Battery Systems and Energy Storage beyond 2020

Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions

Design and Operation of Stationary Distributed Battery Micro-storage Systems

Due to some technical and environmental constraints, expanding the current electric power generation and transmission system is being challenged by even increasing the deployment of distributed renewable generation and storage systems. Energy storage can be used to store energy from utility during low-demand (off-peak) hours and deliver this energy back to the utility during high-demand (on-peak) hours. Furthermore, energy storage can be used with renewable sources to overcome some of their limitations such as their strong dependence on the weather conditions, which cannot be perfectly predicted, and their unmatched or out-of-synchronization generation peaks with the demand peaks. Generally, energy storage enhances the performance of distributed renewable sources and increases the efficiency of the entire power system. Moreover, energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. This research proposes an energy management system (EMS) to manage the operation of distributed grid-tied battery micro-storage systems for stationary applications when operated with and without renewable sources. The term micro refers to the capacity of the energy storage compared to the grid capacity. The proposed management system employs four dynamic models; economic model, battery model, and load and weather forecasting models. These models, which are the main contribution of this research, are used in order to optimally control the operation of the micro-storage system (MSS) to maximize the economic return for the end-user when operated in an electricity spot market system. Chapter 1 presents an introduction to the drawbacks of the current power system, the role of energy storage in deregulated electricity markets, limitations of renewable sources, ways for participating in spot electricity markets, and an outline of the main contributions in this dissertation. In Chapter 2, some hardware design considerations for distributed micro-storage systems as well as some economic analyses are presented. Chapters 3 and 4 propose a battery management system (BMS) that handles three main functions: battery charging, state-of-charge (SOC) estimation and state-of-health (SOH) estimation. Chapter 5 proposes load and weather forecasting models using artificial neural networks (ANNs) to develop an energy management strategy to control the operation of the MSS in a spot market system when incorporated with other renewable energy sources. Finally, conclusions and future work are presented in Chapter 6

Designing a control system Based on SOC Estimation of BMS for PV-Solar System

One of the major challenges for battery energy stowage system is to design a supervisory controller which can yield high energy concentration, reduced self-discharge rate and prolong the battery lifetime. A regulatory PV-Battery Management System (BMS) based State of Charge (SOC) estimation is presented in this paper that optimally addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and utilizes the Maximum Power Point Tracking (MPPT) scheme of the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. A case study (SOC estimation) is demonstrated as well to depict the efficiency (Error 0.082%) of the proposed model using real time data. The numerical simulation structured through real-time information concedes that the projected control mechanism is robust and accomplishes several objectives of integrated PV-BMS for instance avoiding overcharging and deep discharging manner under different solar radiations

Contributions on DC microgrid supervision and control strategies for efficiency optimization through battery modeling, management, and balancing techniques

Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.This dissertation presents a set of equipment, models and control strategies, that have been developed with the final goal of improving the operation of a DC microgrid. Two control strategies are proposed to improve the efficiency of the DC-DC converters that interface the microgrid’s power units with the DC bus. The first strategy is centralized Bus Voltage Optimization Control, which manages the power of the microgrid’s Battery Energy Storage System to make the bus voltage follow the optimum voltage dynamic reference that minimizes the converters’ losses. The second control strategy is Online Optimization of Switching Frequency, which consists in locally operating each converter at its optimum switching frequency, again minimizing power losses. The two proposed optimization strategies have been validated in simulations. Moreover, a new converter-based active balancing topology has been proposed and its control strategy has been designed. This equalizer topology consists of a single DC-DC converter that performs cell-to-cell charge transfer employing power routing via controlled switches. The control strategy of the equalizer has been designed to achieve rapid SOC balancing while avoiding imbalance overcompensation. Its performance has been validated in simulation. Finally, a simple degradation model of an NMC battery cell with graphite negative electrode is proposed. The model combines the simplicity of an equivalent circuit model, which explains the fast dynamics of the cell, with a physical model of the Solid-Electrolyte Interphase (SEI) layer growth process, which predicts the capacity loss and the internal resistance rise in the long term. The proposed model fine-tunes the capacity loss prediction by accounting for the incorporation of unused lithium reserves of both electrodes into the cyclable lithium range to reach the OCV limits after the side reaction has consumed cyclable lithium. The SEI degradation model can be used to perform predictive control of batteries oriented toward extending their lifetime

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)

LITHIUM-ION BATTERY DEGRADATION EVALUATION THROUGH BAYESIAN NETWORK METHOD FOR RESIDENTIAL ENERGY STORAGE SYSTEMS

Batteries continue to infiltrate in innovative applications with the technological advancements led by Li-ion chemistry in the past decade. Residential energy storage is one such example, made possible by increasing efficiency and decreasing the cost of solar PV. Residential energy storage, charged by rooftop solar PV is tied to the grid, provides household loads. This multi-operation role has a significant effect on battery degradation. These contributing factors especially solar irradiation and weather conditions are highly variable and can only be explained with probabilistic analysis. However, the effect of such external factors on battery degradation is approached in recent literature with mostly deterministic and some limited stochastic processes. Thus, a probabilistic degradation analysis of Li-ion batteries in residential energy storage is required to evaluate aging and relate to the external causal factors. The literature review revealed modified Arrhenius degradation model for Li-ion battery cells. Though originating from an empirical deterministic method, the modified Arrhenius equation relates battery degradation with all the major properties, i.e. state of charge, C-rate, temperature, and total amp-hour throughput. These battery properties are correlated with external factors while evaluation of capacity fade of residential Li-ion battery using a proposed detailed hierarchical Bayesian Network (BN), a hierarchical probabilistic framework suitable to analyze battery degradation stochastically. The BN is developed considering all the uncertainties of the process including, solar irradiance, grid services, weather conditions, and EV schedule. It also includes hidden intermediate variables such as battery power and power generated by solar PV. Markov Chain Monte-Carlo analysis with Metropolis-Hastings algorithm is used to estimate capacity fade along with several other interesting posterior probability distributions from the BN. Various informative and promising results were obtained from multiple case scenarios that were developed to explore the effect of the aforementioned external factors on the battery. Furthermore, the methodologies involved to perform several characterizations and aging test that is essential to evaluate the estimation proposed by the hierarchical BN is explored. These experiments were conducted with conventional and low-cost hardware-in-the-loop systems that were developed and utilized to quantify the quality of estimation of degradation