Battery State Of Charge Research Articles

Model-based Parametric Study for Comparison of System Configurations and Control of a Hydrogen Hybrid Cargo Vessel

The current state of research in marine energy systems has concentrated on conventional diesel systems, while limited literature is available on the configuration and control of alternative energy sources such as hydrogen hybrid systems, which have attracted increasing interest recently owing to the energy transition. This paper presents a modelling and control study for conceptual retrofitting of a general cargo vessel to a hydrogen-hybrid version. Generic fuel cell, battery, and converter models are used, enabling easy adaptation to various powerplant sizes and ship types. A robustly coordinated Energy Management Strategy (EMS), which can be implemented for different vessel’s power profiles, was developed for power sharing, DC bus voltage control, and battery State of Charge (SoC) regulation. The total installed fuel cell power and battery capacity were heuristically selected from a range of power profiles of the ship. A database of fuel cells with stacks from different manufacturers was created to test different combinations in terms of fuel consumption, cost, and weight, based on the framework of the problem. Uncertainties in terms of fuel prices are presented using normal distribution graphs. The system configurations and control results are presented for one power profile of the vessel and the average fuel costs. It is demonstrated that with the proposed control method, the power losses are less than 1%, the DC bus voltage fluctuations are less than 0.5%, and the battery SoC remains between 35-65% for the entire duration of the analysed power profile. The configuration with eight stacks of 150 kW has the lowest total fuel cost (730 $) with an average difference of 7.1% from the other solutions, and the lowest total weight (10.54 tons) with an average difference of 15.4% from the other configurations. Overall, this study demonstrates the efficient configuration and control of hybrid energy systems using parameterized components.

Technical, Economic, and Environmental Analysis of Solar System with Management Considering Flexible Energy Sources in an Educational Building

Decades of persistent power outages in Iraq have triggered economic, environmental, and technical woes. Building-integrated solar energy offers a compelling solution. This study explores the viability of a hybrid system combining renewable and conventional sources to power an educational building. Technical and economic analyses using SAM software identified a (PV-DG-Battery-Grid) HES with 5.6kW PV, 400kW DG, 5.5kW inverter, and battery bank as the most feasible, optimal, cost-effective, and environmentally friendly option. Further analysis assessed the impact of factors like solar radiation, fuel price, battery state of charge, cost of energy, performance, and CO2 reduction. Solar panels are estimated to generate 10,210 MWh/year. This research promotes the shift towards renewable energy integration in Iraqi buildings, supporting improved energy management and a more sustainable future.

Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach

Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach

Micro-Grid of Batteray Energy Storage System (BESS) Design for Malaysia’s Net Energy Metering (NEM)

The depletion of fossil fuels and the increasing demand for energy have led to the adoption of renewable energy sources like solar photovoltaic (PV) systems. Net energy metering (NEM) has given individuals and organizations the ability to generate electricity using PV and sell excess energy back to the grid. However, without a battery energy storage system (BESS), the excess energy can only be sold back at a lower feed-in-tariff (FiT) rate. The integration of BESSs with PV systems has become necessary to avoid potential energy savings loss. This study addresses this gap by simulating an AC-coupled BESS with a capacity of 200kWh in MATLAB. Six modes of operation are discussed in the battery management system (BMS) to verify the performance and cost-effectiveness of the BESS for AC-coupled interconnection systems. A lithium-ion battery was chosen due to its high energy density, and long cycle life. The simulation results are based on the state of charge within 20% to 80% of battery capacity and include PV generation, load consumption, battery energy, battery state of charge (SOC), and grid energy. The study also evaluates the cost-effectiveness of the integrated BESS-PV system under NEM policies in Malaysia. The results show that the BESS can effectively reduce the amount of energy purchased from the grid, maximizing cost savings up to 12% per day under NEM 3.0 policies. The study provides valuable insights and recommendations for future implementation of BESS with PV system in the storing and using renewable energy efficiency, contributing to a more sustainable energy future.

A Novel Approach for Real-Time Estimation of State of Charge in Li-ion Battery Through Hybrid Methodology

A Novel Approach for Real-Time Estimation of State of Charge in Li-ion Battery Through Hybrid Methodology

A review of battery state of charge estimation and management systems: Models and future prospective

AbstractBatteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS.

Single-Particle Model of Li-ion Battery – Model Calibration and Validation against Real Data in an Electric Vehicular Application

This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions’ concentrations to the equilibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions’ concentrations and be further used for SoC accurate estimation.

Data Analysis and Traction Performance Evaluation of an Electric All-Wheel Drive Tractor During Agricultural Operation

Highlights The study evaluated the feasibility of using a new type of platform, the electric AWD tractor, for agricultural operations. The data for key components of the electric AWD tractor during plowing were analyzed. The traction performance of the electric AWD tractor was evaluated via tractive efficiency and dynamic ratio. The electric AWD tractor was expected to replace conventional tractors in terms of traction performance. Abstract. Owing to the increasing need for research on electric all-wheel drive (AWD) systems, an investigation on the AWD systems applicable to tractors was conducted in this study. The experimental platform consisted of an electric power transmission system including four sets of electric motors, helical and planetary reducers, wheels, and an electrical system including chargers, batteries (LiFePO4), converters, and inverters. The drive control system of the electric AWD tractor was divided into the AWD and the motor controller. The data measurement system consisted of analog (current and traction force) and digital (battery voltage, current, motor rotational speed, state of charge (SOC) level, and travel speed) components that communicate using a controller area network (CAN) bus. Data measured during plowing were used to calculate motor power, motor torque, traction power, and driving power to analyze the power consumption of an electric AWD tractor. The traction performance of the electric AWD tractor was evaluated using tractive efficiency, calculated values (traction power and driving power), and dynamic ratio. The maximum and minimum average voltage of the electric AWD tractor during plowing were 72.2 and 64.7 V, respectively, and the average value was measured as 68.2 V. The average value of the maximum current applied to the inverter was 362.4 A, and the mean value was measured as 187.5 A. The average value of the maximum rotational speed of the four motors was 2111.5 rpm, while the mean rotational speed during the operation was 1131.9 rpm. The average value of motor torque was 79.4 Nm. The decrease rate of battery SOC level per minute was calculated as an average of 1.4%/min. The maximum values of the traction and driving power during plowing were 42.0 and 73.7 kW. The maximum tractive efficiency (TE) was 0.77, with an average TE of 0.47. The maximum dynamic ratio (DR) was 0.28, with an average DR of 0.18. The TE of a conventional tractor was found to be a maximum of 0.61, with averages of 0.41, respectively. The average TE of the electric AWD tractor was approximately 14% higher than that of the conventional tractor. Data analysis indicates that the electric AWD tractor performing speed control is vulnerable to slip, and further research is necessary to improve AWD system performance through speed and slip control. Keywords: Data analysis, Data measurement system, Electric AWD tractor, Plowing, Traction performance.

LABORATORY AND ON-ROAD CHARACTERIZATION OF EXHAUST EMISSIONS FROM PLUG-IN HYBRID VEHICLES AT MULTIPLE BATTERY STATES OF CHARGE CONDITIONS

This paper discusses emissions from plug-in hybrid vehicles under various driving scenarios and reports experimental data obtained under laboratory and real-world conditions. Two European plug-in hybrid passenger cars were tested using the two test types in use in the EU (chassis dynamometer and on-road), with some modifications. The best-case and near-worst-case battery states of charge were used for testing. Behavior in terms of CO2 emissions, regulated emissions, and unregulated emissions was characterized and analyzed. Differences were generally much greater for on-road testing, especially for urban driving, during which the potential for purely electrical propulsion of the vehicle is greatest. The long distances covered by current EU legislative test procedures limit the impacts of some effects. Regardless of the traction battery’s state of charge, regulated emissions were well below the applicable EU limits under all driving conditions—for example, combined emissions of reactive nitrogen compounds (nitrogen oxides, ammonia, and nitrous oxide) were consistently < 10 mg/km when tested under laboratory conditions. The two vehicles tested showed that the state of the battery had a large impact on the proportion of electrical propulsion and the resulting CO2 emissions, but differences in regulated pollutants decrease with increasing distance and are generally relatively limited for longer journeys, which include non-urban driving.

Road segment and driving schedule effects in real-time operation on electric vehicle performance and operating cost analysis

Electric two-wheelers (E2W) are getting attraction as personal commuting vehicles owing to their key features like zero tailpipe emissions and lower energy consumption. However, the driving range, maximum speed and load-carrying capacity are the major hurdles associated with the E2W penetration rate in modern mobility. Likewise, these performance characteristics are varying based on the powertrain size, driving and atmospheric conditions. To address these challenges, The present study is focused on evaluating the operating regions of E2W powertrain components under three major road segments including rural, highway and urban at different driving schedules such as morning, afternoon and evening. The speed, acceleration and time-related driving parameters for the selected driving routes and trip timings are calculated to study their influence on the performance of E2W along with ambient conditions. Further, this research work is extended to compare the operating cost and CO2 emission of E2W with spark-ignition two-wheeler (SI-2W) based on real-time driving conditions. The energy consumed per km for E2W is 47.10, 40.53 and 41.63 Wh/km for morning, afternoon and evening driving trips. Similarly, the battery state of charge is recorded about 14%, 30% and 27% respectively at the end of the trips. Also, the results revealed that higher energy is consumed on the morning driving schedule due to higher average speeds with unfavourable ambient conditions. On the other way around, the CO2 emission, operating cost per trip and 100 km of E2W are 6.4, 6.5 and 2.4 times lesser than SI-2W.

An Adaptive Extended Kalman Filter for Model-Based Fault Diagnosis and State of Charge Estimation of Lithium-Ion Batteries

Precisely gauging the state of charge (SOC) of a lithium-ion battery poses a considerable obstacle due to the battery's intricate electrochemical properties, which are highly dynamic and nonlinear. The effectiveness of the control strategy employed in electric vehicles heavily hinges on the precise estimation of the battery's SOC. Among the crucial elements, the accurate assessment of SOC for power lithium-ion batteries stands out. Ensuring the durability and reliability of battery management systems in electric vehicles to predict SOC is a multifaceted endeavor. Given the inherently nonlinear degradation pattern of batteries, achieving precise forecasting of SOC while minimizing degradation proves to be a demanding task. The accurate determination of battery SOC holds significant significance for both battery electric vehicles and hybrid electric vehicles. Lithium-ion batteries have gained extensive usage in various facets of daily life due to their positive environmental and resource-related attributes. The precise determination of SOC is pivotal in ensuring proper battery operation. Nonetheless, the challenges of SOC estimation under low temperatures have received limited attention. To tackle this issue, this study introduces an adaptive extended Kalman Filter for model-based fault diagnosis and SOC estimation of lithium-ion batteries.

Cell to Pack Modelling of Li-Ion Batteries for Electric Vehicle Applications: Development and Potential Challenges

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.

Electrical Conductivity Measurements of the Binary NaCl-CaCl2, the Ternary NaCl-CaCl2-BaCl2 and the Quaternary NaCl-CaCl2-BaCl2-ZnCl2 Molten Salts

An emerging set of batteries, called the molten salts or all liquid battery, are designed for cheap energy storage and electrical grid buffering purposes (1). Some molten salt batteries are commercial or close to commercial like the ZEBRA, NaS and Ambri batteries, however, most of these battery technologies are still on an experimental, lower TRL level but can become increasingly important in the future as the shift towards more intermittent energy sources emerge and as the development of these batteries become more mature. One suggested battery technology is the Na-Zn battery (2). This battery technology is currently further investigated in the EU funded SOLSTICE project (Grant Agreement No 963599). The Na-Zn battery will have Na and Zn electrodes and the electrolyte – the molten salt – will be a NaCl-CaCl2 based electrolyte, with additions of BaCl2 or perhaps even BaCl2 and SrCl2. The NaCl content of the electrolyte gives the charge of the battery, CaCl2 lowers the operating temperature and BaCl2 and SrCl2 increases the density of the electrolyte. The operating temperature of the battery is approximately 600 °C and joule heat generated inside battery during charging and discharging due to resistance of the electrolyte allows to maintain this operating temperature. The composition of electrolyte changes with the battery state of charge hence the internal resistance of battery is not constant during normal operation. Therefore, precise data of electrical conductivity of the electrolyte is required for modelling its performance thermal balance.A method for measuring electrical conductivity has been built in the SINTEF laboratory. The setup used for the measurements is presented in Figure 1a. It consists of two tungsten electrodes immersed in molten electrolyte – one electrode is stationary and the other is moving inside a capillary. Position of the moving electrode inside the capillary is precisely controlled by a height gauge. Shielded thermocouple is used for measuring temperature. To avoid polarization of electrodes during measurement, electrical impedance spectroscopy (EIS) at open circuit potential is used. The principle of the method is that resistance between the electrodes is measured at different positions of the moving electrode inside the capillary. Measured resistance can be expressed by the following formula: R=R0+ρ·(l/A) Where R0 is the offset resistance of electrolyte outside capillary including resistance of electrodes and leads,ρ is the resistivity of electrolyte, l is the length between end of capillary and tip of the moving electrode, A is the area of the capillary. The measurement of resistance is repeated at different positions of the moving electrode relative to the chosen reference position. Since R0 and A are constant, resistivity can be calculated knowing only the tangent of function R = f(l) (Figure 1b) divided by A. The presented approach is characterized by high precision and robustness due to no requirement of cell constant values, which is problematic to estimate at high temperature. It is only based on relative movement of one electrode which is easy to control. In addition, a higher number of positions of the moving electrode at which the measurement is performed effectively helps to eliminate errors. Conductivity is the inverse resistivity. Electrical conductivity measurements were done at a range of temperatures, from 600 °C to 800 °C with 50 °C steps and the results are presented in the form of linear formulas intended for use in User Defined Functions in modelling.Conductivity of various chloride salt compositions containing NaCl, CaCl2, BaCl2 and ZnCl2 have been investigated using the presented technique. It is generally found that the electrical conductivity is almost linear with temperature increase in the range of liquidus temperature up to approximately 800 °C for the investigated electrolyte compositions. Above that region the relationship is not linear anymore. This region is not presented in this presentation. It is also found that the conductivity increases with increasing NaCl content and that larger cations like Ca and Ba reduce the conductivity of the molten salt. The method has proven very powerful to measure electrical conductivity on molten salts and the results are comparable to experimental results found in literature (3).

Shunt Current Analysis of Vanadium Redox Flow Battery System with Multi-Stack Connections

Vanadium redox flow battery (VRFB) systems with multiple stacks due to long lifetime, low self-discharge, and flexible design, are commonly used in large-scale electrical energy storage applications In a VRFB system, pumps deliver positive and negative electrolytes to each stack through a piping system including channels and manifolds. However, the electrolyte flowing between cells through channels and manifolds and the electrolyte flowing between stacks through pipes are electrically conductive. Shunt currents are generated due to the voltage difference between the cells and between the stacks, which reduce the energy efficiency of the battery system. In particular, under different load connections, the shunt currents of a multi-stack VRFB system have different distributions and cause different impairments to the system efficiency. Therefore, it is important to predict the shunt currents of the multi-stack system under different load connections before the actual construction of the system. In this paper, a multi-stack VRFB system with 120 cells was explored by circuit-based modeling to evaluate the shunt currents according to the stack configuration such as serial, parallel, and mixed connections.Then, the Coulomb efficiencies (CEs) were investigated with operating currents at 36 and 54A. The results show that shunt currents are generally more significant at the center cell of a stack than at other cells under these stack configurations and exhibit a positive correlation with the battery state of charge (SOC), i.e., larger shunt currents increase with SOC. In addition, the CEs of the system are higher at 54A compared to those at 36A due to smaller shunt current losses regardless of the stack configurations. Furthermore, regardless of the operating current levels, the multi-stack system connected by parallel loads achieves the highest CEs compared to the series and mixed connected systems due to the absence of shunt current losses in the piping system. Figure 1

State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method

The voltage plateau phase of LiFeO4 (LFP) batteries presents a formidable challenge when estimating the state of charge (SOC). Although numerous data-driven methods have been developed to address this issue, they often depend heavily on high-quality training data. Furthermore, these methods are typically regarded as “black-box” models, lacking interpretability. To overcome these challenges, a novel approach that integrates both model-based and data-driven techniques for battery SOC estimation is proposed in this paper. The proposed method is grounded in the battery model and complemented by the data-driven model, thus enhancing interpretability by incorporating domain knowledge. To reduce computational complexity, the Rint model is used for rough SOC estimation, with the eXtreme Gradient Boosting model (XGBoost) used for residual learning. The parameters of the battery model serve as input features for the XGBoost model. The effectiveness of the proposed method is empirically validated under different dynamic testing profiles. Experimental results demonstrate that integration battery domain knowledge into data-driven approaches not only enhances method interpretability but also significantly improves the SOC estimation accuracy for LFP batteries. Moreover, it exhibits robustness and exceptional generalization performance under unseen dynamic conditions, yielding root mean square errors of less than 1 % and maximum errors of less than 2 %.

Research on battery state of charge estimation based on variable window adaptive extended Kalman filter

Battery state of charge (SOC) estimation is one of the indispensable functions in the battery management system. Among them, the Kalman filter series of algorithms are widely used in the battery SOC estimation process. The adaptive extended Kalman filter uses the difference between the measured voltage and the estimated voltage (error innovation) as the innovative covariance matrix update noise in battery SOC estimation. However, the general adaptive Kalman filter does not consider the error innovation in the estimation process. The change. This will lead to inaccurate battery SOC estimation. In order to solve this problem, we proposes a variable window Kalman filter algorithm that takes into account the changes in the error innovation sequence. The algorithm updates the innovation covariance matrix according to the change of error innovation to improve the accuracy of SOC estimation. The results show that compared with the extended Kalman filter and the fixed window Kalman filter, its calculation efficiency is also improved while its accuracy is improved. Finally, its accuracy is verified under different initial parameters, and the study shows that the algorithm proposed in this article is robust.

State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model

Accurate state-of-charge (SOC) estimation of lithium-ion battery is directly related to the reliability, performance, and safety of the battery. In this work, the second order resistor-capacitance (RC) circuit is equivalent to the battery model and the particle swarm optimization (PSO) algorithm is employed for achieving accurate identification of the parameters of circuit model under dynamic stress test (DST) conditions. Furthermore, the values of open circuit voltage (OCV) obtained from the identification results are input into the temporal convolutional network instead of the terminal voltages, and then the SOC of the Li-ion battery is estimated by directly learning the mapping relationship of OCV-SOC curves, which further improves the estimation accuracy and robustness of the proposed method. Finally, the SOC estimation is validated with the public dataset of LiFePO4 batteries under all driving conditions at different temperature and compared with the individual TCN method. Results show that the SOC estimation of the second order resistor-capacitance circuit-PSO-TCN model is optimal with a root mean square error (RMSE) and maximum error (MAXE) less than 1.8% and 7.65%, respectively.

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

With increasing global attention to climate change and environmental sustainability, the sustainable development of the automotive industry has become an important issue. This study focuses on the regenerative braking issues in pure electric vehicles. Specifically, it intends to elucidate the influence of the braking force distribution of the front and rear axles on access to energy recovery efficiency. Combining the I curve of a pure electric vehicle and the boundary line of the Economic Commission of Europe (ECE) regulations, the braking force distribution relationship between the front and rear axles is formulated to satisfy braking stability. The maximum regenerative braking force of the motor is determined based on the motor torque characteristics and battery charging power, and the regenerative braking torque is optimized by combining the constraints of the braking strength, battery state of charge (SOC), and vehicle speed. Six road working conditions are built, including the New European Driving Cycle (NEDC), the World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), the China Light-Duty Vehicle Test Cycle—Passenger (CLTC-P), and the New York City Cycle (NYCC). The efficiency of the regenerative braking strategy is validated by using the Simulink/MATLAB simulation. The simulation results show that the proposed dynamic logic threshold control strategy can significantly improve the energy recovery effect of electric vehicles, and the energy recovery efficiency can be improved by at least 25% compared to the situation without regenerative braking. Specifically, under the aforementioned road working conditions, the braking energy recovery efficiency levels are 27.69%, 42.18%, 49.54%, 47.60%, 49.28%, and 51.06%, respectively. Moreover, the energy recovery efficiency obtained by the current dynamic logic threshold is also compared with other published results. The regenerative braking control method proposed in this article makes the braking control of electric vehicles more precise, effectively reducing energy consumption and improving the driving range of electric vehicles.

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.