Battery Power Research Articles

Sunwoda's Financial Analysis Based on The Harvard Analytical Framework

In order to comply with the strategy of sustainable development, China hopes to further accelerate the rapid development of the new energy vehicle industry and supporting industries. Power battery is the core component of new energy vehicles, and the selection of sunwoda in the battery industry for financial analysis can provide a useful reference for the same type of enterprises.This paper selects sunwoda as an analysis case, applies the Harvard analytical framework to sunwoda's financial analysis, and conducts in-depth discussion and analysis of sunwoda's financial status and existing problems. The analysis found that sunwoda is currently in the growth period,adopting a growth-oriented development strategy to gradually expand the scale of the enterprise, with many projects underconstruction and rapid growth in scale. However, behind the seemingly perfect financial data, there are problems such as low profitability, intensified market competition,and insufficient future development capabilities.In view of these problems,corresponding improvement suggestions are put forward,hoping to help the development of sunwoda.

Electrochemical Characteristics of Ambarella Peel Waste as Liquid Electrolyte for Zn-Cu Biobattery

This study focuses on the electrochemical characterization of Zn-Cu bio-battery cells utilizing electrolytes derived from ambarella peel waste. The primary objectives are to determine the half-cell and full-cell characteristics of these bio-batteries at various concentration ratios and to identify the optimal concentration ratio for maximum performance. Cyclic voltammetry analysis of the half-cells revealed an oxidation peak at 0.5 V vs Ag/AgCl, corresponding to the conversion of uronic acid to aldaric acid. Additionally, two reduction peaks were observed: hydrogen ion reduction to H2 at 0 V vs Ag/AgCl and water reduction at –0.42 V vs Ag/AgCl. The rate-determining step analysis indicated that the redox reactions in the ambarella peel electrolyte solution were surface reactions. The highest rate constant (ks) of 0.722 ± 0.05 s–1 was observed at a 1:2 concentration ratio. This ratio also resulted in the highest battery capacity of 0.0816 mAh and the maximum power density of 16.13 mW/m2. The study concluded that the 1:2 concentration ratio of ambarella peel waste electrolyte solution is optimal, outperforming the 1:1 and 1:3 ratios in terms of battery capacity and power density.

Adaptive frequency driving scan driver based on a‐InGaZnO TFTs for extremely low‐power displays

AbstractIn this paper, we propose a novel scan driver combined with a logic circuit using amorphous indium‐gallium‐zinc‐oxide (a‐InGaZnO) thin‐film transistors (TFTs) in order to enhance the electrical stability and reduce power consumption in display panels. The latest mobile displays with high resolution and refresh rates consume more battery power, which inevitably limits portability and usability. The proposed scan driver can mask the signal by combining the output stage with a logic circuit, thereby blocking the output pulse for static images. This allows the display panel to operate in a partial driving mode depending on the display content. Due to the masking of the scan driver's output pulses, the connected pixel circuits are consistently maintained in the same state as the previous frame, leading to a lower refresh rate and reduced power consumption. Furthermore, by constructing additional control signals, the proposed scan driver can operate stably in depletion mode under a ∆VTH = −3 V.

Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

Power Battery Recycling Model of Closed-Loop Supply Chains Considering Different Power Structures Under Government Subsidies

With the rapid growth of the electric vehicle industry, the recycling of power batteries has attracted significant attention. In light of current circumstances, the question of how the government can incentivize relevant stakeholders to actively engage in recycling and improve its efficiency has become increasingly pressing. In this context, this study analyses and develops four closed-loop supply chain recycling models to investigate how different government subsidy recipients under varying power structures influence recycling efficiency, profitability, and the overall supply chain structures. The following conclusions are derived from numerical simulations: (1) Government subsidies serve to elevate recycling prices, expand profit margins, and consequently boost the volume of recycled batteries, thus incentivizing corporate engagement in recycling initiatives. (2) When the processor assumes the role of the leader in the Stackelberg game framework, it can maximize the overall efficiency and profitability of the supply chain. (3) The sensitivity coefficient and the competition coefficient are closely interrelated, exerting opposing impacts on the recycling decision made by enterprises. (4) The supply chain leader plays a crucial role in ensuring orderly supply chain development, with government subsidies of the supply chain being transmitted to its members through the leader. Consequently, this study offers a theoretical foundation for the government to enhance policy-making and for enterprises to make informed decisions. It also holds significant practical relevance in addressing the challenges associated with power battery recycling.

Optimization and Structural Analysis of Automotive Battery Packs Using ANSYS

The development of new energy vehicles, particularly electric vehicles, is robust, with the power battery pack being a core component of the battery system, playing a vital role in the vehicle’s range and safety. This study takes the battery pack of an electric vehicle as a subject, employing advanced three-dimensional modeling technology to conduct static and dynamic analyses. Through weight reduction and structural optimization, an innovative power battery pack design scheme is proposed, aiming to achieve a more efficient and lighter electric vehicle power system. The main research tasks are as follows: Firstly, we designed the main load-bearing components of a certain electric vehicle’s power battery pack and established a three-dimensional (3D) model. Then, the model was simplified according to the actual stress conditions of the power battery pack of the electric vehicle and imported into finite element analysis (FEA) software. Next, based on the fundamental principles of the finite element method (FEM), we conducted static analyses under three conditions: bumpy road sharp left turn, bumpy road sharp right turn, and bumpy road emergency braking. The analysis results indicate that the strength of the battery pack meets the allowable requirements, suggesting that the lower housing design has significant redundancy, providing guidance for subsequent optimization. Finally, through modal analysis, we extracted the first six modes of the power battery box, with the first mode frequency being 33.69 Hz. This suggests that the battery pack may experience resonance during actual operation. Based on the static and modal analysis results, we proposed a structural optimization and lightweight design solution for a certain electric vehicle battery pack and compared it with the pre-optimization data.

Rapid synthesis of cobalt manganese phosphate by microwave-assisted hydrothermal method and application as positrode material in supercapatteries

Electrochemical energy storage devices with high specific capacity are of utmost important for the next-generation electronic devices. Supercapatteries (SCs) are highly demanded energy storage devices nowadays as these bridge the low energy supercapacitors and low power batteries. Herein, we report a rapid synthesis of cobalt manganese phosphate (COMAP) by microwave-assisted hydrothermal method and facile fabrication of SCs using electrodes comprising of COMAP as positrode material. The effect of precursor concentration on the microstructure and surface morphology of the COMAP samples are examined initially. Further, the electrochemical performance of COMAP electrodes is studied systematically in 3 M KOH (aqueous) electrolyte. COMAP exhibits excellent charge storage capabilities where type of charge storage mechanism is found to be battery-type based on the calculation obtained from Dunn’s method. The SC electrode fabricated with COMAP synthesized using cobalt: manganese precursor ratio as 80:20 exhibits a highest specific capacity of 191.4 C/g at a scan rate of 1 mV/s. An asymmetric SC (ASC) cell fabricated with COMAP as positrode and activated carbon (AC) as negatrode exhibits a specific capacity of 165.5 C/g at a current density of 1.8 A/g. The COMAP//AC ASC cell exhibits an energy density of 34.1 Wh/kg at a corresponding power density of 1875 W/kg at a current density of 1.8 A/g.

Multi-objective optimization of automotive power battery cooling plate structure using response surface methodology

Multi-objective optimization of automotive power battery cooling plate structure using response surface methodology

Experimental study on the low-temperature preheating performance of positive-temperature-coefficient heating film in the prismatic power battery module

The performance of a power battery directly affects the thermal safety performance of the vehicle. Aiming at the improvement of thermal safety of lithium-ion batteries under low temperature condition, this study focuses on the effect of the positive-temperature-coefficient (PTC) heating film on the heating performance of batteries through experimental testing. First, the side and bottom of the cell were heated, and the heat transfer path and mode of the single cell were analyzed. The effects of different power densities and geometric positions on the heating effect were studied by testing the heating time and temperature consistency. Second, for the battery module, a low-temperature heating thermal management heat transfer model was built to analyze the heating mechanism of the heating film under different heating power densities. Finally, the results of these studies were synthesized to obtain the optimal heating power density. The results show that for the cell, the optimum power density of side heating and bottom heating is 0.5 W /cm2 and 0.4 W /cm2, respectively. The time required for side heating from −20 °C to 10 °C is 730 s lower than that of bottom heating, and the maximum temperature difference is 1.885 °C lower. For battery modules, a power density of 0.5 W/cm2 was appropriate for both bottom and side heating methods. Due to the existence of heat conduction between battery packs, the battery modules will be quickly and evenly preheated. Under the optimal power density, the time length for side heating from –20 °C to 10 °C was 1459 s, and the maximum temperature difference was 6.32 °C; bottom heating time required slightly more time (1783 s), and the maximum temperature difference was 6.221 °C. Side heating can be beneficial for the rapid preheating of the battery module. This study will contribute to improving the performance and safety of batteries in cold environments.

Lithium‑sulfur batteries for next-generation automotive power batteries carbon emission assessment and sustainability study in China

The rise of electric vehicles has ushered in a revolution in the automotive industry, propelling the global automotive sector towards sustainable development. However, considerable controversy persists regarding the comprehensive performance of electric vehicle batteries and their environmental impact. The development of next-generation power batteries, aimed at enhancing energy storage performance while mitigating environmental consequences, has become a focal point of research in this field. Lithium‑sulfur batteries (LSBs), with their innovative structural design and environmentally friendly materials, not only enhance energy storage performance but also harbor significant environmental potential. When coupled with an all-solid-state battery structure, the all-solid-state lithium‑sulfur battery (A-LSB) demonstrates even more superior performance. This combination represents an ideal next-generation power battery for automotive applications, offering heightened efficiency and environmental sustainability. This study conducts a prospective life cycle assessment (LCA) to examine the environmental performance, particularly carbon emissions, during the production processes of LSBs and A-LSBs in the Chinese region. The primary objective is to uncover the potential for sustainable development in the future of lithium‑sulfur battery technologies. During the research process, we conducted comparative validation by comparing with traditional ternary lithium-ion batteries (NCM) and lithium iron phosphate batteries (LFP). Experimental setups were designed to simulate both laboratory and industrial production scenarios, with a primary focus on analyzing the sustainable performance of LSB and A-LSB. The results indicate that, compared to traditional lithium-ion batteries, both LSB and A-LSB exhibit lower carbon emissions and superior environmental performance, with A-LSB showing particular promise. However, A-LSB demonstrates higher sensitivity to environmental impacts, displaying significant variations before and after industrial production. Additionally, it exhibits a greater dependency on regional resources and energy structures. In contrast, LSB displays better regional adaptability.

An evaluate of ORR catalysts in fuel cell: Current Advances and Applications

Abstract. This thesis presents a comprehensive review of fuel cell technology, with a specific emphasis at the latest trends and programs of oxygen reduction response (ORR) catalysts. The examine starts offevolved with the aid of using evaluating the benefits and drawbacks of hydrogen fuel cells with different alternative power sources, which include sun power and secondary batteries, highlighting the ability of fuel cells to update traditional inner combustion engines because of their high power density and environmental benefits. The thesis then explores the essential running ideas of fuel cells and examines their operational mechanisms in numerous chemical environments. In the context of ORR catalysts, the assessment evaluates quite a number catalysts, which include precious metal catalysts, non-precious metal catalysts, and single-atom catalysts, that specialize in techniques to decorate their activity and stability. Finally, the thesis discusses the demanding situations and destiny possibilities for the industrial application of ORR catalysts in hydrogen fuel cells.

Electric vehicle battery closed-loop supply chain pricing and carbon reduction decisions under the carbon cap-and-trade and reward-penalty policies

Recycling end-of-life electric vehicles (EVs) batteries to conserve resources and reduce carbon emissions has obtained a great deal of concern. This paper studied how carbon cap-and-trade and reward-penalty measures jointly impacted EV battery pricing and decarbonization strategies. Three recycling modes covering single-participator, mixed-participator, and joint recycling are established. Optimal pricing and carbon mitigation strategies, total revenue, and recycling percentage are solved and compared. The dynamic effects of target recycling percentage and rewards and punishments on total revenue and recycling percentage are analyzed by numerical examples. Results show that: (1) The factory price, selling price, collection price, and carbon emission mitigation scale of power batteries are affected by cap-and-trade and reward-penalty mechanisms; (2) Reward-penalty can improve both total revenue and recycling percentage; (3) The cap-and-trade mechanism optimizes the total revenues, showing that the total revenue increase with the increasement of carbon quota and carbon price, while the increase of carbon price leads to worse recycling percentage; (4) The supply chain performance under different recycling modes is affected by policy intervention, and the joint recycling mode is better.

Techno-Economic Analysis of Hybrid Pv-Battery-Diesel Power System for Rural Electrification: A Case Study of Ibudo Ora, Ogbomoso

Fossil-fueled generators and electrical grid extensions are the most popular energy sources for supplying electricity to rural areas. However, the high cost of running and maintenance, noise pollution, and a need for decarbonization necessitate the hybridization of different energy sources as a viable solution. Using the conventional technique for the optimal design of a Hybrid Power System (HPS), such as the Hybrid of Multiple Energy Resources (HOMER), is inefficient regarding electricity cost and carbon emission reduction. Hence, this research conducted a technical and economic analysis of an optimal photovoltaic (PV), battery, and diesel generator-based power system to electrify Ibudo Ora, a rural community in Ogbomoso. A feasibility study on electricity demand was conducted on the Ibudo Ora community by conducting an onsite survey, and the community load profile was estimated. Mathematical modeling of the HPS component was formulated. A multi-objective function was developed to minimize the Net Present Cost (NPC), Levelized Cost of Electricity (LCOE), and Total Carbon Emission (TCE) and maximize the System Reliability (SR) of the proposed HPS and optimized using the Energy Valley Optimizer (EVO). MATLAB R2021a was used to simulate the developed model. The performance of the developed EVO-based HPS was evaluated using NPC, LCOE, TCE, and SR as metrics and compared with HOMER, which was used for the same purpose. The NPC of the developed EVO-based HPS and HOMER-based HPS were $998702.87 and $1011984.27, respectively, while the LCOE were $0.4889 and $0.4954. The loss of power supply probabilities of the developed EVO-based and HOMER-based HPS was zero, while in terms of TCE, EVO-based HPS and HOMER-based HPS had 775958.15 kg and 832912.49 kg, respectively. The results showed an appreciable reduction in the NPC, LCOE, and TCE using EVO compared with HOMER for an optimized HPS. This research will assist the government, investors, and policymakers in making decisions on rural electrification using HPS.

Development of a top-lit-up-draft biomass gasifier as a sustainable heat source for effective drying of plantain slices in a cabinet dryer

This paper explored the development of a top-lit-up-draft biomass gasifier as a sustainable heat energy alternative to the traditional sun drying and the costly, unreliable heat energy sources such as grid-electricity and petrochemical fuels commonly used for cabinet dryers, especially in developing countries like Nigeria. The gasifier utilized the principle of top-lit-updraft biomass gasification system to thermochemically converts agricultural residues into clean heat energy. The designed gasifier has reactor diameter and height of 28cm and 90cm, fuel consumption rate of 55kg/hr, gasification airflow rate of 82.1m3/hr, DC fan motor of 15W, equipped with 30Ah battery power source, and capable of supplying heat energy of 260.16MJ/h. Performance testing using input variables of fuel loading (10kg-20kg), fan speed (4000rpm-6000rpm), and fuel type (palm-kernel shell, coconut shell, coconut husk, and wood shavings) assessed the gasifier responses such as fuel gasification time, flame temperature, thermal and combustion efficiencies, and carbon monoxide (CO) emission of the flame. Results revealed maximum thermal efficiency, peak flame temperature and longest gasification time of 86.02%, 8750C, and 130min respectively, when operated with palm-kernel shell at maximum fan speed and fuel loading, which emitted average CO of 1ppm lower than the least international air pollutant threshold of 9ppm. Highest combustion efficiency of 99.9% was attained using wood shavings at maximum fan speed and fuel loading. The results highlighted the potential of the gasifier as an effective heat source for drying applications, particularly for drying plantain slices in a cabinet dryer.

Vision assistant for visually impaired individuals

Visually impaired individuals face significant challenges in accessing environmental information, social interactions, and safety in their daily lives. This article aims to increase the independence of visually impaired individuals and simplify their daily lives using an AI-supported image and sound recognition system. The developed prototype is a pair of glasses consisting of a Raspberry Pi 4 microprocessor, integrated headphones, touch controls, and a powerful battery management system. The electronic diagram includes the integration of a camera and microprocessor. The AI algorithms developed successfully perform tasks such as object recognition, text reading, and facial recognition. The article provides a comprehensive literature review and introduces the prototype to visually impaired users. Based on the feedback from the introduction, the prototype's ease of use and effectiveness were evaluated, and improvements were made based on user feedback. The algorithms were tested for accuracy, with the prototype achieving a 78% accuracy score, close to GPT-4's 82%. Additionally, tests with 20 users resulted in an 85% user satisfaction rate.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

All‐Climate Thermal Management Performance of Power Batteries Based on Double‐Layer Flexible Phase Change Materials

AbstractThermal management systems for power batteries based on phase change materials (PCM) are limited by low heat transfer efficiency, leakage issues, and high rigidity, and most of them cannot meet the needs of all‐climate thermal management. A double‐layer flexible phase change material (FPCM) sleeve structure for all‐climate thermal management is proposed in this study for the first time. Innovations in both material and design have enhanced the adaptability of the thermal management system. The outer layer FPCM achieves high thermal conductivity (4.23 W m−1 K−1) and electrical conductivity (0.95 S m−1), the inner layer FPCM achieves insulation (22.76 MΩ) and flexibility, the thermal contact resistance (TCR) between the double‐layer sleeve structure and the battery are measured to be only 0.15 °C W−1, significantly improved thermal management performance. Experimental results show that at 30 °C ambient temperature, 5 C discharge, the system reduces the maximum temperature by 14.5 °C, from 61.4 to 46.9 °C, compared to natural convection. Additionally, during heating, the system achieves heating rates of 7.0, 8.3, and 8.7 °C min−1 at −20, −10, and 0 °C, respectively, extending battery discharge duration by 32.5%, 65.9%, and 33.6%.

Fine-tuning GAN Models with Unpaired Aerial Images for RGB to NDVI Translation in Vegetation Index Estimation

Calculating the Normalized Difference Vegetation Index (NDVI) requires expensive multispectral cameras, posing challenges such as high costs and the need for technical expertise. This research aims to develop a method to transform RGB images obtained from Unmanned Aerial Vehicle (UAV) into NDVI images. Our proposed method leverages the CycleGAN model, an unsupervised image-to-image translation framework, to learn the intricate mapping between RGB values and NDVI. The model was trained on unpaired datasets of RGB and NDVI images, sourced from paddy fields located in Gresik and Yogyakarta, Indonesia. This process successfully encapsulated the complex correlation between these two modalities. Various training strategies were systematically investigated, including weight initialization schemes, fine-tuning procedures, and learning rate policies, to optimize the model's performance. The fine-tuned CycleGAN demonstrated superior performance in creating synthetic NDVI images from unpaired dataset, surpassing other methods in terms of fidelity, quality, and structural coherence. The results were impressive, with a Normalized Root Mean Square Error (NRMSE) of 0.327, a Peak Signal-to-Noise Ratio (PSNR) of 16.330, an Oriented FAST and Rotated BRIEF (ORB) score of 0.859, and a Structural Similarity Index (SSIM) of 0.757. The best performing CycleGAN model was then deployed on a low-spec microcomputer device, specifically the Raspberry Pi 4B with an average computation time of 21.0077 seconds. Raspberry Pi 4B was chosen for its lightweight, compact dimensions, and compatibility with efficient battery power connections, making it suitable for drone deployment.

Research on power battery anomaly detection method based on improved TimesNet

Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving. However, power battery data are often non-linear and unstable due to external factors, such as temperature conditions, which pose challenges for anomaly detection. Existing methods for collecting battery data’s temporal and spatial features are available, but many of them yield suboptimal detection accuracy and feature extraction efficiency due to their disregard for the multi-periodicity of the data. This paper proposes a novel network structure for power battery anomaly detection based on an improved TimesNet. Firstly, the original battery data undergo preprocessing, and the feature correlation coefficient matrix is established using the MIC algorithm. Secondly, the improved TimesNet network is employed to convert the one-dimensional time feature sequence into a two-dimensional tensor, enabling the extraction of temporal features and dependencies at various cycle scales. Finally, the two-dimensional tensor is transformed into a one-dimensional time series, which undergoes information-weighted aggregation to obtain the final anomaly detection results. To assess the effectiveness and generalization of the proposed model, experiments are conducted using Battery and four public datasets for anomaly detection. The experimental results demonstrate that the proposed model outperforms the transformer, autoformer, TimesNet, and Dlinear models, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models.

Self-powered wireless sensor system utilizing a thermoelectric generator for photovoltaic module monitoring application

In this work, we demonstrate a self-powered wireless PV module monitoring system that utilizes a thermoelectric generator (TEG) to convert residual thermal energy from the PV module into electrical power. We investigated the TEG performance with and without the heat sink. Results show that the temperature difference between the hot and cold sides of the TEG increased to 7.2 °C with the heat sink, compared to only 1 °C without it, at a hot side temperature of 50 °C. We integrated the TEG/heat sink with the PV module, which served as the heat source, achieving a maximum output power of 0.981 mW at a voltage of 0.06 V under a temperature gradient of 3.6 °C in a 1 sun condition. We successfully demonstrated a self-powered wireless PV monitoring sensor system by integrating a step-up voltage converter, microcontroller, IR thermometer, Bluetooth communication module, and the TEG/heat sink, which generated sufficient power for the monitoring system operation. The findings introduce a novel solution for wireless PV module monitoring that operates independently of grid connections or battery power. This innovation not only signifies advancements in renewable energy management but also opens new opportunities in the Internet of Things (IoT) sector.