Greenhouse Gas Emissions From Lithium-Ion Batteries Operating In California\u27s Electrical Grid In 2019

This work examines the impact on greenhouse gas emissions of energy storage devices operating on the California electrical grid during the year 2019. As solar power gains a greater share in California’s energy production, tools for storing the intermittent energy produced from solar and other variable generation sources become important in continuing their growth. In this study, the impact of the deployment of energy storage capacity in California was determined using three charging and discharging strategies. The first, meeting peak net-demand with solar, looked at battery charging when solar production was highest and discharging when net-demand is highest. The second used an energy arbitrage strategy that responds to the average price of energy, maximizing profitability. The third strategy examined maximizing emissions reductions using Watttime’s marginal emissions factors (MEF). Each of these operation patterns use MEFs to determine their impact on greenhouse gas emissions and use average pricing data for all locational nodes in California to determine the profitability of operating with a 1MWh change in energy storage capacity. The results show that the deployment of Lithium-Ion batteries can result in a reduction in carbon emissions at a low cost, highlighting the importance of curtailment alleviation to beneficial energy storage device operation

Design Methodology for the Electrification of Urban Bus Lines with Battery Electric Buses

Electrically powered buses reduce CO$_{2}$ and noise emissions in urban areas and thus promote the trend towards more livable cities. Upon this reason, more and more cities are introducing their first electrified lines as pilot projects. However, no standardized technology has yet emerged, which is why statements on interactions between vehicle, operation and infrastructure in public transport are proving to be difficult to make. In order to be able to make statistically significant statements in this respect, a simulation model was developed that depicts the three subsystems vehicle, operation and infrastructure. On the basis of measurement data from the PRIMOVE research project in Mannheim, in which an urban bus line is operated with two electrically powered buses, the simulation model was validated and a data basis was laid for further investigations. As a result, simulation studies with more than 700 simulated operating days could be carried out, the results of which represent the input for the following statistical analysis. Based on this analysis, the interactions described above will be demonstrated in the design of the main technical parameters, the battery lifespan and the energy demand of electrical bus lines. Through the findings of these simulations, an optimized version of the already electrified bus line in Mannheim will then be presented. Finally, a novel design methodology for electrification based on a multi-objective optimization is introduced. All parameters of the system are variable in order to apply the presented methodology to other projects and thus underline the general validity of the work

Driving behavior-guided battery health monitoring for electric vehicles using machine learning

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms

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)

Holistic Management of Energy Storage System for Electric Vehicles

While electric vehicles (EVs) have recently gained popularity owing to their economic and environmental benefits, they have not yet dominated conventional combustion-engine vehicles in the market. This is due mainly to their short driving range, high cost and/or quick battery performance degradation. One way to mitigate these shortcomings is to optimize the driving range and the degradation rate with a more efficient battery management system (BMS). This dissertation explores how a more efficient BMS can extend EVs' driving range during their warranty periods. Without changing the battery capacity/size, the driving range and the degradation rate can be optimized by adaptively regulating main operational conditions: battery ambient temperature (T), the amount of transferred battery energy, discharge/charge current (I), and the range of operating voltage (min/max V). To this end, we build a real-time adaptive BMS from a cyber-physical system (CPS) perspective. This adaptive BMS calculates target operation conditions (T, I, min/max V) based on: (a) a battery performance model that captures the effects of operational conditions on the degradation rate and the driving range; (b) a real-time battery power predictor; and (c) a temperature and discharge/charge current scheduler to determine target battery operation conditions that guarantee the warranty period and maximize the driving range. Physical components of the CPS actuate battery control knobs to achieve the target operational conditions scheduled by the batteries cyber components of CPS. There are two subcomponents for each condition (T, I): (d) a battery thermal management system and (e) a battery discharge/charge current management system that consists of algorithms and hardware platforms for each sub-system. This dissertation demonstrates that a more efficient real-time BMS can provide EVs with necessary energy for the specified period of time while slowing down performance degradation. Our proposed BMS adjusts temperature and discharge/charge current in real time, considering battery power requirements and behavior patterns, so as to maximize the battery performance for all battery types and drivers. It offers valuable insight into both current and future energy storage systems, providing more adaptability and practicality for various mobile applications such as unmanned aerial vehicles (UAV) and cellular phones with new types of energy storages.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143920/1/kimsun_1.pd

A Review of Lithium-Ion Battery Models in Techno-economic Analyses of Power Systems

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored

Strategies to limit degradation and maximize Li-ion battery service lifetime - critical review and guidance for stakeholders

The relationship between battery operation and their degradation and service life is complex and not well synthesized or communicated. There is a resulting lack of awareness about best practices that influence service life and degradation. Battery degradation causes premature replacement or product retirement, resulting in environmental burdens from producing and processing new battery materials, as well as early end-of-life burdens. It also imposes a significant cost on the consumer, as batteries can contribute to over 25% of the product cost for consumer electronics, over 35% for electric vehicles, and over 50% for power tools. We review and present mechanisms, methods, and guidelines focused on preserving battery health and limiting degradation. The review includes academic literature as well as reports and information published by industry. The goal is to provide practical guidance, metrics, and methods to improve environmental performance of battery systems used in electronics (i.e., cellphones and laptops), vehicles, and cordless power tools to ultimately better inform users as well as battery designers, suppliers, vehicle and device manufacturers, and material recovery and recycling organizations.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/154859/1/Woody_Maxwell_Thesis.pd