Optimal Battery Research Articles

Design and Optimization of a Liquid Cooling Thermal Management System with Flow Distributors and Spiral Channel Cooling Plates for Lithium-Ion Batteries

In this study, a three-dimensional transient simulation model of a liquid cooling thermal management system with flow distributors and spiral channel cooling plates for pouch lithium-ion batteries has been developed. The cooling plates play the role of uniforming temperature distribution and reducing the maximum temperature within each battery, while the flow distributors have the function of reducing the temperature difference between batteries in the battery module. The accuracy of the thermophysical properties and heat generation rate of the battery was verified experimentally. The optimal structure and cooling strategy of the system was determined by single factor analysis as well as orthogonal test and matrix analysis methods. The optimal solution resulted in a maximum battery module temperature of 34.65 °C, a maximum temperature difference of 3.95 °C, and a channel pressure drop of 8.82 Pa. Using the world-harmonized light-duty vehicles test cycle (WLTC) conditions for a battery pack in an electric car, the performance of the optimal battery thermal management system (BTMS) design was tested, and the results indicate that the maximum temperature can be controlled below 25.51 °C and the maximum temperature difference below 0.21 °C, which well meet the requirements of BTMS designs.

Local Interpretable Explanations of Energy System Designs

Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers.

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

The parallel hybrid electric vehicles (PHEV’s) superior performance relies on the associated energy management strategy. This work intends to improve the performance of real-time implementable equivalent consumption minimization strategy (ECMS) so that, optimal battery and fuel energy utilization can be guaranteed in the PHEV. In the ECMS algorithm, the incorporation of intelligent technique is the proposed approach for a novel robust equivalence factor correction concerning the battery state of charge (SOC) for various uncertain driving conditions. With this motive, the significance and necessity of dynamic adjustment in equivalence factor value are first elaborated to achieve the global optimal fuel economy. The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been compared in the adaptive ECMS. Their performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The obtained result infers the fuzzy-PI-based AECMS provided the least terminal SOC variance of 3.25 whereas 30.03 for the conventional fixed PI. The proposed ANFIS-ECMS delivers terminal SOC of 29.41%, 28.22% and 28.37% for the three driving cycles which is having variance of 0.42. Even in the real-time validation of the ANFIS-AECMS, achieves a terminal SOC of 27.53% and a fuel economy of 33.37 km/l for the self-developed real-world driving cycle. The entire result of this work proves 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.

Global Product Design Platforming: A Comparison of Two Equilibrium Solution Methods

AbstractGlobal product platforms can reduce production costs through economies of scale and learning but may decrease revenues by restricting the ability to customize for each market. We model the global platforming problem as a Nash equilibrium among oligopolistic competing firms, each maximizing its profit across markets with respect to its pricing, design, and platforming decisions. We develop and compare two methods to identify Nash equilibria: (1) a sequential iterative optimization (SIO) algorithm, in which each firm solves a mixed-integer nonlinear programming problem globally, with firms iterating until convergence; and (2) a mathematical program with equilibrium constraints (MPEC) that solves the Karush Kuhn Tucker conditions for all firms simultaneously. The algorithms’ performance and results are compared in a case study of plug-in hybrid electric vehicles where firms choose optimal battery capacity and whether to platform or differentiate battery capacity across the US and Chinese markets. We examine a variety of scenarios for (1) learning rate and (2) consumer willingness to pay (WTP) for range in each market. For the case of two firms, both approaches find the Nash equilibrium in all scenarios. On average, the SIO approach solves 200 times faster than the MPEC approach, and the MPEC approach is more sensitive to the starting point. Results show that the optimum for each firm is to platform when learning rates are high or the difference between consumer willingness to pay for range in each market is relatively small. Otherwise, the PHEVs are differentiated with low-range for China and high-range for the US.

Treatment Success After Urethroplasty: The Ongoing Quest for a Pragmatic and Universal Definition

There is no consensus on the ideal definition of success after urethroplasty, which makes research, quality control, and comparisons challenging. Ongoing research endeavors are focused on achieving consensus regarding the optimal battery of outcomes for evaluation of patients after urethroplasty.

