Battery Discharge Research Articles

Design of a reliable electrical photovoltaic generator based on power converters control

This work proposes a renewable energy generation system that operates with a similar behavior as a synchronous generator in terms of reliability in the power supply and inertial response properties against power changes and grid voltage disturbances. The main contributions of this work are: (1) the renewable energy variability managing by means of a battery energy storage strategy, whose charge and discharge are determined by a forecasted-based power adjustment algorithm, (2) the dynamical modeling of an inverter that includes physical inertia and its corresponding response to power and voltage variations, and (3) the synthesis of a nonlinear optimal control for the inertia-based inverter system where the reactive power injection is regulated. Simulation results for a photovoltaic-based generator are presented to demonstrate the effectiveness of the proposed methodologies.

Simulation of a Line Voltage Regulator in a Low-Voltage Grid That Is Subject to Strong Voltage Surges Due to the Provision of Fast Frequency Reserve

The increasing adoption of battery storage units alongside private PV systems may prove to be a new challenge for distribution grid operators. This study explored the potential impact of marketing aggregated battery discharge power as a Fast Frequency Reserve (FFR) and its effect on the distribution grid stability. We investigated the efficacy of Line Voltage Regulators (LVRs) in mitigating voltage surges caused by simultaneous battery activation. For this purpose, a simulation was developed via Matlab (Version R2023a) to simulate the voltage at the nodes of an arbitrary distribution grid, using the feed-in and consumed power of the customers as the input. We applied the model to a distribution grid section in Sundsvall (Sweden). The results confirmed that LVRs can amplify voltage surges when their adjustments are not synchronized with the FFR activation. This study underscored the need for proactive measures to address the voltage maintenance challenges arising from the integration of battery storage units and renewable energy sources.

An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an optimized multi-segment long short-term memory (MSLSTM) network strategy for SOC estimation of powered lithium-ion batteries' adaptive wide temperatures. First, the multi-timescale electrochemical processes during the charging and discharging of power lithium-ion batteries are efficiently analyzed, and the analytically measurable external parameters are classified into subsets based on the analysis. Secondly, the idea of segment long short-term memory (SLSTM) estimation is proposed to enhance the data linkage between the SOC and the nonlinearly varying parameters and to improve the prediction accuracy. Finally, an optimized MSLSTM neural network is proposed for nonlinear regression prediction of SOC in subset intervals through a combination of segmented estimation idea and SLSTM neural network. The proposed algorithm is validated under a variety of temperatures and operating conditions, and the accuracy of the SOC estimation is improved by at least 66.770 % or more. It provides a solution idea for intelligent estimation of power lithium-ion battery SOC.

Quantitative failure analysis of lithium-ion batteries based on direct current internal resistance decomposition model

Accurate failure analysis plays a pivotal role in the optimization design and lifetime prediction of 4.45 V high-voltage LiCoO2/Graphite (LCO/Gr) batteries. Multiphysics coupling model brings great opportunities to conduct battery failure analysis quantitatively, although it is quite challenging because many model parameters need to be handled properly. Herein, we systematically elaborate the differences of ion and electron transport properties before and after cycling ageing of LCO/Gr batteries by constructing direct current internal resistance (DCR) decomposition model. The key parameters acquisition method is established, and the mechanism of DCR growth is elucidated. Furthermore, the aforementioned model parameters are refined by using a hybrid power pulse characteristics (HPPC) curve optimization algorithm based on DCR decomposition results obtained from the three-electrode battery system. Through analyzing the influence of single-factor parameter ageing on battery voltage output capacity and discharge temperature rise, the main factors affecting battery failure process are identified. For instance, the account of the positive electrochemical reaction resistance related to the kpos increased from the initial 22.9% of total DCR to 37.3% after ageing. This work provides a reliable quantitative analysis basis for the global optimization design of advanced LIBs.

