Battery Management Research Articles

A Review on Towards Long Lifetime Battery: AI Based Manufacturing and Management

A Battery Management System (BMS) is crucial for maintaining the health and efficiency of batterypowered devices. Integrating Artificial Intelligence (AI) into BMS offers advanced capabilities for optimizing battery performance. AI algorithms analyze real-time data from the battery, such as voltage, temperature, and charge cycles, to predict battery life, enhance safety, and improve energy efficiency.AI can detect patterns and anomalies that traditional systems might miss, allowing for proactive adjustments to charging and discharging processes. This helps prevent overcharging, overheating, and deep discharges, which can damage batteries and reduce their lifespan. Additionally, AIdriven BMS can learn from historical data to refine its predictions and adapt to changing conditions. It also supports better energy management in electric vehicles and renewable energy systems, where efficient battery use is critical. By continuously monitoring and optimizing battery performance, AI enhances the overall reliability and longevity of battery systems, making technology more sustainable and efficient. Thus, the use of AI in BMS represents a significant advancement in battery technology, contributing to improved performance and safety in various applications. This study explores battery management systems by optimizing performance, extending lifespan, and improving efficiency and ensure safety through predictive analysis and real-time monitoring. Keywords: Battery Management system, artificial intelligence, state of charge, state of health, battery life extension, fault detection.

An Ensemble Learning Method for SOC Estimation of Lithium-Ion Batteries Using Machine Learning

Accurately assessing the State of Charge (SOC) is paramount for optimizing battery management systems, a cornerstone for ensuring peak battery performance and safety across diverse applications, encompassing vehicle powertrains and renewable energy storage systems. Confronted with the challenges of traditional SOC estimation methods, which often struggle with accuracy and cost-effectiveness, this research endeavors to elevate the precision of SOC estimation to a new level, thereby refining battery management strategies. Leveraging the power of integrated learning techniques, the study fuses Random Forest Regressor, Gradient Boosting Regressor, and Linear Regression into a comprehensive framework that substantially enhances the accuracy and overall performance of SOC predictions. By harnessing the publicly accessible National Aeronautics and Space Administration (NASA) Battery Cycle dataset, our analysis reveals that these integrated learning approaches significantly outperform traditional methods like Coulomb counting and electrochemical models, achieving remarkable improvements in SOC estimation accuracy, error reduction, and optimization of key metrics like R2 and Adjusted R2. This pioneering work propels the development of innovative battery management systems grounded in machine learning and deepens our comprehension of how this cutting-edge technology can revolutionize battery technology.

One-step green hydrometallurgical recycling of spent lithium-ion batteries’ cathode

The transition towards a low-carbon future hinges on the advancement of Lithium-ion battery (LIBs) technology, which has spurred a significant demand for raw materials and the management of waste batteries containing hazardous substances. Developing efficient and environmentally friendly recycling strategies is essential to tackle these challenges. Here, we introduce a one-step green hydrometallurgical recycling of spent lithium-ion batteries’ cathode on the basis of contact-electro-catalytic (CEC) process. It combines the separation of the cathode materials in pretreatment, and leaching in a single step. Specifically, electrode pieces are directly dealt with the CEC treatment, which can remove organic binders (e.g. Polyvinylidene Fluoride) and reduce high-valence metal ions. Results indicate that the leaching efficiency of lithium, nickel, cobalt, and manganese can reach 99.6%, 98.3%, 99.4%,97.4%. Plus, SiO2 is a cost-effective and readily available catalyst requiring no modification. Therefore, the CEC-assisted one-step hydrometallurgical recycling exhibits sustainability, rapidness, high efficiency, and eco-friendly for LIBs recycling.

