Lithium-ion Battery Energy Storage Research Articles

Comprehensive Reliability Assessment Method for Lithium Battery Energy Storage Systems

Electrochemical energy storage systems have the advantages of fast power response, intensive energy storage, flexible and convenient deployment, but the output characteristics of the battery system directly affect the service effect of the energy storage power plant. Therefore, it is quite necessary to analyze the reliability of battery energy storage and scientifically assess the system’s performance. This paper considers the aging state of the battery storage system as well as sudden failures and establishes a comprehensive reliability assessment method for battery energy storage systems that take into account the battery health index and the impact of thermal runaway failures, which makes up for the shortcomings of existing reliability assessment methods.

An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

Economic Controls Co-Design of Hybrid Microgrids with Tidal/PV Generation and Lithium-Ion/Flow Battery Storage

Due to the uncontrollable generators, islanded microgrids powered only by renewable energy require costly energy storage systems. Energy storage needs are amplified when load and generation are misaligned on hourly, monthly, or seasonal timescales. Diversification of both loads and generation can smooth out such mismatches. However, the ideal type of battery to smooth out remaining generation deficits will depend on the duration(s) that energy is stored. This study presents a controls co-design approach to design an islanded microgrid, showing the benefit of hybridizing tidal and solar generation and hybridizing lithium-ion and flow battery energy storage. The optimization of the microgrid’s levelized cost of energy is initially studied in grid-search slices to understand convexity and smoothness. Then, a particle swarm optimization is proposed and used to study the sensitivity of the hybrid system configuration to variations in component costs. The study highlights the benefits of controls co-design, the need to model premature battery failure, and the importance of using battery cost models that are applicable across orders of magnitude variations in energy storage durations. The results indicate that such a hybrid microgrid would currently produce energy at five times the cost of diesel generation, but flow battery innovations could bring this closer to only twice the cost while using 100% renewable energy.

Research on modeling and control strategy of lithium battery energy storage system in new energy consumption

Energy storage technology is one of the effective means to promote the consumption of new energy. It has the advantages of improving the flexibility and stability of power grid. Energy storage plays an important role in improving the peaking and valley filling function of the load side of the power grid. Based on the two-stage topology of the energy storage system, this paper establishes the mirror model of the practical application engineering of the energy storage system, and uses the data-driven method to establish the energy storage battery model. On this basis, the multi-objective control strategy is adopted for the peak regulating power of the energy storage system and the load state balance of the battery. The support vector machine algorithm is used to predict the daily load data of the power grid, and the constant power algorithm is proposed to control the battery control node signal. Finally, taking the battery compartment of the energy storage system as the simulation object, the effectiveness of the proposed control strategy is verified, which provides a theoretical basis for the topic research.

The state-of-charge predication of lithium-ion battery energy storage system using data-driven machine learning

Accurate estimation of state-of-charge (SOC) is critical for guaranteeing the safety and stability of lithium-ion battery energy storage system. However, this task is very challenging due to the coupling dynamics of multiple complex processes inside the lithium-ion battery and the lack of measure to monitor the variations of a battery’s internal properties. Recently, with the continuous development of Graphic Processing Unit (GPU) computing power, there is an increasing interest in applying deep-learning as SOC estimation approaches. In this paper, a novel SOC estimation scheme for lithium-ion energy storage system is proposed based on Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) neural network. This method is completely driven by the actual operating data from a photovoltaic energy storage system without using any artificial battery models or inference systems. Compared with traditional SOC estimation methods, the CNN-LSTM model can overcome the deviation in estimation caused by voltage jump at the end of charge and discharge, provide satisfied SOC estimation results during stabilized stage and various charging/discharging stages of the assembled lithium-ion batteries in the system. The calculation results indicate that this method enables fast and accurate SOC estimation with an RMSE of less than 0.31% over the entire operating data of the photovoltaic energy storage system for a full day.

Zn3(PO4)2 Protective Layer on Zn Anode for Improved Electrochemical Properties in Aqueous Zn-ion Batteries

<p>Aqueous zinc-ion batteries are considered as promising alternatives to lithium-ion batteries for energy storage owing to their safety and cost efficiency. However, their lifespan is limited by the irreversibility of Zn anodes because of Zn dendrite growth and side reactions such as the hydrogen evolution reaction and corrosion during cycling. Herein, we present a strategy to restrict direct contact between the Zn anode and aqueous electrolyte by fabricating a protective layer on the surface of Zn foil via phosphidation method. The Zn<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub> protective layer effectively suppresses Zn dendrite growth and side reactions in aqueous electrolytes. The electrochemical properties of the Zn<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>@Zn anode, such as the overpotential, linear polarization resistance, and hydrogen generation reaction, indicate that the protective layer can suppress interfacial corrosion and improve the electrochemical stability compared to that of bare Zn by preventing direct contact between the electrolyte and the active sites of Zn. Remarkably, MnO<sub>2</sub> || Zn<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>@Zn exhibited enhanced reversibility owing to the formation a stable porous layer, which effectively inhibited vertical dendrite growth by inducing the uniform plating of Zn<sup>2+</sup> ions underneath the formed layer.</p>

Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method

With the advantages of high energy density, long cycle life and high stability, lithium-ion batteries have been used in a large number of fields such as electric vehicles and grid scale energy storage. To ensure the safe and reliable operation of battery systems, it is important to make an accurate and rapid estimation of the state of health (SOH) of Li-ion cells. A Li-ion cell is a complex nonlinear dynamic system. The SOH of a Li-ion can not be measured directly in actual working conditions; it can only be estimated indirectly by external characteristic parameters that reflects the extent of cell aging. It is difficult to ensure the reliability of method based on a single aging feature or model. Therefore, this paper proposes a multi-feature SOH estimation method that combines data-driven XGBoost and a Kalman filter. Firstly, a principal component analysis algorithm to reconstruct multiple battery aging features based on data is used, and an XGBoost online estimation model incorporating multiple features based on the reconstructed feature data is constructed. Finally, the joint optimal estimation of SOH of Li-ion cells by introducing a time-domain Kalman filter based on the real-time correction of the XGBoost model is achieved in this method. The results show that the method improves the accuracy and robustness of the estimation model and achieves a high-precision joint estimation of SOH for Li-ion cells.

A surface-controlled process renders high-power Li-ion storage in Mn3O4/RGO composites for both lithium-ion capacitors and batteries

Mn3O4/reduced graphene oxide composites were synthesized with a high capacity ratio via a surface-controlled reaction, making them ideal for fast energy storage in both lithium-ion batteries and lithium-ion capacitors.

Review of machine learning method for safety management of lithium-ion battery energy storage

With the broad implementation of electrochemical energy storage technology, the noteworthy issue of ensuring safe operation and maintenance of battery energy storage power plants has become more and more prominent. The conventional battery management system solely acquires data on the voltage, current, and temperature of individual battery cells. Constrained by hardware processing capabilities, limitations in data transmission bandwidth, and latency issues, effectively monitoring the health and safety of large-scale battery energy storage systems has become a critical technological challenge. The implementation of machine learning techniques in predicting the operating conditions of lithium-ion batteries has provided opportunities for enhancing the safety management of energy storage systems. To address the safety management requirements of lithium-ion batteries, this paper firstly introduces research related to the risk mechanism of abusive use and thermal runaway of such batteries. Next, the architecture and application characteristics of the lithium-ion battery management system will be discussed. The implementation of machine learning techniques for analyzing the health and safety status of lithium-ion batteries is extensively discussed. Finally, a safety assessment of lithium-ion batteries for energy storage power stations is anticipated.

Random Forest-Based Online Detection and Location of Internal Short Circuits in Lithium Battery Energy Storage Systems With Limited Number of Sensors

Random Forest-Based Online Detection and Location of Internal Short Circuits in Lithium Battery Energy Storage Systems With Limited Number of Sensors

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.

Optimal planning of lithium ion battery energy storage for microgrid applications: Considering capacity degradation

By adding battery energy storage (BES) to a microgrid and proper battery charge and discharge management, the microgrid operating costs can be significantly reduced. But energy storage costs are added to the microgrid costs, and energy storage size must be determined in a way that minimizes the total operating costs and energy storage costs. This paper presents a new method for determining the optimal size of the battery energy storage by considering the process of battery capacity degradation. In this method, initially, the battery degradation functions are modeled; then, these functions are incorporated in the problem of determining the optimal battery size. The impact of different cycles with various depths of discharge is also considered that is the prominence of the present work. The proposed method is formulated as mixed-integer linear programming (MILP). Notably, the degradation model was implemented through two different formulations, by considering cycles with various depths of discharge in the second formulation, and the results of each formulation is compared that demonstrates the effectiveness of the second formulation.

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

Battery energy storage systems (BESSs) play a major role as flexible energy resource (FER) in active network management (ANM) schemes by bridging gaps between non-concurrent renewable energy sources (RES)-based power generation and demand in the medium-voltage (MV) and low-voltage (LV) electricity distribution networks. However, Lithium-ion battery energy storage systems (Li-ion BESS) are prone to aging resulting in decreasing performance, particularly its reduced peak power output and capacity. BESS controllers when employed for providing technical ancillary i.e. flexibility services to distribution (e.g. through ANM) or transmission networks must be aware of changing battery characteristics due to aging. Particularly of importance is BESSs' peak power changes aiding in protection of the Li-ion BESS by restricting its operation limits of it for safety reasons and improving its lifetime in the long run. In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i.e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel‑manganese‑cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot.

Early stage internal short circuit fault diagnosis for lithium-ion batteries based on local-outlier detection

Internal short circuit (ISC) is considered to be one of the main causes of battery thermal runaway, which is a critical obstacle to the application of lithium-ion batteries for energy storage. Aiming at inconspicuous characteristics and slow detection speed of early stage ISC faults, this paper proposes a fast diagnostic method for ISC based on local-gravitation outlier detection. In the serial battery module, the cell terminal voltages are normalized to characterize voltage trends that are more sensitive to faults than voltage magnitudes, which improves the speed of fault diagnosis. The normalized voltages are then evaluated by a local gravity outlier detection algorithm to detect faults, by which the anomalies caused by the faults are amplified to achieve the ability to diagnose early ISC faults. The performance of the method is validated under the Urban Dynamometer Driving Schedule test, where several sets of experimental results for ISC faults of varying severity showed that the proposed method could detect them accurately and rapidly even when the fault characteristics are not obvious.

Energy arbitrage optimization of lithium-ion battery considering short-term revenue and long-term battery life loss

To achieve the ambitious goal of net-zero by 2050, the power sector in many countries has been increasingly using renewable energy sources (RES). The power system balance will be significantly challenged due to the intermittency of RES. Lithium-ion battery energy storage systems (BESS) can effectively address the challenge by providing flexibility to fill the mismatch between the intermittent supply and the varying demand, generating revenues for BESS to payback the initial investment. However, every grid participation also results in an inevitable lifetime loss. From the asset owner’s perspective, therefore, it is important to balance the short-term revenue from the fluctuating electrical market with the long-term battery life loss. To achieve this in this study, first, a novel online-applied battery life model is established, which is computationally efficient to comprehensively consider the battery’s historical aging, state of charge (SOC), charge–discharge rate (Crate). Then, an online-applied sliding-window dynamic programming (SWDP) strategy is proposed to optimize short-term grid service revenues and long-term battery life losses. The SWDP is also compared with an offline-applied dynamic programming (DP) strategy. The simulation results show that the cost due to battery life decay is significant, which indicates the importance of tracking the battery life loss in the battery asset management. Additionally, the prediction accuracy of electricity price significantly influences the performance of SWDP strategy. The BESS’s total profit can reduce by half in the considered case study if the electricity prediction error is 12.5%.

Explosion protection for prompt and delayed deflagrations in containerized lithium-ion battery energy storage systems

Explosion hazards can develop when gases evolved during lithium-ion battery energy system thermal runaways accumulate within the confined space of an energy storage system installation. Tests were conducted at the cell, module, unit, and installation scale to characterize these hazards. Three installation level tests show that explosion scenarios can occur as prompt ignitions within seconds of cell gas venting or delayed ignitions where gases ignite after a longer duration of accumulation, especially when fire protection systems are actuated. Deflagration venting and exhaust ventilation system design approaches that can be implemented at the installation level are evaluated using a dataset generated from cell, module, and unit level tests. Data from the installation level tests demonstrate the use and effectiveness of deflagration venting for containerized li-ion battery energy storage systems.

Recent progresses in state estimation of lithium-ion battery energy storage systems: A review

Battery storage has been widely used in integrating large-scale renewable generations and in transport decarbonization. For battery systems to operate safely and reliably, the accuracy of state estimation is extremely crucial in battery management system (BMS). However, due to the complex working conditions and electrochemical reactions, it is difficult to accurately estimate the internal state of batteries. This survey focuses on categorizing and reviewing some of the most recent estimation methods for internal states, including state of charge (SOC), state of health (SOH) and internal temperature, of which internal temperature estimation methods have been rarely reviewed in the existing literature. The main advantages and disadvantages of these methods are compared, with a focus on the new sensing technologies for internal state estimations. Then, the future prospects of battery state estimation technologies are discussed.

Strain and Temperature Behavior of Lithium-Ion 18650 Cylindrical Batteries

Modern technology, from portable electronics to electric vehicles, is increasingly reliant on lithium-ion batteries for energy storage, but their use poses safety concerns, namely due to the risk of thermal runaway [1]. This process results in toxic off-gassing, smoke, fire, and explosions, causing damage to property and endangering people. Therefore, early detection of thermal runaway is critical. Voltage, current, and temperature sensors are used to monitor batteries and identify the onset of thermal runaway, but these have limitations. For example, the heat generated inside the battery will take time to be detected due to the low thermal conductivity of polymers and other battery components. Past studies have measured the stress generated in each electrode during cycling [2] and investigated the volumetric expansion of batteries during cycling [3,4]. Therefore, strain measurement of cells could provide insight into the reversible and irreversible phenomenon inside of a battery in real time. To this end, strain and temperature response of a commercially available high capacity (3.4 Ah) 18650 lithium-ion battery were investigated in this study. Strain measurements were conducted while cycling in a temperature-controlled environment. During battery cycling the maximum strain was recorded at 100% state-of-charge (4.2V) in the hoop direction at the center of the battery. Strain was returned to a baseline value upon discharge. In all the cycling scenarios, the electrochemical reaction peaks coincided with change in strain response. Repeated measurements across multiple cycles and multiple identical cells have produced similar strain behavior. The results from this study indicate that strain measurements on a battery’s surface are directly correlated to the reversible electrochemical behavior of a battery, and therefore be used as a diagnostics tool for battery operation to detect irreversible phenomenon. This presentation will describe the methods of strain measurement as well as potential applications of this technique as a diagnostic or early fault detection tool. Figure 1: Middle hoop strain and battery potential during a full cycle. Figure 1

Impact of the splitting of the German–Austrian electricity bidding zone on investment in a grid-scale battery energy storage system deployed for price arbitrage with gray and green power in Austrian and German day-ahead power markets

The splitting of the German–Austrian electricity bidding zone in October 2018 established limits for cross-border exchanges of power between Germany and Austria, which led to the decoupling of German and Austrian wholesale power prices. We analyze the impact of the split on the investment in lithium-ion grid-scale battery energy storage systems (BESS) used for intertemporal arbitrage in German and Austrian day-ahead power markets with hourly contracts operated by the Energy Exchange of Austria. The paper was written in response to the increasing share of green power in the national energy mix of the two countries, followed by a similar development in the volume of green power traded. Arbitrage with green power was analyzed and compared to arbitrage with power from conventional sources using a robust mixed-integer linear programming (MILP) model extended with a battery degradation process. The model was solved for different levels of depth of discharge (DOD) to identify the optimum between cycle life and calendar aging with respect to prevailing market conditions. Using predicted costs of battery, a break-even point was reached with 60% DOD identified as the optimal pattern for the dispatch of the BESS. A separate scenario was considered for the analysis of the impact of moving from the perfect knowledge of future prices to a situation where day-ahead prices are forecasted using the seasonal autoregressive integrated moving average (SARIMA) model.

Design of Remote Monitoring System for Lithium Battery Operating Statu

The safety of lithium batteries has always been an important problem that restricts the rapid development of lithium batteries. In view of the problems such as the untimely and difficult monitoring of the thermal runaway behavior of the lithium battery energy storage system, a remote monitoring system for the operating state of lithium batteries based on ZigBee and NB-IOT technology is designed. The real-time collection of the operating parameters of lithium batteries is realized through various sensors, the whole system is constructed by the Internet of things technology, and the uploading of operating parameters is realized through the network. It is verified that the system can operate stably, collect the operating parameters of lithium battery and upload relevant data in real time, facilitate the remote monitoring of staff, and provide a strong guarantee for the safety of lithium battery energy storage system.