Optimal Battery Research Articles

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Evaluating the Battery Management System's Performance Under Levels of State of Health (SOH) Parameters

Batteries in electric vehicles are the primary focus battery health care. The Battery Management System (BMS) maintains optimal battery conditions by evaluating the system's Htate of health (SOH). SOH identification can recommend the right time to replace the battery to keep the electric vehicle system working optimally. With suitable title and accuracy, the battery will avoid failure and have a long service life. This research aims to produce estimates and identify SOH parameters so that the performance of the battery management system increases. The central parameter values obtained are R0, Rp, and Cp based on Thevenin battery modeling. Then, to get good initialization and accurate results, the parameter identification is completed using an adaptive algorithm, namely Coulomb Counting and Open Circuit Voltage (OCV). The two algorithms compare the identification results of error, MAE, RSME, and final SOH. The focus of this research is to obtain data on estimation error values along with information regarding reliable BMS performance. The performance of the current estimation algorithm is known by calculating the error, which is presented in the form of root mean square error (RMSE) and mean absolute error (MAE). The SOH estimation results using Coulomb Counting have a better error than OCV, namely 1.770%, with a final SOH value of 17.33%. The Thevenin battery model can model the battery accurately with an error of 0.0451%.

Double-battery configuration method for electric bus operation in cold regions

Managing the energy supply of electric buses (EBs) is critical to balance operational reliability and cost reduction. However, ambient temperature significantly affects energy consumption, posing challenges in providing adequate energy supply, especially in cold regions. In this study, we propose a double-battery configuration approach for EBs operating in cold regions with substantial temperature variations between seasons. The method involves utilizing a higher-capacity battery during winter months and a lower-capacity battery for summer operations. To determine the optimal fleet size, battery capacities, and EB scheduling plans in both winter and summer seasons, we formulate an integer programming model, focusing on minimizing the fleet's average annual operating costs (OC) and carbon emissions (CE). The model is solved using the branch and bound method. A case study is conducted, examining EB routes in 11 cold cities. Results reveal that the double-battery configuration method effectively reduces the fleet's average annual OC and CE. Specifically, the reduction ratio range for OC is from 3.34% to 5.18%, while the reduction ratio for CE ranges from 4.06% to 5.16%.

Guest molecule-directed conversion of covalent organic framework into carbon with synergistic high content N dopants and defects for efficient oxygen reduction

Creating intrinsic defects and N dopants in metal-free carbon matrix has proven to be an effective approach for enhancing their electroactivity toward oxygen reduction (ORR), though it is still a big challenge. Herein, metal-free carbon materials with tunable contents of defects and N dopants were prepared through the guest molecule-directed conversion of covalent organic framework (COFs). Due to the corrosion of guest molecules and subsequent substitution by ammonia (NH3) during the pyrolysis, the optimal carbon catalyst is provided with high content of N dopants (8.22 at%) and carbon defects (ID/IG, 1.41). It delivers remarkable ORR activity, with a half-wave potential (E1/2) of 0.863 V versus reversible hydrogen electrode (vs. RHE) and impressive durability, with a 10.9 % current loss over 40 h. Moreover, the optimal carbon catalyst-assembled Zn-air battery achieves a maximum power density of 107.1 mW cm−2, demonstrating its potential in practical devices. Theoretical calculations reveal that the synergistic effect between carbon defects and heteroatom N dopants is responsible for the enhancement of ORR activity. This work might inspire the development of highly efficient metal-free carbon catalysts for various energy conversion applications.

Optimizing Electric Vehicle Battery Performance: Comprehensive Study of Battery Heating Challenges and Sustainable Battery Swapping System

This paper conducts a comprehensive study on battery heating challenges in electric vehicles (EVs) and proposes a sustainable battery swapping system to optimize battery performance. With the increasing demand for EVs and the necessity for extended driving ranges, notable progress has been achieved in battery technologies. However, battery heating remains a crucial concern, particularly in cold weather conditions. This research thoroughly examines various factors contributing to battery heating, including battery size, chemistry, design, and ambient conditions. Diverse battery heating techniques, such as insulation and thermal management systems, are extensively analyzed. Underlining the significance of effective battery heating solutions, this study emphasizes the importance of maintaining an optimal battery temperature to enhance performance and prolong battery life. Additionally, the paper introduces a sustainable battery swapping system controlled by a Blynk app and Node MCU controller, facilitating seamless battery replacement and promoting the integration of renewable energy sources. This system ensures uninterrupted EV operation while providing users complete control and real-time monitoring capabilities via mobile devices, revolutionizing the EV charging paradigm.

Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties

Grid scale battery integration plays an important role in renewable energy integration and the formation of smart grid. To mitigate the problems of insufficient frequency response and peak regulation capacities faced by modern power grids with high wind energy uptake, a day-ahead optimization dispatch strategy considering operational risks is proposed in this study. In the day-ahead dispatch model, generation units and a large-scale battery energy storage station (LS-BESS) are coordinated to participate in multi-type frequency control ancillary services (FCASs). For optimal performance, scheduling in different timescales and the complementarity between power and energy types of requirements are coordinated, with various service uncertainties considered. Then the conditional value-at-risk theory is utilized to achieve the optimal mix of multiple resources for day-ahead regulation reserves, to realize the minimum operation cost. To tackle the uncertainty of wind power over a large timescale, a robust optimization (RO) approach based on the budget uncertainty set is employed, which considers robustness and economy. This can help avoid over-conservation of the standard RO and enhance the applicability of the decisions made. Simulation studies and comparison analysis of multiple schemes verify the effectiveness of the proposed optimal day-ahead dispatch strategy, which also demonstrate that a LS-BESS participating in multiple FCASs for day-ahead dispatch can help realize secure, reliable, and economic power grid.

A Security-constrained Unit Commitment-based Optimal Battery Usage Pattern Model

This paper discusses multiple strategies of security-constrained unit commitment (SCUC)-based optimal battery usage pattern models to drive the charging and discharging of different batteries and help the operation cycles satisfy their preferences according to battery characteristics. Previous studies mainly focus on either battery storage integration as a controllable resource into the grid with high penetration of renewable energy, or battery degradation costs as additional terms in the objective function of SCUC. In comparison, this paper first considers whether battery operation cycles satisfy their preferences according to specific battery characteristics. It discusses a full charging/discharging strategy for Ni-Cd batteries, which have a memory effect and prefer deep cycles. Then, the paper explores a shallow cycle strategy for Lithium-ion, lead acid, and Ni-H batteries, which have no memory effect and prefer shallow cycles for long service life. Subsequently, it is extended to a hybrid strategy when batteries with/without memory effects are at the same node. The proposed model minimizes the total operation cost of SCUC while including the penalties for battery usage strategies. The original problem is a mixed integer non-linear programming (MINLP) one, and then it can be mathematically converted into a mixed integer linear programming (MILP) one. Consequently, the models with multiple strategies are tested through IEEE 6-node power system. Computational results show that, compared to the base case without any strategy, the proposed optimal battery usage pattern approaches have an advantage in driving Ni-Cd batteries toward deep cycles, guiding Lithium-ion batteries towards shallow cycles, and improving the flexibility of performance with hybrid applications.

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets.This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. The study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

Improving the Efficiency of Modern Warehouses Using Smart Battery Placement

In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.

Optimal battery thermal management for electric vehicles with battery degradation minimization

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical–thermal-aging model of the LiFePO4 battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules ”fast cooling, slow cooling, and temperature-maintaining” from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 2.18% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided.

The effect of battery charging method on reducing battery capacity in off-grid solar home systems

Due to the widespread use of lithium batteries in the off-grid solar home systems (SHS), to reduce system costs, it is necessary to accurately determine the required battery capacity instead of choosing a larger battery capacity, as well as ways to increase battery life. For the first time in this paper, instead of a fixed depth of discharge battery, the effect of different depths of discharges battery, that occurs during the day and night, as well as the possible and optimal battery charging method to reduce the required battery capacity in a real off-grid SHS have been investigated. It has been shown that with this method, it is possible to use a battery with a lower capacity than the usual selection method. Moreover, the comparison of both battery charging methods shows that the pulse charging method has a significant performance in increasing the battery life and thus reducing the required battery capacity.

Fiber-Bragg-Grating-Based Sensor System to Measure Battery State of Charge Based on a Machine Learning Model

Real-time monitoring of the state of charge (SOC) of the batteries used in a wide variety of applications is becoming increasingly important, especially given the impetus by the current targets towards “net-zero”. In this research, an advanced approach was used involving fiber Bragg grating (FBG)-based sensors that were developed and implemented for the measurement of the key parameters required to ensure optimum battery performance. In this work, one of the biggest challenges to assess (and then map) the data from the sensor system developed is tackled in order to better understand the key parameters of the battery in an efficient and improved way. It is well known that the relationship between the changes in the resonance wavelength of the FBGs used in the sensor system, arising due to change in the electrical parameters of the battery, is complex and dependent on several different factors. In this work, this effect was evaluated by coupling the sensor data to a data-driven regression model approach that was developed for the measurement of the SOC of the batteries used, and this was obtained directly and conveniently from the FBG data. In this comprehensive study, FBG-based sensors were fabricated and then installed onto the battery, which then was subjected to a range of charging–discharging cycles, following which the electrical parameters of the battery were estimated from recorded data using a black-box machine learning (ML) model. Data-driven regression algorithms were employed for the training of the black-box model. The efficiency of the estimation of the SOC of the battery from the FBG-based sensor data was found to be high, at 99.62% (R2 values of Estimated SOC and True SOC line), creating a very satisfactory result for this key measurement. Thus, the work shows the robustness of the FBG-based sensor system combined with the neural network algorithm as an effective way to evaluate the electrical parameters of the battery, which is particularly important, as no physical/electrochemical/electrical model of the system is thus required.

A - 23 Neuropsychological Assessments Are Highly Predictive of Β-Amyloid Positive Alzheimer's Disease.

Alzheimer's disease (ad) is the most common cause of dementia, but it is often difficult to accurately diagnose. The current gold standard is β-amyloid positivity on a positron emission tomography (PET) scan. However, these scans are not covered by insurance and are cost prohibitive, making them infeasible for many patients. The aim of this study was to quantitatively assess the accuracy of a neuropsychological battery in distinguishing people with biomarker-confirmed ad versus people who are amyloid-negative. In total, 60 participants completed a full neuropsychological assessment and Florbetapir-PET imaging. We then used linear discriminant analysis to assess the extent to which a neuropsychological battery could accurately distinguish the biomarker-confirmed groups. Sensitivity, specificity, positive and negative likelihood ratio, area under the curve (AUC), and accuracy were computed for each model using the classification results based on neuropsychological assessments versus Florbetapir-PET imaging. Participants between the ages of 55-84years (M = 70.17, SD = 6.65), including 37 with amyloid-positive ad and 23 amyloid-negative participants, completed all aspects of the present study. Overall, the sensitivity of the model was 94.59% (95% CI: 81.81%-99.34%), specificity was 91.30% (95% CI: 71.96%-98.93%), the AUC was 0.93 (95% CI: 0.85-1.00), the positive likelihood ratio was 10.88 (95% CI: 2.89-40.99), and the negative likelihood ratio was 0.06 (95% CI: 0.02-0.23). Assessments from a neuropsychological battery accurately discriminated β-amyloid positive participants with ad from β-amyloid negative participants. These findings represent a potential alternative, cost-effective method for confirming ad status. Further research is warranted to investigate the most optimal neuropsychological battery for ad diagnosis.

Conceptual Design of Solid-State Li-Battery for Urban Air Mobility

The negative impact of internal combustion engines on the environment is a major concern in metropolitan areas due to the continued rapid growth and high overall level in the number of vehicles, population, and traffic congestion. Electric vertical take-off and landing (eVTOL) aircraft promises a new era for urban regional transportation and air mobility to address the challenges mentioned above. Nonetheless, providing electrical energy storage systems, like batteries, is one of the key issues with such aircraft. Here, the non-flammable technology of all-solid-state Li batteries with high theoretical gravimetric energy is an attractive option. Modelling allows for a knowledge-driven assessment of the potential of this technology. We here used a combination of a pseudo-2-dimensional cell model with a microstructure surrogate model approach to acquire a better understanding of the effect of the cathode microstructure on the internal process limitations. This model is incorporated into a global optimisation algorithm to predict optimum battery size with respect to the dynamic load demand of eVTOL. When carbon black and active materials are premixed, the battery performs better than when solid electrolyte and active materials are premixed, particularly for low amounts of carbon black in the cathode combination, i.e., 5%. Further, results indicate that future electrification of transportation powertrains would necessitate optimising the composition and distribution of electrode components to fulfil the high demands for power and energy density. By enhancing transport through the microstructure and improving the material’s intrinsic conductivity, it is possible to significantly increase the effective diffusivity and conductivity of ASSB, and hence the mission range.

Optimal Battery Energy Storage Control for Multi-Service Provision Using a Semidefinite Programming-Based Battery Model

Battery energy storage systems (BESS) capable of simultaneously providing multiple grid services can assist a distribution grid operator in overcoming various challenges due to the high penetration of distributed solar photovoltaics and accelerated electrification. The seamless provision of multiple services can be ensured with BESS control decisions based on an accurate model reflecting the battery characteristics. Thus, this paper presents a novel Li-ion battery model based on linear matrix inequalities along with a semidefinite programming (SDP)-based model to determine the optimal BESS control decisions for the provision of multiple services in distribution systems. Peak shaving, power factor improvement, and electricity cost savings are considered for the services. Moreover, the mathematical analysis reveals that the original nonconvex problem can be equivalently transformed into the SDP model. The effectiveness of using BESS in the distribution system is verified through simulations using real-world data. Ultimately, a comparative analysis with a conventional linear model demonstrates that the proposed battery model reduces the energy losses in the batteries, in the simulation condition, and help maintain the battery states within normal operating limits.

Evaluation of the Effectiveness of Treatments to Remove Per- and Polyfluoroalkyl Substances from Water—Are We Using the Right Approach? Proposal of a Paradigm Shift from “Chemical Only” towards an Integrated Bio-Chemical Assessment

Per- and polyfluoroalkyl substances (PFASs) have been under intense investigation by the scientific community due to their persistence in the environment and potentially hazardous effects on living organisms. In order to tackle the presence of these compounds in water, to date, the research has been strongly focused on the evaluation of the effectiveness of different types of technologies. Considering the extreme complexity of the matter of PFASs and our relatively low knowledge in this topic, the following question arises: is the “chemical only” approach that is followed for evaluating the effectiveness of technologies for PFAS removal from water reliable enough? In this work, some limitations of the present approach are discussed, highlighting the reasons why it cannot be considered a reliable tool to correctly estimate the effectiveness of technology when referring to emerging compounds such as PFASs. Bioassays can play a key role in moving towards an integrated bio-chemical evaluation (chemical analysis and ecotoxicological evaluation), which is strongly encouraged. This represents the only way to completely characterize a water matrix and fully evaluate the impact of technologies when dealing with micropollutants in water, such as PFASs. Future research should focus on defining an optimal battery of bioassays that specifically fit to best represent changes in water quality in terms of short- and long-term impacts on living organisms.

Hierarchical optimal intelligent battery thermal management strategy for an electric vehicle based on ant colony sliding mode control

Excessive temperature of battery pack causes the performance degradation, affecting the normal operation and safety of the electric vehicles (EVs). This paper proposes an ant colony optimization-fuzzy sliding mode control (ACO-FSMC) hierarchical strategy for the battery thermal management system (BTMS) of EVs. Ignoring the dynamic process of the pump and compressor leads to the deviation of the cooling efficiency of EVs. Therefore, a control-oriented BTMS model is established to describe the temperature dynamics of battery pack and coolant, considering driving current effect on the speed of pump and compressor. Then, a hierarchical optimization strategy is designed to realize the control of the battery cooling rate as well as the speed of the pump and compressor. The upper layer of controller use an ant colony optimization (ACO) to solve the reference speed of the pump and compressor while satisfying constraints of physical characteristics of actuators, aiming at reducing battery temperature deviation and saving energy of BTMS. Considering the nonlinearity of lower layer of BTMS caused by the disturbance of time-varying load torque, fuzzy sliding mode control (FSMC) is applied to control the speed of pump and compressor, in which an integral sliding surface is designed to reduce the influence of disturbance. In order to weaken the chattering of sliding mode control and reduce energy consumption, fuzzy controller is used to adjust the reaching law parameters of sliding mode surface. The simulation results show that compared with PID, the maximum temperature deviation of ACO-FSMC is reduced by 43%, and the energy consumption is reduced by 23%.

Enhancement of an Air-Cooled Battery Thermal Management System Using Liquid Cooling with CuO and Al2O3 Nanofluids under Steady-State and Transient Conditions

Lithium-ion batteries are a crucial part of transportation electrification. Various battery thermal management systems (BTMS) are employed in electric vehicles for safe and optimum battery operation. With the advancement in power demand and battery technology, there is an increasing interest in enhancing BTMS’ performance. Liquid cooling is gaining a lot of attention recently due to its higher heat capacity compared to air. In this study, an air-cooled BTMS is replaced by a liquid cooled with nanoparticles, and the impacts of different nanoparticles and flow chrematistics are modeled. Furthermore, a unique approach that involves transient analysis is employed. The effects of nanofluid in enhancing the thermal performance of lithium-ion batteries are assessed for two types of nanoparticles (CuO and Al2O3) at four different volume concentrations (0.5%, 2%, 3%, and 5%) and three fluid velocities (0.05, 0.075, and 0.1 m/s). To simulate fluid flow behavior and analyze the temperature distribution within the battery pack, a conventional k-ε turbulence model is used. The results indicate that the cooling efficiency of the system can be enhanced by introducing a 5% volume concentration of nanofluids at a lower fluid velocity as compared to pure liquid. Al2O3 and CuO reduce the temperature by 7.89% and 4.73% for the 5% volume concentration, respectively. From transient analysis, it is also found that for 600 s of operation at the highest power, the cell temperature is within the safe range for the selected vehicle with nanofluid cooling. The findings from this study are expected to contribute to improving BTMS by quantifying the benefits of using nanofluids for battery cooling under both steady-state and transient conditions.

Energy Management and Economic Considerations of Intermittent Photovoltaic-Driven Electrochemical Ammonia Production.

As the energy sector shifts from fossil fuels to renewable energy, there is a need for long-duration energy storage solutions to handle the intermittency of renewable electricity. Electrofuels, or fuels synthesized from excess electricity, are an emerging medium poised to meet long-duration energy storage requirements. Ammonia as an electrofuel is potentially ideal because ammonia has a relatively low liquefaction pressure, indicating that ammonia can be easily stored and transported. Here, we develop a framework to optimize the electrochemical production of ammonia powered by intermittent photovoltaic power. We also explore various buyback policies to understand the impact that policy has on the cost of intermittent ammonia and optimal sizing ratios. The optimal ratio of the photovoltaic to the electrolyzer is ∼3.7 MWPV/MWELEC for a system that is completely powered by renewable photovoltaic power and operates intermittently. The optimal ratio of the photovoltaic to the electrolyzer is ∼3.3 MWPV/MWELEC for a system that uses photovoltaics in conjunction with grid electricity and operates continuously. For the purchase price at the avoided cost of electricity, the optimal ratio of the solar panel to the electrolyzer increases to ∼4 MWPV/MWELEC for a system that can only sell to the grid and ∼5 MWPV/MWELEC for a system that can buy and sell electricity to the grid at the avoided cost. Optimizing energy management by setting auxiliary battery size limits is essential to reducing ammonia costs, and the optimal battery size decreases as the buyback price of electricity increases. Finally, we find that systems connected to the grid and operating continuously have emissions comparable to the Haber-Bosch process because of the current emissions tied to the United States electricity generation. Thus, unless the grid is completely decarbonized, it is essential to create electrofuels that rely minimally on grid electricity.