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The importance of batteries is increasing, especially in the field of the high power requirement systems like electric driven vehicles. Mobile energy storage makes it possible to accelerate with incredible torque, without any accruing air pollution. Due to the high costs of real components, it is of great use to simulate battery driven systems before building them. Transient processes within a cell are highly dependent on the operating point of the complete system, which makes it difficult to create equations and model arameterizations. This paper shows which data is important for cell modeling and how to parameterize simplified physical cell models.
In this paper a design method for mechatronic functions in an early design phase under consideration of the requirements of functional safety is proposed [14]. The goal of the method is to discover threats for the safety goals and to prove or optimize the diagnostic mechanisms. At the current design process a lifetime endurance test is performed in a late project phase. In order to avoid unexpected late design changes the method should be applied in an early design phase. The method is an additional way to avoid design failures for E/E functions with special focus on aging in the field of functional safety applications. The method is developed out of three standards VDI 2221 methodology for development and construction of technical systems and products [1]), VDI 2206 design methodology for mechatronic systems [2] and the ISO 26262 road vehicles - Functional Safety [3] by using the V-Model. Furthermore, a core element of the design method is simulating aging effects by applying VHDL - AMS. To demonstrate the design methodology a sensor out of the EGAS function is chosen as a safety relevant automotive system. The limitation is the availability of aging data for electrical components.
For the planning of photovoltaic power plants standard software tools are used. Most of these software tools use statistical solar data to determine the overall energy harvest of a photovoltaic plant over one year. The calculations rely on stationary location and ideal boundary conditions, e.g., constant ambient temperature. Even though, for example, shadowing may be considered by standard software, the investigation of untypical configurations and problems cannot be performed by such software, as most configurations cannot be changed by the user.
The presented PhotoVoltaics library was developed with the intention to provide a flexible framework for standard and non-standard problems. Particularly, the PhotoVoltaics library can be coupled with other Modelica libraries to perform systemic investigations. An application library, PhotoVoltaics_TGM, is provided as add-on, where measured data of two photovoltaic pants of the TGM in Vienna can be compared with simulation results. This add-on library serves as validation of the PhotoVoltaics library.
Conventional battery management systems typically adopt hierarchical master-slave architectures. With regard to an uninterruptible power supply, the most significant disadvantage of central structures is the dependency of the errorfree function of the superior master board. The decentralized battery management system presented in this paper, consisting of consumers, generators and intelligent battery cells, is controlled without any central coordination authority. For this purpose, an intelligent control algorithm and a leader election algorithm are implemented on the microcontrollers of the battery cells. To test different control and election strategies, a software framework is presented for the complete simulation of the decentralized battery management system consisting of equal participants.
The automotive industry is eventually evolving into a complex network of services. The heterogeneous and distributed nature of automotive software systems demands flexible software components which can operate in different environments. Because of heterogeneous automotive development environments, the domain experts, must cope with too many diversities, adaption layers, and incompatibilities to design applications for the current generation of autonomous driving vehicles. In this context, interface adaptation is a promising approach to achieve flexibility without directly changing the respective components. AUTOSAR, which is the de-facto standard for describing automotive system architecture and is a hugely comprehensive standard allowing designers full control from abstract system description to bare metal level deployment. However, the vehicle subsystems have still evolved to include multifarious high-level domains not covered by AUTOSAR e.g. Infotainment, Telematics etc. Therefore, it seems beneficial to bridge the semantic gaps between AUTOSAR applications and other standards of automotive application domains. The goal of this paper is to investigate interface semantic mapping and achieve transparent integration of domain-specific applications using the translation of semantics among the AUTOSAR platform software component models and other software components models of open source development platforms e.g. GENIVI. A key goal of such a modelling approach is the reuse of existing interface description languages and respective code generators. This will enhance future interoperability and decrease in incompatibility among these platforms.
