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Energy management is essential for the efficient operation of microgrids with reduced energy costs and minimized emissions. Energy management of PV/battery/fuel cell/diesel generator-based microgrid to minimize the operations cost considering battery degradation and emissions for a fully functional microgrid existing in the campus of Sultan Qaboos University, Oman, is presented in this work. A microgrid with a state-of-the-art hydroplus fuel cell without the necessity for hydrogen storage is presented in this study with experimentally obtained parameters. Also, a comparison of operations cost with microgrids using two different technologies of PEM fuel cells, one with hydroplus fuel cell and the second with the methanol fuel cell which requires provision for hydrogen storage is performed with three different cases; the scheduled, grid-tied, and islanded with different scenarios under grid-tied mode. The analysis proved that using a hydroplus fuel cell instead of a methanol fuel cell with hydrogen storage reduces the cost of the daily operation by 6.9% in the scheduled mode and 18.2% in the islanded mode. In the grid-tied mode three different grid limits, 20 kW, 15 kW, and 10 kW are considered. The analysis showed no reduction, 1.3% and 5.9% reduction in the operations cost respectively. The results obtained are highly promising to be applied in microgrids where conventional fuel cells are currently employed. The new technology of fuel cells introduced in this study, possesses the advantages of near zero emissions and reduced operations costs besides avoiding the perilousness of hydrogen storage. [Display omitted] • Energy management strategy to minimize the operations cost of microgrid is proposed. • Mathematical modelling is performed with experimentally obtained parameters. • Comparison of operations costs with two technologies of PEM fuel cells is performed. • Significant reduction in daily operations costs with hydroplus fuel cell. • Near-zero emissions in hydroplus fuel cell. [ABSTRACT FROM AUTHOR]

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries are widely used in various mobile applications, particularly in electric vehicles, due to their high energy and power density. However, repeated charge and discharge cycles and inappropriate charging protocols can lead to its early degradation, resulting in reduced capacity and high internal resistance. Even though some research has proposed an optimal charging method of a lithium‐ion battery, an effective method is yet to be identified for both time and degradation reduction. Herein, an effective charging protocol that minimizes battery life degradation thereby enhancing its remaining‐useful‐life is proposed. The proposed protocol is an adaptive multistage constant current (MCC) and pulse charging (PC) protocol, utilizing time‐dependent current charging profiles to prevent battery degradation with state‐of‐charge (SOC) variation and pulse relaxation intervals. An extended Kalman filter algorithm for accuracy SOC estimation is embedded with the charging protocol. The proposed method is evaluated with other charging profiles, including constant current, MCC, and PC protocols, to evaluate its performance. The results show that among the four cases proposed, only the PC protocol outperforms other charging protocols, achieving a balance between fast charging and battery degradation prevention, making it better applicable for use in practical battery charge applications. [ABSTRACT FROM AUTHOR]

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs' life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 104, 1.12 × 104 and 1.01 × 104 kg CO2-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector. [ABSTRACT FROM AUTHOR]

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation. [ABSTRACT FROM AUTHOR]

Rapid advancements in battery technologies led to dramatic growth in adoption of electric vehicles (EVs) all over the world. On the other hand, ever-increasing renewable energy sources (RES) in microgrids (MGs) posing numerous challenges ahead. In this context, EVs can be used as virtual storage units to confront the intermittency aspect of RES in MG scenarios. This work proposes an EV fleet control strategy to implement a three-layer energy management system: Optimal storage distribution (OSD), optimal power exchange (OPE) and smart EV ranking (SER). The key objectives are minimizing grid dependency, energy cost, EV battery degradation and to maximize EV storage usage. Water filling algorithm is used to obtain OSD and multi-objective optimization problem is formulated and solved by e-constraint method to obtain OPE. SER is implemented using a fuzzy logic controller where a number of decision variables are involved. EV battery degradation has been considered through SER by including a key decision variable, EV usage probability (EUP). EUP has been obtained using a probabilistic approach that accounts all possible state transitions of each EV in the given time interval. An on-grid MG scenario with EV fleets and RES is considered to implement the proposed EMS. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure. [ABSTRACT FROM AUTHOR]

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%. [ABSTRACT FROM AUTHOR]

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Abstract The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

As the demand for high-performance and long-lasting batteries continues to escalate, understanding the degradation mechanisms of Li-ion batteries (LIBs) has become a pressing concern. In this study, we employed the acoustic emission (AE) technique to detect and quantify the internal changes occurring within LIBs during the degradation processes. Our goal was to propose a comprehensive method to categorize the AE data and correlate them with different battery events, which has not yet been properly established in the state of the art. Two commercial pouch cells at different levels of degradation were monitored using the AE technique during their cycling, and the changes in their electrochemical and mechanical behavior were analyzed. A thorough investigation of the AE hits enabled us to identify 4 distinct AE types in terms of frequency, which could reflect multiple battery degradation events, including intercalation-induced stress, gas generation, and particle/electrode cracking. Our proposed approach was compared with the conventional methods presented in past studies, demonstrating its compatibility in explaining different battery phenomena and the coupled behavior of those phenomena. Overall, this work offers a new approach to in-operando AE analysis of LIBs, which helps further development of the AE technique as a real-time and nondestructive diagnostic tool for LIBs.

This paper describes an experimental dataset of used lithium-ion battery cells cycled on grid storage synthetic duty cycles to study their feasibility for second-life applications. Data were collected at the Stanford Energy Control Laboratory at Stanford University, CA, USA. The ten INR21700-M50T battery cells with graphite/silicon anode and Nickel–Manganese–Cobalt (NMC) cathode had been previously tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile. In this paper, six out of these ten cells are tested for a period of 24 months. The aging campaign is a combination of calendar aging and cycling. The cycling portion is designed to replicate real-world usage patterns based on synthetic duty cycles for residential and commercial grid energy storage systems (ESS). Battery cycling alternates between 20 °C and 35 °C to simulate seasonal temperature variations encountered in grid applications. The calendar aging, on the other hand, occurs at room temperature. Periodic assessments of battery degradation during second-life testing are accomplished via Reference Performance Tests for second-life (RPT S) comprising of a combined capacity and pulse power test, and Electrochemical Impedance Spectroscopy (EIS) at three state-of-charge (SOC) values. The data set captures the combined effects of cycling-induced stress and long-term storage.

Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency.

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Batteries are the lynchpin of electric vertical takeoff and landing (eVTOL) aircraft, and the high discharge rates pose a critical challenge to the battery thermal management system (BTMS). This work presents a channeled liquid cooling technology-based BTMS for eVTOL aircraft. During the flight, the heat generated from the batteries is partly extracted by circulating liquid coolant within a wavy channel (WC) attached firmly to the battery cells. The heat is then transported into a plate-fin compact heat exchanger (HEX), where all the heat is dissipated into the atmosphere. We report sensitivity analyses of the HEX model to shed light on the impact of the relevant design parameters on the BTMS size, weight, and power. We also examine critical parameters of the coupled WC-HEX BTMS in a one-dimensional off-design analysis. We demonstrate that the liquid cooling system can maintain the battery operating temperature within acceptable levels with a mass of less than 20% of the battery pack mass. Battery degradation using the liquid cooling system is reduced by over three times compared to an air-cooled system for both tilt-wing and lift+cruise eVTOL aircraft. [ABSTRACT FROM AUTHOR]

The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries. [ABSTRACT FROM AUTHOR]

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased. [ABSTRACT FROM AUTHOR]

The variability of solar radiation presents significant challenges for the integration of solar photovoltaic (PV) energy into the electrical system. Incorporating battery storage technologies ensures energy reliability and promotes sustainable growth. In this work, an energy analysis is carried out to determine the installation size and the operating setpoint with optimal constant monthly power through an iterative calculation process, considering various operating setpoints and system parameters. A degradation model is integrated according to the curves offered by battery manufacturers and the charge–discharge cycles are calculated using the rainflow method to guarantee a reliable analysis of the plant. Through massive data analysis in a long-term simulation, indicators are generated that allow for establishing a relationship between the energy unavailability of the system and the BESS dimensions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are crucial in mitigating global emissions by replacing internal combustion engines. The capacity of EV batteries, coupled with their charging infrastructure, offers the added advantage of supplying flexible demand capacity and providing demand response benefits to the power grid, which is essential as overall demand increases. EVs ready for vehicle-to-everything (V2X) applications and chargers that support them enhance this flexibility by allowing for varied storage applications. However, to fully harness these benefits, it is vital to consider EV drivers' charging habits and optimize the charging and discharging controls to minimize battery life impact. This study examines various V2X applications in North America and their effects on battery longevity, considering EV charging patterns. Additionally, it investigates advanced aging-aware optimization algorithms for managing bidirectional charging. [ABSTRACT FROM AUTHOR]

As Electric Vehicles (EVs) become increasingly prevalent, accurately estimating Lithium-ion Batteries (LIBs) Remaining Useful Life (RUL) is crucial for ensuring safety and avoiding operational risks beyond their service life threshold. However, directly measuring battery capacity during EV operation is challenging. In this paper, we propose a novel approach that leverages measurable features based on the discharge time and battery temperature to estimate RUL. Our framework relies on a novel feature extraction strategy that accurately characterizes the battery, leading to improved RUL predictions. Multiple machine learning algorithms are employed and evaluated. Our experimental results demonstrate that the proposed method accurately estimates capacity with minimal hyperparameter tuning. The R 2 scores across various battery numbers indicate strong predictive performance for models like XGBoost, RF, AdaBoost, and others, with improvement percentages ranging from 85% to 99%, which the model's generalizability verifies across other batteries. The results show the effectiveness of our proposed method in accurately estimating the RUL of LIBs in EVs. [ABSTRACT FROM AUTHOR]

Batteries are crucial to manage the rising share of intermittent energy sources and variability in demand. Most technoeconomic models in the literature oversimplify battery degradation representation. Accounting properly for battery degradation allows for better cost tradeoffs and optimal battery usage, especially in dynamic settings. We propose a highly accurate and scalable formulation for battery degradation that considers the combined impact of cycle depth (CD) and state of charge on calendar and cycle aging. This includes a novel way to track charge‐discharge cycles. We test the consequences of battery degradation in a stylized price arbitrage model on battery operation and solution times. When ignoring battery degradation, ex post calculations reveal hidden degradation costs that exceed revenues and hence turn seemingly profitable trades into losing trades. Considering battery degradation leads to smaller CDs and lower average states of charge. Overall, we show that a much‐improved representation of battery degradation is possible at modest computational cost, allowing better decisions and higher profits. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for accurate state‐of‐health (SOH) predictions in lithium‐ion batteries, crucial for maintaining efficient battery operation and extending lifespan. We propose a novel approach utilizing the Seasonal autoregressive integrated moving average with exogenous variables (S‐ARIMAX) model, integrating multiple battery degradation factors as exogenous variables for precise SOH estimation. Our methodology involves correlation analysis to identify optimal factors, which are then incorporated into distinct cases of the S‐ARIMAX model. We evaluate the model's performance using mean absolute percentage error (MAPE) and identify voltage deviation as a highly correlated exogenous variable significantly impacting predictive accuracy. Notably, our findings underscore the S‐ARIMAX model's superiority in longer cycles, demonstrating a MAPE as low as 0.1113 at 160 cycles, emphasizing the model's adeptness at capturing cyclic patterns for precise long‐term SOH forecasts. Comparing S‐ARIMAX and ARIMAX models highlights the pivotal role of seasonality, particularly in long‐term predictions. The study's quantitative findings emphasize the necessity of integrating highly correlated variables and accounting for seasonal patterns in optimizing lithium‐ion battery SOH predictions. Our approach presents practical implications for maintaining battery performance and lifespan in electric vehicles and energy storage systems, underlining the importance of selecting strongly correlated variables and integrating seasonal components for accurate and reliable SOH forecasts. [ABSTRACT FROM AUTHOR]

A longer battery life is a highly sought-after feature for most smartphone users when considering their next device. However, with the emergence of new hardware technology and software applications that require heavy processing, the demand for battery power has significantly increased. Unfortunately, the development of battery technology has not kept up with the rapid advancements in smartphone hardware and software, which rely heavily on battery power. To address this issue, several approaches have been proposed to regulate battery consumption and the charging process on smartphones. In this paper, we summarize the different approaches related to this problem that managed to achieve up to a 61% increase in battery daily usage in simulation testing, highlighting their strengths, limitations, and current challenges. Furthermore, we provide a comprehensive review of various open-source datasets that have the potential to be used in developing new approaches to improve battery drainage and degradation in smartphones. We also discuss the methodology for collecting each dataset. Finally, we propose a new approach to address the current limitations and challenges to solving the problem of battery drainage and degradation that could be developed using the currently available datasets. These new approaches may involve incorporating Machine Learning (ML) techniques to predict battery charging patterns and minimize battery drainage. [ABSTRACT FROM AUTHOR]

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%. [ABSTRACT FROM AUTHOR]

As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements. [ABSTRACT FROM AUTHOR]

