It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single-battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, leading to limited error compensation. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on a cloud master–slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open-circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, Nonlinear Auto-Regressive Moving Average (NARMA-L2) models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting and neural network (CC + NN) method achieves the fastest error convergence rate and outperforms other methods. In addition, the proposed cloud master–slave BMS architecture significantly improves system reliability by enabling real-time cross-verification of the SoC data from the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the Federal Test Procedure (FTP)-75 driving cycle and verifies the high efficiency of this method in practical applications. The final analysis shows that the CC + NN combination exhibits optimal error-suppression performance in complex scenarios and provides a high-precision solution for electric vehicle battery management.

Fast charging techniques have attracted significant attention for their potential to substantially reduce charging time, despite the associated risk of accelerated degradation. However, most existing fast-charging strategies rely on open-loop control, limiting their ability to mitigate degradation across varying operating conditions. In response to this challenge, the study introduces a data-driven, health-aware battery fast charging (HABFC) approach that employs an adaptive neuro-fuzzy inference system (ANFIS) to determine the real-time transition point between constant current (CC) and constant voltage (CV) operation. The proposed framework integrates knowledge-driven and data-driven paradigms, where expert-defined rule-based guidelines provide structural initialization and enforce safety constraints, while the ANFIS model captures complex nonlinear relationships between battery states and optimal charging decisions. This hybrid approach enables accurate and adaptive prediction of the CC-CV transition, ensuring both operational safety and physical consistency across diverse conditions. To enhance robustness, the strategy incorporates state-of-health (SoH) and state-of-charge (SoC) as key inputs. A real-time degrading equivalent circuit model (ECM) is employed to estimate SoC, thereby mitigating estimation errors and improving decision reliability. The resulting ANFIS-based HABFC strategy facilitates dynamic, real-time switching between CC and CV modes, achieving faster charging without compromising battery longevity. The proposed method is experimentally validated using a customized charging setup. Results demonstrate approximately 12% reduction in charging time alongside a 23% improvement in cycle life compared to the conventional charging technique. The electrochemical impedance study indicates mitigation of lithium plating by reduced impedance growth, which stabilizes electrode kinetics, thereby enhancing electrochemical stability and prolonging battery life. Furthermore, comprehensive comparisons with benchmark strategies highlight superior performance across charging efficiency, degradation mitigation, and thermal behavior. Overall, this work establishes the effectiveness of real-time adaptive CC-CV switching at the cell level and provides a scalable foundation for future extension to battery pack-level applications.

The utilization of wind and solar energy-based distributed energy sources (DERs) has emerged as an important alternative due to their low carbon footprint and eco-friendly nature. This study presents a control methodology that effectively performs utility-synchronization DER-based microgrid and ensures continuous supply to locally connected loads during mode transitions. Additionally, it maintains the power quality of microgrid, even under unbalanced load conditions. Proposed power quality feature is achieved using an adaptive current control based on generalized robust logarithmic family (GRLF) that utilizes least mean square (LMS) based weight updating. This algorithm offers superior convergence and accuracy under both Gaussian and non-Gaussian noises. GRLF-LMS adaptive current control is utilized for fundamental extraction from input current signals. Control estimates phase angle and frequency of microgrid using a double-loop inertia phase-locked loop (PLL) method. Double-loop structure includes a synchronous reference frame PLL, which operates in a dual-loop fashion to provide synchronization and inherent antidisturbance capability. An overall control is tested on an experimental setup in emulated adverse operating conditions.

A good strategy for energy management is essential to control the power distribution between fuel cells and batteries in hybrid electric cars. Various energy management systems have been explored in the literature, focusing on optimizing the fuel cell characteristics. The literature review reveals researchers have not adequately addressed the effect of key battery parameters for developing energy management strategies for realistic driving conditions. This research proposes a novel energy management strategy with a multi-objective optimization for fuel cell battery hybrids, focusing on fuel efficiency, energy utilization, and drivability. Energetic macroscopic representation is a framework for powertrain modeling, aiding in creating the energy management system (EMS). The main goal is to provide a systematic control framework that integrates local bus voltage and traction control controllers with a global controller for energy management systems. The unique EMS regulates power flows by dynamically modifying battery and fuel cell operation's rate limitations and saturation levels. The thresholds for rate restriction and saturation are optimized offline using the multi-objective optimization. The impact of optimization parameters on the optimization goals is examined using three standard driving cycles. The simulation findings demonstrate that the efficacy of local controllers is contingent upon the driving cycle. Battery management excels in low dynamic power cycles, whereas fuel cell management is superior in high constant power cycles. The EMS may allocate power between the battery and the fuel cell, allowing the battery to manage transients. Altering the operational restrictions modifies the power distribution ratio while meeting the power requirements. Restricting battery power improves battery longevity by 50%. The modification of weights among the optimization targets is also taken into account. Conversely, a greater emphasis on reducing gasoline usage undermines battery energy. Minimization of power errors or drivability is prioritized above everything else. The results demonstrate that the suggested method can function effectively with an accuracy of 91% relative to optimal circumstances. The energy distribution between the battery and fuel cell enhances the longevity of both power sources.

This article presents an innovative method for rapidly charging batteries while considering health factors. The strategy utilizes a closed-loop control approach based on a mathematical model. Initially, an intricate thermo-electric aging cell model (TEACM) was constructed based on electrochemical aging study. The model’s parameters are continuously updated in real-time by pulling data from the battery pack while it is being charged. The TEACM algorithm analyzes the parameter values and calculates different battery statuses. These estimations are then used to improve the charging process. The charge optimization phase optimizes the precharging and boosts charging processes individually. Several pulse precharging approaches have been analyzed, and by calculating the diffusion coefficient, the most effective precharging method has been identified. The use of multistage constant current technique is utilized to optimize the battery charging process. The optimization is accomplished by implementing the non-linear model predictive controller. Ultimately, the two refined charging methods are combined and sent to the charger. The hypothesis was validated using a cell cycling test bench. The acquired findings were compared with other recognized benchmark charging techniques, demonstrating that the suggested approach charges the cell in half the time compared to the 1C-CCCV charging methodology while also increasing the cycle life by 6.67%.

Lithium-ion batteries (LIBs) have received enormous attention as the core components of Electric vehicles (EVs). An unavoidable issue is that battery performance will continue to degrade as materials age and cycle time increases. Accurately predicting the Remaining useful life (RUL) of LIBs is an important prerequisite to ensure the safe driving of EVs. However, the actual battery management system may encounter sensor or communication system failures, resulting in missing or incomplete data, which will result in inaccurate battery RUL predictions. This article presents a novel method based on hybridization of Generative adversarial imputation nets (GAIN) and Stacked denoised autoencoder with Kriging (SDAE-Kriging) for the prediction of RUL of LIBs in scenarios of missing and incomplete data. In the proposed method, the GAIN is leveraged to realize the filling of missing and incomplete data. The SDAE-Kriging is used to predict the RUL of LIBs with filled data. Different missing rates (10%, 20%, 30%, and 40% ) are investigated to establish RUL prediction models. It is manifested that the GAIN has better data filling results on two datasets. After completing the data filling, the results show that SDAE-Kriging has high prediction accuracy (RMSE is less than 0.1) for both datasets, even when the missing rate is 40% . The proposed scheme can provide an effective solution for the RUL prediction of LIBs in the scenario of missing and incomplete data in practical industrial applications.

In electric vehicle industry, rechargeable multicell battery packs commonly with fixed configurations are adopted. Such fixed battery configurations have several drawbacks, including limited fault tolerance during unusual operating situations, poor cell state variation management, etc. Thus, this article proposes a self-reconfigurable battery pack design considering two scenarios: with and without active cell balancing. Herein, the issue of cell state variation is mostly solved by the proposed configuration mode, and further monitoring, control, and protection can be easily appended to accomplish other functionalities, for example, meeting dynamic voltage requirement. Each proposed design is validated by simulation results for a six-cell polymer lithium-ion battery pack. The proposed design can maximally utilize the battery's capacity and help to protect cells from over-charging and over-discharging as well. This research could be further extended to other application scenarios involving wide-range dynamic voltage requirements.

In past studies, the designs of the liquid cooling channel for battery packs usually emphasized the parametric optimization of coolant and battery parameters on pre-defined or fixed geometric designs. This significantly restricts the possibilities for geometric modifications of cooling channels, consequently limiting the potential improvement in heat dissipation performance. Therefore, to address this problem, this work illustrates a topology optimization (TO) for the design of a double input single output battery module liquid cooling plate with improved Thermal Performance. This design aims to provide the most efficient heat transfer from the prismatic cells to the liquid flowing within the cooling plate. TO approach determines the optimal channel structure in a two-dimensional domain. The established two-dimensional structures create a pathway for the development of three-dimensional cold plate models. This article further comprehensively examines the impact of the location and velocity of the liquid inflow on the cooling process. The optimal input location has been determined by presenting ten case studies with different intake positions. Each of these instances has undergone testing under five different velocity settings for inputs. Consequently, the optimal cooling plate design has been identified based on its superior heat exchange capability. These experiments aid in determining the optimal cooling plate configuration, with the intake located 19.5 mm out from the center. The findings have been confirmed by comparing the thermal and pressure drop performance with the traditionally used straight channel design. The findings obtained suggest validating the module-level battery pack design suggests that the proposed design can be further extended at the battery pack level.

This research introduces a new charging methodology, the multistep constant-current constant-temperature constant-voltage (MSCCCTCV) strategy. The main objective of this approach is to achieve rapid battery charging while giving utmost importance to maintaining battery longevity. The thermoelectric aging cell model (TEACM) is introduced, which incorporates impedance, thermal, and aging models within a robust semi-empirical framework. The accuracy of voltage detection, current detection, and cycle life prediction by the TEACM was found to be 91%, 97%, and 99%, respectively, as evidenced by experimental results obtained from a cell cycling test bench. A personalized charging strategy is developed by employing a quad-objective genetic algorithm (QOGA) based on the TEACM parameters. The optimization process employed by the QOGA considers the maximum allowable temperature and charging rate values, aiming to minimize charging time, degradation rate, energy loss, and temperature increase. Additionally, this research presents three novel charging strategies based on the number of CC steps employed, namely single-step, dual-step, and triple-step. The findings indicate that implementing the triple-step CCCTCV approach leads to a reduction of 31% in charging time and an enhancement of 66% in cycle life compared to the conventional 1C CC-CV charging technique.

The conventional battery packs with set configurations, often used in electric vehicles (EVs), include several safety attributes, although they cannot effectively optimise cell selection for powering the system. Neglect of battery's recovery effect results in inefficient use of the energy at hand. Furthermore, certain amount of energy is used in process of voltage control to operate the motor at designated velocity. Hence, the primary objective of this work is to mitigate the aforementioned energy wastage by enabling the battery to autonomously choose optimal configuration that maximises overall efficiency. Moreover, it should be noted that the power demands of EV vary at different velocities. Hence, it is essential to advance battery technologies to accommodate increasing energy demands of automobiles. This study presents novel feedback control method that aims to optimise prediction of output voltage for certain configuration. Additionally, algorithm has capability to adapt and reconfigure itself, exhibiting significant tolerance level of around 50% of nominal voltage of the cell. Method used in this context effectively reduces number of switch toggles necessary to get desired voltage level through implementation of binary search technique. Consequently, this approach enhances the battery pack's life.

Cell modelling provides accurate real-time battery performance forecasts. Researchers have investigated many cell modelling techniques, with the most common being a single model for the charging cycle. In such case, cell degradation impacts the accuracy of predictions. Present study employs a novel multizoned equivalent circuit modelling (ECM) approach. Based on findings of literature review, it is generally observed that degradation tends to be more prominent on anode. Consequently, charging-zones are established by comprehensive examination of anode's characteristics. Galvanostatic intermittent titration technique is employed to determine diffusion coefficient of anode at various time intervals. Moreover, the validation of these zones is accomplished through the estimation of charge transfer resistance using galvanostatic electrochemical impedance spectroscopy. This obtained data is subsequently utilised to establish distinct charging zones. Trizoned ECM is established by developing ECM for each respective zone. This model is validated using cell cycling test bench, where high level of accuracy in predicting voltage (98.14 %), current (97.95 %), and ageing (98.35 %) is demonstrated. In addition, proposed methodology is tested on three distinct cells to verify its reproducibility. Furthermore, comparison with benchmark strategies demonstrates its efficacy. This raises possibility of using similar method to produce health-aware battery fast charging systems based on digital twins.

Lithium-ion cells exhibit a great degree of dynamism. Using a single model, the conventional cell modelling technique presents challenges in accurately mapping and predicting the battery's performance. This paper proposes employing a novel multi-zoned equivalent circuit model to accurately represent the battery's charging characteristics. The charging zone has been divided according to the findings of electrochemical estimation. The galvanostatic intermittent titration method and electro-impedance spectroscopy measure the cell's diffusion coefficient and charge transfer resistance at an SoC interval of 1.18%. These indicate the primary degradation events that occur throughout the charging process of a lithium-ion battery. Electro-impedance spectroscopy is used to evaluate the multi-zoned equivalent circuit model. Three zonal modelling techniques have been discussed: dual, triple, and quad zoned. The accuracy of this model is proved by validating it using a cell cycling test bench, which predicts the voltage (98.14%), current (97.95%), and ageing (98.35%). Moreover, when compared to benchmark strategies, it clearly shows its efficacy. This presents the potential for using a similar approach to develop battery-fast charging systems that prioritize health and are built around digital twins.

In the manufacturing process of lithium-ion batteries, capacity grading is a crucial step. Precisely anticipating the capacity of lithium-ion batteries exhibits significant importance in saving production time, reducing production costs, and conserving energy. Therefore, this paper proposes a lithium-ion battery capacity prediction method based on partial voltage curve and autoencoder. Using the partial voltage curve of the grading process, multiple dynamic features (DF) are extracted based on the dynamic changes of the lithium-ion battery voltage. A CLAE feature extraction model based on autoencoder (AE) is proposed, which is composed of convolutional neural network (CNN), long-short-term memory (LSTM) and AE. It can extract spatial features and temporal features of voltage curves. Finally, a support vector regression (SVR) capacity prediction model optimized by differential evolution algorithm (DE) is established. Compared with other capacity prediction methods, the DF-CLAE-DE-SVR prediction model outlined in this study exhibits superior prediction performance for battery capacity, especially for low-capacity batteries.

In this article, developing an optimized health-aware battery-fast-charging strategy is proposed using multistep constant-current constant-voltage (MSCCCV)-charging technique. First, the thermal-aging cell model (TACM) is formulated utilizing a 1D radial heat-conduction phenomenon. The utilization of this model facilitates the generation of a simulated cell model, thereby mitigating the necessity for conducting multiple experimental assays. A cycle life predictor based on multi-input elastic net regression is then developed, which forecast cell's cycle life based on inputs from TACM. The accuracy of the projected life is 87% which is validated using APR18650M1B cell cycling dataset. This is then utilized to devise an adaptive MSCCCV-charging strategy with four-step constant-current (CC), which is optimized using whale optimization algorithm, have C-rate of 5.2C, 4.4C, 5.2C, and 4.52C. An alternate cell (LGEBM26R) is used to validate the reproducibility of the proposed charging method. The second cell charging is optimized using the aforesaid steps and four-step CC, thus obtained have C-rate of 1.3C, 1.95C, 2.23C, and 1.24C. Compared to 1C-CCCV charging, the algorithm increases LGEBM26R cell cycle life by 17.23% and APR18650M1B cell cycle life by 28%. The MSCCCV technique's superiority is demonstrated through a comparison with benchmark techniques considering charging time and cycle life as performance parameters.

The reason behind developing a fuel cell hybrid electric vehicle is to achieve the most suitable usage of both energy sources without compromising on the driving performance. This can be achieved by developing an appropriate energy management system for optimally splitting the power among the battery and fuel cell. However, the performance of such a system is strongly dependent on the road and traffic scenarios which are often characterized by driving cycles. In this contribution, four driving cycles are compared to evaluate the energy and fuel consumption of the battery and fuel cell respectively. The power demand and power supplied are analyzed to evaluate the efficiency of the energy management system. It is observed that with Artemis driving cycle, battery energy consumption is least whereas the minimum fuel consumption is achieved with UDDS driving cycle. The WLTP cycle has higher energy as well as fuel consumption compared to the other three cycles while UDDS has the least. It is also observed that the energy management system has the ability of always maintaining the power demand and supply balance.

The advancement of digital twin (DT) technology improves battery performance and lifespan. Although precise forecasting, selection of design variables, and risk reduction are challenging. Therefore, it is critical in implementation of practical DT to investigate the sensitivity of feature implications on state estimation thoroughly. Hence in this paper, an analysis of features has been piloted using voltage and current characteristics. First, features have been extracted from performance values. Secondly, genetic programming (GP) has been set up to reflect the impact on state estimations. Structural risk minimization is used as a fitness function to maximize the DT's objective function, while GP-battery state estimation is implemented. An illustrative example is presented to evaluate the state of experimental data generated in the lab under controlled environmental conditions. Based on the analysis, the state of charge shows precision incorporation of all features, while the change in current over voltage shows the improvement in state of energy estimation. State of power is more sensitive towards changes in voltage concerning changes in current, and state of health offers better accuracy to the present voltage over the current applied. A sensitivity rating has been compared to design the role of the feature variable.

This paper proposes a novel dual step-constant current-constant temperature-constant voltage (DS-CC-CT-CV) charging strategy for a health-aware battery fast charging (HABFC). Firstly, a highly robust thermo-electric ageing cell model (TEACM) is created where the impedance, thermal, and ageing models are intercoupled to obtain a semi-empirical model. These models are verified using a cell cycling test bench where the accuracy of TEACM is 91% for voltage detection, 97% for current detection and 99% for cycle life prediction. Second, these parameters obtained in TEACM are used to optimize the proposed charging strategy with quad objective genetic algorithm (QOGA). This helps in providing a customized charging strategy for every charging cycle. The QOGA helps to reduce the charging time, degradation rate, energy loss and temperature rise by setting the values of maximum permissible temperature and charging rate. The charging strategy is fed into the charger, and the cells are cycled up to 80% of State of Health condition. The results indicate that the cell charges 17.6% faster and has 17.23% more cycle life than a cell charged at 1C using CC-CV. Finally, the superiority of the DS-CC-CT-CV technique is proved by comparing it with the benchmark techniques with respect to charging time, temperature rise, and cycle life.

High charging times for EVs are a hurdle to mainstream adoption since quick charging shortens battery life. The paper proposes a quick charging method that reduces charging time and battery deterioration. The proposed charging method modifies the conventional Constant Current – Constant Voltage (CC-CV) charging approach, where the switching point from CC to CV mode is altered. Sixteen charging techniques with varied CC-CV switchover points have been explored, comprising nine charging strategies with capacity switching and seven with voltage switching. The gathered data is processed using a mathematical relationship to determine the State of Health, capacity deterioration, and Internal Resistance characteristics. In addition, a Fuzzy Logic Controller is employed to determine the two effective switchover points based on the least amount of cyclic capacity deterioration, SoH, and charging time. The optimal CC-CV transition point between these two points is estimated using Genetic Algorithm Optimization. Compared with the traditional CC-CV charging approach, the effectiveness of the resulting switchover point is evaluated. The results indicate that the recommended charging approach costs an additional time of 4.22 %, and it increases the cell's cycle life by 51.77 % compared to CC-CV. Thus, the comparative analysis reveals that the proposed charge technique offers more efficient performance.

The health-aware battery fast charging (HABFC) strategy for electric vehicles (EVs) is predominantly dependent on the input conditions. These conditions are set using a set of various type of sensors (temperature, current, voltage, etc.), which can seldom be erroneous. Also, one of the primary challenges associated with physical battery management system (BMS) is the lack of memory required to execute intricate state estimation methodologies. Therefore, this paper proposes the Cloud-BMS based health-aware battery fast charging (HABFC) architecture with error correction strategy for reducing the charging time and increasing the cycle life of the EV battery. A cloud BMS has been integrated in the HABFC architecture to reduce the dependency on erroneous sensors. To design an effective cloud BMS, this paper reviews the seven most critical battery state estimation techniques (state of charge (SoC), state of health (SoH), state of power (SoP), state of energy (SoE), state of temperature (SoT), state of function (SoF), and state of safety (SoS)). Based on comparison of mathematical computational methods, its accuracy and challenges, the best computational method for each state estimation technique has been determined. The cloud BMS makes all state estimations based on parameters estimated using the virtual battery. Further, this paper presents an error correction strategy that helps correct the error in physical BMS employing a master-slave configuration. The proposed cloud BMS based HABFC architecture is validated using a cell cycling setup. It can be concluded that the proposed architecture produces 21.24% more cycle life as compared to fast charging without HABFC and 11% more cycle life as compared to cell charged using HABFC without cloud BMS.

The long charging time of Li-ion batteries in comparison to ICEV (Internal Combustion Engine Vehicle) refuelling time is a barrier to the adoption of Li-ion-based EV. The electric vehicle industry believes that increasing the current rate (C-rate) will reduce charging time, but this increases the cell degradation rate. As a result, the need of the hour is to develop a health-aware battery fast charging strategy. Numerous research was conducted to develop an optimal charging algorithm, but the high complexity of the algorithms attracted fewer industries to adopt these strategies. This paper reviews twenty-six charging strategies that have a low computational burden and easy adaptability. The critical research direction facilitates understanding the limitations and provides a solution for the strategical industrial adaptability. The keys have been investigated further, and steps for developing a comprehensive charging framework have been devised. The proposed framework helps in charging management system development using a graphical user interface-based fast-charging controller. This controller aims to provide an industry-ready hybrid fast charging strategy adaptation. The fast charging rate, low battery degradation, and low complexity will facilitate faster industrial adaptation and encourage consumers to switch to EVs.

In hybrid electric vehicles (HEV), the power management unit helps to control the distribution of power between various power sources with the aim of meeting a set of predefined objectives. Often, the minimization of difference between power demand and supplied power is a prime objective. However, further challenges need to be addressed are the selection of suitable set of rules and adapting them to the vehicle drive cycle. In this paper, a battery–supercapacitor hybrid vehicle control with two schemes, viz. fuzzy rule-based (FRB) and hybrid fuzzy-PID controllers are proposed. The MATLAB simulation results indicate that the application of hybrid fuzzy-PID control scheme results in meeting the power demand with slower rate of discharge. This ensures improved efficacy of the proposed scheme.

The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.

Due to the increase in global warming, the pollutant sources of energy have been replaced with renewable sources and electric vehicles. Vehicles have long halt-time, thus the energy stored in Electric Vehicle can be used for energy supply to grid and reactive power compensation. This work primarily deals with reactive power compensation for industrial load using a STATCOM. The switching and control of S TATCOM is done using hysteresis control scheme. Results indicates that the architecture can successfully generate and absorb the reactive power demand according to the load variations at industrial load end and the THD analysis verify the usability of the system. The model has been simulated in MATLAB/Simulink environment.

In hybrid electric vehicles parameters, the battery management systems play an important part in state estimation techniques which has to be reliable and precise. A HEV when running checks several parameters constantly to make the control strategies on the upcoming driving conditions. A battery management system consists of various components such as sensors and actuators that helps in the safety of battery, improving the range for driving and helps in cost minimization. A battery management system installed in a HEV assures the vehicle efficiency by estimation of these several parameters and processes the HEV according to it. These parameters are a vital information to the hybrid electric vehicles as this information decoded from the sensors or algorithm provides the update on the vehicle's component running data. Data such as State of charge, cell's temperature, state of health of the cell, state of power, state of life, etc. are all taken into the consideration for the running of a hybrid electric vehicles. These parameters are the vital information to the hybrid electric vehicles as this information decoded from the sensors or algorithm provides the update on the vehicle's component running data. Data such as State of charge, cell's temperature, state of health of the cell, state of power, state of life, etc. are all taken into the consideration for the operation of a hybrid electric vehicles. In this framework a battery model is planned which is put under direct measurement techniques to successfully estimate State of Charge. These two methods used are OCV method and coulomb counting method technique. A basic thermal model is also projected in this paper to measure temperature of the battery, to verify all these operations, a simulation results has been briefed at last.

The increasing use of non-renewable sources has led to an increase in global warming and carbon footprint. In this scenario EV, BESS and renewable sources such as wind turbine generator and SPVA have proved to be highly beneficial in creating a sustainable future. This work makes one such attempt and creates an islanded microgrid with renewable energy sources, BESS and V2G. A novel rule-based energy management unit has been developed in this work to help supply of energy generated as per the load demand. The results indicate that the developed system can successfully meet load requirements and this will help to reduce the carbon emissions to a bare minimum. The complete system has been primarily simulated on MATLAB/Simulink and then verified using Hardware-in-loop configuration on a Hardware Test Bench.

The increasing demand of EV has led to an increase in need of reliable charging infrastructure which can be integrated with the conventional grid. The deployment of smart grid projects has resulted in modernization of conventional grid and evolving it into a more reliable form by integrating renewable sources and EV into the same infrastructure. This work proposes to use SPVA-battery-grid based EV charging infrastructure which will increase the reliability on the system and provide instantaneous supply of power. Further, this work presents case scenario of workspace which operates from 9am to 5pm. The MATLAB simulation shows that proposed power flow control logic can successfully switch between available multiple modes and supply demanded power to EV.

Since last few decades, a rapid change in the generation of electricity has been seen with the world shifting from nonrenewable sources of energy towards the renewable sources with solar as the major source of electricity. In order to increase the amount of generation and reliability, this work aims to design a Maximum Power Point Tracking (MPPT) algorithm and dual axis based Solar Photo voltaic array (SPVA) which has proved to be a better solution as compared to MPPT based SPVA. All the modeling and simulation has been done in MATLAB/ Simulink.

Due to the increasing demand of Solar Photovoltaic Array (SPVA) a need for a stable technology is a must. Push Pull Flyback Converter is commonly used in Switched mode Power Supply (SMPS) Devices for industrial applications since a very long time. This work uses this Push Pull Flyback Converter in order to boost the input voltage and to provide a continuous forward supply current. Further a Micro inverter is used which converts the incident DC voltage into AC voltage and the operation of the microinverter has been verified by doing the Total Harmonic Distortion (THD) analysis and FFT analysis of the output AC voltage. The complete mode has been simulated using MATLAB/ Simulink.

The generation of HEV has already arrived and will live for many ages. Hybrid vehicles comprises of multiple energy sources just because to match the performance demands and has vast advantages such as reducing air pollution, fuel depletion and emission reduction. In order to provide better efficiency power management plays a vital role in HEV. There are many intelligent power management techniques that can achieve optimal power handling which are suitable for real time application and can provide better efficiency for different driving condition. We will basically study on Rule based methods and optimization based methods. In rule-based methods, methods are easily implemented and is very simple in real time but cannot provide best of optimality whereas in optimized based methods, it can provide better optimal results but it all comes with the cost of high computational requirements and price. Thus, in this review-based paper we will brief about the latest trends of power management strategies that are based upon rule based and optimization based and will highlight on which one will yield better optimal results with its pros and cons.

The problem of electrification is still prevailing in many parts of India. The non-electrified rural areas can be fed power by the use of standalone system photovoltaic system. The output of PV array mainly depends on the physical conditions like irradiation and temperature. Therefore, a robust converter is required which can operate under variable input conditions. Flyback converter is widely used in the industrial switched mode power supply (SMPS) applications because of its isolated voltage output, low cost and high efficiency. In the present work, the push - pull flyback converter interfaced with solar PV system for domestic and small-scale industrial applications has been designed. The design has been simulated with fixed and variable irradiation inputs to the PV array in the MATLAB/SIMULINK environment.

Synchronous phasor measurements are important parameter for wide-area monitoring and control applications. The main application of signal processing in the area of power systems is to determine the parameters to be measured and up to what level of accuracy. The purpose of this study is to compare various methods of Signal Processing for Phasor Measurement Unit through MATLAB and to find which signal processing methods provide the best characterization and analysis of the signals to be investigated.

The extensive use of fossil fuels has resulted into depletion of this valuable resource at an alarming rate. Though the supply of fossil fuel is reducing i.e. generation is reducing but the demand of power is ever increasing. The need of finding alternative energy sources in order to bridge the gap between supply and demand is a must. Moreover the disproportionate distributions of load between peak load and off peak load period have created serious power grid stability issues. Thus a system to solve both these problems is a must. In this paper we have used the vehicle to home infrastructure to solve these problems. In vehicle-to-home (V2H) systems, electric vehicles interact with the home battery as distributed energy storage systems that offer many potential benefits as an energy interface between a vehicle and the grid, the bidirectional converter plays a crucial role in their interaction. A model of the topology is derived, its behavior is analyzed, and its converter design method is developed. In this paper, we have used a super capacitor at home end, which will help to store more energy. Simulation results are employed to verify the design and analysis using MATLAB. The desired results help to determine the suitability of the technology for V2H system.