Integrating ensemble learning and meta bagging techniques for temperature-specific State of Health prediction in Lithium-ion Batteries

Accurately predicting the State of Health (SOH) of lithium-ion batteries is crucial in the field of battery technology to guarantee operational dependability and durability. This study introduces a novel methodology that integrates ensemble learning and meta-bagging techniques to enhance the precision of SOH prediction, particularly in scenarios characterized by fluctuating temperature conditions. We start our methodology by conducting an extensive data-gathering phase, specifically targeting crucial variables: temperature, voltage, current, and capacity. Subsequently, a thorough data preparation stage ensues, encompassing tasks such as cleansing, standardization, and curating relevant features, with particular attention given to temperature characteristics. Next, we employ a temperature-adaptive ensemble learning model that combines different prediction algorithms, such as Linear Regression (LR) and Long Short-Term Memory (LSTM) networks. These algorithms are trained on temperature-stratified data subsets using a Meta-Bagging Estimator (MBE). This strategy enhances the precision of predictions and the ability to overcome the obstacle of temperature changes impacting battery performance. The efficacy of the ensemble model is thoroughly assessed utilizing diverse performance criteria, showcasing its improved predictive capability compared to conventional approaches. Our findings indicate that this customized strategy is better equipped to manage the intricate dynamics of lithium-ion battery behavior, resulting in more dependable and precise assessments of battery SOH. This work substantially contributes to battery health monitoring and can significantly advance battery management systems.

Analysis of cell balancing of Li-ion batteries with dissipative and non-dissipative systems for electric vehicle applications

This article describes the essential components of contemporary battery management systems (BMS), such as power electronics bidirectional charging and discharging, reverse protection against constant current and voltage, and Li-ion battery cell balancing, which is the process of introducing Li-ion The majority of domestic electrical applications, including e-cars, are powered by batteries. These qualities are crucial for ensuring the safety, longevity, and effectiveness of batteries when it comes to renewable energy sources and electric vehicles (EVs). Two other crucial BMS characteristics that protect against reverse current and overcharging destroying batteries are constant current and voltage reverse protection. Ensuring that every single cell in a battery is in balance is known as evenly charging and discharging every single cell. In-depth discussions of these characteristics' advantages and operation, as well as their uses in EVs, renewable energy systems, and other contexts, are provided in this article. It is seen from the analysis that the non-dissipative lithium-ion battery cell balancing strategy, which significantly enhances safety and efficiency, provides greater benefits than the dissipative balancing approach. The modelling of an SoC charge-controlled Li-Ion battery with an optimum battery voltage of 3.6 V. Its rated capacity of 4 Ah is considered a test cell that has contrasted dissipative and non-dissipative balancing in MATLAB/Simulink with five cells in the battery bulk. It is seen from the analysis that the non-dissipative lithium-ion battery cell balancing strategy provides greater benefits than the dissipative balancing approach.

The knowledge contributions from science to technology on lithium-ion batteries from the industry chain view

The lithium-ion industry chain comprises multiple links such as battery components, battery (pack) and management, and recycling. Each link has its features, and they are interlinked. Industry chain development cannot be separated from the mutual promotion of all links in the industry chain. The scientific and technological innovation of the lithium-ion battery chain is widely studied because it is considered one of the most promising energy storage systems. Moreover, scientific development is widely considered to promote technological innovation, and the contribution of knowledge from science to technology is studied by analyzing citations from patents to papers. However, there are two research gaps. First, there is little research on the contributions of science and technology in the lithium-ion battery field. The second gap is the lack of an industrial chain view in the study of science–technology linkages. Therefore, this study proposed a framework to reveal the knowledge contributions from science to technology from the industry chain view and applied it to the lithium-ion battery industry. The findings are summarized as follows: (1) A total of 130 topics were mined, summarized, and classified from the industry view. The electrode, battery management and electrolyte are relatively complex and are more discussed, especially regarding the electrode. (2) Knowledge contributions from science to technology have concentrated on the electrolyte, electrode and battery management. The knowledge contributions from paper topics on the electrode to patent topics on the electrode are the strongest. Patent inventors who focus on these three elements should refer to science on this element and science on the other two elements. Besides, policy- makers could promulgate promotion policies to stimulate the scientific development of these three elements, especially for the electrode. (3) Garnet-type ceramic sintering was the topic that had the greatest contributions in 2017 and 2018, and the patent topic to which it contributed as the most was “garnet-type ceramic sintering. Silicon has a consistently high degree of knowledge interaction between science and technology. These findings could serve as a reference for policy-makers when making incentive policies and scholars when selecting research content.

