinetics. In addition, the preferential formation of NiOOH at high OER potentials ensures the catalytic stability of zinc-air batteries by trapping Fe ions escaping from carbon corrosion to form FeOOH. The optimized catalyst (FeNi-NC) has an overpotential of only 351 mV at a current density of 50 mA cm−2 under alkaline conditions. More importantly, the assembled ZABs can be stably charged and discharged for more than 500 h at 10 mA cm−2, demonstrating excellent battery charging and discharging stability. This work shows the great potential of strong electronic interactions between polymetals to improve Fe-N-C catalysts.

Predictive Synchronous Rectification Control Scheme for Resonant DC–DC Converters for Battery Charging and Telecom Application

Predictive Synchronous Rectification Control Scheme for Resonant DC–DC Converters for Battery Charging and Telecom Application

An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries

Abstract Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned power battery capacity. Firstly, according to the electrochemical aging characteristics of decommissioned power batteries, combined with the ampere-time integration algorithm, a new lossless extraction method of the capacity characteristics of Lithium-ion batteries based on the segment voltage is proposed. Second, to minimize the interference of environmental factors and sampling errors on the charging capacity, the relative amount of charging capacity is introduced. Finally, to complete the capacity sorting before the ladder utilization, an improved RIC-KMC method is proposed, which combines the electrochemical aging feature values of decommissioned power batteries extracted from the segment voltage charging capacity with the K-means clustering algorithm. Using three sets of battery charging and discharging data to validate the sorting algorithm, the capacity sorting errors are reduced by 0.926%, 12.381%, and 10.185%, respectively, which verification the validity of the proposed RIC-KMC method. This study provides a new solution for capacity pre-sorting of decommissioned power batteries for stepwise utilization.

The Embedded System of Battery Charging with Constant Current System using Microcontroller and IC LM338

The battery has the function of an electrical energy storage system. However, charging the battery is prone to damage. Damage to the battery is often caused by overcharging. The time used during the battery charging process is an important factor that must be considered when charging. thus, efficient battery charging is important and very necessary to avoid damage. An another important aspect of a battery charging method is the current and voltage values supplied during its charging process. The presence of inappropriate values damages and reduce battery life, while the maintenance of a constant current is an important charging process step that extends service life. This research is expected to create a safe and optimal battery charging system with a simple low-cost circuit based on constant current by utilizing the LM388 Integrated Circuit (IC) regulator. This study discusses a 12 V battery charging system with monitored and controlled constant current using an R-Shunt sensor. Voltage monitoring while charging was carried out with voltage sensors using a constant current of 0.1 to 1.3 Ampere. Furthermore, voltages and currents were displayed through a Liquid Crystal Display (LCD) screen controlled with Arduino Uno during battery charging. The measurement results showed that the circuit reached voltages and currents of 14.07 V and 0.19 A respectively for a charge duration of 8 hours.

Design and Experimental Verification of Electric Vehicle Battery Charger Using Kelvin-Connected Discrete MOSFETs and IGBTs for Energy Efficiency Improvement

This research investigates the advantages of Kelvin-connected 4-pin discrete transistors, both MOSFETs and IGBTs, in onboard battery chargers for electric vehicles. The study compares the standard 3-pin and the extended 4-pin packages based on averaged data collected from leading manufacturers. The investigation shows significant potential power loss reduction, thermal operation mitigation, and reduced gate-drive oscillation for the 4-pin package. The benefits have been quantified by analysing the operation of actual switches in an automotive battery charger based on Boost-PFC and DC-DC LLC converters. The converters’ practical design demonstrates a procedure for integrating the Kelvin-connected package into the design methodology. The results have been verified experimentally.

Design and Implementation of an Automated Instrument for Battery Charging Experiments

Accurate and long-term measurements are essential in battery charging research and development, to evaluate and compare different techniques and their effectiveness. In such approaches, a very good control of certain charging parameters is also necessary, during several hours or even days. Thus, it became necessary to obtain an automatic instrument that could ensure the precision and repeatability of the experiments, but also the flexibility regarding the type and size of the b attery. This article presents the design, implementation and preliminary experimentation of an instrument for automation and monitoring of battery charge-discharge cycles. The tool adds exponential current pulses to the battery charging process, especially in the constant current phase. Such pulses are rarely addressed in the literature, although some studies suggest that they have the potential to improve batteries performance.

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.

Operando Stack Pressure Measurement of LFP/Graphite and LMFP/Graphite Cells to aid in State of Charge Prediction

Abstract One of the biggest issues facing electric vehicles with LiFePO4 (LFP) batteries is state of charge management. Over much of the state of charge window, the voltage of both LFP and LiMnxFe1-xPO4 (LMFP) batteries does not vary. Therefore, state of charge management for LFP batteries relies on coulomb counting, which requires frequent charging to 100% to remain accurate. We propose to use the signal from pressure sensors placed between prismatic cells in battery modules to help determine the state of charge of LFP and LMFP batteries. Using LFP and LMFP pouch cells to demonstrate this principle, the pressure vs. time profiles of cells constrained in fixed volumes reveal distinct and repeatable profiles, which can be used to determine the state of charge in combination with voltage monitoring. Additionally, a simulated LFP battery pack was constructed and the pressure vs. time profile was monitored in the presence of compliant material under low initial force loadings to demonstrate how this could work in an EV battery.

Optimizing Charge and Discharge of Lithium-Ion Batteries by Deploying PID Controller with Coupled Electro-Thermal-Aging Dynamic

Optimizing Charge and Discharge of Lithium-Ion Batteries by Deploying PID Controller with Coupled Electro-Thermal-Aging Dynamic

High gain super lift Luo converter for hybrid energy management systems

Recently, there has been a resurgence of interest in the development of alternative energy supplies due to rising fossil fuel prices and issues about the effects of greenhouse gas emissions on the environment. Today's demand is the design of a hybrid electric power generation system that uses solar and wind energy for remote places. This paper presents the design of an optimized hybrid renewable energy system consisting of a photo voltaic wind turbine (savinous type) with a battery and a super lift LUO converter. A solar PV module with an intelligent inverter facilitates the tracking of the maximum power point and the implementation of a battery charge controller for greater battery lifespan as well as reliable performance. Higher power output and clean energy are available from the hybrid power generation system, which combines innovative techniques with solar and wind power.

Design of a Safety Discharge System for Prevent Thermal Runaway in Shared Battery Charging Stations

Design of a Safety Discharge System for Prevent Thermal Runaway in Shared Battery Charging Stations

Design of Stabilizing Network for Capacitive Power Transfer Transmitter Operating at Maximum Power Transfer Limiting the Voltage Gain in Resonant Capacitors

Capacitive power transfer (CPT) is a technology that is emerging as an alternative to inductive power transfer (IPT) in applications requiring low to medium power. A great interest has been developed in the implementation of CPT systems in battery charging systems, where a condition to compete with IPT systems is the need to increase the power transfer in the CPT systems without significant losses. This paper puts forth a design methodology for a stabilizing network, which has been applied to a CPT system. This methodology has been developed through impedance analysis of the circuit, in order to achieve maximum power transfer, with total gains of voltage and current reaching a value close to unity. The methodology allows for the calculation of the value of the components of the stabilizing network, which has been designed with the objective of stabilizing the resonant frequency against changes in the capacitance of the transmission plates. To validate the design procedure, an experimental prototype was developed at 25 W and an operational frequency of 1.55 MHz. The results obtained validate the design methodology.

Real-time electrochemical-strain distribution and state-of-charge mapping via distributed optical fiber for lithium-ion batteries

Deep learning has opened new avenues for estimating the dynamic state of charge in batteries. However, hindered by macroscopic and limited electrochemical inputs, data-driven models still struggle to accurately capture local states inside batteries. This study aims to leverage distributed fiber optic sensing and long short-term memory to enhance the accuracy and reliability of the state of charge estimation and to visualize its distribution. First, the internal strain is closely linked to the lithium embedded state of graphite particles, with a coefficient of 0.992 between strain amplitude and capacity over long cycles. Incorporating strain as an additional physical parameter significantly improves accuracy. The proposed model achieves a prediction error of only 2.01 %, representing a 41.2 % improvement compared to models that exclude stain data. Factors such as current and ambient temperature are considered to prove the effectiveness of this method. Furthermore, by combining the trained model with internal strain distribution, the developed intuitive imaging for local state visualization reveals aging tendencies and uneven behavior in lithium-ion batteries. This predictive and imaging approach offers valuable insights for local state analysis and advances battery research and design.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Understanding the heat generation mechanisms and the interplay between joule heat and entropy effects as a function of state of charge in lithium-ion batteries

The thermal performance of lithium-ion battery cells is critical for ensuring their safe and reliable operation across various applications. In this study, we employed an isothermal calorimetry method to investigate the heat generation of commercial 18650 lithium-ion battery fresh cells during charge and discharge at different current rates, ranging from 0.05C to 0.5C, and across various temperatures: 20 °C, 30 °C, 40 °C, and 50 °C. Our findings revealed a direct correlation between heat generation and current rates, indicating that higher current rates lead to increased heat generation within the cells. Conversely, we observed that heat generation remained relatively stable as the temperature rose, suggesting that temperature changes within this range may not significantly impact the heat generation of fresh cells during typical operations. Furthermore, our study explored irreversible heat generation, which depends on the applied current and overpotential, using the galvanostatic intermittent titration technique at 0.05C–0.5C and 30 °C. Additionally, electrochemical impedance spectroscopy was performed on the same cells during charge and discharge at 20 °C, 30 °C, and 40 °C to analyze cell impedance. Our results indicated a consistent dependence of impedance on the state of charge and depth of discharge, with a significant increase in impedance observed at the end of the discharge process.

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

A machine-learning-based column generation heuristic for electric bus scheduling

Bus scheduling in public transit consists in determining a set of bus schedules to cover a set of timetabled trips at minimum cost. This planning process has evolved recently with the advent of electric buses that introduce constraints related to vehicle autonomy and battery charging process. In particular, column-generation algorithms have regained popularity for solving problems similar to the one considered in this paper, namely, the multi-depot electric vehicle scheduling problem (MDEVSP) with a piecewise linear charging function and capacitated charging stations. To tackle large-scale MDEVSP instances, we design a column generation (CG) heuristic that relies on reduced-sized networks to generate the bus schedules. The reduction is achieved by selecting a priori a subset of the arcs. Multiple selection techniques are studied: some are based on a greedy heuristic and others exploit a supervised learning algorithm relying on a graph neural network. It turns out that combining both selection types yields the best computational results. On 405 artificial instances involving between 568 and 1474 trips and generated from real bus lines in Montreal, the network reduction technique produced an average computational time reduction of 71.6% (compared to the same CG heuristic but without network reduction), while deteriorating solution cost by an average of 2.2%. On 8 larger instances containing more than 2500 trips on average, the proposed solution method also provided an average time saving of 52.5% with an average gap of 4.2% thanks to a transfer learning approach.

Joint Model Parameter Identification and EKF Algorithm for the SOC Estimation of LFP Battery

Abstract Accurately estimating the state of charge (SOC) of batteries is crucial for achieving the safety and efficient driving of electric vehicles. To address the negative impact of voltage platform flatness and accumulated errors in current sampling, the SOC estimation method jointing model parameter identification and extended Kalman filter (EKF) algorithm is proposed and verified through simulation in this paper. Firstly, the parameter identification method is obtained based on the second-order dual polarization model, and effective identification of the parameters under different SOC is achieved using experimental conditions of hybrid pulse power characteristic and constant current discharge. On this basis, a function model with SOC as the independent variable and model parameters as the dependent variable is established by jointing model parameter identification and EKF algorithm, and the iterative estimation of SOC is achieved through the 1stopt and cftool methods. Finally, the SOC estimation accuracy of the proposed method is validated under three operating conditions that adopt the latest standards and are closer to the actual driving environment. The simulation results show that the SOC estimation method jointing model parameter identification and EKF algorithm has higher accuracy and smaller fluctuations than the traditional AH integration method, and the mean squared error (MSE) of estimation for the four test conditions are less than 0.29%, 0.72% and 0.25%, respectively.

A Novel Feedforward Scheme for Enhancing Dynamic Performance of Vector-Controlled Dual Active Bridge Converter with Dual Phase Shift Modulation for Fast Battery Charging Systems

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort.