Battery Charging Research Articles

Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network

Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.

Development of GaN-Based, 6.6 kW, 450 V, Bi-Directional On-Board Charger with Integrated 1 kW, 12 V Auxiliary DC-DC Converter with High Power Density

Automotive-grade GaN power switches have recently been made available in the market from a growing number of semiconductor suppliers. The exploitation of this technology enables the development of very efficient power converters operating at much higher switching frequencies with respect to components implemented with silicon power devices. Thus, a new generation of automotive power components with an increased power density is expected to replace silicon-based products in the development of higher-performance electric and hybrid vehicles. 650 V GaN-on-silicon power switches are particularly suitable for the development of 3–7 kW on-board battery chargers (OBCs) for electric cars and motorcycles with a 400 V nominal voltage battery pack. This paper describes the design and implementation of a 6.6 kW OBC for electric vehicles using automotive-grade, 650 V, 25 mΩ, discrete GaN switches. The OBC allows bi-directional power flow, since it is composed of a bridgeless, interleaved, totem-pole PFC AC/DC active front end, followed by a dual active bridge (DAB) DC-DC converter. The OBC can operate from a single-phase 90–264 Vrms AC grid to a 200–450 V high-voltage (HV) battery and also integrates an auxiliary 1 kW DC-DC converter to connect the HV battery to the 12 V battery of the vehicle. The auxiliary DC-DC converter is a center-tapped phase-shifted full-bridge (PSFB) converter with synchronous rectification. At the low-voltage side of the auxiliary converter, 100 V GaN power switches are used. The entire OBC is liquid-cooled. The first prototype of the OBC exhibited a 96% efficiency and 2.2 kW/L power density (including the cooling system) at a 60 °C ambient temperature.

Hierarchical Game Theory-Based Optimal Power Management for Both Seaports and Ships

Unlike land-based power systems, a seaport microgrid is not only equipped with extensive electrification but also connected with ships, which turns maritime power management into a comprehensive transportation-power coordination problem. To fully exploit the potential of shipboard power systems for energy efficiency improvement of seaports, a hierarchical Stackelberg game-based framework is developed in this paper to achieve efficient power management for both seaports and ships. In this framework, the seaport microgrid serves as a leader to manage the local sources and determine the price for ships to maximize revenue, considering the influence of both the power side and transportation side. On the other hand, the ships act as followers to decide the charging and service strategies for an optimal trade-off between the benefits from onboard battery charging and the ship service cost. The proposed game is solved as a bilevel optimization problem via mathematical programming with the equilibrium constraints method. The proposed algorithm's efficiency is evaluated through numerical simulations. The simulation results demonstrate that the proposed power management method can simultaneously increase the economic profits of seaports and improve the service of ships at berth. Compared to traditional energy management methods, the comprehensive benefits can be increased by approximately 36%.

Pre-magnetization smashing hydrated vanadium ions to improve redox flow batteries performance

The vanadium redox flow battery (VRFB) has received extensive attention due to its intrinsic safety and high scalability. Currently, there are still deficiencies of low energy efficiency and higher unit capacity cost in VRFB energy storage. Continuously optimizing the battery performance through reducing electrode polarization losses is a necessary way to achieve efficient VRFB energy storage technology. In this study, a method of pre-magnetizing the electrolyte to improve the performance of the battery was proposed. A permanent magnet with an intensity of 0.9 T was used to pre-magnetize the vanadium-ion aqueous electrolyte before feed into the battery stack and working-on, it was found that the diffusion capacity of vanadium ions increased by 133.6 % after pre-magnetized even if the external magnetic field was removed. Meanwhile, both of the charge transfer and concentration polarization resistance of the electrode were reduced. The energy efficiency (EE) of the battery was increased by 6.4 % in the charge–discharge test at current density of 300 mA cm−2. The power density increased by 6.19 % with current density 400 mA cm−2 at most, and it prefers to continuously increase as current larger. The electrolyte pre-magnetization showed significant positive improvements on the electrode reaction kinetics and battery charge–discharge cycle.

Development of an advanced current mode charging control strategy system for electric vehicle batteries

Electric vehicles (EVs) face customer hesitancy due to challenges in locating fast charging stations, lengthy recharging times, and incompatible charging ports. This research addresses these issues by proposing a novel current mode control strategy for EV battery charging. Traditional charging methods often result in suboptimal rates, battery degradation, and safety risks. The primary objective is to enhance charging efficiency, safety, and battery lifespan by optimizing parameters such as voltage and current. Control mode charging offers significant advantages over plug-in charging by minimizing stress factors that contribute to degradation, such as high temperatures and excessive charging cycles. This approach aims to extend the lifespan of EV batteries while ensuring safe, efficient, and fast charging. The control system offers three charging modes: slow (0.49 A, 6.31 W, 264 mins), medium (2.74 A, 34.85 W, 50 mins), and fast (4.62 A, 50.80 W, 30 mins) using a 12 V single-phase supply. This advanced strategy significantly improves EV charging system efficiency, with fast charging achieving 80% higher efficiency than slow charging in both simulations and experimental testing. The key contribution of this research is the development of a tailored current mode charging strategy that optimizes charging efficiency while ensuring battery longevity and safety.

Battery charging system for electric vehicle

Selecting the appropriate charger for electric vehicles (EVs) is crucial for enhancing performance, with non-isolated DC-DC converters playing a significant role in charging EV batteries. The efficient conversion of input power into output as per the requirement is main perspective in the design of DC-DC converters. This paper delves into the landscape of non-isolated DC-DC converters utilized in EV charging, emphasizing their pivotal role. Additionally, it introduces a novel approach by incorporating machine learning-based pulse width modulation (PWM) control for the buck DC-DC converter. By integrating machine learning algorithms into the control scheme, the efficiency and performance of the charging system can be greatly enhanced, resulting in improved overall EV operation. This innovative application of machine learning not only optimizes charging efficiency but also enables adaptability to varying input/output conditions, ultimately leading to more efficient and effective charging processes for EVs.

Autonomous Street Lighting System based on Solar Energy and LEDs

This paper introduces a solar-powered autonomous street lighting system designed primarily for areas without access to the grid, such as rural roads and intersections. It utilizes solar panels as the primary energy source, with batteries serving as backup, and employs light-emitting diodes (LEDs) for illumination. The proposed system is optimized for efficiency by employing direct current (DC) power at all stages. To replace a 70W high-pressure sodium (HPS) lamp, an LED fixture design is discussed, considering human scotopic vision. Experimental outcomes for the LED driver and battery charger are provided, demonstrating features such as maximum power point tracking (MPPT), constant current, and constant voltage modes. The system ensures MPPT under varying solar irradiance and temperature conditions by analyzing the battery charger input impedance.

Accurate Evaluation of Commutations of 650 V GaN Power Switches Assisted by Electromagnetic Simulations in a 7 kW Dual Active Bridge Converter for Automotive Battery Charging Applications

The exploitation of high-voltage GaN power switches enables the development of power converters with superior characteristics with respect to components developed with heritage silicon-based technologies. One of the key advantages of GaN switches is their very fast commutation capability due to reduced parasitic capacitance and inductance with respect to silicon devices. The capability of an accurate evaluation of the switch commutations in the design phase is of crucial importance to maximize performance and avoid reliability or electromagnetic compatibility issues in the final converter. In this paper, an accurate evaluation of high-voltage GaN HEMT commutations is performed, exploiting detailed non-linear dynamic models of transistors and electromagnetic simulations of a PCB. A deep insight into the commutation waveforms in the intrinsic device (i.e., conductive drain current and intrinsic node voltages) is proposed to evaluate and explain the mechanisms of almost lossless turn-off and turn-on commutations in a 7 kW DAB converter. The influence on the performance of the PCB parasitics and the driver characteristics are accurately reproduced by simulations, suggesting important guidelines for the optimal design of power converters fully exploiting GaN HEMT’s potential. This detailed simulation/analysis approach for transistor commutation is typically adopted in Radio Frequency amplifier design but also becomes very valuable in power converter design when the very fast commutations of a GaN HEMT at a high switching frequency cannot be fully described and taken under control with conventional approaches used in power electronics design. The simulation results are confirmed by experimental data.

Exploring the Impact of Battery Charge Reduction Rate and the Placement of Chargers on AGV Operation

This paper presents a simulation model to study the effect of the battery charging rate of Automated Guided Vehicles (AGVs) on the overall output of a toy car production environment. This paper uses Modular Petri Nets for modeling and the General Petri Net Simulator (GPenSIM) for model implementation on MATLAB and simulation. The main focus of this paper is to analyze the operational efficiency of AGVs under varying conditions, such as the impact of battery charge reduction rates and the strategic placement of Charging Stations within the production line. By employing Modular Petri Nets implemented with GPenSIM, this paper presents a detailed model that captures the dynamics (movements and interactions) of AGVs in a simulated manufacturing environment. The model is also extensible, as newer functionalities can be added to it as Petri Modules. This paper specifically focuses on two critical operational parameters: (a) the number of AGVs and their battery charge reduction rate; (b) the number of Charging Stations. In summary, the goal, aim, and novelty of this paper is to provide a simpler yet effective model to practitioners so that they can study and experiment without needing advanced mathematical skills.

Design and Simulation of Non-Linear Control System fed Bidirectional Converter for Solar Energy Controlled Battery Charging Applications

Design and Simulation of Non-Linear Control System fed Bidirectional Converter for Solar Energy Controlled Battery Charging Applications

Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.

On the description of electrode materials in lithium ion batteries based on the quantification of work functions

During charging of a lithium ion battery, electrons are transferred from the cathode material to the outer circuit and lithium ions are transferred into the electrolyte. Here, the energy required to take electrons and lithium ions out of two prototypical cathode materials, LixFePO4 and LixMn2O4 is investigated as a function of the state of lithiation, x. Both electronic and ionic work functions vary significantly with x for LixFePO4 but rather little for LixMn2O4. The relevance of these work functions for improving the thermodynamic description of lithium ion batteries is discussed.

False Data Injection Attacks on Reinforcement Learning-Based Charging Coordination in Smart Grids and a Countermeasure

Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase their chances of charging compared to honest consumers. As a result, honest consumers experience reduced charging levels for their batteries, leading to a degradation in the system’s performance in terms of fairness, consumer satisfaction, and overall reward. These negative effects become more severe as the amount of power allocated for charging decreases and as the number of attackers in the system increases. Since the total available power for charging is limited, some honest consumers with genuinely low SoC values are not selected, creating a significant disparity in battery charging levels between honest and malicious consumers. To counter this serious threat, we develop a deep learning-based FDI attack detector and evaluated it using a real-world dataset. Our experiments show that our detector can identify malicious consumers with high accuracy and low false alarm rates, effectively protecting the RL-based charging coordination system from FDI attacks and mitigating the negative impacts of these attacks.

To the selection of battery parameters for a M1 category hybrid vehicle

Background: To optimize the operation of a hybrid vehicle power plant, it is necessary to correctly select the parameters of its traction battery, which significantly affect the control algorithm and charge-discharge balance. The presence of a methodology that allows one to make a reasonable choice depending on the characteristics of the target vehicle, the conditions and modes of its operation will make it possible to increase energy efficiency indicators.Aims: The work aim was to develop a methodology for calculating the parameters of a hybrid vehicle traction battery using the energy charge-discharge balance of its power plant.Matherials and methods: A mathematical model of the charge-discharge energy balance has been proposed. Using this model, it is possible to make appropriate calculations on the amount of energy expended by the power plant when a vehicle moving through the stages of the European urban cycle, and analyze various algorithms for controlling it to achieve maximum energy efficiency, as well as select capacity and pover of traction battery. The basis for the calculations was the cycle described in UN Regulation No. 83.Results: The process of energy accumulation in a traction battery is considered, and criteria for choosing an algorithm for the operation of the vehicle’s power plant are indicated. The energy of vehicle movement in the urban cycle was determined, taking into account the operating time of the units, and the charge-discharge energy balance was calculated, which, in turn, made it possible to find the proper battery capacity. It has been established that the battery charge value at the end of the cycle should be equal to the initial value, which allows the use of a lower capacity battery and ensure maximum energy efficiency of the power plant. Also, based on the calculation results, recommendations were made regarding the optimization of the specific energy and weight-size parameters of the battery.Conclusions: The proposed methodology allows for a reasonable choice of а hybrid vehicle power plant parameters to ensure its energy efficiency.

Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging

To enable a shift from fossil fuels to renewable and sustainable transport, batteries must allow fast charging and exhibit extended lifetimes—objectives that traditionally conflict. Current charging technologies often compromise one attribute for the other, leading to either inconvenience or diminished resource efficiency in battery-powered vehicles. For lithium-ion batteries, the way to meet both objectives is for the lithium plating potential at the anode surface to remain positive. In this study, we address this challenge by introducing a novel method that involves real-time monitoring and control of the plating potential in lithium-ion battery cells throughout their lifespan. Our experimental results on three-electrode cells reveal that our approach can enable batteries to charge at least 30% faster while almost doubling their lifetime. To facilitate the adoption of these findings in commercial applications, we propose a machine learning-based framework for lifelong plating potential estimation, utilizing readily available battery data from electric vehicles. The resulting model demonstrates high fidelity and robustness under diverse operating conditions, achieving a mean absolute error of merely 3.37 mV. This research outlines a practical methodology to prevent lithium plating and enable the fastest health-conscious battery charging.

Characterizing thermal distribution of electric heating plates for power battery

Electric preheating of power batteries within specific temperature ranges is recognized as a promising technology for enabling fast charging and discharging. Among various methods, electric heating plate (EHP) has gained widespread attention. Nevertheless, its performance remains insufficiently understood. In this study, the EHP is comprehensively characterized through the construction of a realistic three-dimensional transient model, which is experimentally validated. Detailed analysis is conducted on the influence of resistance wire (RW) width and thermal power, with a particular focus on temperature profiles and uniformity. The results show that a circular fillet structure is recommended to ensure uniform current distribution in RW instead of the traditional right-angle fillet structure. The temperature of the EHP gradually decreases from its center to the outwards, forming an O-shape distribution of isotherm. The temperature uniformity and maximum temperature difference are primarily determined by the width and spacing of RW, demonstrating a positive correlation. Our finding also reveals that an EHP with a RW width of 1 mm displays a maximum temperature difference within ±0.5 K, meeting the requirements imposed by major industrial power battery producers. These results not only provide guidance for optimizing EHP architecture but also support enhanced performance in the fast charging and discharging of power batteries.

A large-scale study of the impact of node behavior on loosely coupled data dissemination: The case of the Distributed Arctic Observatory

A Cyber-Physical System (CPS) deployed in remote and resource-constrained environments faces multiple challenges. It has, no or limited: network coverage, possibility of energy replenishment, physical access by humans.Cyber-physical nodes deployed to observe and interact with the Arctic tundra face these challenges. They are subject to environmental factors such as avalanches, low temperatures, snow, ice, water and wild animals. Without energy supply infrastructures and humans available, nodes must achieve long operational lifetime from a single battery charge. They must be extremely energy-efficient. To reduce energy costs and increase their energy efficiency, cyber-physical nodes sleep most of the time, and avoid to communicate when they are unreachable.But, a CPS needs to disseminate data between the nodes for multiple purposes including data reporting to a back-end service, resilient operations, safe-keeping of observational data, and propagating nodes updates. Loosely-coupled data dissemination policies offer this possibility [1]. Although, investigations should be made on their applicability to large-scale CPS.In this paper, we evaluate and discuss the efficiency in energy, time and number of successful delivery of four data dissemination policies proposed in [1]. This evaluation is based on flow-level simulations. We study small and large-scale CPS, and evaluate the effects of the number of nodes and the size of the disseminated data on the nodes energy consumption and the dissemination's delivery success. To mitigate negative effects raised on large-scale CPS and large disseminated data sizes, different strategies are proposed and evaluated. We show that energy saving strategies do not always imply energy efficiency, and better data dissemination often comes at a cost. This last result highlights the importance of simulation prior to real CPS deployments in constrained environments.

Idirectional Converter For Conductive And Inductive Charging Of Battery Controlled Electric Vehicles

idirectional Converter For Conductive And Inductive Charging Of Battery Controlled Electric Vehicles

A comprehensive approach of design and analysis of hybrid solar and pedal assisted electric three-wheeler

Electric three-wheelers consume a great deal of power causing load shedding in industrial and residential areas. This research investigates the feasibility of a solar-assisted electric three-wheeler for deployment in Bangladesh, integrating solar photovoltaic system and pedaling generator. This research focused on enhancing the energy efficiency and sustainability of electric three-wheelers using renewable energy reducing dependency on the national grid. The rooftop space of the vehicle is insufficient for the solar PV system, necessitating the whole surface area of the vehicle with a 600 W solar panel. Simulations for Dhaka, Cox’s Bazar, and Rangpur assessed the solar energy potential across Bangladesh. A 48 V solar PV array and a Maximum Power Point Tracking (MPPT) charge controller were designed to optimize battery charging, achieving converter efficiencies between 97%–99.2%. Partial shading significantly impacted performance, while tilt angle optimization suggested a 0∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ ^{\\circ }$$\\end{document} tilt for moving vehicles and a 23∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ ^{\\circ }$$\\end{document} tilt during idle periods from October to March for optimal energy capture. A pedaling DC generator was introduced to charge the battery during idle times without grid reliance. The simulations for energy harvesting estimated an annual energy production of approximately 820 kWh. The integration of these systems increased drag, reducing maximum speed and requiring additional power. 172.28 kWh annual additional energy consumption was calculated for the solar PV system and 200.75 kWh for the pedaling charger. The study found the solar-assisted system with enhanced efficiency and sustainability but the limited economic viability of the pedaling generator. In future, the system can be developed for other countries with different vehicles.

Internal Temperature Estimation for Lithium-Ion Cells Based on a Layered Electro-Thermal Equivalent Circuit Model

In the domain of Battery Management System (BMS) research, the precise acquisition and estimation of internal temperature distribution within lithium-ion cells is a significant challenge. The commercial viability precludes the use of internal temperature sensors, and existing methodologies for online estimation of internal temperatures under various electrical loads are constrained by computational limitations and model accuracy. This study presents a layered electro-thermal equivalent circuit model (LETECM), developed by integrating a layered second-order fractional equivalent circuit model with a layered thermal equivalent circuit model. A lithium-ion battery divided into three layers was employed to illustrate the development of this LETECM. The model’s precision was validated against a 3D Newman Finite Element Model (3DNFEM), constructed using actual battery parameters. Given that the thermal gradient inside the battery is usually more pronounced under high load conditions, a 10C direct current discharge for 60 s followed by a rest period of 240 s was adopted as the test condition in the simulation. The results indicate that at the end of the DC discharge, the temperature difference between the inner layer and the surface of the battery was the largest and the maximum temperature difference predicted by the LETECM was 3.58 °C, while the 3DNFEM exhibited a temperature difference of 3.74 °C. The trends in each layer temperature and battery surface temperature obtained by the two models are highly consistent. The proposed model offers computational efficiency and maintains notable accuracy, suggesting its potential integration into BMS for real-time online applications. This advancement could provide critical internal temperature data for refining battery charging and discharging performance assessments and lifespan predictions, thereby optimizing battery management strategies.