Thermal Management of Battery Using Phase Change Material and Air Cooling

Abstract: Fossil fuels produce large quantities of carbon dioxide when burned. Carbon emissions trap heat in the atmosphere and lead to climate change. Electric vehicles could help diversify the energy needed to move people and goods thanks to their reliance on the broad mix of primary energy sources used in power generation, significantly improving energy security. A battery is an energy storage system in an electric car. The battery of the electric car begins to rise in temperature during its usage. An average optimum battery working conditions varies from 40˚C – 55˚C. Various cooling methods have been implemented to prevent the damage and thermal runaways of the battery cells, such as air, liquid, phase changing materials and a combination of either method. This project has an aim to develop a battery thermal system capable of managing the temperature of the battery within safety limits and providing a cost-effective solution.

State of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction

The particle filter (PF) has been widely used for state of charge (SOC) estimation. However, the particle degradation phenomenon will affect the estimation accuracy. In response to this issue, a state of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction (ACDPF-WR) is designed in this paper. This method combines the preferred importance density function and the optimized resampling strategy. Firstly, the central difference Kalman filter (CDKF) is used to update the sampled particles to reduce the influence of particle degradation. Secondly, the Gaussian process regression (GPR) model of particles and weights is constructed by combining the offline learning experimental battery data set, and the GPR is used to generate the weight distribution of the particle filter. Then, an adaptive step size mechanism is introduced, which determines the optimal step size by calculating the mean square error of the weight distribution under different steps. Finally, the weight distribution generated by the central difference particle filter (CDPF) based on the optimal step size is combined with the typical resampling algorithm to select high-quality particles to achieve the optimal estimation. The adaptability and robustness of the algorithm are verified under Beijing Dynamic Stress Test (BJDST), US06 Highway Driving Schedule (US06), and Dynamic Stress Test (DST) conditions, and evaluated by mean absolute error (MAE) and root mean square error (RMSE) indicators. The average comparison results of the three working conditions show that the MAE of ACDPF-WR is 54.1 % higher than that of EPF and 24.3 % higher than that of CDPF, and the RMSE of ACDPF-WR is 64.6 % higher than that of EPF and 24.5 % higher than that of CDPF. The proposed algorithm achieves better performance and provides new insights and methods for the optimization and improvement of the battery management system.

Governance of Waste Batteries as a Circular Resource: The Role of Policy Actors and Stakeholder Collaboration

Objectives : The study aims to address the gap in research on governance strategies for managing used electric vehicle (EV) batteries in Korea. The goal is to explore strategies that enhance governance and promote a sustainable ecosystem for used batteries, positioning them as a circular resource to aid in reducing greenhouse gas emissions in the transportation sector.Methods : An expert survey involving 37 respondents was conducted to gather insights into the governance models for EV battery management. The survey sought to identify key actors and preferred governance models necessary to establish a comprehensive ecosystem for used batteries.Results and Discussion : The survey results revealed that companies (54%) were identified as the primary actors needing adjustments and expanded authority in EV battery governance, followed by the central government (40%) and local governments (5%). Regarding preferred governance models, the Business-to-Government (B2G) model was the most favored (40%), followed by Business-to-Central Government (B2CG) (37%), Business-to-Local Government (B2LG) (16%), and Central Government-to-Local Government (CG2LG) (5%). These findings emphasize the importance of cooperative governance in fostering the growth of the EV battery industry.Conclusion : The study provides valuable insights for developing a sustainable ecosystem for used batteries as a circular resource. The findings offer critical evidence to inform policy formulation, underscoring the need for a collaborative governance framework to support industry growth and sustainability.

Advanced integration of bidirectional long short-term memory neural networks and innovative extended Kalman filter for state of charge estimation of lithium-ion battery

The state of charge (SoC) of a battery is a crucial monitoring indicator for battery management systems and it helps to assess how much further an electric vehicle can travel. This work proposes a novel approach for predicting battery SoC by developing a closed-loop system that integrates a bidirectional long short-term memory neural network with an innovative algorithm-extended Kalman filter. A second-order equivalent circuit model is selected, and its parameters are computed using the variational and logistic map cuckoo search approach. Further, an Extended Kalman filter is combined with an innovation algorithm to update process noise in real-time, and a bidirectional long short-term memory neural network takes the input from the Extended Kalman filter and gives the compensated error value for the final SoC estimation. 75% of dynamic stress test data from the Extended Kalman filter is used for training purposes, remaining data sets are used for testing purposes. The addressed algorithm is validated by evaluating its performance in comparison to individual algorithms and various combined approaches. Empirical analysis demonstrates that the proposed model achieves a root mean square error of 0.11 % and mean absolute error of 0.1 % positioning it as a valuable tool for battery management systems.

A Compact Overview on Li-Ion Batteries Characteristics and Battery Management Systems Integration for Automotive Applications

A Compact Overview on Li-Ion Batteries Characteristics and Battery Management Systems Integration for Automotive Applications

Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter

With the European Union legislative push to phase out internal combustion engines by 2035, the demand for electric vehicles and efficient energy storage solutions, particularly lithium-ion batteries, is set to rise. Addressing this demand necessitates both the optimization of battery lifespan and the development of robust methodologies for real-time assessment of state of health and prediction of remaining useful life. This study introduces a novel hybrid grey-box prognostic and health management framework that combines a physical battery model with a multi-layer perceptron particle filter (MLP-PF) for real-time estimation of degradation parameters. The approach leverages an electrochemical model developed in Modelica to simulate battery voltage and track degradation parameters, thereby capturing the battery dynamic behavior over time. By integrating a data-driven MLP-PF, this method adapts the physical degradation parameters, ensuring ongoing and precise estimation of remaining useful life. Experimental validation, in terms of accuracy and confidence interval coverage, confirms the framework capability in prediction and relative quantification of uncertainties. These results underscore the framework practical utility for battery management systems in electric vehicles, providing an adaptable and accurate tool for decision-makers in battery maintenance and replacement.

Modeling and Analysis of Current Loading Effects on Electric Vehicle’s Lithium-Ion Batteries: A MATLAB-Based Model Approach

Beyond portable mobile devices, lithium-ion batteries play a crucial role in electric vehicle operations and stationary grid power generation. However, the aging of lithium-ion batteries, often accelerated by extreme temperatures and load current influences, requires thorough examination and solution. The high load current, cycling, temperature differential, and operational conditions are factors contributing to the reduction in capacity and shortened lifespan of lithium-ion batteries. In this study, a lithium-ion (LiNixMnyCozO2) battery was modeled by using the MATLAB/Simulink model technique. In order to investigate the effect of resistance build-up in the batteries, the capacity of the batteries (old and new batteries) was analyzed over different usage periods: 360 cycles, 1000 cycles, and 2000 cycles. A cooling system was introduced to explicitly carry out an inductive analysis of the effect of temperature on the performances of the batteries. The effect of load current on the capacity of the battery was examined between 30 A and 100 A. The results showed that the available capacity of a battery is proportional to its usage rate. Generally, when the load current on the batteries (old and new batteries) was 30 A, the battery was ideally in good health even after 1000 cycles for a 2 h discharge time. In addition, the old battery, however, showed a capacity decrease to about 74.15% and 74.94% for scenarios 1 and 2 after 1000 cycles for a 2 h discharge time when the batteries were subjected to a 100 A discharge current. Amongst other factors, scenarios 1 and 2 can be differentiated by whether the battery pack discharges uniformly or non-uniformly, whether the individual cells operate under the same or different discharge cycles, and whether the batteries are with cooling or without a cooling system. The voltage and temperature differences between the old and new batteries, after 2000 cycles for the 100 A load current, are 4.0 V and 5.3 °C (scenario 2), respectively. Moreover, after 360 cycles at a 100 A discharge current, the temperature difference between the old and new batteries was 4.5 °C in scenario 1 and 2.3 °C in scenario 2. Based on the results obtained in this study, useful equations for proper calibration, voltage, and cooling switching time characteristics were proposed. Additionally, the study results indicated that at higher load currents, battery degradation became less affected by temperature differentials. The results of this study will aid in the adequate load optimization and thermal management of lithium-ion batteries for electric vehicle applications.

Comparative study of methodologies for SOH diagnosis and forecast of LFP and NMC lithium batteries used in electric vehicles

The exponential growth of electric mobility requires alternatives to extend the life of batteries in new applications and reduce the environmental impact of retired lithium batteries. The second life is an economic and environment-friendly alternative for battery management. The development of fast, low-cost, and reliable diagnostic methodologies makes it possible to increase the economic benefits and reduce the remanufacturing time of second-life batteries (SLBs). In the present work, battery state of health (SOH) distribution analysis, incremental capacity (IC), internal resistance (IR), and electrochemical impedance spectroscopy (EIS) were applied as diagnostic methodologies for two different chemistries of lithium-ion batteries previously used in electric vehicles (EV). In addition, module equalization in batteries was done in order to assess whether the state of module charge (SOC) variation affects the SOH. The results demonstrate that the diagnosis methodology depends on the chemistry of the battery, and that there is no single reliable diagnostic procedure that can be applied to all types of lithium-ion batteries. It was determined that the most adequate diagnostic method for LFP batteries (LiFePO4 cathode) is the IC method, while for NMC batteries (LiNi0.33Mn0.33Co0.33O2 cathode) the IR and EIS diagnostic methods are the most appropriate. Similarly, the present work proposed a simple methodology for IC capacity diagnosis and a general expression for SOH determination by IC and incremental voltage diagnosis of LFP batteries. Higher stability of LFP respect to NMC modules was observed during the SLBs performance, retaining >99 % after 500 cycles for LFP, compared with 90.2 % for NMC. The remaining useful life (RUL) shows that there are kinetics and lithium inventory changes in the batteries temperature-dependent.

A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time.

Research on Fast SOC Balance Control of Modular Battery Energy Storage System

Early SOC balancing techniques primarily centered on simple hardware circuit designs. Passive balancing circuits utilize resistors to consume energy, aiming to balance the SOC among batteries; however, this approach leads to considerable energy wastage. As research progresses, active balancing circuits have garnered widespread attention. Successively, active balancing circuits utilizing capacitors, inductors, and transformers have been proposed, enhancing balancing efficiency to some extent. Nevertheless, challenges persist, including energy wastage during transfers between non-adjacent batteries and the complexity of circuit designs. In recent years, SOC balancing methods based on software algorithms have gained popularity. For instance, intelligent control algorithms are being integrated into battery management systems to optimize control strategies for SOC balancing. However, these methods may encounter issues such as high algorithmic complexity and stringent hardware requirements in practical applications. This paper proposes a fast state-of-charge (SOC) balance control strategy that incorporates a weighting factor within a modular battery energy storage system architecture. The modular distributed battery system consists of battery power modules (BPMs) connected in series, with each BPM comprising a battery cell and a bidirectional buck–boost DC-DC converter. By controlling the output voltage of each BPM, SOC balance can be achieved while ensuring stable regulation of the DC bus voltage without the need for external equalization circuits. Building on these BPMs, a sliding mode control strategy with adaptive acceleration coefficient weighting factors is designed to increase the output voltage difference of each BPM, thereby reducing the balancing time. Simulation and experimental results demonstrate that the proposed control strategy effectively increases the output voltage difference among the BPMs, facilitating SOC balance in a short time.

Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks

The prediction of the lifespan of lithium-ion batteries (LIBs) is crucial for gaining early insights into battery degradation, enabling the optimization of battery usage and management strategies. This work assesses the expansion and aging characteristics of LIBs under various operating conditions and develops a combined machine-learning method to predict the aging trajectory of LIBs based on mechanical and electrical features. To avoid errors caused by secondary data processing, five mechanical-electrical features are directly extracted from the stress and electrochemical characteristic curves to reflect the battery aging state. A nonlinear autoregressive (NAR) neural network is adopted to predict the change trend of aging features (AFs) using historical data. Subsequently, the nonlinear regression (NR) neural network is employed for aging trajectory prediction using the aging feature dataset, which consists of both historical and predicted data. The experimental results demonstrate that the proposed method can ensure that the mean absolute error (MAE) in aging trajectory prediction is within 0.25 %. The method comprehensively analyzes and utilizes the mechanical-electrical properties of LIBs to enhance the predictive accuracy, expanding the scope of the predictive model in practical applications.

Revealing Relationship Between In Situ Impedance and Lithium Plating Onset Based on Lithium–Graphite Half-Cells

Lithium plating may occur during charging, especially at high rates or overcharging conditions for lithium-ion batteries (LIBs), which would cause battery capacity degradation and even trigger thermal runaway. Thus, it is essential to detect lithium plating onset during the charging processes. Electrochemical impedance can reveal the dynamic electrode properties of the battery, which is promising for use in battery management systems for the online detection of lithium plating onset. In this article, the impedance at 1 Hz is measured during the over-discharge and fast discharge processes using lithium–graphite half-cells. For half-cells, the variation in graphite electrode potential vs. Li/Li+ during discharging is directly recorded. An equivalent circuit model is proposed and adopted to estimate the real lithium plating reaction overpotential, which is deemed the thermodynamic indicator of lithium plating and is used as validation for the detection of lithium plating onset. Through the auxiliary validation of the estimation of lithium plating overpotential and the shape of incremental capacity curves, the relationship between impedance changes at specific frequency and the lithium plating onset is revealed. The results lay a good foundation for proposing the online diagnostic method of lithium plating onset based on the in situ impedance.

Research on a fault-diagnosis strategy of lithium iron phosphate battery in an energy-storage system based on multi-feature fusion

Quickly and accurately detecting the voltage abnormality of lithium-ion batteries in battery energy storage systems (BESS) can avoid accidents caused by battery faults. A triple-layer battery fault diagnosis strategy is proposed based on multi-feature fusion. The threshold are mainly based on the dynamic quartile method and the adaptive multiple is automatically adjusted according to the data situation. The first layer fault is based on the frequency of voltage values exceeding the limit to detect over-charge or over-discharge faults. The features such as improved stack autoencoder, improved standard deviation and improved Shannon entropy of the voltage sequences in a sliding-time window are mutually validated to identify open circuit or short circuit fault batteries in the second layer. The above features are input into the sliding local outlier factor (SLOF) method in the third layer to obtain the fused outlier factors. The grades of inconsistency are divided by fused outlier factor and verify the fault diagnosis results of the first two layers. Finally, validation is carried out using battery data from two actual operating BESSs. Moreover, a comparative analysis with existing methods has been executed. This proposed method can determine the type and severity of battery faults, which is conducive to the practical fault diagnosis application of battery management system in BESSs.

Optimal battery management in PV + WT micro-grid using MSMA on fuzzy-PID controller: a real-time study

In modern energy systems, managing energy within a microgrid (MG) poses significant challenges due to the unpredictable nature of renewable energy sources. This article introduces a novel approach for optimal battery management in a photovoltaic–wind microgrid using a Modified Slime Mould Algorithm (MSMA) combined with a fuzzy-PID controller. The microgrid comprises a wind turbine (WT) generator, solar photovoltaic (PV) generator, and a battery energy storage system (BESS). The BESS plays a crucial role in meeting high power demand during outages, while the fuzzy-PID controller ensures accurate prediction of the battery’s state of charge (SOC). The proposed method’s performance is evaluated by comparing the MSMA-based fuzzy-PID controller with a PSO-based fuzzy-PID controller to establish its effectiveness. The optimal energy management of the BESS in the microgrid is achieved by fine-tuning the fuzzy-PID controller using the MSMA algorithm. Simulation results demonstrate that the battery management system (BMS) effectively optimizes charging and discharging based on renewable energy availability and load demand. The fuzzy-PID controller adjusts battery operation by minimizing the error between the desired and actual battery voltage. Performance validation has been conducted in RTS-lab using five distinct load scenarios—45 kW, 35 kW, 75 kW, 4.5 kW, and 12.5 kW, which confirming the effectiveness of the proposed control strategy for energy management.