Energy Storage Technologies for Smoothing Power Fluctuations in Marine Current Turbines

With regard to marine renewable energies, significant electrical power can be extracted from marine tidal current. However, the power harnessed by a marine current turbine varies due to the periodicity of the tidal phenomenon and could be highly fluctuant caused by swell effect. To improve the power quality and make the marine current generation system more reliable, energy storage systems will play a crucial role. In this paper, the power fluctuation phenomenon is described and the state of art of energy storage technologies is presented. Characteristics of various energy storage technologies are analyzed and compared for marine application. The omparison shows that high-energy batteries like sodiumsulphur battery and flow battery are favorable for smoothing the long-period power fluctuation due to the tide phenomenon while supercapacitors and flywheels are suitable for eliminating short-period power disturbances due to swell or turbulence phenomena. It means that hybrid storage technologies are needed for achieving optimal performance in marine current energy systems

Electric vehicle battery model identification and state of charge estimation in real world driving cycles

This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry

Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

ENERGY REDUCTION IN AUTOMOTIVE PAINT SHOPS A REVIEW OF HYBRID/ELECTRIC VEHICLE BATTERY MANUFACTURING

Automotive industry is facing fundamental challenges due to the rapid depletion of fossil fuels, energy saving and environmental concerns. The need of sustainable energy development has motivated the research of energy reduction and renewable energy sources. Efficient use of energy in vehicle manufacturing is demanded, as well as an alternative energy source to replace gasoline powered engines. In this thesis, we introduce a case study at an automotive paint shop, where the largest amount of energy consumption of an automotive assembly plant takes place. Additionally, we present a summary of recent advances in the area of hybrid and electrical vehicles battery manufacturing, review commonly used battery technologies, their manufacturing processes, and related recycling and environmental issues. Our study shows that energy consumption in paint shops can be reduced substantially by selecting the appropriate repair capacity, reducing the number of repainted jobs and consuming less material and energy. Also, it is seen that considerable effort needs to be devoted to the development of batteries for hybrid and electric vehicles in the near future, which will make this area challenging and research opportunities promising

Analysis and optimal design of micro-energy harvesting systems for wireless sensor nodes

Presently, wireless sensor nodes are widely used and the lifetime of the system is becoming the biggest problem with using this technology. As more and more low power products have been used in WSN, energy harvesting technologies, based on their own characteristics, attract more and more attention in this area. But in order to design high energy efficiency, low cost and nearly perpetual lifetime micro energy harvesting system is still challenging. This thesis proposes a new way, by applying three factors of the system, which are the energy generation, the energy consumption and the power management strategy, into a theoretical model, to optimally design a highly efficient micro energy harvesting system in a real environment. In order to achieve this goal, three aspects of contributions, which are theoretically analysis an energy harvesting system, practically enhancing the system efficiency, and real system implementation, have been made. For the theoretically analysis, the generic architecture and the system design procedure have been proposed to guide system design. Based on the proposed system architecture, the theoretical analytical models of solar and thermal energy harvesting systems have been developed to evaluate the performance of the system before it being designed and implemented. Based on the model’s findings, two approaches (MPPT based power conversion circuit and the power management subsystem) have been considered to practically increase the system efficiency. As this research has been funded by the two public projects, two energy harvesting systems (solar and thermal) powered wireless sensor nodes have been developed and implemented in the real environments based on the proposed work, although other energy sources are given passing treatment. The experimental results show that the two systems have been efficiently designed with the optimization of the system parameters by using the simulation model. The further experimental results, tested in the real environments, show that both systems can have nearly perpetual lifetime with high energy efficiency

Parametric Study of Alternative EV1 Powertrains

The General Motors (GM) EV1 is an electric vehicle originally powered by either a PbA or NiMh battery pack. This paper examines the possibility of alternative powertrain configurations. These alternatives include an ultracapacitor (UC) storage system, fuel cell system with UC storage, and a fuel cell system with a NiMh battery pack. The configurations were simulated using ADVISOR. Parametric tests were performed by varying the size of the energy storage systems. The study of these combinations is followed by an examination of the current art of the hybrid energy storage topologies used to combine battery and ultracapacitor storage. These topologies include passive parallel, active parallel, cascade parallel, and multi-input bidirectional converter

Lifecycle Energy and Air Emission Differences between Electric and Internal Combustion Vehicles

The U.S. Federal Government has encouraged shifting from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) with three objectives, reducing foreign oil dependence, greenhouse gas emissions, and criteria pollutant emissions. This thesis uses Monte Carlo simulation to predict lifecycle emissions and energy consumption differences per kilometer driven from replacing ICEVs with three EV options: lead acid, nickel cadmium (Ni-Cd), and nickel metal hydride (NiMH). All three EV options reduce U.S. foreign oil dependence by shifting to domestic coal. The probabilities that lifecycle energy consumption per km driven improve are lead acid 76%, Ni-Cd 64%, and NiMH 90%. The probabilities that EV substitution reduce global warming gas emissions are lead acid 41%, Ni-Cd 34%, and NiMH 64%. All three EV options increase sulfur oxides emissions. The probability that EV substitution will decrease nitrogen oxides emissions is only 12-14%. The probability that EV substitution reduces particulate matter emissions is less than one percent. The probability that EV substitution reduces volatile organic carbon emissions is lead acid 66%, Ni-Cd 98%, and NiMH 100%. Probabilities indicate that EVs will reduce foreign oil dependence, volatile organic carbon and lead emissions. However the other air emissions will increase and greenhouse gas emissions remain relatively unchanged

A H2 PEM fuel cell and high energy dense battery hybrid energy source for an urban electric vehicle

Electric vehicles are set to play a prominent role in addressing the energy and environmental impact of an increasing road transport population by offering a more energy efficient and less polluting drive-train alternative to conventional internal combustion engine (ICE) vehicles. Given the energy (and hence range) and performance limitations of electro-chemical battery storage systems, hybrid systems combining energy and power dense storage technologies have been proposed for vehicle applications. The paper discusses the application of a hydrogen fuel cell as a range extender for an urban electric vehicle for which the primary energy source is provided by a high energy dense battery. A review of fuel cell systems and automotive drive-train application issues are discussed, together with an overview of the battery technology. The prototype fuel cell and battery component simulation models are presented and their performance as a combined energy/power source assessed for typical urban and sub-urban driving scenario

A hardware-in-the-loop test rig for development of electric vehicle battery identification and state estimation algorithms

This paper describes a hardware-in-the-loop (HIL) test rig for the test and development of electric vehicle battery parameterisation and state-estimation algorithms in the presence of realistic real-world duty cycles. The rig includes two electric machines, a battery pack, a real-time simulator, a thermal chamber and a PC for human-machine interface. Other parts of a vehicle powertrain system are modelled and used in the real-time simulator. A generic framework has been developed for real-time battery measurement, model identification and state estimation. Measurements are used to extract parameters of an equivalent circuit network model. Outputs of the identification unit are then used by an estimation unit trained to find the relationship between the battery parameters and state-of-charge. The results demonstrate that even with a high noise level in measured data, the proposed identification and estimation algorithms are able to work well in real-time