Remaining Battery Capacity Research Articles

IMPLEMENTATION OF ENERGY EFFICIENT SECURITY MECHANISMS FOR WIRELESS SENSOR NETWORKS

Designing energy-efficient security mechanisms is critical for WSNs due to their limited power resources. Techniques like low-power cryptography, duty cycling, and energy-aware security protocols can help in achieving security without significantly impacting the energy consumption of sensor nodes. In this paper, we propose an energy aware geographical multipath routing scheme for WSNs. The distance to the destination location, remaining battery capacity, and queue size of candidate sensor nodes in the local communication range are taken into consideration for next hop relay node selection, and Analytical Hierarchy Process (AHP) and Geographical Routing Algorithm (GRA) are applied for decision making. Simulation results show that these schemes can extend the network lifetime longer than the original geographical routing scheme which only considers distance to the destination location. KEYWORDS: WSN, linear discriminate packet flow analysis system, optimized rout path switch, Analytical Hierarchy Process (AHP) and Geographical Routing Algorithms(GRA).

State of charge estimation for lithium batteries based on multi-timescale FFRLS-UKF

Abstract State of charge (SOC) can represent the remaining battery capacity, and accurately estimating SOC is very useful for the drivers of electric vehicles. This study proposes a lithium battery SOC estimation technique based on multi-timescale forgetting factor recursive least squares as well as an unscented Kalman filter (MTS-FFRLS-UKF). The MTS-FFRLS-UKF uses a circuit with a 2-RC network as the battery model, whose parameters are continuously updated at a timescale that is different from SOC estimation. The experimental results under a dynamic test prove the availability and accuracy of MTS-FFRLS-UKF. Continuous parameter update enhances the accuracy of battery modelling and furthermore the SOC estimation accuracy. The mean absolute error (MAE) and root-mean square error (RMSE) of SOC estimation decrease to 0.58% and 0.62%, respectively.

Smart Home System With Battery Backup and Internet of Things

This research enhances Smart Home Systems by integrating an Automatic Transfer Switch (ATS) for seamless power source switching between the grid and a backup battery, ensuring uninterrupted operation during power disruptions. An Automatic Battery Charging (ABC) system optimizes battery charging based on its condition, improving energy storage and efficiency. The system provides on-site electrical equipment control and sensor data access via a Human Machine Interface (HMI). Remote monitoring and control through the Blynk app offer convenience. Additionally, an energy consumption estimation feature allows users to estimate billing costs, with the Battery State of Charge (SoC) indicating the remaining battery capacity. Hardware testing showed the system's reliability with a 2-4 second ATS response and ±2-second ABC response. This research offers homeowners reliable power continuity and energy optimization. It contributes to IoT-based smart home systems, demonstrating ATS and ABC effectiveness, advancing both theory and practice for modern smart living.

Optimal operation of microgrid with consideration of V2G's uncertainty

AbstractThe aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

A hybrid energy storage system (HESS) by integrating Lithium-Ion Battery and Wind Turbine System for Electric Vehicle is designed and implemented. An advanced model of lithium ion/wind turbine HESS model is developed to improve the battery’s charging capacity. The behavior of this model is tested by using Machine Learning Algorithms to identify remaining battery capacity of the energy storage components. The Machine learning Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) Decision Tree (DT) and Linear Regression (LR) are implemented which helps in predicting the State of Charge (SoC) in the electric vehicle to estimate the battery’s lifetime. The SoC of the EV without HESS is calculated and compared with the SoC of HESS implemented EV model. Sensors are used to measure the current and voltage parameters of the Lithium-Ion Battery setup (Internal Battery) and Storage Battery (External Battery). The measured data is sent to the LPC2148 ARM microcontroller Board. The data that is obtained from LPC2148 is given as input to the machine learning algorithms which are executed in the Computer. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square (RMSE), R-Square Error (R2E) and Mean Square Error (MSE) are calculated and the SoC values are predicted. The Random Forest algorithm produces better results for the HESS model as the Error values produced are less than 2.5%. The battery capacity of the electric vehicle has been improved by using external battery that is charged by wind turbine. Thus, the driving range of the electric vehicle implemented with HESS is increased twice when compared to conventional electric vehicle [1].

Deploying Fast Charging Infrastructure for Electric Vehicles in Urban Networks: An Activity-Based Approach

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users’ daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Reliable Ship Emergency Power Source: A Monte Carlo Simulation Approach to Optimize Remaining Capacity Measurement Frequency for Lead-Acid Battery Maintenance

<div>The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance. The methodology employed in this study includes outlining the theoretical development of a fully automated condition monitoring system and describing data cleansing steps to account for environmental effects on system performance. A Monte Carlo simulation is used to evaluate the sensitivity of the remaining useful life prediction to varying measurement frequencies, prediction models, and parameter settings, leading to an estimate of the optimal measurement frequency for the system. The results show that a certain minimum measurement frequency is required to achieve the target prediction accuracy while balancing cost-efficiency and operational stability. Reliable failure prediction with negligible changes in prognostic accuracy can be achieved by performing useful remaining lead-acid battery capacity measurements twice a day or every 5 ship voyage cycles with the underlying utilization.</div>

The theoretical Algorithm Analysis of LOC for LiFePO4 Batteries

LOC represents the usage time of a single battery, which is different from the battery capacity calibrated by SOC and the remaining battery capacity of RUL. It uses the usable time of a single battery as a scale, which can clearly reflect the remaining life of the battery under specific cycle times, current, voltage, temperature, discharge depth, and other conditions. Based on LOC (Life of Charge), using AI algorithm to accurately establish a single battery model, taking this as the starting point to establish a BMS system.

A system dynamics model for end-of-life management of electric vehicle batteries in the US: Comparing the cost, carbon, and material requirements of remanufacturing and recycling

End-of-life (EOL) electric vehicle (EV) batteries can be remanufactured into second-life batteries (SLBs) for residential and utility-level stationary storage applications. However, it is important from a sustainability perspective to assess that there is no increase in material requirement, cost, or carbon footprint of lithium-ion batteries by adding a remanufacturing stage before recycling. A system dynamics model is developed to evaluate the changing EOL battery availability, the change in virgin raw material demand, net present economic value, and carbon footprint of EV batteries with the added remanufacturing stage in the US from 2020 to 2050 depending on the battery state-of-health and battery chemistry over time. We find that compared to recycling, remanufacturing can reduce the life-cycle carbon footprint of the EV battery by 2–17%, by reducing the virgin raw material mined for new batteries. Extended abstractEnd-of-life (EOL) electric vehicle (EV) batteries can either be recycled or remanufactured into second-life batteries (SLBs) for residential and utility-level stationary storage applications. There is a lot of research and policy efforts to support SLB for storage application based on the assumption that using the remaining battery capacity after the in-vehicle use will be environmentally beneficial. However, remanufacturing could negatively impact new EV battery manufacturing by reducing recycled material availability, thereby increasing mining. It is important from a sustainability perspective to ensure that there is no increase in material requirement, cost, or carbon footprint of lithium-ion batteries by adding a remanufacturing stage before recycling. A system dynamics model is developed to evaluate the changing EOL battery availability, the change in virgin raw material demand, net present economic value, and carbon footprint of EV batteries with the added remanufacturing stage in the US from 2020 to 2050 depending on the battery state-of-health and battery chemistry over time. We find that compared to recycling, remanufacturing can reduce the life-cycle carbon footprint of the EV battery by 2–17%, by reducing the virgin raw material mined for new batteries. However, as new battery prices decrease in the long term, the net present economic value of remanufacturing decreases, reducing the overall EOL economic value of the recycling with added remanufacturing stage. This reduction can be compensated for by providing subsidies and incentives for SLBs, which can increase profit.

Data-driven state-of-charge prediction of a storage cell using ABC/GBRT, ABC/MLP and LASSO machine learning techniques

Electric vehicles (EVs) will be the dominant technology for the automobile industry due to efficiency and environmental reasons. Lithium-ion batteries lead the energy supply business for the most recent group of EVs and many other electronic consumer devices. One of the most important pieces of information for EV users is the state-of-charge of the battery, also known as SOC. The SOC works like the fuel gauge for the battery. Information about remaining battery capacity is essential to avoid running out of battery power. Battery remaining charge is not easy to estimate, due to non-linear phenomenon inside the battery. This work is concerned with SOC prediction using machine learning techniques. Three machine learning tools, called Artificial Bee Colony-Multilayer Perceptron (ABC/MLP), Artificial Bee Colony gradient boosting regression tree (ABC/GBRT) and Least Absolute Selection and Shrinkage Operator (LASSO) have been used to build models that enable the prediction of the SOC of a storage cell. The predictive results confirm the enhanced performance of the ABC/GBRT-based model over the other methods for SOC prediction. SOC errors remain below 1%, 10% and 17% for ABC/GBRT, ABC/MLP and LASSO, respectively. The goodness of fit, calculated using R2, was 0.99, 0.95 and 0.81 for the three methods, respectively. A comparison of the results obtained using all the methods has also been carried out.

Use of life cycle assessment to evaluate circular economy business models in the case of Li-ion battery remanufacturing

PurposeThe purpose of this study is to advance and illustrate how life cycle assessment (LCA) can assess circular economy business models for lithium-ion batteries to verify potential environmental benefits compared to linear business models. Scenarios for battery repurpose are assessed to support future decision-makers regarding the choice of new versus second life batteries for stationary energy storage. A procedure to determine the substitution coefficient for repurpose and reuse of batteries is proposed.MethodsTwo different circular economy business models are assessed by applying primary data from two Norwegian companies for the development of a new life cycle inventory. With this new data, the authors compare second life battery (from first life in electric vehicle) scenarios and avoided production potential by performing a complete consequential LCA. Building on earlier work, a procedure to identify the substitution coefficient (i.e., potential for avoided production) for battery life cycle assessments is proposed. Interviews during factory visits were performed to identify a technical and a market factor affecting the substitution coefficient.Results and discussionThis study illustrates how life cycle assessment methodology can detect and thus enhance the potential environmental benefits and trade-offs of circular economy business models. Results show that the CBMs which use second life batteries correspond to 16% (for global warming potential) of manufacturing a new battery. This means that a second life battery must avoid > 16% production of a new battery to become the preferred alternative. Hence, circular economy business models with second life batteries can generate net environmental benefits while the remaining battery capacity and market price are identified factors that can alter the potential environmental benefits. The findings suggest that assumptions concerning the avoided production emissions are crucial for understanding the overall impacts of battery value chains.ConclusionsCircular economy business models which enable second life batteries show lower environmental impacts compared to a new battery when it can partly avoid production of a new battery. Based on the identified technical and market factor affecting this potential, a key message to industry and other organizations is that second life batteries should be chosen over new batteries. This depends on the remaining capacity being satisfactory for the new application, and the investment is not performed because of a low price compared to a new battery. Consequential LCA practitioners adopting a market approach while evaluating battery reuse and repurpose should model and account for the avoided production potential.

Effective estimation of battery state-of-health by virtual experiments via transfer- and meta-learning

The continuous monitoring of the state-of-health (SOH) of electric vehicles (EV) represents a problem with great research relevance due to the time-consuming battery cycling and capacity measurements that are usually required to create a SOH estimation model. Instead of the widely used approach of modelling the battery’s degradation behaviour with as little cycling effort as possible, the applied SOH monitoring approach is the first of its kind that is solely based on commonly logged battery management system (BMS) signals and does not rely on tedious capacity measurements. These are used to train the digital battery twins, which are subsequently subjected to virtual capacity tests to estimate the SOH. In this work, transfer-learning is applied to increase the data and computational efficiency of the digital battery twins training process to facilitate a real-world application as it enables SOH estimation for unknown ageing states due to the selective parameter initialisation at less than a tenth of the common training time. However, the successful SOH estimation with a mean SOH deviation of 0.05% using transfer-learning still requires the presence of pauses in the dataset. Meta-learning extends the idea of transfer-learning as the baseline model simultaneously takes several ageing states into account. Learning the basic battery-electric behaviour it is forced to preserve a certain level of uncertainty at the same time, which seems crucial for the successful fine-tuning of the model parameters based on three pause-free load profiles resulting in a mean SOH deviation of 0.85%. This optimised virtual SOH experiment framework provides the cornerstone for a scalable and robust estimation of the remaining battery capacity on a pure data basis.

ELECTRIC VEHICLE (EV) POWER CONSUMPTION (BATTERY) ON UPHILL ROAD CONDITIONS

Electrical Vehicle (EV) has been developing since the mid-18th century and reached its peak at the end of the 18th century. Dwindling fuel reserves and environmental damage caused by exhaust emissions from millions of fossil-fuel vehicles are the driving factors for the development of electric cars, technological developments in the battery sector have also contributed to the development of electric cars. One of the important components in an electric car is the battery which will provide resources to the system, so determining the capacity and knowing the performance of the battery used is very important. To find out the battery performance, the model can be made using MATLAB/SIMULINK. The model built consists of electric (DC motor, control and battery) and mechanical (transmission, wheels, brakes and body) parts. Of the models made for speeds of 35, 40, and 45 kmph on level ground, the distances that can be covered are 2,292, 2,597, and 2,899 m, while the remaining battery capacity (SoC, %) is 92.93%, 91.16%, and 89.17%. This is proportional to the distance traveled which also proves that the model made is appropriate. Uphill roads affect the use of battery capacity, the greater the angle of incline, the greater the battery capacity used.

A Preliminary Study on the Development of a New UAV Concept and the Associated Flight Method

This article presents a preliminary study on the development of a new concept for an unmanned aerial vehicle (UAV) design that incorporates the use of four wings and attached systems to improve overall performance, it being classified as a hybrid quadcopter (a quad tilt wing, tiltrotor UAV). By simulation, it was determined that the developed concept has significant advantages compared with a conventional quadcopter. By implementing this concept, an increase in the maximum speed by 59.21% can be obtained; it reduces time to complete a 10 km route by 36.4%, decreases the energy consumption by 37%, and increases the maximum travel distance by 56.9% at 30% remaining battery capacity. Additionally, the concept improves maneuverability by allowing turning movements to be performed by changing the angle of incidence of the rear wings, resulting in less energy consumption compared to traditional turning methods applied in the case of a conventional quadcopter.

Optimal control of energy storage system of high-permeability distributed photovoltaic low-voltage distribution network

Abstract Photovoltaic (PV) is one of the very promising renewable energy sources, but its output power is fluctuating. To maintain PV-energy storage system-load power balance in low-voltage distribution networks, we propose a new optimized sag control strategy, which is no longer indexed by the battery voltage but by the battery state of charge (SOC) because the battery SOC can better reflect the remaining battery capacity for better performance. In addition, to further optimize its performance, we try to use the ratio of intercell SOC for optimization. We analysed the deficiencies of sag control in detail, derived a control strategy with cell SOC as an indicator and finally analysed its performance in detail. To verify the performance of the proposed strategy, based on MATLAB, we designed the power variation of 2 lithium-ion battery packs to maintain the bus power stability under two cases of sudden rise as well as sudden fall of PV power and 3 lithium-ion battery packs to maintain during the bus power stabilization with one set of performance after failure, and the simulation results prove the feasibility of the proposed strategy.

State-of-health estimation by virtual experiments using recurrent decoder–encoder based lithium-ion digital battery twins trained on unstructured battery data

Due to the large share of production costs, the lifespan of an electric vehicle’s (EV) lithium-ion traction battery should be as long as possible. The optimisation of the EV’s operating strategy with regard to battery life requires a regular evaluation of the battery’s state-of-health (SOH). Yet the SOH, the remaining battery capacity, cannot be measured directly through sensors but requires the elaborate conduction of special characterisation tests. Considering the limited number of test facilities as well as the rapidly growing number of EVs, time-efficient and scalable SOH estimation methods are urgently needed and are the object of investigation in this work. The developed virtual SOH experiment originates from the incremental capacity measurement and solely relies on the commonly logged battery management system (BMS) signals to train the digital battery twins. The first examined dataset with identical load profiles for new and aged battery state serves as proof of concept. The successful SOH estimation based on the second dataset that consists of varying load profiles with increased complexity constitutes a step towards the application on real driving cycles. Assuming that the load cycles contain pauses and start from the fully charged battery state, the SOH estimation succeeds either through a steady shift of the load sequences (variant one) with an average deviation of 0.36% or by random alignment of the dataset’s subsequences (variant two) with 1.04%. In contrast to continuous capacity tests, the presented framework does not impose restrictions to small currents. It is entirely independent of the prevailing and unknown ageing condition due to the application of battery models based on the novel encoder–decoder architecture and thus provides the cornerstone for a scalable and robust estimation of battery capacity on a pure data basis.

Remaining battery capacity estimate based on extended Kalman filtering

Remaining battery capacity estimate based on extended Kalman filtering

Battery Management System to Estimate Battery Aging using Deep Learning and Machine Learning Algorithms

One of the greatest challenges faced by Electric Vehicle (EV) manufactures is insufficient charging stations. Estimating the aging of the battery in the electric vehicle helps the driver to predict the driving range of the vehicle. This paper proposes a battery management system that is developed to predict remaining battery charge of the Electric Vehicle. The aging of the lithium-ion (Li-Ion) battery present in the electric vehicle is predicted using different machine learning and deep learning algorithms. The parameters such as voltage, current and temperature are taken from the sensors connected to the LPC2148 ARM board and the values are given as dataset to the Long Short-Term Memory (LSTM), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) Algorithms. The experimental results indicate that for real-time data Naïve Bayes algorithm gave the best results in terms of metrics such as Accuracy, Precision, Recall and F1-score. Naïve Bayes produced results with the accuracy rate of 88% and used to calculate the Remaining Battery Capacity which helps predicting the aging of the lithium- ion battery.

Embedded Instrumentation System Using Acquisition Mechanism for BLDC-Powered Electric Vehicle

The primary component of an electric vehicle is the electric motor. In order for the motor to operate properly, some measurement and data acquisition are required for monitoring and controlling its performance. For this purpose, an embedded system is developed and attached to the vehicle. This paper presents the design and implementation of an embedded instrumentation system that includes a data acquisition device, data processor, and data display. A complete prototype-scale electric vehicle was developed and equipped with an embedded instrumentation system. A Brushless DC (BLDC) motor is employed as prime-mover taking power from a 10-Ah Battery. The input parameter is determined by the vehicle’s throttle opening, and the output variables are measured and processed. For the purpose of data acquisition, the system relies on Arduino and Raspberry Pi as processing and monitoring devices. The data and information are displayed on the vehicle dashboard to indicate the real-time vehicle performance and some related information. These include speed, power, motor temperature, distance achieved, and estimated distance that can still be reached with the remaining battery capacity. The data and information are graphically and numerically displayed, which would be useful for steering that enhances system efficiency. The system was tested in the lab and real system, where it demonstrated fine accuracy. The average deviations of the electrical data displayed in the instrumentation system with those given by the standard meter are 0.25 Volt, 0.03 Amp, and 0.43 Watt for voltage, current, and power, respectively. From a mechanical standpoint, the average deviations of speed and torque are 1.2876 km/h and 1.218*10-4 Nm, respectively. The contributions of this research are the development of a complete system to be operated in real conditions, and validation of the displayed information with standard measurement and manual calculation.

An Algorithm to Predict E-Bike Power Consumption Based on Planned Routes

E-bikes, i.e., bikes equipped with a small electrical engine, are becoming increasingly widespread, thanks to their positive contribution to mobility and sustainability. A key component of an e-bike is the battery that feeds the drive unit: clearly, the higher the capacity of the battery, the longer the distances that the biker will cover under engine support. On the negative side, the additional weight incurred by the electric components is likely to ruin the riding experience in case the battery runs out of power. For this reason, an integrated hardware-software system that provides accurate information about the remaining range is essential, especially for older or “not-in-shape” bikers. Many e-bikes systems are already equipped with a small control unit that displays useful information, such as speed, instantaneous power consumption, and estimated range as well. Existing approaches rely on machine learning techniques applied to collected data, or even on the remaining battery capacity and the assistance level required by the drive unit. They do not consider crucial aspects of the planned route, in particular the difference in altitude, the combined weight of bike and biker, and road conditions. In this paper, we propose a mathematical model implemented in an application to compute battery consumption, and hence the presumed remaining range, in a more accurate way. Our application relies on external sources to compute the route and the elevation data of a number of intermediate points. We present the mathematical model on which our application is based, we show the implemented application in shape of an app, and we report the results of the experiments.