A magnetoelectric (ME) voltage transformer is fabricated on the basis of a ferromagnetic (FM)-piezoelectric (PE) heterostructure comprising two equally thick laminated layers of an amorphous FM alloy and a piezoceramic lead zirconate-titanate layer sandwiched between them. The structure, placed inside an excitation coil, is electrically poled and magnetized in the direction of the long axis. The primary voltage is applied to the coil and the secondary voltage is measured between the electrodes of the PE material. It is shown for the first time that the change in the total thickness of magnetic layers significantly influences the transformer ' s characteristics. At the largest total thickness of FM layers of 138 mu m, the open-circuit voltage transformation ratio K has a maximum value of about 20, and the power transfer efficiency eta at a matched resistive load of about 20 k omega reaches 45%. The variation of the control magnetic field in the range of 0-21.6 kA m(-1) makes it possible to change the voltage transformation ratio K from zero to the maximum value. A simple model allows one to calculate the dependence of the characteristics of the ME transformer on the frequency of the primary voltage, thickness of the FM layers, control magnetic field, and the load.
How is energy storage integrated and currently implemented in the electricity supply, heating supply, and mobility sectors? This chapter provides both theoretical and practical answers to that question. The chapter focuses on the integration of renewable energy. Cross-sectoral energy storage systems that link the electricity, heating, and mobility sectors are discussed in Kap. 14. This chapter focuses on storage integration in the electricity sector. After considering stand-alone networks, the chapter uses practical examples to analyze the various storage applications in the European network. The chapter concludes with a discussion of storage integration in the heating and transportation sectors.
Energy supply always requires energy storage—either as an intrinsic property or as additional system. It is an intrinsic property of solid, liquid, and gaseous fuels, although less so of water-borne heat, but not of electricity. So to meet variable demands and supplies, heat and electricity networks usually require additional storage systems. When they are added to an energy network, should they be viewed as ‘suppliers’ or as ‘consumers’? Who is responsible for covering the costs of storage systems? To categorize storage systems in the energy sector, they first need to be carefully defined.
This chapter defines storage as well as storage systems, describes their use, and then classifies storage systems according to temporal, spatial, physical, energy-related, and economic criteria.
Electricity is becoming the primary source of energy, a trend that is particularly apparent through the coupling of the electricity sector with other energy sectors. In addition to the established links between the electricity and heating sectors using combined heat and power (CHP), which is supplemented by electric heat-pumps and power-to-heat (PtH), other new links are also emerging. These links are manifesting in the form of electro-mobility and electric fuels in the electricity and transport sectors; and in the electricity and gas sector they are appearing in the form of power-to-gas (PtG). The production of basic chemical materials such as methanol or polymers using electrical energy, water, and CO2 will also play a role in the future. However, the latter will not be dealt with explicitly here. Instead we will consider in detail other aspects of electricity as a primary energy source and its integration and application for energy storage.
Human beings have relied on stored energy since time immemorial. The planet’s first mechanism for storing energy arose two billion years ago. Photosynthesis captures solar energy in chemical bonds; it is a process on which all life depends. With the discovery of fire around one-and-a-half million years ago, early man learned to access this stored energy by burning wood. Only since the Industrial Revolution have humans used fossil fuels, which are the results of biomass produced millions of years ago, then subjected to geological processes. Today, the long-term objective is to utilize sustainable biomass storage, replicate it by technical means, and to develop new storage technologies.
This chapter is about the history of energy storage as it pertains to the carbon cycle. It begins with a natural energy storage system—photosynthesis—and examines its products biomass, peat, and fossil fuels before turning to storage technology in the era of renewable energies. It will also discuss how stored energy is used. This chapter focuses on natural biogenic and fossil energy storage. Other chapters are devoted to artificial storage technologies, including batteries, pumped-storage, and power-to-gas (PtG). Each begins with a short history of its respective technology.
Unlike the electricity sector, heating and cooling storage requirements have attracted little public attention. This is because these storage requirements have generally already been met, and will not change significantly in the future. In the electricity sector by contrast, there will be a significant shift from primary energy storage to electricity and final energy storage. Both sectors have remarkably high storage requirements. Almost all households have thermal buffers. The same is true of renewable energy heating systems such as pellet heating, geothermal, or solar-thermal systems. Some households with liquid gas or oil heating even have two storage units: a fuel tank and a thermal buffer. Exceptions include heating systems with upstream storage such as district heating or gas storage. In the future, integration of the electricity and heating sectors by combined heat and power (CHP) generation, heat pumps, power-to-heat (PtH), and power-to-gas (PtG) will facilitate the use of renewable energy, and lead to a paradigm shift. Relying on results from various studies, this chapter examines the development of heating supply in Germany and the resulting thermal storage requirements. The chapter’s later sections provide surplus and storage potential estimates. Cooling requirements are included as ‘process cooling’ under ‘process heat’, and as ‘air-conditioning’ over ‘room heating’. It is primarily integrated into electricity demand.
Energy storage systems (in the past as well as today) are one significant part in the energy supply. The following three chapters describe how storage demand will develop in the future for the electricity, heat, and traffic sectors, as well as for non-energetic consumption of fossil resources (the chemical industry). Chapter 3, the core of this section on storage demand, makes clear how and why the electricity sector is the nucleus of the energy supply of all sectors and why it creates essential bridges between electricity, heat, and transport sectors, as well as with the chemical industry.
If planned electricity network expansion takes place and flexibilities in generation and consumption are fully exploited, the demand for electricity storage, according to present estimates, will only reach a significant scale at 60–80% shares of renewable energy in the power supply. Network expansion has a great impact on the storage demand, as well as flexible power generation in power plants, combined heat and power (CHP), and flexible consumption via demand-side management (DSM). Four studies in the context of storage demand and the role of energy storage systems for flexibility are comprehensively addressed. The authors and the co-authors were themselves participants in these studies, which will be complemented by ongoing research. A meta-study summary of the main results is shown in Abschn. 3.7, and these results are compared with seven further studies.
In the transport sector, energy transition is still in its beginnings: shares of renewable fuels are at 5% and are, with the exception of a small percentage in electrical rail transport, almost entirely restricted to biofuel. The transport sector, i.e., road, air, shipping, and rail traffic, consumes around 30% of all final energy in Germany and its dependency of over 90% on petroleum is still very high. As a result, its shares in greenhouse gas emissions are at 20%. The necessary structural change in mobility, based on energy transition, is closely linked to the question of operating energy and of energy storage also. Aside from vehicles directly powered by wind or solar energy, mobility without storage is not possible: fuel tanks in cars, gas stations, and airplanes are omnipresent. The focus of the considerations on storage demand in the transport sector is on the question of how these storages can be used with renewable energies via bio and synthetic fuels, and on the question of how much storage is necessary for these new drive technologies, such as e-mobility. Before this, mobility needs today and in future need to be examined. In the chemical sector, the situation is very much alike: there is a great dependency on fossil resources, and decarbonization is inevitable to achieve ambitious climate goals. The structural change to convert and store renewable electricity as primary energy via power-to-X (PtX) represents a storage demand. First estimates will conclude this chapter.
Chemical Energy Storage
(2019)
Purely electrical energy storage technologies are very efficient, however they are also very expensive and have the smallest capacities. Electrochemical-energy storage reaches higher capacities at smaller costs, but at the expense of efficiency. This pattern continues in a similar way for chemical-energy storage. In terms of capacities, the limits of batteries (accumulators) are reached when low-loss long-term storage is of need. Chemical-energy storage and stocking fulfills these requirements completely. The storing itself may be subject to significant efficiency losses, but, from today’s point of view and in combination with the existing gas and fuel infrastructure, it is the only national option with regards to the long-term storage of renewable energies.
Chemical-energy storage is the backbone of today’s conventional energy supply. Solid (wood and coal), liquid (mineral oil), and gaseous (natural gas) energy carriers are ‘energy storages’ themselves, and are stored using different technologies. In the course of energy transition, chemical-energy storage will be of significant importance, mainly as long-term storage for the power sector, but also in the form of combustibles and fuels for transport and heat. Not only are conventional storing technologies discussed within this chapter, but a detailed explanation is also given about the storage of renewable energies in the form of gaseous (power-to-gas, PtG) and liquid (power-to-liquid, PtL) energy carriers for electricity, heat, chemicals, and in the form of synthetic fuels.
There are several approaches to classifying energy storage systems (see Chaps. 1 and 2). Storage systems are used in a large number of different technologies at various stages of development, and in a wide range of application areas (see Chaps. 3 to 5). This chapter compares the capabilities of the different storage systems using the following criteria:This comparison of storage systems also provides a convenient overview of the various storage systems and their capabilities.
In this paper it is presented how the network calculation program PowerFactory is used to implement automated network planning algorithms. PowerFactory’s internal functions and data sets are discussed and it is shown how the functions and data sets have been extended by specially developed scripts to enable the automation of network planning steps. Finally, the methodology applied to calculate grid use cases is briefly discussed.
Coupling energy sectors within the emerging residential PV prosumer systems is necessary for an optimised use of the houseowners’ own produced electricity. But the pure availability of different energy technologies in the system is not enough. By optimising the electricity usage as well as the capacities of PV generators, storage technologies, heat pumps and battery electric vehicles, not only the best solution in a technical point of view can be achieved, the need of finding the most financially beneficial system composition for single-family houses and tenements is possible. The study provides a detailed model for average German single-family houses and tenements and results for the energy transition period until 2050 for the optimised energy systems regarding optimised PV and stationary battery capacities and different heat storage capacities. Most noticeable outcomes can be observed by using a vehicle-to-home car, where a car can mostly take over the tasks of a stationary battery and by introducing a solidarity model using this type of car in tenement systems.
Based on a prospective scenario analysis, possible vehicle fleet developments for the individual motor car traffic (vehicle categories N1 and M1) are investigated for Germany in order to determine the long-term vehicle fleet structure, final energy demand, and related carbon dioxide emissions until the year 2050. In this framework, a vehicle fleet model was developed which combines a bottom-up consumer demand model with a dynamic stock-flow approach. Special emphasis is thereby given to different electric power-trains and synthetic gaseous fuels based on the power-to-gas technology. In detail, two different main scenarios are developed and, in addition, the impact of different carbon dioxide taxation levels of fossil fuels on the vehicle fleet structure are analysed. The scenario results reveal a broad range of possible future vehicle fleet structures. In the short to medium timeframe, the internal combustion engine dominates the fleet as a result of efficiency improvements and an increased use of natural gas as automotive fuel. The development of electric power-trains is initially marked by hybrid vehicles, whereas battery electric vehicles dominate the fleet structure in the long-term. Under favourable conditions, also synthetic gaseous fuels are competitive which can reduce carbon dioxide emissions even further.
The feasibility of implementing power-to-gas systems, to absorb surplus solar power from electricity distribution networks and carbon dioxide from biomass anaerobic digestion (AD) plant, in order to produce synthetic methane was investigated for a region of Southern Germany that has a high solar power penetration. The analysis was based on time series electricity data for 2012 from which future load profiles were computed in accordance with the expected installed capacities of solar power across the period 2015–2025. The electrolyser capacity required to absorb 20% of excess solar energy occurring within the region's low voltage network in 2025 was estimated to be 370 MWe. First order considerations of the region's gas grid, electricity network and existing AD sites suggest that such a deployment could be achieved by installing sub-MW (and some multi-MW) power-to-gas plant at several hundred AD sites.
Quantization and Sampling Effects in Fast Digitally Controlled Switch-Mode DC/DC Buck Converters
(2021)
Software-defined control of DC/DC switch mode power converters features unequaled flexibility compared to analog control. For fast switching converters as recently used for low power applications, digital control may cause steady-state oscillations known as limit cycles. This effect based on a quantized duty cycle is compared to other aberrations such as output ripple voltage, which is inherent to any pulse-width modulator. A distinction is made between DC and AC errors. The latter are divided into synchronous and asynchronous output voltage disturbances. Synchronous AC errors are composed of the switching frequency and its harmonics.