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Lithium-ion battery second life usage might be an interesting solution for certain applications and it can bring not only environmental benefits but also e.g. lower electric vehicles or hybrid electric vehicles total cost of ownership. However, the electrical performance and performance degradation of the battery systems in the second life application are still unknown what brings significant economic risk and hinders second life battery usage. This work investigates the capacity and power degradation of the 2.5Ah nanophosphate LiFePO4/C cells under different the second life calendar ageing regimes.

Lithium Sulfur batteries are receiving a lot of research interest because of their intrinsic characteristics, such as very high energy density and increased safety, which make them a suitable solution for zero-emission vehicles and space application. This paper analyses the influence of the temperature on the performance parameters of a 3.4 Ah Lithium-Sulfur battery cell. Furthermore, the values of the internal resistance and entropic heat coefficient, which are necessary for the parametrization of a heat generation model, are determined experimentally.

Lithium-ion batteries have already gained acceptability for Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) applications because of several reasons such as high theoretical capacity, their cycle-life, and high specific energy density. The intention of this experimental research is to study the surface temperature evolution of a 13 Ah Nano Lithium-Titanate battery cell for the usage of rechargeable energy storage system under fast charging conditions. The nominal voltage of the cell is 2.26V and the nominal capacity is 13.4 Ah. In this research, contact thermocouples were employed in order to measure the surface temperature evolution of the cell. A relatively non-uniform temperature distribution was observed over the cell surface.The position of the maximum temperature was seen around the negative tab of the cell.

Despite the cost of li-ion batteries is gradually falling, the price for li-ion batteries is still too high in order to significantly impact the mass market adoption of e-mobility and household battery applications. It is expected that it might take another several years before lithium-ion batteries obtain grid parity and Electric Vehicles (EVs) will become competitive in cost with conventional vehicles (Figure 1). In consequence, a different approach for battery cost reduction can be investigated.

The impedance behavior of a 3.4 Ah pouch Lithium-Sulfur cell was extensively characterized using the electrochemical impedance spectroscopy (EIS) technique. EIS measurements were performed at various temperatures and over the entire state-of-charge (SOC) interval without applying a superimposed DC current. The obtained results have revealed a high dependency of the pouch cell’s impedance spectrum on the operating conditions. An equivalent electrical circuit was proposed to further analyze the results and to quantify the contributions of different resistances to the total impedance of the Li-S pouch cell at different SOCs and temperatures.

Thermal modeling of lithium-ion batteries is gaining its importance together with increasing power density and compact design of the modern battery systems in order to assure battery safety and long lifetime. Thermal models of lithium-ion batteries are usually either expensive to develop and accurate or equivalent thermal circuit based with moderate accuracy and without spatial temperature distribution.This work presents initial results that can be used as a fundament for the cost-efficient development of the two-dimensional thermal model of lithium-ion battery based on multipoint electrothermal impedance spectroscopy.

Current lithium-ion battery research aims in not only increasing their energy density but also power density. Emerging applications of lithium-ion batteries (HEV, PHEV, grid support) are becoming more and more power demanding.The increasing charging and discharging power capability rates of lithium-ion batteries raises safety concerns and requires thermal management of the entire battery system. Moreover, lithium-ion battery’s temperature influences both battery short term (capacity, efficiency, self-discharge) and long-term (lifetime) behaviour. Thus, thermal modelling of lithium-ion battery cells and battery packs is gaining importance.Equivalent thermal circuits’ models have proven to be relatively accurate with low computational burden for the price of low spatial resolution; nevertheless, they usually require expensive equipment for parametrization.Recent research initiated by Barsoukov et al. proposed electrothermal impedance spectroscopy (ETIS) as a novel and non-destructive method of characterizing the thermal properties of batteries by defining frequency dependent thermal impedance.This paper aims to systematize the state of knowledge about ETIS, presents measurement methods on the example of a high-power LiMO2/Li4Ti5O12 battery cell and discusses the prospect.

Modern lithium-ion batteries, like LiMO2/Li4Ti5O12 chemistry, are having very high power capability, which drives the need for precise thermal modelling of the battery. Battery thermal models are required to avoid possible safety issues (thermal runaways, high-temperature gradients) but also for the optimal battery pack design and for predicting battery electrical performance during operation (especially at low temperatures). Equivalent electrical circuit battery models (thermal and electro-thermal) are able to offer relatively good performance with relatively low computational efforts and may be executed on-line (e.g. in the electric vehicle Battery Management System). The Electrothermal Impedance Spectroscopy (ETIS) can be used as a relatively simple and fast method for non-destructive parametrization of the battery thermal models. However, the ETIS requires expensive galvanostat with high-bandwidth and high-current capability for large format battery cells. Thus, this paper evaluates the possibility and accuracy of performing ETIS measurements with a standard battery test station (or bidirectional power supply) with low-bandwidth.

Wind power plant operators are often faced with extra charges when their power production does not match the forecasted power. Because the accuracy of wind power forecasts is limited, the use of energy storage systems is an attractive alternative even when large-scale aggregation of wind power is considered. In this paper, the economic feasibility of lithium-ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium-ion battery was developed considering both cycling and calendar life. The economic analysis considers two different energy management strategies for the storage systems and it is performed for the Danish market. Analyses have shown that the price of the Li-ion BESS needs to decrease by 6.7 times in order to obtain a positive net present value considering the present prices on the Danish energy market. Moreover, it was found that for total elimination of the wind power forecast error, it is required to have a 25-MWh Li-ion battery energy storage system for the considered 2 MW WT.

This paper studies the impedance characteristics of a fresh 13 Ah high-power lithium titanate oxide (LTO) battery cell and analyses its dependence on the temperature and state-of-charge. The impedance of the battery cell was measured by means of the electrochemical impedance spectroscopy (EIS) technique for the entire state-of-charge (SOC) interval and considering five temperatures between 5oC and 45oC. By analyzing the measured impedance spectra of the LTO-based battery cell, it was found out that the cell’s impedance is extremely dependent on the operating conditions. By further processing the measurements data by means of curve fitting, it was found that the parameters of the equivalent electrical circuit (and the corresponding electro-chemical processes) are following different dependencies on the SOC, which in most of the cases are only slightly changing with the measurement temperature.

Lithium-Sulfur (Li-S) batteries are a promising energy storage technology, which draws interest due to their high theoretical limits in terms of specific capacity, specific energy and energy density. However as a drawback, they suffer from a high self-discharge rate, which is mainly caused by ongoing polysulfide shuttle. In this paper, the self-discharge behavior of Li-S batteries is experimentally investigated, considering various conditions as depth-of-discharge, temperature and idling time. The self-discharge rate under different conditions is identified and quantified. Moreover, a methodology for estimating the capacity of the high voltage plateau based on a self-discharge constant was analyzed; however, the method needs further improvements in order to estimate this capacity accurately for all conditions.

Lithium ion batteries with iron phosphate cathodes are gradually improving in their performance and gaining importance, and are more and more considered for new applications. Different aspects of this chemistry were studied in numerous publications; however, very little research was devoted to detailed empirical investigations in order to find out how self-discharge of this chemistry depends on different storing conditions. Precise knowledge about the level of the self-discharge of lithium ion battery cells is very important for improving the performance of the battery management system since it allows also for more precise determination of the actual battery SOC after prolonged storage. In this paper the self-discharge of the nanophosphate LiFePO4/C is studied at different temperature, SOC conditions and at different SOH levels of the battery. Moreover, cell to cell differences in self-discharge caused by the manufacturing tolerances are investigated.

Lithium ion (Li-ion) batteries have been extensively used in consumer electronics because of their characteristics, such as high efficiency, long life, and high gravimetric and volumetric energy. In addition, Li-ion batteries are becoming the most attractive candidate as electrochemical storage systems for stationary applications, as well as power source for sustainable automotive and back-up power supply applications. This paper gives an overview of the Li-ion battery chemistries that are available at present in the market, and describes the three out of four main applications (except the consumers’ applications), grid support, automotive, and back-up power, for which the Li-ion batteries are suitable. Each of these applications has its own specifications and thus, the chemistry of the Li-ion battery should be chosen to fulfil the requirements of the corresponding application. Consequently, this paper discusses as well, the suitability of the presented Li-ion battery chemistries for different applications.

Fulfilling ambitious goals of the full transition from the centralized, fossil fuel-based conventional generation units into distributed and eco-friendly renewables can be difficult to achieve without energy storage systems due to technical and economical challenges. Energy storage system addition to wind turbines/wind farms is one of the most promising solutions to problems related with the further integration of wind power into the grids with already high wind penetration.

Future large Wind Power Plants (WPP) will be intended to function like today's conventional power plants seen from the transmission system point of view, by complying with future, more stringent, grid codes and providing ancillary services. This is possible to achieve by integrating WPPs with Battery Energy Storage Systems (BESSs) into the so called Virtual Power Plants (VPP). Relatively new energy storage technologies based on Lithium ion (Li-ion) batteries are constantly improving their performance and are becoming attractive for stationary energy storage applications due to their characteristics such as high power, high efficiency, low self-discharge, and long lifetime.The family of the Li-ion batteries is wide and the selection of the most appropriate Liion chemistries for VPPs is one of the topics of this thesis, where different chemistries are compared and the most suitable ones for VPP integration are selected based on the accelerated lifetime tests.The knowledge of the BESS lifetime is a key factor for their integration with WPPs. This is because an accurate knowledge of the BESS lifetime is a crucial factor in the project planning stage for the investigation on the BESS investment profitability. Moreover, the information about the BESS State of Health (SOH), at every point, is very important since the performance of the Li-ion BESS is changing with its age. In applications, the replacement of the BESS takes place usually before the end of their actual life, depending if the batteries are able to meet several performance requirements, which are application dependent. Furthermore, for the VPP, the degradation or failure of the interconnected BESS can lead to costly downtime.Thus, an accurate estimation of the battery cells lifetime becomes mandatory. However, lifetime degradation of the Li-ion batteries is a complex process where many different degradation processes are involved and degradation is dependent on the battery operational conditions. Moreover, Li-ion battery cells are degrading slowly and the process of their ageing is time and resource consuming. Thus, the main purpose of this thesis is the development of a lifetime model, for a LiFePO4/C, battery based on accelerated laboratory cycle and calendar lifetime tests under different stress factors and stress levels, which are characteristic for the selected services of the VPP.The developed Li-ion battery lifetime model is a used for the investigation on the lifetime of Li-ion BESS under different operational conditions for two selected services that the VPP could provide: primary frequency regulation and wind power forecast accuracy improvement. Different energy management strategies for these services are studied and different battery operational conditions are investigated. Moreover, simple economic analyses are performed; using the developed Li-ion battery lifetime model, for assessingthe profitability of the considered services provided by the VPP.

Energy storage systems based on Lithium-ion batteries have the potential to mitigate the negative impact of wind power grid integration on the power system stability, which is caused by the characteristics of the wind. This paper presents a performance model for a Li4Ti5O12/LiMO2 battery cell. For developing the performance model an EIS-based electrical modelling approach was followed. The obtained model is able to predict with high accuracy charge and discharge voltage profiles for different ages of the battery cell and for different charging/discharging current rates. Moreover, the ageing behaviour of the battery cell was analysed for the case of accelerated cycling ageing with a certain mission profile.

Given the increasing penetration of renewable energy technologies as distributed generation embedded in the consumption centres, there is growing interest in energy storage systems located very close to consumers. These systems allow to increase the amount of renewable energy generation consumed locally, they provide opportunities for demand-side management and help to decarbonise the electricity, heating and transport sectors. In this paper, the authors present an interdisciplinary review of community energy storage (CES) with a focus on its potential role and challenges as a key element within the wider energy system. The discussion includes: the whole spectrum of applications and technologies with a strong emphasis on end user applications; techno-economic, environmental and social assessments of CES; and an outlook on CES from the customer, utility company and policy-maker perspectives. Currently, in general only traditional thermal storage with water tanks is economically viable. However, CES is expected to offer new opportunities for the energy transition since the community scale introduces several advantages for electrochemical technologies such as batteries. Technical and economic benefits over energy storage in single dwellings are driven by enhanced performance due to less spiky community demand profile and economies of scale respectively. In addition, CES brings new opportunities for citizen participation within communities and helps to increase awareness of energy consumption and environmental impacts.

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is developing rapidly. To ensure the battery safe usage and to reduce its average lifecycle cost, an accurate State of Charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper reviews recently proposed online SOC estimation methods and classifies them into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

This paper proposes a novel fast open circuit voltage prediction approach for Lithium-ion battery, which is potential to facilitate a convenient battery modeling and states estimation in the energy storage system. Open circuit voltage measurement suffers from a long relaxation time (several hours, even days) to reach the thermodynamic equilibrium of the battery. On the basis of the feedback control theory, the proposed multiple correction approach utilizes the constrained nonlinear optimization of the power function in each curve fitting step. The voltage measurement in a short period is divided into several segments to correct the voltage prediction multiple times with the feedback errors after each curve fitting. The similarity between the shape of the power function and the variation of the terminal voltage during the relaxation time is utilized. The proposed method can speed up the time-consuming open circuit voltage measurement and predict the open circuit voltage with high accuracy. Experimental tests on a LiFePO4 battery prove the validation and effectiveness of the proposed method in accurately predicting the open circuit voltage within a very short relaxation time (less than 15 min).

This paper proposes a low-complexity online state of charge estimation method for LiFePO4 battery in electrical vehicles. The proposed method is able to achieve accurate state of charge with less computational efforts in comparison with the nonlinear Kalman filters, and also can provide state of health information for battery management system. According to the error analysis of equivalent circuit model with two resistance and capacitance, two proportional-integral filters are designed to compensate the errors from inaccurate state of charge and current measurements, respectively. An error dividing process is proposed to tune the contribution of each filter to the finial estimation results, which enhances the validation and accuracy of the proposed method. Recursive least squares filter can provide the state of health information and updates the parameters of battery model online to eliminate the errors caused by parameters uncertainty. The proposed method is compared with extend Kalman filter in regards to accuracy and execution time. The execution time of the proposed method is measured on Zynq board platform to validate its suitability for online implementation. In this paper, the proposed method is able to obtain less than 1% error for state of charge estimation.

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories,that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.