When a molecule absorbs a photon of appropriate energy, a chain of photophysical events ensues, such as internal conversion or vibrational relaxation (loss of energy in the absence of light emission), fluorescence, intersystem crossing (from singlet state to a triplet state) and phosphorescence, as shown in the Jablonski diagram for organic molecules (Fig. 1). Each of the processes occurs with a certain probability, characterized by decay rate constants (k). It can be shown that the average length of time τ for the set of molecules to decay from one state to another is reciprocally proportional to the rate of decay: τ = 1/k. This average length of time is called the mean lifetime, or simply lifetime. It can also be shown that the lifetime of a photophysical process is the time required by a population of N electronically excited molecules to be reduced by a factor of e. Correspondingly, the fluorescence lifetime is the time required by a population of excited fluorophores to decrease exponentially to N/e via the loss of energy through fluorescence and other non-radiative processes. The lifetime of photophycal processes vary significantly from tens of femotoseconds for internal conversion1,2 to nanoseconds for fluorescence and microseconds or seconds for phosphorescence.1






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Figure 1


Jablonski diagram and a timescale of photophysical processes for organic molecules.

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Fluorescence Lifetime Measurements and Biological Imaging

DC microgrids (MGs) have been gaining a continually increasing interest over the past couple of years both in academia and industry. The advantages of dc distribution when compared to its ac counterpart are well known. The most important ones include higher reliability and efficiency, simpler control and natural interface with renewable energy sources, and electronic loads and energy storage systems. With rapid emergence of these components in modern power systems, the importance of dc in today's society is gradually being brought to a whole new level. A broad class of traditional dc distribution applications, such as traction, telecom, vehicular, and distributed power systems can be classified under dc MG framework and ongoing development, and expansion of the field is largely influenced by concepts used over there. This paper aims first to shed light on the practical design aspects of dc MG technology concerning typical power hardware topologies and their suitability for different emerging smart grid applications. Then, an overview of the state of the art in dc MG protection and grounding is provided. Owing to the fact that there is no zero-current crossing, an arc that appears upon breaking dc current cannot be extinguished naturally, making the protection of dc MGs a challenging problem. In relation with this, a comprehensive overview of protection schemes, which discusses both design of practical protective devices and their integration into overall protection systems, is provided. Closely coupled with protection, conflicting grounding objectives, e.g., minimization of stray current and common-mode voltage, are explained and several practical solutions are presented. Also, standardization efforts for dc systems are addressed. Finally, concluding remarks and important future research directions are pointed out.

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DC Microgrids—Part II: A Review of Power Architectures, Applications, and Standardization Issues

Over the past decade, electrochemical energy storage (EES) devices have greatly improved, as a wide variety of advanced electrode active materials and new device architectures have been developed. These new materials and devices should be evaluated against clear and rigorous metrics, primarily based on the evidence of real performances. A series of criteria are commonly used to characterize and report performance of EES systems in the literature. However, as advanced EES systems are becoming more and more sophisticated, the methodologies to reliably evaluate the performance of the electrode active materials and EES devices need to be refined to realize the true promise as well as the limitations of these fast-moving technologies, and target areas for further development. In the absence of a commonly accepted core group of metrics, inconsistencies may arise between the values attributed to the materials or devices and their real performances. Herein, we provide an overview of the energy storage devices from conventional capacitors to supercapacitors to hybrid systems and ultimately to batteries. The metrics for evaluation of energy storage systems are described, although the focus is kept on capacitive and hybrid energy storage systems. In addition, we discuss the challenges that still need to be addressed for establishing more sophisticated criteria for evaluating EES systems. We hope this effort will foster ongoing dialog and promote greater understanding of these metrics to develop an international protocol for accurate assessment of EES systems.

Towards establishing standard performance metrics for batteries, supercapacitors and beyond.

Commercialization of Lithium Battery Technologies for Electric Vehicles

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.

Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids

In order to achieve lower fuel consumption and less greenhouse gas (GHG) emissions, we need higher efficiency vehicles with improved performance. Electrification is the most promising solution to enable a more sustainable and environmentally friendly transportation system. Electrified transportation vision includes utilizing more electrical energy to power traction and nontraction loads in the vehicle. In electrified powertrain applications, the efficiency of the electrical path, and the power and energy density of the components play important roles to improve the electric range of the vehicle to run the engine close to its peak efficiency point and to maintain lower energy consumption with less emissions. In general, the electrified powertrain architecture, design and control of the powertrain components, and software development are coupled to facilitate an efficient, high-performance, and reliable powertrain. In this paper, enabling technologies and solutions for the electrified transportation are discussed in terms of power electronics, electric machines, electrified powertrain architectures, energy storage systems (ESSs), and controls and software.

Making the Case for Electrified Transportation

This paper presents an online optimal energy/power control method for the operation of energy storage in grid-connected electricity microgrids. The approach is based on a mixed-integer-linear-program optimization formulated over a rolling horizon window, considering predicted future electricity usage and renewable energy generation. Performance objectives include electricity usage cost, battery operation costs, and utility oriented goals related to the peak demand and load smoothing. A robust counterpart formulation of the optimization problem is also proposed to handle uncertainty in energy demand/generation prediction in a computationally efficient way. Further reduction in the computations is achieved by employing variable time steps and relaxing binary constraints. A series of simulations demonstrate the effectiveness of various features of the proposed energy/power management methodology in different scenarios.

An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

Recently, increased emissions regulations and a push for less dependence on fossil fuels are factors that have enticed a growth in the market share of alternative energy vehicles. Readily available energy storage systems (ESSs) pose a challenge for the mass market penetration of hybrid electric vehicles (HEVs), plug-in HEVs, and EVs. This is mainly due to the high cost of ESS available today. However, tremendous research efforts are going into reducing the cost of these storage devices, increasing their lifespan, and improving their energy density. This paper aims to give an overview of the current state of readily available battery and ultracapacitor (UC) technologies as well as a look ahead toward promising advanced battery chemistries and next generation ESS. Energy management systems and various battery balancing configurations are discussed in addition to battery state/parameter estimation and protection mechanisms. Finally, hybrid ESSs (HESS) are reviewed as a mitigation strategy to the shortcomings of traditional battery and UC technologies. Consideration is given to the combination of advanced battery chemistries with UCs to portray HESS performance, which can meet and exceed the performance of current ESS technologies.

Electrochemical and Electrostatic Energy Storage and Management Systems for Electric Drive Vehicles: State-of-the-Art Review and Future Trends

The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique.

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Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

An online optimal control strategy for power flow management in microgrids with on-site battery, renewable energy sources, and integrated electric vehicles (EVs) is presented in this paper. An optimization problem in the form of a mixed integer linear program is formulated. It is executed over a rolling time horizon using predicted values of the microgrid electricity demand, renewable energy generation, EV connection and disconnection times, and the EV state of charge at time of connection. The solution to this optimization problem provides the on-site storage and EV charge/discharge powers. Both bidirectional and unidirectional charging scenarios are considered for EVs. The proposed optimal controller maximizes economic benefits and ensures user-specified charge levels are reached at the time of EV disconnection from the microgrid. By formulating the problem as a stochastic chance constraints optimization, significant improvement is shown in the system robustness over conventional rolling horizon controller, while dealing with uncertainties in the predictions of demand/generation, and EV state of charge and connection/disconnection times. Results of Monte Carlo simulations show that the proposed chance constraints-based controller is highly effective in reducing cost and meeting the user desired EV charge level at time of disconnection from the microgrid, even in the presence of uncertainty.

Pawel Malysz

Papers

Making the Case for Electrified Transportation

An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

Electrochemical and Electrostatic Energy Storage and Management Systems for Electric Drive Vehicles: State-of-the-Art Review and Future Trends

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

A Chance-Constraints-Based Control Strategy for Microgrids With Energy Storage and Integrated Electric Vehicles