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Results for “Battery sizing optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Economic Analysis of On-Route Fast Charging for Battery Electric Buses: Case Study in Utah

Battery electric buses (BEBs) are increasingly being embraced by transit agencies as an energy-efficient and emission-free alternative to bus fleets. However, because of the limitations of battery technology, BEBs suffer from limited driving range, great battery cost, and time-consuming charging processes. On-route fast charging technology is gaining popularity as a remedy, reducing battery cost, extending driving range, and reducing charging time. With on-route fast charging, BEBs are as capable as their diesel counterparts in relation to range and operating time. However, transit agencies may have the following concerns about on-route fast charging: 1) on-route fast charging stations require massive capital costs; 2) on-route fast charging may lead to high electricity power demand charges; and 3) charging during peak hours may increase electricity energy charges. This study conducts a quantitative economic analysis of on-route fast charging for BEBs, thereby providing some guidelines for transit agencies. An integrated optimization model is first proposed to determine battery size, charger type, and recharging schedule for a general BEB route. Based on the model, an economic analysis of on-route fast charging is then performed on 10 real-world bus routes and a simplified general bus route with different parameters. The results demonstrate that given the current prices of on-route fast charging stations and batteries, it is always beneficial to install on-route fast charging stations for BEBs. A sensitivity analysis is also conducted to show the impact of potential price reductions of batteries.

Engineering↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2022

Cylindrical battery design app

Software that delivers optimal design parameters and performance predictions for cylindrical cells, which can range in size from microbatteries to EV batteries, is developed. The software utilizes machine learning and includes a graphical user interface to enable rapid prototyping and to accelerate energy storage research, development, and manufacturing. The Cylindrical battery design V1.0 comprises of three types of cylindrical batteries, Microbattery (Primary), Microbattery (Secondary) and 18650/21700/xxxxx Cylindrical battery. The software was developed in MATLAB. The software has the capability to output the cell design with the capacity ranges from several mAh to several million Ah.

Xiao, Jie↗
DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Machine Learning Software for Cylindrical Battery Design and Performance Prediction

A software that delivers optimal design parameters and performance predictions for cylindrical cells, which can range in size from micro batteries to EV batteries, is developed. The Cylindrical battery design V1.0 is comprised of three types of cylindrical batteries, Micro battery (Primary), Micro battery (Secondary) and 18650/21700/xxxxx cylindrical battery. The software was developed in MATLAB. The software has the capability to output the cell design with the capacity ranges from several mAh to several million Ah. The software utilizes machine learning and includes a graphical user interface to enable rapid prototyping to accelerate energy storage research, development, and manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2019

REopt: Enabling Renewable Energy, Storage, and Combined Heat and Power

Deployment of distributed energy resources including combined heat and power (CHP), photovoltaics (PV), and battery storage is growing as building and campus owners seek to reduce energy costs and increase resilience to grid outages. While deployment of a single technology is widespread, integrated systems of multiple technologies are also becoming more common to provide maximum economic and resilience benefits. To accelerate deployment, decision makers require tools to evaluate the technical and economic feasibility of these integrated systems. This paper presents a framework for optimizing the size and dispatch strategy of CHP with PV and battery storage to minimize life-cycle cost of energy. The modeling methodology is presented, with a particular focus on the linearization of cost and performance metrics required for implementation in a mixed integer linear program. A case study is used to examine the technical and economic viability of integrated CHP, PV, and battery systems, compared to CHP-only or PV+battery storage systems. In the case study we show how the integrated systems can provide increased economic and resilience benefits compared to single technologies on their own. The case study presented shows the combined CHP, PV, and battery system resulted in a 7% lower life cycle cost than CHP only, and a 22% lower life cycle cost than PV+battery storage. The life cycle emissions for CHP, PV, and battery storage resulted in a 24% reduction than CHP only and a 17% increase than PV+battery storage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Optimization of mesoporous carbon structures for lithium–sulfur battery applications

Mesoporous carbon (MC) with tunable pore sizes (22nm, 12nm, 7nm, and 3nm) and pore volumes (from 1.3 to 4.8 cc/g) containing sulfur inside the pores were systematically studied as mesoporous carbon-sulfur (MCS) composite electrodes for Li-S batteries. Investigation on these MCS composites reveals that the pore structure has no influence on the battery performance at full sulfur loading conditions (the pore volume is fully filled by sulfur) but the maximum sulfur loading capability is higher for MC with larger pore volume. MC with large pore volumes, partial sulfur filling (part of the pore volume left unfilled), and surface modification, can have reasonably high sulfur loading, improved electrical and ionic contacts of sulfur with MC and with electrolytes, which subsequently promotes the battery performance. An initial capacity of ~1250 mAh/g (based on sulfur) and 650 mAh/g capacity retention over 100 cycles were obtained with 50 wt% sulfur loading in the MC with 22nm pore size (4.8 cc/g). When the surface of MCS was coated with Clevios P to reduce the dissolve of polysulfide anions in electrolytes, it exhibits a high initial discharge capacity of ~1390 mAh/g and improved cycling stability with capacity retention of ~840 mAh/g over 100 cycles. The reported correlation among the structure, sulfur filling, surface modification and the electrochemical performance of the MCS composite cathodes provides guidance in designing new electrodes for lithium-sulfur batteries

25 ENERGY STORAGE↗
DOE OSTI2025

Data-Driven Energy Resilience Assessment and Enhancement in Urban Communities: A Case Study in Detroit

This paper presents a data-driven framework for assessing and enhancing energy resilience in urban communities. The resilience assessment is based on two datasets: 1) annual aggregated power outage data and 2) 15-minute interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme conditions. To enhance resilience, an optimization framework utilizing mixed integer linear programming is developed to determine the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). This method offers a cost-effective and practical solution for improving energy resilience in vulnerable communities. Furthermore, a case study of the City of Detroit in Michigan demonstrates the effectiveness of the framework through simulation and validation.

Energy resilience assessment↗
DOE OSTI2020

Optimized System for On-Route Fast Charging of Battery-Electric Shuttle Buses

Electrifying cars, buses, and trucks is an attractive means of reducing energy use and emissions because it involves more efficient drivetrain components and minimal restructuring of the transportation network. Transit buses, driving fixed routes, help minimize driver range anxiety by properly sizing energy storage systems. A major challenge to fully electrifying transit buses is providing the amount of energy they consume in a day of driving. To enable a full day of operation without charging, batteries need to be large, which could be expensive and heavy. This work utilizes real-world transit bus data used in a battery-electric drivetrain model to co-optimize charger locations, charger power levels, and vehicle battery sizes.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Optimal Sizing of Resilience Solutions for the U.S. Army Reserve

Power or water outages in buildings threaten the ability of the military to support surrounding communities during natural disasters. Outages can last for days, weeks or months. Typical solutions include very expensive batteries and onsite generation that are sized based on historical power needs. The U.S. Army Reserve (USAR) has teamed up with the Pacific Northwest National Laboratory (PNNL) to develop a simulation framework that optimizes future power needs to reduce the cost of resilience solutions. The approach starts with the building. Power needs during an emergency event are simulated at the building end-use level, then loads are reduced through the selection of life cycle cost-effective building-level technology improvements. These new optimized loads are then fed into a microgrid sizing tool that dynamically constructs many different combinations of solar photovoltaic, battery storage, and generator resources to meet the load for hundreds of statistically generated outage scenarios. Six site assessments completed by USAR and PNNL in 2019 have resulted in a 1-14% reduction in overall investment when optimizing the buildings first before determining the generation requirements. This approach helps the Army secure critical missions and provide a 14 day-minimum supply of necessary energy and water in the most efficient manner.

buidlings, efficiency, resilience, FEDS, MCOR↗
DOE OSTI2022

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

The electric grid is rapidly evolving as small-scale, demand-side resources play increasingly important roles in grid operations and decarbonization. Maximizing the potential of demand-side resources involves incentivizing electricity customers to use those resources in ways that benefit the broader electrical grid. These incentives depend largely on the electricity cost savings that customers can realize from demand-side resource adoption. Determining these potential cost savings is a complex task. Cost savings depend on numerous factors, including the characteristics of different technologies, the algorithms that control these devices, system performance, customer behavior, electricity rate structures, and climatic factors. Another challenge is that estimated cost savings are frequently based on modeled rather than observed system performance, particularly in the academic literature. In this study, we begin to fill the gap in empirical research of demand-side resources using data from a new construction residential community equipped with rooftop solar and storage (S+S) in Arizona. We use these data to analyze the factors that determine customer electricity cost savings and emissions impacts of S+S in the real world. We then compare these data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, we explore ways to improve such models and, conversely, use modeled results to suggest improvements to actual S+S deployment. The results of these analyses can be summarized in four key findings: 1) rate structures play a central role in the grid and customer value of demand-side resources; 2) certain customers can benefit more from demand-side resource adoption than others; 3) modeled battery dispatch and sizing reveals opportunities for additional cost savings; and 4) optimal dispatches can reduce grid emissions while maximizing bill savings.

14 SOLAR ENERGY↗
DOE OSTI2018

REopt Lite Web Tool

NREL developed a free, publicly available web version of the REopt (TM) renewable energy integration and optimization platform called REopt Lite. REopt Lite recommends the optimal size and dispatch strategy for grid-connected photovoltaics (PV) and battery storage at a site. It also allows users to explore how PV and storage can increase a site's resiliency during a grid outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Addressing the Split Incentive Challenge for Enhanced Solar Adoption in Multifamily Rental Properties [Abstract]

The split incentive problem is particularly pronounced in rental markets, where landlords prioritize investments that directly increase property value or rental income. Since energy savings from solar photovoltaic (PV) systems primarily benefit tenants, landlords may perceive little return on investment unless mechanisms exist to recapture some of the financial gains. The primary objective of this project is to develop a publicly available, web-based tool to analyze the U.S. Department of Energy’s ResStock database, which models the U.S. residential building stock. The tool allows users to filter buildings by location, type, HVAC system, square footage, and other characteristics, and outputs typical electric load profiles. By leveraging location-specific electric load data, Fram Energy aims to advance business strategies that address the split incentive barrier and promote the adoption of solar PV installations in rental properties. In addition, a machine learning model will be developed to weigh the marginal contribution of building features across the dataset in predicting electricity demand, supporting guided decision making in forecasting electric load profiles. Lastly, based on each building’s location, load profile, and utility’s electricity rate, an optimized solar photovoltaic array and battery energy storage system will be sized to provide energy arbitrage opportunities.

14 SOLAR ENERGY↗
DOE OSTI2025

Hierarchical Resilience Planning for Networked Microgrids: A Case Study of Puerto Rico

Microgrids can be designed to enhance the energy resilience of communities and critical infrastructures, such as hospitals, data centers, and communication networks, which are vulnerable to frequent weather-related disruption. Coordinating multiple microgrids in a network can leverage the geographical diversity of load and generation resources while enabling resilient and cost-effective planning of the distribution system. Designing a networked microgrid is complex, involving intricate technical assessment, cost-benefit analysis, site-specific requirements, and the evaluation of existing resources. Therefore, this paper proposes a hierarchical resilience planning framework and performs an extensive techno-economic analysis for the design of a networked microgrid. Hierarchical resilience planning involves technology sizing at an individual community level to meet the critical load and satisfy resilience criteria, and resource optimization at networked microgrid level to provide a higher level of resilience and energy adequacy. A real-world case of Puerto Rico's cooperative microgrid “Microrred de la Montaña” is investigated considering localized electricity tariffs, site-specific demand profiles, solar generation, and existing hydro resources. Multiple optimization scenarios are developed based on the resiliency requirement to estimate the capacity of solar photovoltaic and battery energy storage (BES) to be installed at each substation. The results provide the optimal sizing for individual community and networked microgrid to withstand 1day and 3-day outages along with the criteria for critical load.

13 - HYDRO ENERGY↗
DOE OSTI2010

Electricity storage for grid-connected household dwellings with PV panels

Classically electricity storage for PV panels is mostly designed for stand-alone applications. In contrast, we focus in this article on houses connected to the grid with a small-scale storage to store a part of the solar power for postponed consumption within the day or the next days. In this way the house owner becomes less dependent on the grid and does only pay for the net shortage of his energy production. Local storage solutions pave the way for many new applications like omitting over-voltage of the line and bridging periods of power-line black-out. Since 2009 using self-consumption of PV energy is publicly encouraged in Germany, which can be realised by electric storage. This paper develops methods to determine the optimal storage size for grid-connected dwellings with PV panels. From measurements in houses we were able to establish calculation rules for sizing the storage. Two situations for electricity storage are covered: - the storage system is an optimum to cover most of the electricity needs; - it is an optimum for covering the peak power need of a dwelling. After these calculation rules a second step is needed to determine the size of the real battery. The article treats the aspects that should be taken into consideration before buying a specific battery like lead-acid and lithium-ion batteries. (author)

14 SOLAR ENERGY↗
DOE OSTI2018

Economic Sizing of Batteries for the Smart Home: Preprint

This paper presents a methodology to economically size a home battery based on parametric analysis using home energy management system (HEMS) software to optimally dispatch the battery along with controllable loads under several use cases. We account for connected equipment, controls, renewable resources, and other factors such as building characteristics and utility tariffs. The paper defines an analytical pathway for such a sizing tool, develops initial sizing guidance, and clarifies technical and market opportunities for home batteries in the context of existing and emerging equipment and control technologies. A parametric analysis that included 132 scenarios has been performed based on different combinations of pertinent parameters. Results indicate that four variables dominate the decision-making process: application scenarios (new construction or retrofit), utility tariffs, existence of HEMS, and the anticipated payback time. Life-cycle cost analysis indicated that in the absence of utility incentives, batteries plus HEMS have a payback time of longer than 10 years for new construction under a time-of-use rate structure and feed-in tariff; larger batteries have a longer payback time but may provide more benefits to utilities on reducing power backfeed under certain circumstances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