Battery Energy Storage Systems

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1		Levon Gevorkov	--	3900	2023-09-11 12:02:10	\|
2	format correct	Catherine Yang	Meta information modification	3900	2023-09-12 03:19:09	\|

This entry is adapted from the peer-reviewed paper 10.3390/en16176344

The battery energy storage system can be applied to store the energy produced by renewable energy sources (RESs) and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources.

battery energy storage system battery management systems (BMSs) energy management techniques power electronics for BESS

1. Introduction

Energy storage systems (ESSs) can become a good solution to these issues as well as reduce power output variances, regulate frequency, provide voltage reliability, and enhance the quality of the supply. There are various methods for storing power, including battery energy storage systems, compressed air energy storage, and pumped hydro storage. Energy storage systems are employed to store the energy produced by renewable energy systems when there is an excess of generation capacity and release the stored energy to meet peak load demands ^[1]. The ability of the electricity distribution system to include additional RESs is another benefit of ESSs ^[2]^[3]. Among the other types of ESSs, battery energy storage systems (BESSs) play an important role. For instance, BESSs paired with renewable energy sources can be a cost-competitive solution in microgrid scenarios. The Statista Research Department anticipates that 57 GW of BESSs will be installed only in Europe by 2030 ^[4].

2. Power Electronic Converters for BESSs

Power electronics converters (PEC) play a crucial role in battery management systems and for battery storage systems in general. They are used to ensure proper power flow. Each BESS is required to control its power flow and a power balance during operation. As it was previously mentioned, the BESS generally provides a power balance between customers connected to an MG and renewable energy sources responsible for energy generation. Another of BESS’s key functions is battery balancing, which is used to make sure all batteries are running at the same state-of-charge level. Balanced batteries can increase the system’s overall useful capacity while also enhancing its dependability. Under discharging operation, the battery with the lowest SoC in a battery pack with series-connected batteries will be entirely discharged first. Therefore, power electronic devices are responsible both for providing proper power flow and balancing options. The power electronic converter is the BESS’s primary means of energy processing. Figure 1 represents typical applications of PECs for battery energy storage systems.

Figure 1. Typical application of PECs in BESS configuration.

Storage batteries, converters, and a control system make up the battery energy system. Energy can be stored and released using batteries. The real charging and discharging power and response speed can vary depending on the battery’s power, capacity, and changing and discharging characteristics. Battery DC power can be transformed into grid-connected AC power with the use of a converter. Bidirectional AC/DC and DC/AC converters are needed for battery energy storage systems. Battery energy storage system converters often use two-level or three-level topologies in modern applications. For instance, in ^[5], the authors outline the creation of an inverter that stabilizes the electricity from a wind farm utilizing sodium–sulfur batteries. The authors described the largest energy storage system in the world, which consists of 17 sets of power conversion systems (PCS) and 34 MW NAS batteries for a 51 MW wind power system. Through a voltage source converter (VSC), based on the control system of the fully-controlled power electric device, PCS can achieve four-quadrant and decoupled control of grid-side active and reactive power. According to a power generation planning system that considers the meteorological conditions, charge/discharge power status, etc., this power conditioning system can control the fluctuations in wind power with a power control precision of 2%. Consequently, the wind power plant can provide electricity to the grid without being impacted by variations in wind output. The field tests demonstrated good operating performance.

In ^[6], the authors describe the construction of utility-scale power conversion systems for BESSs that are 500 kVA and 100 kVA in size. Power conversion systems are reported to be effective across a large operating range because of adequate hardware and software design. The inclusion of some crucial features—such as soft-start, anti-islanding, stand-alone operation is intended to increase stability and dependability under a variety of demanding dynamic operation circumstances. Developed PCSs are particularly adaptable because they may be coupled to work in parallel with other PCS modules, taking into consideration the rising battery storage capacities. Factory tests have shown their good performance in the electrical, control, and thermal areas.

The modular multilevel cascade converter (MMCC) family is discussed in ^[7]. The MMCC family is based on cascading multiple bidirectional chopper cells or single-phase full-bridge cells. The author provides a classification of MMCCs. The single-star bridge cells (SSBC), single-delta bridge cells (SDBC), double-star chopper cells (DSCC), and double-star bridge cells (DSBC) are the four circuit configurations that make up the MMCC family. Although there is a clear difference in application between the SSBC and DSCC, the SSBC and DSCC are more practical than the other three members of the MMCC family in terms of cost, performance, and market. In addition, the author provides examples of the SSBC’s applications to battery energy storage systems, the SDBC’s applications to static synchronous compensators (STATCOM) for negative-sequence reactive-power regulation, and the DSCC’s applications to motor drives for fans and blowers, together with the results of their experiments.

In ^[8], the authors briefly discuss a battery energy storage system based on a multilevel cascade pulse-width-modulated (PWM) converter. To enable charging and discharging the battery units at various power levels while generating a three-phase balanced line-to-line voltage, the active-power regulation of individual converter cells is described. Due to this, even when the battery units’ power-handling capacities differ, the battery energy is utilized to the fullest extent possible. The effectiveness of the proposed active-power control is confirmed by experimental data from a 200 V, 10 kW, and 3.6 kWh battery energy storage system.

A technical review of battery energy storage systems is provided in ^[9]. The others provide an overview of the difficulties in integrating solar power into the electrical grid, and examples of various operational modes for battery energy storage systems in grid-tied solar applications. For the power electronics converters, the ramp rate control, frequency droop response, power factor correction, solar time shifting, and output leveling are some of the real-time control techniques that are covered in the paper. It is shown that energy storage control systems for PECs can be connected with energy markets in addition to these applications that concentrate on system stability to increase the economics of solar energy.

In ^[10], a multi-input power converter for a hybrid system is proposed. It connects two unidirectional ports for input power sources: a bidirectional port for a storage component, and a port for an output load in a single, integrated structure. The two input ports are used to simultaneously convert two distinct low-voltage input power sources to consistent high-voltage output power. The suggested converter’s operational states can be split into three states based on battery usage depending on various circumstances. The authors offer a power management control strategy that regulates the bidirectional converter running in boost mode in accordance with the operation state of the PV/wind, ensuring that the system functions with high efficiency and enabling the battery to be charged or discharged.

A reliable and adaptable active balancing topology is proposed in ^[11]. It can help to balance the state-of-charge level of the battery modules in a high-voltage pack, which is a frequently ignored subject, as well as the charge imbalance within a module, or intramodular equalization. When the lithium–ion battery (LIB) was both idle and under load, experimental verification on parallel and series topologies of cells in BESS’s hardware and genuinely sized modules proved the proposed concept. Without the use of additional converters or auxiliary accumulators, the switching converter is capable of performing intramodular architecture at the pack level.

In ^[12], the authors propose a fuzzy logic control (FLC)-based non-dissipative equalization methodology to reduce the inconsistency of series-connected lithium-ion batteries. To achieve cell-to-cell equalization, a bidirectional equalization circuit with energy-transferring inductors is used. It is suggested to equalize cells on the basis of a state of charge, and a temperature-dependent battery model is created for the state-of-charge estimation. The flyback converter is suggested to cut down on energy usage and equalization time for efficient equalization. To verify the benefits of the proposed program, a comparison of the proposed flyback converter based on mean-difference math is performed.

In ^[13], the authors suggest a novel architecture for the power electronics converter to shorten the equalization time, and simulation results are used to confirm the system’s viability. The issue with the traditional inductor-based balancing method is that because the energy is transferred cell by cell, it takes a long time to equalize when it moves from the first to the final cell. This issue is resolved by the suggested improved topology which enhances the balancing time when compared to traditional ones. The middle cells will equalize using this strategy just as quickly as the top and bottom ones. In comparison to the standard topology, which equalizes four cells, it equalizes eight cells faster. The simulation results are used to validate the system’s viability.

An established method of next-to-next balancing is described in ^[14]. Its operational and design restrictions are examined. According to experimental findings, the magnetic coupler’s size was significantly reduced while retaining an efficiency of more than 90%. The main objective of the research work was to identify a better topology that can best address applications of BESSs such as electric automobiles in terms of integration, performance, and affordability. A real-world application has demonstrated the regularity of the currents. It is successful at reducing the magnetic coil using a flux adjustment strategy. The new equalization converter’s balancing process has been examined.

The available literature in some cases is concentrated on isolated topologies for battery energy storage systems, namely dual-active bridges (DAB). The typical topology of a full-bridge FBDAB is shown in Figure 2.

Figure 2. Typical FBDAB configuration.

By adjusting the on/off status of the semiconductor switches on the converters’ primary and secondary sides, several control strategies can be applied. Among the control strategies are the so-called single-phase-shift (SPS) strategy which is the most common type of control method now in use is conventional DABs. The second method is the extended-phase-shift (EPS) strategy which is an additional switching technique. The zero-voltage switching (ZVS) range widens with this modulation. In addition, there are dual-phase-shift (DPS) and triple-phase-shift (TPS) control techniques, that can be used for full-bridge DAB circuits ^[15]^[16].

Another typical topology for the battery storage systems is neutral point connected (NPC)-based DAB. In ^[17], a bidirectional DC/DC converter is designed with a dual-active bridge and a single-phase three-level neutral point connected construction on the high-voltage side. This architecture decreases the isolation transformer’s turn ratio and the voltage stress on the switches. Additionally, all switches in full bridge and NPC with bidirectional power flow have zero-voltage switching. The grid side inverter with current hysteresis control is NPC based. The authors show that the whole set-up is appropriate for utility applications thanks to its increased reliability and low maintenance requirements.

For the balancing of the cells in battery storage systems, various topologies are proposed. Among them are capacitor-based, inductor-based, and transformer-based topologies, as shown in Figure 3.

Figure 3. Common types of cell balancing.

In ^[18], the authors proposed to connect capacitors between adjacent cells. The capacitors are constantly changing their charge. The capacitor is charged by one cell and then discharged through the neighboring cell, transferring surplus charge between the two if the charge states of the two cells differ. Since every cell is interconnected, the entire pack finally becomes balanced (Figure 4).

Figure 4. Cell equalization circuit based on switched capacitors.

The serious drawback is the poor balancing speed; in the worst case, the first cell in the string will be out of balance compared to the string with the last cell. In this case, the imbalance could only be corrected by passing the extra charge through all of the cells. Scalability becomes a challenge due to this issue being made worse by the battery pack having additional cells as shown in ^[19].

In ^[20], the authors propose a novel bidirectional buck–boost + Cuk converter for series-connected battery cells. In the past, either the buck–boost converter or the Cuk converter required (2n − 1) switches to balance a string of n battery cells. The proposed buck–boost + Cuk converter, however, only needs n switches because it skillfully combines the buck–boost converter and the Cuk converter. Unlike many other existing one-switch-per-cell topologies, it reduces the switch count by almost half without sacrificing the modularization benefit or the device voltage stress. The buck–boost battery charge equalizer’s best feature, simple pulse width modulation, with a 50% duty cycle, is still present.

The application of multiport converters plays an important role for BESSs. In ^[21], the authors describe various types of multiport converters and their characteristics. A comprehensive examination of the key features of multiport converters, including their topologies, types according to different characteristics, advantages, and disadvantages, and areas of application are presented. The presentation includes a thorough analysis of the criteria for choosing multiport converters for various applications. It is shown that three main types of multiport converters have the own advantages for specific power range applications. For instance, non-isolated topologies are more suitable for low-power BESS application, and isolated, partially isolated topologies are more suitable for high-power BESS applications.

Table 1 contains some of the research results regarding the implementation of various cell equalization topologies.

Table 1. Application of different cell equalization topologies for BESSs.

Balancing Type	Main Results
Inductor based	For series-connected lithium-ion batteries, an innovative non-dissipative two-stage equalization circuit architecture based on the classic buck–boost circuit is developed.	^[22]
Inductor based	A flyback-based cell-balancing circuit for a string of lithium-ion batteries is suggested.	^[23]
Capacitor based	An innovative switched capacitor equalizer that combines the benefits of double-tiered switched capacitors and switched capacitors with chain structures is proposed.	^[24]
Transformer based	The dual transformer-based asymmetrical triple port active bridge (DT-operating ATAB) multiport converter with a closed-loop controller is offered.	^[25]
Transformer based	The modular balancer-based balancing topology, consisting of an outer-module balancer based on a switched capacitor converter and an intra-module balancer based on a multi-winding transformer circuit is proposed.	^[26]

Taking into account employing cutting edge power devices such as GaN semiconductors, and enhanced control algorithms, it is possible to improve current solutions. The charging circuitry can share resources and reduce costs by integrating modern power electronics technologies. At the same time, providing a fully flexible battery pack that can be adjusted for various voltage and current requirements is quite promising from a research point of view.

3. Battery Management System for BESSs

The future role of lithium-ion batteries (LIB) in the energy market is clearly defined. LIBs serve as potential allies in supporting distributed renewable resources and facilitating the transition towards zero-carbon emissions in the mobility sector. However, handling LIB products is a challenging task. This is due to the environmentally hazardous raw materials used to build cells ^[27], capacity losses over the service life, as well as the instability associated with certain electrode composites, which may hinder safe operation. Consequently, three critical challenges for LIB technology to overcome are system sustainability, performance, and safety ^[28]. To address these challenges, BMS plays a crucial role in the evaluation of these factors.

On one hand, traditional BMS architecture (specifically for high-voltage applications) at its highest level is primarily designed for managing power and energy during battery charging and discharging, using a Battery Control Unit (BCU), which acts as the master controller for the whole battery, measures pack insulation, and sample pack voltage/current values ^[29]. Additionally, BCU determines battery status, calculating SoX predictions: commonly SoC, state of health (SoH), state of power (SoP), and state of temperature (SoT) are used to determine cell performance and remaining useful life (RUL), and track cell safety status to ensure a reliable operation over the life cycle, in order to determine proper battery end of life (EoL) and define second life possible applications ^[30]^[31].

SoH plays an important role because it is essential to maintain the LIBs’ operational safety and enhance their output. A trustworthy BMS can deliver precise state-of-health estimations and guarantee battery safety, which can enable the most effective operation and energy management. There are various methods proposed to estimate SoH. In ^[32], the authors use the weighted quantile regression (WQR) and light gradient-boosting machine (LightGBM) methods to learn a non-linear mapping between the measurable attributes and the SOH. The model is known as LightGBM-WQR. The suggested LightGBM-WQR model estimates SOH with good accuracy, and the average absolute error (MAE) of all cells is constrained to 1.57%. A thorough analysis of the many techniques used for SOH estimate, including experimental methods, model-based techniques, and machine learning algorithms is carried out and the benefits and drawbacks of each strategy are examined critically and in depth in ^[33]. To estimate the battery’s SOH and SOC, the well-known Kalman filter (KF) and relatively recent sliding innovation filter (SIF) are used in ^[34]; the dual-KF-interacting multiple model (IMM) and dual-SIF-IMM are the resulting techniques, respectively. Accurate Li-ion battery SoC and SoH model for online estimation is proposed in ^[35]. The link between the SoH equation and the modification factor, which is a function of SOC, is discovered to be linear.

On the other hand, each battery incorporates several Battery Management Units (BMU). The primary function of the BMU is to optimize the overall battery module performance, by measuring the voltage of individual cells, balancing, and equalizing module cell branches, as it is shown in Figure 2. Moreover, BMU also samples temperature and other important values such as cell strain, in order to prevent cell malfunctioning, or hazardous situations such as thermal runaway ^[36].

Subsequently, all the measures gathered by BMU are packaged into different data frames and sent to BCU. Once BCU receives cell measurement information, it can estimate cell SoX and determine the optimal strategy to distribute the energy among the pack depending on the battery pack’s instantaneous input/output current, and consequently predict the next step cell voltage, temperature, and pressure, accordingly, to adapt BMU balancing and protection systems in a closed-control loop. Moreover, BCU-BMU interoperability can find synergies with other auxiliary control systems, such as the Battery Thermal Management System (BTMS), which is responsible for maintaining the cells within allowed temperature ranges.

To perform SoX calculations, BCU uses battery models ^[37]; nowadays, in LIB industry, ECM is widely adopted in BMSs due to successful results in its practical implementation and its relatively inherent simplicity. However, the ECM working principle is fully based on experimental cell data parameterized with representative electric components. Thus, the ECM lack of physical insight restricts the precision of SoX estimates and their ability to accurately adapt to the electrochemical processes driven by lithium-ion diffusion and transport that govern the real LIB system response. To address this discrepancy, the industry is mainly focused on developing better methods and techniques to enhance ECM capabilities, to update cell states adaptatively according to the stochastic variations that cells experience during a cycle.

Significant advancements have been made in the improvement of adaptive filter-based methodologies for adjusting SoC estimations based on measured voltage, temperature values, as well as noise covariances. However, as the cell approaches its EoL, these models gradually lose precision due to SoC deviations ^[38]. This precision loss poses challenges to BMS decision making and EoL definition, resulting in the underutilization of battery cell energy and consequently LIB sustainability reduction.

To address this issue, it is crucial to complement filter-based methods with alternative approaches that can adapt model parameters to battery capacity fades. Many studies advocate for the implementation of data-driven techniques as the optimal solution ^[39]. These approaches use huge experimental datasets and combine them with genetic algorithms, neural networks, or particle swarm optimization techniques, to effectively adapt SoC and ECM parameters to battery nominal capacity changes ^[40]; these methods contribute to establishing better SoH predictions, and as a result, pack/cell overall SoX estimations are also enhanced ^[41].

However, these advanced models require significant provisions in terms of computational requirements and data storage to effectively be run. Therefore, considering the rapid progress and the expected future widespread implementation of 5G technologies, research is focused on migrating modeling BCU capabilities and functions to cloud-based BMS architectures. The structure shown in Figure 3 represents how these advancements will change the BMS ecosystem, paving the way for the materialization of the LIB Digital-Twin (DT) concept, all in an effort to provide more efficient and sustainable battery systems ^[42].

Nevertheless, as can be intuited from Figure 5, DT architectures still require a simplified version of BCU (Battery Control Unit) integrated within the battery pack. These systems are expected to optimize BMU energy distribution and serve as data clusters to connect the entire battery to the cloud. Additionally, aside from the cloud model, BCU also should use an offline model to determine short-term control strategies and act as a backup model in case of cloud loss connection, fails, or crashes.

Figure 5. BMS-DT ecosystem concept.

Therefore, BCU should have implemented a Reduced Order Model (ROM) which should be updated periodically every time the cloud Full Order Model (FOM) performs an iteration to estimate SoX. The values calculated from the cloud can be used for BCU to estimate the error due to ROM parametrization deviances. Consequently, the ROM parameters can be adaptively updated between FOM iterations.

Moreover, with the advancement in computational capabilities, the BMS-DT concept can explore emerging LIB modeling fields, such as the Pseudo-Two-Dimensional (P2D) approach for the macroscopic representation of physics-based models. P2D more complex variants, such as the Doyle–Fuller–Newman (DFN) model, offer a competitive alternative to data-driven approaches. Since they are based on PDE governing electrochemical equations and precise cell constructive and electrochemical parameterization, LIB behavior and degradation mechanisms can be accurately captured, without the need for extensive databases ^[43]. Furthermore, simplified versions such as the Single Particle Model (SPM) can provide more accurate estimations for pack/cell control systems compared to traditional ECM approaches, enhancing efficiency and overall LIB safety.

Artificial intelligence (AI)-based energy management solutions are advancing the sophistication and intelligence of BMS systems. Real-time battery data analysis and performance forecasting can be carried out using artificial intelligence techniques. This makes it possible to better optimize battery use, extending its life and increasing its effectiveness. Additionally, AI algorithms can be employed to spot irregularities in the battery’s operation and notify the user or maintenance staff of potential problems. This can lower maintenance costs and help prevent safety issues.

Overall, BMS systems are becoming more intelligent, secure, and efficient thanks to energy management solutions utilizing AI. When it comes to sifting through enormous volumes of observational data for patterns and insights, machine learning is quickly becoming a crucial tool. As a result, the future seems promising for the creation of a cloud-based BMS that has been upgraded by AI. By fusing the advantages of physical process models with the adaptability of machine learning approaches, this will significantly increase the predictive and modeling capability for long-range connections across multiple timelines ^[44].

Predictive AI-based algorithms in BMSs can enhance the accessibility of test datasets and reliable real-time data processing for electrical vehicle applications. According to the analysis provided by the authors in ^[45], further research utilizing the Kalman filter algorithm is required to enhance the current algorithms by incorporating both SoH and SoC estimators to determine how old the battery is in terms of power management over prolonged use. Additionally, from a theoretical and practical standpoint, machine learning (ML) technologies play a significant role in battery SoH estimation.

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Subjects: Engineering, Electrical & Electronic

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Sergi Obrador Rey

Juan Alberto Romero

Lluis Trilla Romero

Àlber Filbà Martínez

Xavier Sanchez Roger

Muhammad Attique Qamar

José Luis Domínguez-García

Levon Gevorkov

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Update Date: 12 Sep 2023

Table of Contents

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1. Introduction

2. Power Electronic Converters for BESSs

3. Battery Management System for BESSs

References