In any electricity grid system, a balance must be found at all times between production and demand. However, the growing use of renewable energies poses new challenges for grid operators, i.e., imbalances that can cause undesirable effects on the electricity network, including frequency deviations. In response, ancillary services have been introduced to serve as mechanisms to support the continuous flow of electricity, ensuring that demand and production are met in real-time. Given the rapid response capabilities of batteries, battery owners are encouraged to participate in one of the most crucial ancillary services, the Frequency Containment Reserve (FCR). Through such participation, battery owners can generate new revenue opportunities and support the stability of the electricity grid. In this study, we explore mathematical models and heuristics for planning and coordinating cellular network systems interested in providing FCR-D ancillary services. By leveraging spare battery capacity associated with their multiple cellular base stations, communications service providers (CSPs) emerge as a potential player in this market. We compare different mathematical models and heuristics applied to the Swedish frequency market, considering a CSP with one thousand cellular base stations. The results demonstrate the effectiveness of the proposed models in terms of transparent participation, profit and associated costs. Furthermore, we validate the technical and economic feasibility of frequency regulation provided by cellular network systems, thus revealing a new potential source of revenue for CSPs.

Consensus has been widely used in distributed control, where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal. However, the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost, which is an important performance indicator to assess the performance and sustainability of battery energy storage (BES) systems. In this paper, we propose a consensus-based optimal control strategy for multi-microgrid systems, aiming at multiple control objectives including minimizing battery degradation cost. Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge (SoC) among all microgrids while each microgrid is trying to reach its individual optimality. In order to reduce the pressure of commu-nication links and prevent excessive exposure of local information, this ratio is the only state variable shared between microgrids. Since our complex nonlinear problem might be difficult to solve by traditional methods, we design a compressive sensing-based gradient descent (CSGD) method to solve the control problem. Numerical simulation results show that our control strategy results in a 74.24 % reduction in battery degradation cost on average compared to the control method without considering battery degradation. In addition, the compressive sensing method causes an 89.12 % reduction in computation time cost compared to the traditional Monte Carlo (MC) method in solving the control problem.

In recent years, battery degradation has become a critical concern in various industries, including electric vehicles, renewable energy systems, and portable electronics. To address this issue, data-driven techniques have emerged as a promising approach for lithium-ion battery (LIB) degradation analysis and estimation. This paper focuses on the application of incremental capacity curves (ICCs) in battery degradation analysis using data-driven techniques. The incremental capacity curve is a powerful tool that provides valuable insights into the capacity degradation of a battery. By analyzing the changes in the ICC over time, it is possible to identify and quantify battery degradation phenomena such as capacity fade, impedance growth, and aging effects. However, manually analyzing ICCs can be time-consuming and subjective, leading to potential errors and inconsistencies. To overcome these challenges, hyperparameter-tuned Long Short-Term Memory (LSTM) techniques are employed to automate the analysis of ICCs and extract meaningful degradation information. These techniques leverage statistical models to process large volumes of ICC data and identify degradation patterns. By training these models on historical data, they can accurately predict battery degradation and estimate the remaining useful life (RUL) of a battery. Further, to enhance the performance of estimation of RUL of the battery. A hyperparameter-tuned LSTM technique has been proposed. The proposed technique has been compared with well-known techniques (i.e. Fully Connected Neural Network (FNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)). The results depict that the proposed robust LSTM technique outperforms well in terms of computational cost and speed. To demonstrate the efficiency of the proposed technique, error analysis has been carried out. The simulation and experimental results depict that the proposed hyperparameter-tuned LSTM model results in very low error indices such as RMSE, MEA, and MAPE as 0.0246, 0.0159 and 1.03 compared with models such as FNN, ANN and CNN. The proposed hyperparameter-tuned LSTM technique depicts a lesser error. By leveraging machine learning and statistical models. The results of this study contribute to the advancement of battery management systems and the optimization of battery usage in various applications.

Microgrids are fundamental elements in modern energy systems. Among the various microgrid components, the Energy Storage System (ESS) plays a pivotal role in ensuring system reliability, but its high cost and inevitable degradation over time pose significant challenges. Many current studies overlook the impact of ESS degradation on operational optimization, potentially leading to cost-ineffective systems. To address this gap, we introduce a quadratic ESS degradation model that captures intricate battery dynamics, such as State of Charge (SoC) and Depth of Discharge (DoD), using Markovian properties. Based on this model, we propose an optimal energy management framework for DC microgrids using Quadratic Programming (QP). The objective is to minimize the combined costs of degradation and electricity, considering the Time-of-Use (ToU) tariff while adhering to ESS constraints. This financially focused approach provides a pragmatic and economically aligned optimization strategy. Testing across various State of Health (SoH) scenarios demonstrates that our proposed model reduces total operational costs by 3-18%. This research advances microgrid optimization techniques and offers practical insights to enhance efficiency and economic resilience in real-world scenarios.

This paper develops intelligent energy management in Microgrid using forecasting-based multi-objective optimization using genetic algorithm framework. In this work, the energy storage system is included in Microgrid network, which is essential for effective energy management and smooth power transfer. The developed model incorporates the forecasted values of solar PV and wind generation obtained using time series long short-term memory network for the next 24 h and provides the optimal values of battery and grid powers to meet the deficit of power to meet the demand in the Microgrid. The proposed model incorporates grid power consumption cost and battery degradation cost as the objectives with battery status and renewables utilization as the constraints of the optimization model. The fuzzy decision-making strategy obtains the compromise solution in the Pareto optimal front from multiple solutions. The proposed model is investigated with fixed and variable grid tariff conditions achieving efficient performance in energy management with optimal utilization of renewables, grid and battery in the system for 24 hours-time horizons. The system parameters in the Microgrid network are also investigated in the time horizon with the optimal values of proposed model. The proposed intelligent energy management system model is tested in 2.5 MW PV/wind/energy storage Microgrid system in MATLAB 2020 simulation platform and experimental setup of 1 kW grid connected Microgrid with solar PV and battery.

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning. [ABSTRACT FROM AUTHOR]

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R2 value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R2 value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals. [ABSTRACT FROM AUTHOR]

The number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of these ships, it is important to monitor the capacity that can be stored in the batteries, and classification societies typically require that this can be verified by independent tests—annual capacity tests. However, this paper discusses data‐driven alternatives based on operational sensor data collected from the batteries. There are different strategies for such data‐driven state of health (SOH) estimation. Some approaches require full operational history of the batteries in order to predict SOH, and this may be impractical due to several reasons. Thus, methods that are able to give reliable estimation of SOH based on only snapshots of the data streams are more attractive from a practical point of view. In this paper, data‐driven snapshot methods are explored and applied to degradation data from battery cells cycled in different laboratory tests. Hence, data from different sources are fused together with the aim of achieving better predictions. The paper presents the battery data show how relevant features can be extracted from snapshots of the data and presents data‐driven models for SOH estimation. It is discussed how such methods could be utilized in a data‐driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for nearly 40% of the tested cells, although the fusion of data from different laboratory tests did not improve results significantly. Results are greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) is increasing due to governmental policies focused on curbing climate change. EV batteries are retired when they are no longer suitable for energy-intensive EV operations. A large number of EV batteries are expected to be retired in the next 5–10 years. These retired batteries have 70–80% average capacity left. Second-life use of these battery packs has the potential to address the increasing energy storage system (ESS) demand for the grid and also to create a circular economy for EV batteries. The needs of modern grids for frequency regulation, power smoothing, and peak shaving can be met using retired batteries. Moreover, these batteries can also be employed for revenue generation for energy arbitrage (EA). While there are articles reviewing the general applications of retired batteries, this paper presents a comprehensive review of the research work on applications of the second-life batteries (SLBs) specific to the power grid and SLB degradation. The power electronics interface and battery management systems for the SLB are also thoroughly reviewed. [ABSTRACT FROM AUTHOR]

In this paper, the planning of a hybrid system of wind turbine units, photovoltaic panels, and battery storage is presented by taking into account the limitation of the storage degradation. The scheme minimizes the construction and maintenance cost of power sources and storage equipment. The constraints of the problem include the operating model of the mentioned elements, the limitation of the number of the mentioned elements, the limitation of the storage degradation, and the power balance in the hybrid system. This scheme is subject to uncertainties of the demand and output power generation of wind turbines and photovoltaics, which are modeled using a scenario-based stochastic optimization. The problem has a mixed-integer non-linear structure, and the paper adopts the firefly algorithm to solve the problem. The contributions of the paper include considering the degradation model of the battery, presenting a stochastic modelling for planning the islanded system, and taking into account the uncertainties of load and renewable power. Finally, based on the numerical results, a low planning cost is obtained for the hybrid system in the case of using renewable resources. Batteries are capable of providing flexibility for the hybrid system so that they can cover oscillations of renewable power with respect to the load. The firefly algorithm can find a reliable optimal solution. Stochastic modeling raises the planning cost of the islanded system in comparison to the deterministic model, but it yields a more reliable solution. The battery degradation model incurs no additional costs in system planning, although it offers a far more precise representation of the battery's behavior.

Energy arbitrage is a potential revenue stream for battery operators with access to variable electricity prices. However, the power shifted by grid-scale energy storage has the potential to influence the production mix in real time, impacting the carbon emissions of the electricity system. Little research is available on the CO2 emissions induced by arbitrage operations, and studies that consider arbitrage-related CO2 emissions often neglect battery degradation. To address this gap, this study proposes a novel modeling and assessment framework based on mixed-integer linear programming to analyze the trade-offs between profit and CO2 emissions of battery arbitrage operations as well as the impact of degradation on arbitrage profit and emissions. We present the results in terms of Pareto-optimal solutions that identify maximum profit and minimum CO2 emissions. We illustrate our model through a case study in Germany and we show that performing maximum-profit arbitrage increases the system emissions by up to 7.5 tCO2 per MWh of storage capacity (or about 12% of battery life cycle emissions per year). 60% of the added emissions can be avoided by sacrificing only 1.5% to 2.7% of the net arbitrage profit, and CO2-neutral operation can be achieved by sacrificing about 7% of the profit. Our findings also highlight the importance of modeling battery degradation, as degradation-unaware arbitrage models may lead to a substantial profit loss (potentially to negative profits) and higher CO2 emissions (up to +260%) with respect to degradation-aware models.

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. We provide an innovative Energy Management Strategy (EMS) for MG with grid support in this academic publication. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. The period of 24 h is divided into 96 intervals (t) and storage requirements (kWh/t) are estimated based on the estimated load and RES together with the necessary storage volume. We employ three approaches that are frequently used for communication channel power allocation optimization to accomplish OAES. With two objectives: minimum network power loss plus voltage fluctuations, the Multi-Objective Optimization Problem (MOOP) is solved for each 't' based on OAES to provide the Optimal Power Flow (OPF). The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG.

The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion) battery occupies a crucial position, although it is faced with challenges related to performance battery degradation over time due to electrochemical processes. This battery degradation is a crucial factor to account for, based on its potential to diminish the efficiency and safety of electrical system equipment, thereby contributing to increased system planning costs. This implies that the health of battery needs to be diagnosed, particularly by determining remaining useful life (RUL), to avoid unexpected operational costs and ensure system safety. Therefore, this study aimed to use machine learning models, specifically extreme gradient boosting (XGBoost) algorithm, to estimate RUL, with a focus on the temperature variable, an aspect that had been previously underemphasized. Utilizing XGBoost model, along with fine-tuning its hyperparameters, proved to be a more accurate and efficient method for predicting RUL. The evaluation of the model yielded promising outcomes, with a root mean square error (RMSE) of 90.1 and a mean absolute percentage error (MAPE) of 7.5 %. Additionally, the results showed that the model could improve RUL predictions for batteries within BESS. This study significantly contributed to optimizing planning and operations for BESS, as well as developing more efficient and effective maintenance strategies.

Abstract Bidirectional charging allows energy from the electric vehicles (EV) to be fed back into the grid, offering the possibility of price-optimized charging. However, such strategies cause higher charging cycles, which affect the cyclic aging of the battery and reduce its service life, resulting in additional costs for the user. Various approaches are used to account for battery degradation in optimizations models of bidirectional charging use-cases. In this paper, a systematic literature review is carried out to identify existing battery degradation models and to determine the most suitable one. In the models under review, degradation is integrated into the optimization’s objective function. The review shows that there are mainly two strategies suitable for vehicle-to-grid (V2G) optimization problems: A weighted Ah-throughput model (wAh-model) with a constant degradation cost factor and a performance based model (pb-model) linking the degradation to measurable parameters such as capacity loss. Both models were implemented and analyzed. The results show that the wAh-model is the better optimization option, as in the pb-model the current state of health of the battery has an excessively large impact on the calculated degradation cost. It leads to excess costs due to a higher aging rate at the beginning of life which proves to be not ideal in the optimization. The sensitivity analysis reveals that altering the initial State of Health (SoH) from 95 % in the base scenario to 100 % leads to an increase in average degradation costs by factor 9.71 in the pb-model. From the evaluated base scenario the average degradation costs for the pb-model are 0.45 cent/kWh and for the wAh-model 0.23 cent/kWh.

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs’ life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 104, 1.12 × 104 and 1.01 × 104 kg CO2-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector.

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators.

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure.