A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization

The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric

Electric vehicle as a service (EVaaS):applications, challenges and enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Optimal Energy Scheduling of Grid-connected Microgrids with Battery Energy Storage

The coupling of small-scale renewable-based energy sources, such as photovoltaic systems, with residential battery energy storages forms clusters of local energy resources and customers, which can be represented as controllable entities to the main distribution grid. The operation of these clusters is similar to that of grid-connected microgrids. The future distribution grid of multiple grid-connected microgrids will require proper coordination to ensure that the energy management of the microgrid resources satisfies the targets and constraints of both the microgrids’ and the main grid’s operation. The link between the battery dispatch and the induced battery degradation also needs to be better understood to implement energy management with long-term economic benefits. This thesis contributes to the solution of the above-mentioned issues with an energy management model developed for a grid-connected microgrid that uses battery energy storage as a flexible energy resource. The performance of the model was evaluated in different test cases (simulations and demonstrations) in which the model optimized the schedule of the microgrid resources and the energy exchange with the connected main grid, while satisfying the constraints and operational objectives of the microgrid. Coordination with the distribution system operator was proposed to ensure that the microgrid energy scheduling solution would not violate the constraints of the main grid.Two radial distribution grids were used in simulation studies: the 12-kV electrical distribution grid of the Chalmers University of Technology campus and a 12.6-kV 33-bus test system. Results of the Chalmers’ test case assuming the operation of two grid-connected microgrids with battery energy storage of 100-200 kWh showed that the microgrids’ economic optimization could reduce the cost for the distribution system operator by up to 2%. Coordination with the distribution system operator could achieve an even higher reduction, although it would lead to sub-optimal solutions for the microgrids. Application of decentralized coordination showed the effectiveness of utilizing microgrids as flexible entities, while preserving the privacy of the microgrid data, in the simulations performed with the 33-bus test system. The developed microgrid energy management model was also applied for a building microgrid, where the battery energy storage was modeled considering both degradation and real-life operation characteristics derived from measurements conducted at real residential buildings equipped with stationary battery energy storages. Simulation results of a building microgrid with a 7.2 kWh battery energy storage showed that the annual building energy and battery degradation cost could be reduced by up to 3% compared to when the impact of battery degradation was neglected in the energy scheduling. To demonstrate the model’s practical use, it was integrated in an energy management system of the real buildings, where the buildings’ battery energy storages and, by extent, their energy exchange with the main grid, were dispatched based on the model’s decisions in several test cases.The test cases’ results showed that the model can reduce the energy cost of the microgrid both in short-term and in long-term. Moreover, with the help of this model, the microgrid can be employed as a flexible resource and reduce the operation cost of the main distribution grid

Development of optimal energy management and sizing strategies for large-scale electrical storage systems supporting renewable energy sources.

284 p.El desarrollo e integración de las fuentes de energía renovable (RES) conducirá a un futuro energético más sostenible. Las plantas renovables deberán mejorar su participación y operación a través de los mercados de electricidad de una manera más controlada y segura. Además, el diseño actual del mercado está cambiando para permitir una participación inclusiva en mercados de flexibilidad. En este contexto, los sistemas de almacenamiento de energía (ESS) se consideran una de las tecnologías flexibles clave que pueden apoyar la operación de las energías renovables, mediante servicios como: 1) control de la potencia generada, 2) mejora de los errores de predicción, y 3) provisión de servicios auxiliares de regulación de frecuencia. Sin embargo, el desarrollo del almacenamiento ha sido frenado también por sus altos costos. Por lo tanto, esta tesis doctoral aborda el tema del ¿Desarrollo de estrategias óptimas de gestión y dimensionamiento de los sistemas de almacenamiento eléctrico a gran escala como apoyo a fuentes de energía renovable¿, con el objetivo de desarrollar una metodología con una perspectiva global, mediante una estrategia de gestión de energía avanzada (EMS) que aborda la gestión de activos (RES + ESS) a largo plazo y por otro lado, el cálculo del dimensionamiento y operación del almacenamiento a corto plazo (en la operación en tiempo real), para asegurar un marco adecuado que permita evaluar la rentabilidad de la integración del almacenamiento en aplicaciones conectadas a la red. La estrategia de gestión de energía propuesta es validada a través de dos casos de estudio: una planta renovable individual (eólica o solar) con almacenamiento, y un porfolio de renovables y almacenamiento

Electric Vehicles Charging Technology Review and Optimal Size Estimation

AbstractMany different types of electric vehicle (EV) charging technologies are described in literature and implemented in practical applications. This paper presents an overview of the existing and proposed EV charging technologies in terms of converter topologies, power levels, power flow directions and charging control strategies. An overview of the main charging methods is presented as well, particularly the goal is to highlight an effective and fast charging technique for lithium ions batteries concerning prolonging cell cycle life and retaining high charging efficiency. Once presented the main important aspects of charging technologies and strategies, in the last part of this paper, through the use of genetic algorithm, the optimal size of the charging systems is estimated and, on the base of a sensitive analysis, the possible future trends in this field are finally valued

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Battery Second Use: A Framework for Evaluating the Combination of Two Value Chains

A Battery Second Use (B2U) strategy is the design and development of a battery system with the intention of having it serve two purposes: (1) the initial use in the vehicle and (2) another mobile or stationary application. An optimal battery second use strategy requires the design and use of the battery to maximize the value of the system over its entire extended life cycle. Within this thesis a framework is developed which allows the evaluation of tradeoffs along the operational second use value chain