The Zinc/Bromine Flow Battery: Fundamentals and Novel Materials for Technology Advancement

Flow batteries are a promising solution for solving intermittency challenges and increasing uptake of renewable power sources such as wind and solar. In particular, zinc/bromine batteries are an attractive option for large-scale electrical energy storage due to their relatively low cost of primary electrolyte and high theoretical specific energy of 440 Wh kg-1. However, inefficient materials of construction hinder practical utilization of this capability and reduce power delivery. The work presented in this thesis aims to overcome these limitations by providing an understanding of the fundamental physical and electrochemical processes governing interactions within the bulk electrolyte and at the electrode–electrolyte interface. Suitable alternative materials to improve system performance are developed via electrochemical investigations, physical characterization and molecular modelling. It is shown that conventional chloride-based supporting electrolytes significantly influence the morphology of zinc electrodeposits generated. High chloride concentration causes removal of zinc from the bulk, causing coulombic losses in the system. It is shown that sulfates, phosphates or even a higher proportion of bromides, are potentially suitable alternatives. Single-halide type tetrahedral zinc complexes exist in conventional electrolytes, and a previously unreported Raman vibrational band at 220 cm-1 is assigned to the [ZnBr2Cl(H2O)]– complex. Ionic liquid additives are proven not to be merely spectators in the zinc half-cell, due to the effects of their chemical structures. Studies using hybrid ionic liquid mixtures indicate that each half-cell benefits from the use of different compounds. It is expected that the approaches and findings presented in this thesis contribute towards aiding and guiding the future search for novel materials to further improve Zn/Br battery technology

State of Charge Estimation for Electric Scooters by Using Learning Mechanisms

This paper presents a state of charge (SOC) learning system designed to improve the performance of current methods of measuring SOC in electric scooters or electric vehicles. The SOC, or residual capacity of an electric vehicle battery, is difficult to determine because of the battery’s nonlinear discharge characteristics. The proposed learning system uses learning controllers, fuzzy neural networks, and cerebellar model articulation controller (CMAC) networks for estimating and predicting the nonlinear characteristics of the battery’s energy consumption. In addition to estimating the availability or residual battery power, the learning system could also provide addition information, such as an estimated traveling distance and the maximum allowable safe speed

Lithium-Ion battery SOC estimation

Lithium-ion batteries are frequently used in Hybrid electric vehicles (HEVs), which are taking the place of gas-engine vehicles. An important but not measurable quantity in HEVs is the amount of charge remaining in the battery in a drive cycle. The remaining charge is normally identified by a variable called state of charge (SOC). A potential way of estimating the SOC is relating this variable with the state of a dynamical system. Afterwards, the SOC can be estimated through an observer design. As a precise model, electrochemical equations are chosen in this research to estimate the SOC. The first part of this thesis considers comparison studies of commonly-used finite-dimensional estimation methods for different distributed parameter systems (DPSs). In this part, the system is first approximated by a finite-dimensional representation; the observer dynamics is a copy of the finite-dimensional representation and a filtering gain obtained through observer design. The main outcome of these studies is comparing the performance of different observers in the state estimation of different types of DPSs after truncation. The studies are then expanded to investigate the effect of the truncated model by increasing the order of finite-dimensional approximation of the system numerically. The simulation results are also compared to the mathematical properties of the systems. A modified sliding mode observer is improved next to take care of the system's nonlinearity and compensate for the estimation error due to disturbances coming from an external input. It is proved that the modified SMO provides an exponential convergence of the estimation error in the existence of an external input. In most cases, the simulations results of the comparison studies indicate the improved performance of the modified SMO observer. Approximation and well-posedness of two general classes of nonlinear DPSs are studied next. The main concern of these studies is to produce a low-order model which converges to the original equation as the order of approximation increases. The available results in the literature are limited to specified classes of systems. These classes do not cover the lithium-ion cell model; however, the general forms presented here include the electrochemical equations as a specific version. In order to facilitate the electrochemical model for observer design, simplification of the model is considered in the next step. The original electrochemical equations are composed of both dynamical and constraint equations. They are simplified such that a fully dynamical representation can be derived. The fully dynamical representation is beneficial for real-time application since it does not require solving the constraint equation at every time iteration while solving the dynamical equations. Next, the electrochemical equations can be transformed into the general state space form studied in this thesis. Finally, an adaptive EKF observer is designed via the low-order model for SOC estimation. The electrochemical model employed here is a variable solid-state diffusivity model. Compared to other models, the variable solid-state diffusivity model is more accurate for cells with Lithium ion phosphate positive electrode, which are considered here, than others. The adaptive observer is constructed based on considering an adaptive model for the open circuit potential term in the electrochemical equations. The parameters of this model are identified simultaneously with the state estimation. Compared to the experimental data, simulation results show the efficiency of the designed observer in the existence of modeling inaccuracy

Investigation of energy storage system and demand side response for distribution networks

PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks. Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated. The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks