Battery Energy Management System Research Articles

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

A proactive energy management strategy for battery-powered autonomous systems

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies.

Optimal energy management system for grid-connected hybrid power plant and battery integrated into multilevel configuration

A novel optimal energy management system (EMS) using a nonlinear constrained multivariable function to optimize the operation of battery energy storages (BESs) used in a hybrid power plant with wind turbine (WT) and photovoltaic (PV) power plants is proposed in this work. The hybrid power plant uses a configuration based on a battery-stored impedance-based cascaded multilevel inverter to integrate renewable energy sources (PV power plants and WT) and BESs into the grid. The new optimal EMS seeks for satisfying the demanded power while dispatching power between BESs to optimize their efficiency. A grid-connected configuration is implemented to assess the efficiency of the suggested supervisory control under changes in renewable energy (changes in wind speed and irradiation), and in a varying active and reactive powers’ request. The BES efficiency obtained from the suggested EMS is set side by side to the BES efficiency got from a conventional EMS and a model predictive control (MPC), both working based on the state-of-charge (SOC) of the BES and balancing power EMS. The results from MATLAB simulation and the experimental results with the real-time OPAL-RT simulator (OP4510, OPAL-RT) and dSPACE MicroLabBox show the effectiveness of the suggested approach and the improvement in long-term BES efficiency.

Mechanical engineering in automotive innovation: A review of electric vehicles and future trends

This paper provides a succinct overview of the extensive landscape of automotive innovation within the realm of mechanical engineering, focusing on the transformative emergence of electric vehicles (EVs) and the anticipated future trends in this dynamic field. As the automotive industry undergoes a paradigm shift towards sustainable and eco-friendly transportation solutions, the role of mechanical engineering in shaping the future of mobility becomes increasingly pivotal. The review begins by delving into the evolution of electric vehicles, tracing their historical development and highlighting key milestones that have propelled them from niche alternatives to mainstream contenders in the automotive market. A comprehensive examination of the underlying mechanical engineering principles governing EV design and performance is presented, emphasizing advancements in battery technology, electric drivetrains, and energy management systems. The document scrutinizes current challenges and opportunities associated with electric vehicles, addressing issues such as range anxiety, charging infrastructure, and the environmental impact of battery production and disposal. It explores ongoing research efforts within the field of mechanical engineering aimed at overcoming these challenges and enhancing the overall efficiency, safety, and sustainability of electric vehicles. Furthermore, the paper outlines the anticipated future trends in automotive innovation, providing insights into cutting-edge technologies and methodologies poised to shape the next generation of electric vehicles. Topics include advancements in materials science for lightweight construction, artificial intelligence integration for autonomous driving, and the integration of renewable energy sources to further enhance the environmental footprint of EVs. This review underscores the pivotal role of mechanical engineering in the ongoing revolution of automotive technology, emphasizing the transformative impact of electric vehicles and offering a glimpse into the exciting future trends that will define the landscape of sustainable mobility.

Impact of energy storage devices on microgrid frequency performance: A robust DQN based grade-2 fuzzy cascaded controller

This article intended an advanced deep-Q network (A-DQN) based algorithm for the optimal design of a robust grade-2 fuzzy cascaded controller (G2-FCC) concerning frequency control of an autonomous AC microgrid under several electrical disturbances. The proposed AC microgrid is equipped with a hydraulic electrolyzer fuel cell (HE-FC) that empowers electrically to a variety of local consumers. Apart from the HE-FC, the proposed microgrid model is associated with few fractional megawatts (MW) rated power generating sources such as diesel generators (DEG), micro-turbines (MT), solar power stations (SPS), wind power stations (WPS) and geothermal to maximize the total power of a system. However, to increase system performance and energy quality some energy-storing devices such as ultra-capacitors (UC), flywheel (fw), and battery energy management systems (BEMS) are integrated into the system. Most renewable energy sources such as solar and wind power plants are associated with high uncertainties and exhibit low inertia which severely affects the grid frequency. The proposed fuzzy G2-FCC controller dominates the frequency disturbance gracefully and shows its superiority over a few standard approaches like fuzzy Proportional Integral Derivative (PID) and PID controllers. The energy-storing components also have shown their potential in regard to frequency profile improvement of the microgrid system. Finally, the suggested A-DQN algorithm is proved as the most effective tool to optimal design the parameters of the G2-FCC controller. Finally, it is verified that the suggested G2-FCC controller is found to be the most effective tool to improve the settling time of ΔF by 154.54 and 249.64 % over well-existing grade-1 fuzzy PID and PID approaches, respectively.

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

Edge-based solution for battery energy management system: Investigating the integration capability into the building automation system

Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.

An intelligent converter and controller for electric vehicle drives utilizing grid and stand-alone solar photovoltaic power generation systems

In this study, a battery energy management system for electric vehicle (EV) applications is proposed with a standalone photovoltaic (PV) source and controlled based on the availability of grid, PV source, load consumption, and energy stored in the battery. This paper proposes a single-ended primary-inductance converter (SEPIC) DC-DC converter for charging the battery through the utility and PV source that provides good load regulation. The bidirectional nature of the proposed DC-DC converter provides the charging and discharging of the EV battery in the succeeding modes of operation, i) grid-tied charging, ii) PV-tied charging, iii) discharging to the load in the absence of utility and PV source, and iv) regenerative braking. An improved perturb and observe-based maximum power point tracking (MPPT) algorithm is proposed to track the maximum power from the PV source. In addition, to handle the four modes of operation, a dedicated controller is also proposed. Firstly, the proposed system is validated using MATLAB/Simulink software by considering different operating conditions, and the performance is compared with the traditional MPPT algorithms. Finally, the effectiveness of the suggested system is validated through an experimental prototype. The result proved the superiority of the converter and controller over the traditional systems.<div align="center"> </div>

A battery energy management system to improve the financial, technical, and environmental indicators of Colombian urban and rural networks

This paper addresses the problem of managing battery energy in urban and rural alternating current networks, aiming at improving their financial, technical, and environmental indicators. To this end, a mathematical model was formulated that proposes as objective functions the optimization of energy operational costs of the grid, the minimization of power losses associated with energy transport, and the reduction in CO2 emissions related to the production of energy. This model also considers the set of constraints involved in the operation of an alternating current network in an environment with distributed energy resources (Photovoltaic generators + Batteries storage systems). A master–slave strategy that combines a parallel version of the Vortex Search Algorithm (VSA) and an Hourly Power Flow method based on Successive Approximations (HPFSA) was proposed as the solution methodology. To create the test scenarios, urban and rural electrical networks documented in the specialized literature were adapted using generation and demand data of an average day of operation in Medellín-Antioquia (Urban) and Capurganá-Chocó (Rural). These data represent the energy behavior of an urban and a rural network within the Colombian territory. In relation to the distributed energy devices, the integration of three Photovoltaic Distributed Generators (PV DGs) and three lithium-ion batteries of different types into the electrical networks was considered. In addition, the energy production costs and CO2 emissions of the local network and the diesel fuel in Colombia were determined, as well as the maintenance costs associated with the batteries and the PV DGs. With the purpose of validating the effectiveness of the proposed method in terms of solution, repeatability, and processing times, two comparison methods reported in the specialized literature were employed to solve the problem addressed in this paper. In the urban and rural networks under study, the proposed solution methodology achieved the best results in terms of solution quality, repeatability, and processing time.

Battery Power Control Strategy for Intermittent Renewable Energy Integrated Modular Multilevel Converter-Based High-Voltage Direct Current Network

Modular multilevel converters (MMC) play a dominant role in integrating remotely located renewable energy resources (RER) over the high-voltage direct current (HVDC) transmission network. The fault ride-through capabilities of the MMC-HVDC network during low-voltage faults and the power fluctuation due to RER intermittency are the major obstacles to the effective integration of renewable energy. In response, this article proposes a local voltage-based combined battery energy control scheme for a PV-wind-battery connected MMC-HVDC system to regulate the HVDC-link voltage during low-voltage faults at the point of common coupling of alternating current grids and to reduce the intermittent RER power fluctuation. The proposed technique removes the dynamic braking resistor from the HVDC-link and smoothly integrates the RER without active power reduction of renewable energy under low-voltage faults. Symmetrical and unsymmetrical low-voltage faults have been conducted to validate the effectiveness of the proposed control scheme for the battery in mitigating surplus energy in the HVDC-link. Additionally, wind speed, solar radiation, and temperature have been changed to confirm the improved performance of the battery energy management system. The complete systems have been simulated and tested in a real-time digital simulator (RTDS) and using dSPACE-based controller hardware in a loop setup.

Artificial Rabbits Optimized Neural Network-Based Energy Management System for PV, Battery, and Supercapacitor Based Isolated DC Microgrid System

Artificial Rabbits Optimized Neural Network-Based Energy Management System for PV, Battery, and Supercapacitor Based Isolated DC Microgrid System

Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

In recent years, the power system has been evolved into micro grids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on micro grids is gaining popularity Day-by-Day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. An efficient energy management system for a small-scale Hybrid Wind-Solar- Battery based microgrid is proposed in this paper. The wind and solar energy conversion systems and battery storage system have been developed along with power electronic converters, control algorithms and controllers to test the operation of hybrid microgrid. The power balance is maintained by an energy management system for the variations of renewable energy power generation and also for the load demand variations. In this system, for reliable and stable operation of a microgrid with multiple DGs, coordinated control of energy management system is proposed. The control algorithms of microgrid system are verified by Matlab Simulation.

Coordinated control strategies for unbalanced voltage suppression and optimization of SOC of battery in a bipolar DC microgrid with voltage balancer

The unbalanced loads and distributed generations (DGs) will lead to an unbalanced current in the neutral line and further result in a voltage imbalance which will increase circuit power consumption and affect the stable operation of the bipolar DC microgrid. The application of a voltage balancer (VB) can address the issue of unbalanced voltage and current. In this paper, the proposed coordinated control strategy can realize VB to provide a reference voltage to voltage source converter (VSC), battery energy management system (BEMS), and photovoltaic (PV) power generation. The essence of the proposed coordinated control strategy is to adaptively adjust the voltage droop coefficient, which can realize the suppression of unbalanced voltage, power sharing, and regulation of battery state of charge (SOC). In this research, the coordinated control structure of VB and the control scheme of BEMS are investigated. In addition, the small-signal model of the bipolar DC microgrid is also presented to show the system involved is stable. Subsequently, the effectiveness of the proposed coordinated control strategies is verified in MATLAB/Simulink software.

Development of Battery Energy Management System for EV Charging Station

Designing energy storage technologies for applications such as power and smart grids has become a need in the last era, and it is heavily reliant on accurate battery conditions as well as characteristic predictions. A variety of techniques are used to identify battery properties and describe battery states, depending on the kind of battery cell and how it is utilized. This study focuses on estimating the State of Charge (SOC), which is one of the most critical battery states. The most significant aspect of batteries is their State of Charge, which represents their accuracy and state. For charging and discharging, the Coulomb-counting approach is used in mathematical modeling to determine the battery's SOC. The proposed system maintains the SOC of the battery when it undergoes deep outflow in the input ports that exceeds its limitation during the charging of EVs in electric vehicle charging stations. Simulations utilizing MATLAB/ Simulink software are used to assess the suggested method's performance.

Big data acquisition of parallel battery back state and energy management system using edge computing

Big data acquisition of parallel battery back state and energy management system using edge computing

Big data acquisition of parallel battery back state and energy management system using edge computing

Big data acquisition of parallel battery back state and energy management system using edge computing

Design of a Battery Energy Management System for Capacity Charge Reduction

This paper presents a novel dispatch and evaluation framework for battery energy storage systems (BESSs) to minimize a load servicing entity’s coincident demand during system peak hours. The framework consists of i) a two-step BESS dispatch process that accounts for uncertainties in forecasting system peak and using limited battery cycle life, and ii) procedures to design control parameters, determine BESS duration, and estimate the corresponding net benefits. In the proposed dispatch, a rule-based triggering mechanism is executed to determine whether to dispatch a BESS on an operating day by comparing the peak-day probability with a predetermined threshold. Once the dispatch is triggered, a model predictive control is carried out to maximize the expected reduction in peak demand. By exercising this two-step dispatch method with different thresholds, one can explore the trade-off between peak demand reduction effectiveness and loss of battery life, and thereby identify the optimal thresholds to maximize cumulative economic benefits. Case studies are conducted using the data provided by utilities in North Carolina. Simulation results are presented to demonstrate the effectiveness of the proposed method.

An approach of battery energy management systems for DC micro-grids considering stateflow controller

This paper presents an approach of battery energy management systems for DC micro-grids considering Stateflow controller. In micro-grids, batteries have been the favorite energy storage technology focus on addressing and accelerate United Nations Sustainable Development Goal 7 'Affordable and Clean Energy' as well as contributing to the discussion around the long-term mitigation of climate change. However, their improper or non-effective operations can cause over-current, overvoltage or overcharging/discharging and significantly impact on their lifespan and on the micro-grid reliability. Comparatively to Lithium Ion (Li-ion) battery, most of the technologies present some issues, which can impact negatively on the battery lifetime as well as the average of energy stored in the battery. In case of battery bank, the deep discharge or the premature charge of battery can lead to a reduced lifetime of the storage system. The aim of this study was to develop a battery management system algorithm to control the charging/discharging of a battery bank and to keep its state of charge in the admissible limits to avoid the deep charging/discharging of the battery within a DC micro-grid. The objectives of this proposed work were to develop a control system schemes for the DC microgrid and to develop an energy management system using MATLAB/Simulink software. The results of the scenarios of the developed algorithm have shown that this algorithm could be able to ensure the reliability, the resiliency, the robustness and the proper operation of the battery systems in micro-grids. The results have shown that the initial investment cost could comparatively be lower and could decrease the economic analyze in terms of LCOE.

Optimization of battery charging strategy based on nonlinear model predictive control

With the increased applications of lithium-ion batteries in energy storage systems and electric vehicles, there is a growing demand for battery energy storage systems and management systems. Considering that the temperature especially internal temperature significantly can affect the performance and safety of the battery, a triple-objective optimization charging method which can reduce the cell charging time, energy loss, and internal temperature rise is proposed based on a thermoelectric coupling model in this paper. Specifically, a thermoelectric coupling model suitable for a wide temperature range from −5 °C to 45 °C is formulated. On this basis, a nonlinear model predictive control framework is proposed to obtain the real-time charging current by solving the nonlinear optimization problems. The impacts of the objective function weights and internal temperature thresholds on the charging result are discussed through experiments, and another multi-stage constant current charging method is conducted as a comparison. Results show that the nonlinear model predictive control can achieve a good balance between three objectives while satisfying constraints. Compared with the traditional multistage constant current charging method, the proposed strategy can reduce energy loss by 150 J and temperature rise by 1–2 °C in similar charging time.