Comparison of prediction performance of lithium titanate oxide battery discharge capacity with machine learning methods

Comparison of prediction performance of lithium titanate oxide battery discharge capacity with machine learning methods

Effects of Deformation Microstructures on the Electrochemical Properties of Mg-Hg-Ga Alloy Anode

Mg-Hg-Ga alloy is one of the anode materials for seawater-activated batteries. Here, Mg-3.0 wt%Hg-3.0 wt%Ga alloys were prepared by extrusion and rolling and the microstructure of the Mg-3.0 wt%Hg-3.0 wt%Ga alloy after plastic deformation was studied. The effects of deformation microstructures on the discharge properties and corrosion behavior of the alloys were investigated by galvanostatic discharge, potentiodynamic polarization, electrochemical impedance spectra experiments, and Mg/AgCl battery test. The results show that the microstructure of the extruded alloy is uniform, and the corrosion activity and discharge activity of the extruded alloy are relatively weak. Hot rolling deformation refines the grains and promotes the precipitation of the Mg5Ga2 phase distribute more dispersively, which results in faster and more uniform activation of the alloy, thus improving the corrosion activity and discharge activity of the Mg-3.0 wt%Hg-3.0 wt%Ga alloy. The results of galvanostatic discharge in half battery and Mg/AgCl battery discharge tests show that rolling deformation improves the discharge properties of the alloy, and the alloy with 78% rolling reduction has the best discharge performance.

Energy storage capacity optimization of optical storage microgrid considering customer satisfaction and depth of energy storage discharge

Abstract The influence of the depth of battery discharge (DOD) and user satisfaction on the capacity configuration of the optical storage microgrid cannot be ignored. In this paper, the minimum comprehensive cost of an optical storage microgrid is taken as the objective function, and the model is established by considering the SOC after DOD, user satisfaction with electricity consumption, power balance, and other constraints. The numerical examples show that the proposed method can allocate suitable energy storage capacity for optical microgrids and ensure user satisfaction.

A Study on Fishing Vessel Energy System Optimization Using Bond Graphs

Recently, environmental regulations have been strengthened due to climate change. This change comes in a way that limits emissions from ships in the shipbuilding industry. According to these changes, the trend of ship construction is changing installing pollutant emission reduction facilities such as scrubbers or applying alternative fuels such as low sulfur oil and LNG to satisfy rule requirements. However, these changes are focused on large ships. Small ships are limited in size. So, it is hard to install large facilities such as scrubbers and LNG propulsion systems, such as fishing boats that require operating space. In addition, in order to apply the pure electric propulsion method, there is a risk of marine distress during battery discharge. Therefore, the application of the electric–diesel hybrid propulsion method for small ships is being studied as a compromised solution. Since hybrid propulsion uses various energy sources, a method that can estimate effective efficiency is required for efficient operation. Therefore, in this study, a Bond graph is used to model the various energy sources of hybrid propulsion ships in an integrated manner. Furthermore, based on energy system modeling using the Bond graph, the study aims to propose a method for finding the optimal operational scenarios and reduction ratios for the entire voyage, considering the navigation feature of each different maritime region. In particular, the reduction gear is an important component at the junction of the power transmission of the hybrid propulsion ship. It is expected to be useful in the initial design stage as it can change the efficient operation performance with minimum design change.

Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.

A novel thermal management system for a cylindrical battery based on tubular thermoelectric generator

This study introduces an innovative thermal management system tailored for cylindrical Lithium-ion batteries. It integrates a tubular thermoelectric generator (TTEG) to handle both battery thermal management and the utilization of waste heat for power generation. The main focus lies in evaluating the waste heat recovery capabilities of the TTEG to enhance effective thermal management. The research investigates key geometric and operational parameters such as battery discharge rate (C-rate), thermocouple count (NTC), heat transfer coefficient (HTC), and thermoelectric leg height (HL) to comprehensively assess the overall system performance. The results highlight that integrating the TTEG improves thermal management and waste heat recovery in lithium-ion batteries. It demonstrates temperature reductions of up to 21 °C at a discharge rate of 3C compared to batteries without the TTEG thermal management system. Additionally, increasing the NTC leads to a rise in the maximum battery temperature, thereby enhancing waste heat recovery performance. For instance, increasing the number of thermocouples from 100 to 144 results in a substantial up to 70 % increase in TTEG voltage.Furthermore, enhancing HTC and HL positively impacts thermal performance, showing a significant decrease in the maximum battery temperature. Elevated HTC and HL also contribute to improving thermoelectric voltage, power, and conversion efficiency, highlighting their dual role in enhancing waste heat recovery. For instance, at peak conditions, there is a notable reduction of 2.66 °C in the maximum battery temperature with an HL of 12 mm compared to 4 mm. Additionally, increasing HTC from 10 to 20, particularly at maximum discharge, leads to a surge of 15.87 % in voltage and a substantial 34.28 % increase in output power.

A thermal management design using phase change material in embedded finned shells for lithium-ion batteries

Phase change material (PCM) has recently been regarded as a promising thermal management means for lithium-ion (Li-ion) batteries. However, solid-liquid phase change of many current PCMs occurs in a narrow temperature range, and the cooling performance of their liquid phase is rather poor. To tackle these issues, this article presents a battery thermal management (BTM) design which injects PCM into an aluminum shell around each cell, and machines the shell inner surfaces into a row of straight rectangular fins immersed into the PCM and its outer surfaces into a plain-fin shape exposed in the air. In the mild thermal conditions, the finned shells of two adjacent cells can be embedded and passive PCM cooling is employed. Once the PCM completely melts, the shells will move apart and an airflow will be driven through the gap between them. The BTM design can also protect Li-ion batteries in a freezing environment, by taking advantage of PCM solidification when the finned shells return to their embedded configuration. A series of experiments were conducted to examine its effectiveness on two 50Ah lithium nickel cobalt manganese oxide (NCM) prismatic batteries, which were discharged at a capacity-rate of 2C and at room temperatures of T0=25∘C, 40∘C and −10∘C. It is shown that in the battery discharge at T0=25∘C, PCM in the embedded finned shells effectively reduced the average battery-surface temperature (T¯) and the maximum temperature difference (ΔTmax) by 21% and 36.7%, respectively, compared to bare batteries. At T0=40∘C, this BTM design successfully limited the growth of T¯ to 9.0∘C and led to ΔTmax no more than 1.5∘C. As to the low-temperature scenario, it maintained the batteries at temperatures above T0 for 55.2% longer than those without any BTM protections. Its cooling performance was also compared with that of the conventional liquid-cooling scheme using a bottom serpentine-channel cooling plate. All these results clearly demonstrate that the BTM design in this article can compensate for the deficiencies of conventional PCM-based BTM schemes. Regardless of whether the PCM phase change is available, it has well coped with different thermal management demands of Li-ion batteries.

Analysis of partial load loss of the PCS and internal storage battery loss in residential PV power generation and storage battery systems

AbstractThis study aims to quantify the amount of loss due to partial load of power conditioning system (PCS) and internal loss of storage battery in residential photovoltaic (PV) power generation and storage battery system by using home energy management system (HEMS) data. After identifying the configuration of the PV power generation and storage battery system, HEMS data on PV power generation, household demand, storage battery charging, storage battery discharging, purchased power and sold power were extracted and analyzed according to the research purpose. As a result, the loss due to partial load of the PCS and the average internal loss of the storage battery were quantified. In addition, it was confirmed that a more realistic system simulation could be performed by considering the impact of the partial load of the PCS and the average internal loss of the storage battery.

Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm

Due to the large-scale application of electric vehicles, the remaining service life prediction and health status diagnosis of lithium-ion batteries as their power core is particularly important, and the essence of RUL prediction and SOH diagnosis is the prediction of remaining capacity. Through the aging experiment of cycle charging and discharging of lithium-ion batteries, the health features of experimental data are extracted for the prediction of remaining capacity. In this paper, a deep feature extraction method based on Bilinear CNN combined with CatBoost algorithm based on fractional order method optimization particle swarm optimization, and ant colony optimization algorithm is proposed for battery remaining capacity prediction. Seven groups of health features extracted from ten groups of battery data were used to input them into the optimized CatBoost algorithm for regression prediction. The results show that the proposed model achieves accurate SOH and RUL prediction, the three evaluation indicators MAE, RMSE, and MAPE of SOH are all within 1.7 % and the error rate of RUL is not higher than 1.5 %, and the test of multiple batteries also proves its strong robustness.

Hyperbolic enhancement of a quantum battery

A quantum system which can store energy, and from which one can extract useful work, is known as a quantum battery. Such a device raises interesting issues surrounding how quantum physics can provide certain advantages in the charging, energy storage, or discharging of the quantum battery as compared to their classical equivalents. However, the pernicious effect of dissipation degrades the performance of any realistic battery. Here, we show how one can circumvent this problem of energy loss by proposing a quantum battery model which benefits from quantum squeezing. Namely, charging the battery quadratically with a short temporal pulse induces a hyperbolic enhancement in the stored energy, such that the dissipation present becomes essentially negligible in comparison. Furthermore, we show that when the driving is strong enough, the useful work which can be extracted from the quantum battery, that is the ergotropy, is exactly equal to the stored energy. These impressive properties imply a highly efficient quantum energetic device with abundant amounts of ergotropy. Our theoretical results suggest a possible route to realizing high-performance quantum batteries, which could be realized in a variety of platforms exploiting quantum continuous variables. Published by the American Physical Society 2024

Direct reconstruction of the temperature field of lithium-ion battery based on mapping characteristics

Lithium-ion (Li-ion) batteries are susceptible to the working temperature so that the monitoring is essential to the battery internal temperature. The electrochemical-thermal coupling model completely reflects the battery internal behavior. However, the high complexity and multi-field coupling make it difficult to be applied to reconstruct the battery temperature field based on state observer. Based on mapping characteristics, this work proposes a temperature field direct reconstruction scheme. And the electrochemical-thermal coupling model plays a large role in this scheme. Mapping characteristic vectors of the transient temperature field are obtained from the step response model of the Li-ion battery temperature field to charge and discharge current. Furthermore, the quantitative connection of the mapping characteristic vectors between the surface points of and the internal nodes of the battery is characterized by a relevance degree matrix. And a direct reconstruction model of the internal transient temperature field is established. The internal temperature field is reconstructed directly online and in real time via exploiting the temperature measurements on battery surface. In numerical experiments, the three-dimensional temperature field of the Li-ion battery charge and discharge processes is reconstructed. The effects of charge and discharge rates, model mismatch, and measurement errors on the reconstruction results are investigated. During the charge and discharge processes, the maximum values of the transient average error of the reconstructed temperature field are about 0.4 K and 0.3 K, respectively. And the transient average error is generally below 0.8 K when the standard deviation of measurement errors is 0.5 K.

Improvement of Power Factor in Wireless Electric Vehicle Charging Using Inductive Power Transfer Topology

The EV requires energy supplied to its motors to function properly. This required energy either can be transmitted by a physical wired connection or by wireless power transfer firstly to the battery using charging of the EV system and then battery discharges to supply to the vehicle. This paper deals with the wireless transfer of power using the induction method for charging battery of the vehicle using electric energy for its functioning as it is the most suitable and fast mode to charge the batteries of many vehicles altogether at charging station. The challenges recently faced by EV’s includes the movement of energy from the energy supply point to the destination i.e., EV’s without the direct interaction of battery and energy source. Many topologies have been introduced in this regard and several simulation models are built.

Transactive energy based on virtual power plant and ancillary services: A real case application in Brazil after the Law 14.300/2022

This paper introduces a transactive energy model that incorporates a Virtual Power Plant composed of photovoltaic systems, batteries and hybrid systems within an ancillary services framework. In response to the enactment of Law 14.300/2022 in Brazil, which has led to diminished commercial incentives for the installation of Distributed Energy Resources, investors are compelled to explore alternative avenues to offset these losses. The paper explores the coordination of Distributed Energy Resources as a Virtual Power Plant for commercial purposes, simultaneously addressing technical challenges such as promoting voltage control, mitigating electric losses, and reducing power demand. Simulations conducted in this paper utilizing a real case system in Brazil demonstrate that the proposed approach holds promise. This is attributed to the flexibility afforded by charging the batteries, as well as the inverter's capacity in the Distributed Energy Resources to inject reactive power, thereby enhancing the overall value of the proposed business model. The simulations conducted reveal that economic benefits are notably attained by charging batteries during off-peak periods and discharging them during peak period. Furthermore, due to the significant integration of photovoltaic systems, charging during off-peak hours contributes to the mitigation of overvoltage issues and reduction of energy losses. It is important to highlight that, during peak periods, the optimization of commercial gains is contingent upon adhering to a prescribed 3-h limit for battery discharging. Notably, the prioritization of commercial gains during peak times has the potential to enhance power demand reduction, as evidenced in the case study presented in this paper.

Enhanced EV Battery Monitoring Using IoT with Improved SEPIC - ZETA Converter and Modified Lion Optimization for Photovoltaic Systems

The rising popularity of electric vehicles (EVs) and the increasing emphasis on sustainable transportation have highlighted the importance of advanced monitoring systems. These systems play a crucial role in providing real-time information about battery parameters to ensure optimal performance and longevity. Moreover, the integration of renewable energy sources, specifically photovoltaic (PV) systems, with EV charging infrastructure presents an exciting prospect to enhance energy efficiency and minimize environmental harm. As a consequence the proposed work develops an Internet of Things (IoT) based PV fed EV charging system. The system incorporates an improved SEPIC-Zeta converter together with Modified Lion Optimization algorithm (LOA) assisted Proportional Integral (PI) controller for optimizing PV system for charging EV battery. IoT connectivity monitors parameter such as PV voltage, PV current, and State of Charge (SOC) of battery. Real-time monitoring of these parameters is achieved by leveraging sensors. The collected data is transmitted through IoT networks to a central monitoring system, enabling operators to track the performance of PV system and EV battery. This facilitates optimized energy generation, efficient battery charging and discharging, and overall system reliability. IoT-enabled monitoring system provides remote accessibility, data analytics, and the generation of alerts and notifications for timely actions. Experimental validation demonstrates that, the proposed concept enhances the monitoring capabilities, improves energy efficiency, and ensures reliable operation of EV charging infrastructure.

Two-phase cooling system for electric vehicles’ battery

The objective of this research project is to design an innovative cooling system for effectively managing the thermal conditions of batteries in electric vehicles. Electric vehicles commonly utilize Lithium-Ion cells as their power source. Despite significant advancements in Lithium-Ion technology from an electrochemical standpoint, the thermal management of these batteries remains a formidable challenge. This is primarily due to the demanding operational conditions that Lithium-Ion cells face during battery discharge, motion, and charging. The power supply unit in electric vehicles often demands high power outputs within short durations, leading to the generation of substantial heat by the batteries. This elevated working temperature poses a risk of decreased battery performance or even malfunction. Therefore, an efficient battery thermal management system is essential to optimize the performance of the batteries. In our research project, we propose and investigate a cooling system that is directly integrated into the power supply unit. Our study introduces an innovative thermal management system that combines two-phase direct liquid cooling with pulsating heat pipes. This system provides a compelling solution by combining high thermal efficiency, passive operation, and cost-effectiveness. Within this configuration, batteries are immersed in a low-boiling dielectric fluid contained in a Plexiglas container, facilitating efficient heat exchange. Simultaneously, the pulsating heat pipe operates to manage heat spikes by promoting vapor recondensation, thereby maintaining safe operational temperatures. The proposed battery thermal management system has demonstrated remarkable efficiency, ensuring that battery temperatures remain within the recommended range even under high load conditions. A notable advantage of this cooling system is its complete passivity, eliminating the need for energy-consuming coolant circulation.

Theoretical screening of electrocatalytic performance of Fe@BNC materials in lithium-sulfur batteries

Advanced cathode materials can suppress the shuttle effect and improve the slow sulfur reduction kinetics during discharge in lithium-sulfur batteries. The catalytic performance of Fe@BNC single-atom catalysts as cathode materials was evaluated through simulations based on density functional theory in this article. By calculating the Gibbs free energy curve and adsorption energy of polysulfides on the substrate, it was found that Fe@BNC has a reasonable adsorption energy range and good catalytic activity. Moreover, calculations of reaction energy barriers indicate that Fe@B1N3 performs well in delithiation when charged. The Research investigates in detail the electrocatalytic properties and electronic structure properties of Fe@BNC as a cathode material for lithium-sulfur batteries. This paper highlights its potential to improve battery performance by reducing the shuttle effect and accelerating the redox kinetics.