Abstract: Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.
Abstract: Considering the demand to reduce global warming potential and importance of solidification in various applications, there is an increasing interest in energy storage systems to find the efficient phase change materials. Therefore, this paper presents an experimental study and comparison on the potential of titania nanofluids with and without surfactant for cooling energy storage systems. A designed cooling generation device based on compression refrigeration cycle is used to explore nanofluids solidification characteristics. In this work, titania nanoparticles of 0.01, 0.02 and 0.04 wt.% are dispersed in deionized water as base fluid. Measurement of phase change parameters of nanofluids illustrates that the addition of polyvinylpyrrolidone (PVP) as surfactant to titania nanofluids advances the onset nucleation time and leads to lower solidification time. Also, the experimental results show that only adding 0.02 wt.% titania nanoparticles, especially in the case of nanofluids with a surfactant, can evidently reduce the supercooling degree by nearly 70%. Hence, it is concluded that there is a great energy saving potential in the energy storage systems using titania nanofluid with PVP.
Abstract: Efficient energy storage is a crucial factor in facilitating the uptake of renewable energy resources. Among the many options available for energy storage systems required to balance imbalanced supply and demand cycles, compressed air energy storage (CAES) is a proven technology in grid-scale applications. This paper reviews the current state of micro scale CAES technology and describes a micro-scale advanced adiabatic CAES (A-CAES) system, where heat generated during compression is stored for use in the discharge phase. It will also describe a thermodynamic model, developed in EES (Engineering Equation Solver) to evaluate the performance and critical parameters of the discharge phase of the proposed system. Three configurations are explained including: single turbine without preheater, two turbines with preheaters, and three turbines with preheaters. It is shown that the micro-scale A-CAES is highly dependent upon key parameters including; regulator pressure, air pressure and volume, thermal energy storage temperature and flow rate and the number of turbines. It was found that a micro-scale AA-CAES, when optimized with an appropriate configuration, could deliver energy input to output efficiency of up to 70%.
Abstract: Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.
Abstract: Equivalent circuit models (ECMs) are widely used in
battery management systems in electric vehicles and other battery
energy storage systems. The battery dynamics and the model
parameters vary under different working conditions, such as different
temperature and state of charge (SOC) levels, and therefore online
parameter identification can improve the modelling accuracy. This
paper presents a way of online ECM parameter identification using a
continuous time (CT) estimation method. The CT estimation method
has several advantages over discrete time (DT) estimation methods
for ECM parameter identification due to the widely separated battery
dynamic modes and fast sampling. The presented method can be used
for online SOC estimation. Test data are collected using a lithium ion
cell, and the experimental results show that the presented CT method
achieves better modelling accuracy compared with the conventional
DT recursive least square method. The effectiveness of the presented
method for online SOC estimation is also verified on test data.
Abstract: The application of ESS (Energy Storage Systems) in
the future grids has been the solution of the microgrid. However, high
investment costs necessitate accurate modeling and control strategy of
ESS to justify its economic viability and further underutilization.
Therefore, the reasonable control strategy for ESS which is subjected
to generator and usage helps to curtail the cost of investment and
operation costs. The rated frequency in power system is decreased
when the load is increasing unexpectedly; hence the thermal power is
operated at the capacity of only its 95% for the Governor Free (GF) to
adjust the frequency as reserve (5%) in practice. The ESS can be
utilized with governor at the same time for the frequency response due
to characteristic of its fast response speed and moreover, the cost of
ESS is declined rapidly to the reasonable price. This paper presents the
ESS control strategy to extend usage of the ESS taken account into
governor’s ramp rate and reduce the governor’s intervention as well.
All results in this paper are simulated by MATLAB.