Collective self-consumption of solar photovoltaic and batteries for a micro-grid energy system

The paper carried out a techno-economic analysis in order to obtain the optimum battery storage capacity in conjunction with a photovoltaic array that can match the desired household load at highest possible self-consumption. In addition, it determined the economic feasibility of a proposed micro-system. Annual energy consumption, irradiance, and ambient temperature were measured at 1-min resolution for the year 2021. Simulations of a stationary economic model are performed for the years 2021 through 2030.The results showed that the photovoltaic array at 2.7 kWp capacity can generate an annual energy of approx. 4295.4 kWh and the optimal battery capacity which can satisfy 91.1% of self-consumption and energy cost of $0.256/kWh is 14.4 kWh. Furthermore, two third-order polynomial relationships between self-consumption and net present cost with energy cost were solved.

Solar Self-Sufficient Households as a Driving Factor for Sustainability Transformation

We present a model to estimate the technical requirements, including the photovoltaic area and battery capacity, along with the costs, for a four-person household to be 100% electrically self-sufficient in Germany. We model the hourly electricity consumption of private households with quasi-Fourier series and an autoregressive statistical model based on data from Berlin in 2010. Combining the consumption model and remote-sensed hourly solar irradiance data from the ERA5 data set, we find the optimal photovoltaic area and battery capacity that would have been necessary to be self-sufficient in electricity from July 2002 to June 2022. We show that it is possible to build a self-sufficient household with today’s storage technology for private households and estimate the costs expected to do so.

A consolidated decision-making framework for nano-additives selection in battery thermal management applications

The selection of appropriate nano-additives for optimum battery thermal management system (BTMS) performance is an exigent task for decision-makers and is a multi-attribute decision-making (MADM) problem. This work presents a framework that utilizes the application of various MADM schemes. These techniques are integrated with a subjective weighting criteria approach to prioritize suitable nano-additives as promoters towards heat transfer enhancement to mitigate the thermal issues in energy storage devices. The COPRAS, PROMETHEE-II, and VIKOR methods are used to present ranking schemes for alternatives based on the criteria weights assigned by the AHP method. The results show that Graphene and MWCNT nano-additives are the best-suited alternatives. The sensitivity tests are carried out to describe the effect of parametric variations in the most critical risk preferences over the rankings of the alternatives. In addition, the problem of rank reversal associated with these methods is also addressed. Therefore, the evaluation criteria and methodology in this work can provide a theoretical reference for nano-additive selection. An attempt is made to discuss the technical index of efficiency assisting decision-makers to make reliable and robust decisions by employing the CCR model.

Stochastic multi-objective optimal sizing of battery energy storage system for a residential home

The deployment of renewable local generation sources at home can help reduce emission contributions within residential homes. Furthermore, the adoption of the residential PV system is increasing as the economics of installation continues to decrease. An optimally sized battery energy storage system can help maximise the benefits of the power generated from the PV systems while being economical. In this paper, a stochastic optimisation problem is formulated to determine the optimal size of the energy storage system and investigate the benefits of uncertainty consideration in the formulation of the sizing optimisation problem. A multi-objective problem is formulated consisting of two objectives: minimise the cost of purchasing the battery energy storage system, and minimise the amount of energy imported from the grid within the period. The stochastic problem is formulated as a two-stage scenario stochastic problem. The optimal sizing and operation problem was formulated as a mixed-integer linear programming problem and solved using the CPLEX solver. This work also utilises a Monte Carlo approach to deal with the uncertainty in load forecasting. Simulation results show that the proposed approach can estimate an optimal battery energy storage system at the current cost of BESS and clearly indicate the benefit of a stochastic approach.

Battery optimization by machine learning algorithms: Research gap via bibliometric analysis

Technological developments enable low-carbon transitions to be accelerated by conceptualization systems and innovations for research and development to generate clean energy. Batteries are becoming one of the essential parts of the science of electrical power sources. Lithium-ion batteries are part of the change and development factors in technologies that significantly impact the portable devices sector and the development of electric vehicles. Designing the material structure and composition of battery manufacturing with the help of engineering system design will form a much more optimal battery. Machine learning algorithms can easily optimize the battery’s composition through battery experiment test data history to produce a more optimal battery configuration. This study is prepared to identify research gaps in topics related to machine learning for battery optimization. Related studies about machine learning for battery optimization are identified using bibliometric analysis and systematic literature review of the study search index through database Scopus-indexed publications. The results from this paper reveal energy management systems and strategies, hybrid vehicles, other optimization algorithms, battery electrodes, and the safety of batteries as the particular research gap according to machine learning for battery optimization. This paper expects research on battery optimization using machine learning methods will continue to be developed to maximize the potential of machine learning algorithms in helping the research process.

Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro

Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro

Prescriptive analytics for optimal multi-use battery energy storage systems operation: State-of-the-art and research directions

This paper presents the state-of-the-art and latest advances in implementing multi-use practices on BESS applications to the power system grid. Representative papers on modeling and optimization methods were selected, most of them working with realistic use cases, but none reporting on real-world implementations. Some major findings from reviewing key representative papers are that current optimization methods were able to handle uncertainty related to prices and other parameters either in the look-ahead planning stage or real-time control for obtaining an optimal dispatch schedule, thus addressing previously pointed-out gaps. Further, a recommendation for future work is to implement current multi-use methods, found to be mature enough, to real-world BESS use cases for validation and gaining experimental experience. Furthermore, the multi-use performance can further be improved by joining the strong contributions from each paper's method. Current methods have shown great potential for bringing simultaneous benefits to the grid, as well as being economical for the owner. A continued exploration and inclusion of new future BESS applications in prescriptive analytics for BESS multi-use suggest robustness for future market changes. However, further assessments are needed to evaluate whether the technology is economical and green for each particular use case and also for a potential systematic upscaling of the technology as a means to facilitate the green shift.

Optimal Sizing and Siting of PV and Battery Based Space Microgrids Near the Moon’s Shackleton Crater

Space mission cost and feasibility depend mainly on the size and mass of the payload. This paper investigates the optimal photovoltaic (PV) array and battery size and mass for an islanded PV-battery powered space microgrid (MG) at the lunar south pole. The PV arrays are considered to be installed on top of towers to increase solar energy harvesting. Considering the dependency of the generated power from PV arrays on the tower height, different tower heights of 10, 50, and 100 m are investigated. The paper presents the methodology to estimate the available power from the PV system using the information of illumination time-series at the location of potential sites with different tower heights. Besides, considering the power demand of several power-consuming units at different operating states, the power demand profile of the lunar base is generated. The optimal sizing of the PV and battery system for a 1-year horizon, without considering battery degradation, results in a total mass of approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5 \times 10^{5}~\text{kg}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \times 10^{5}~\text{kg}$ </tex-math></inline-formula> with a tower height of 10 m depending on the solar illumination profiles at different sites. For a 5-year optimization horizon of the same sites with 10 m tower height and considering the battery yearly capacity degradation, total system mass ranges approximately from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {2 \times 10^{5}~kg}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.5 \times 10^{5}~\text{kg}$ </tex-math></inline-formula> . Although increasing the tower height may considerably reduce the total size and mass of the battery and PV system, the mass of the PV tower will increase. Thus, a satisfactory trade-off in selecting the site location and tower height is required. In this regard, 15 highly illuminated sites at different locations and with different PV tower heights are assessed in this paper. To improve the reliability and flexibility of the power system, the multi-microgrid (MMG) concept is deployed to distribute the power-consuming units of the base among different MGs having their local energy production and storage systems. Finally, based on the total power demand served at a candidate site and the corresponding total system mass, a criterion, mass-per-unit-load (MPUL), is used to identify the sites that serve the highest power demand with less total system mass.

Real-time digital twin machine learning-based cost minimization model for renewable-based microgrids considering uncertainty

This research study aims to investigate the microgrid operation for distributing energy including of a local user, a wind turbine, 5 photovoltaics (PV), and a battery, which is linked by a transformer to the external network. This paper examines a reinforcement learning (RL) method that uses 2steps-ahead to schedule the batteries, which is essential for achieving the objective of the users. There is an essential architecture to make multi-criteria decisions via an individual user to increase the battery's usage at peak times and increase the wind turbine's usage for local consumption. RL algorithms select the optimum battery planning measures based on forecasts of wind power and photovoltaic availability. Through the suggested learning, the user can better understand the optimum battery planning measures for various time-varying environment factors. By using the proposed architecture, smart users are capable of learning the uncertain environment and selecting optimum energy management measures based on their experiences.

Optimisation of Electric Vehicle Battery Size

Energy storage and battery technologies have taken centre stage in the race to meet the UK government target to ban new petrol and diesel cars by 2030. However, underlying key issues such as resource demand and negative public opinion must be solved before the high uptake of electric vehicles. The research conducted in this paper proposed viable solutions to these challenges through modelling of real driver data utilising an agent based modelling approach. Per month state of charge analysis confirmed that the current charging infrastructure in circulation will not accommodate the miniaturisation of electric vehicle battery size. Thus, an improved alternative charging infrastructure was proposed, which enabled the optimal battery size to be reduced by up to 40%. The users stop times were analysed to assign an optimal battery size based upon monthly driving behaviour concluding daily inner city drivers require a 30kWh battery and daily long distance drivers require a 40kWh battery. When decreasing the battery size by the proposed 40% there is a £2650.60 saving and a 6.4kg lithium demand decrease per battery when compared to the current average battery size.

Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing

Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing

Performance optimization of electric vehicle battery thermal management based on the transcritical CO2 system

Thermal management of electric vehicles, especially battery thermal management, is critical to driving range and operational safety. To find a vehicle thermal management system with higher energy efficiency and environmental protection, an environmentally-friendly and efficient battery and cabin parallel cooling thermal management system was evaluated with CO2 as the working fluid. First, different control strategies of the evaporation temperature were compared regarding the battery cooling performance. Then, the effect of the battery cooling evaporation temperature on the coefficient of performance (COP) was explored. It was found that the maximum COP increased by 8.38% as the evaporation temperature decreased from 17 to 5.8 °C. Besides, it was found that the optimal battery cooling evaporation temperature range is 10.2–11 °C when the battery heating power is 0.4 kW. The vapor quality at the cold plate outlet should be lower than 0.95. Finally, the battery cooling performance under variable operating conditions was investigated. The influence of operating parameters on the battery cooling evaporation temperature and CO2 outlet vapor quality was also analyzed. Simulation results showed that the optimum evaporation temperature range varied significantly under different working conditions. The vapor quality at the cold plate outlet decreased slightly with the evaporation temperature.

Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication

A guided proportional integral controller-based Pontryagin’s minimum principle (PI-PMP) energy management control strategy based on dynamic traffic information for plug-in fuel cell vehicles is proposed in this paper. Combined with the real-world traffic flow data of high-way driving scenarios, an improved equivalent consumption minimization strategy (ECMS) based on dichotomy is used to quickly search for the optimal initial costate value and reference battery state of charge (SOC) trajectory. A horizon velocity predictor based on artificial neural networks (ANNs) is used to achieve short-term velocity prediction, and a PI-PMP control strategy is adopted to realize SOC trajectory following. Simultaneously, the sensitivity of the costate in ECMS and PI-PMP is analyzed in depth. The simulation results of three scenarios with no/static/dynamic traffic flow information show that the guided PI-PMP energy management strategy based on dynamic traffic flow information has a significant energy-saving effect and real-time optimization potential.

Bridging the Affordability between Battery-Supported Electric Cooking and Conventional Cooking Fuel

Cooking with electricity (eCook) in low-and-middle-income developing countries is still an expensive option compared to using conventional fuels such as charcoal and firewood, so there is a need to identify solutions to make it affordable for all in line with the targets of SDG7. The initial aim of this paper is to understand and provide a comparative analysis of the cooking energy costs through a range of contextualized cooking scenarios—when cooking either with conventional fuels (e.g., charcoal, firewood) or with eCook appliances (e.g., electric pressure cookers (EPC) or hotplates), that are connected to and supplied from an integrated or portable battery (eCook battery). The cooking cost for the eCook cooking scenarios is assessed with and without fuel stacking where a degree of eCook exists alongside other conventional fuels. These studies offer a more realistic concept of the cooking practice to characterize the best fit for cooking scenarios and the main factors required to close the gap between the energy cost of conventional fuel and eCook batteries. The results suggest that to close the affordability gap between firewood and battery-based eCook, three main factors must be considered when estimating the cooking cost: the non-market cost of firewood; ensuring a low mini-grid tariff that is within range of the national grid tariff; and using an optimal eCook battery size with the capability to meet the required demand. In fact, when taking these factors into account; for the 2022 analysis, cooking 100% using an eCook battery is in a range of USD 29–30/month implying that there could be an opportunity for parity when the firewood cost reaches USD 29/month. The same applies to the cooking prediction costs for 2030, reducing further to USD 20–21/month. However, if the firewood is harvested sustainably, ensuring it is high in quality and dry, it would be difficult for battery eCook to compete. It should be emphasized that this research has relevance for governments, practitioners, and researchers.

Review on Battery Packing Design Strategies for Superior Thermal Management in Electric Vehicles

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed.