Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs

Two weighted-order classes of iterative root-finding methods

In this paper we design, by using the weight function technique, two families of iterative schemes with order of convergence eight. These weight functions depend on one, two and three variables and they are used in the second and third step of the iterative expression. Dynamics on polynomial and non-polynomial functions is analysed and they are applied on the problem of preliminary orbit determination by using a modified Gauss method. Finally, some standard test functions are to check the reliability of the proposed schemes and allow us to compare them with other known methods.This research was supported by Ministerio de Ciencia y Tecnologia MTM2011-28636-C02-02 and FONDOCYT 2011-1-B1-33 Republica Dominicana.Artidiello Moreno, SDJ.; Cordero Barbero, A.; Torregrosa Sánchez, JR.; Vassileva, M. (2015). Two weighted-order classes of iterative root-finding methods. International Journal of Computer Mathematics. 92(9):1790-1805. https://doi.org/10.1080/00207160.2014.887201S1790180592

Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs

Online health-conscious energy management strategy for a hybrid multi-stack fuel cell vehicle based on game theory

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FCHEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multiagent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system

A framework for models of movement in geographic space

This article concerns the theoretical foundations of movement informatics. We discuss general frameworks in which models of spatial movement may be developed. In particular, the article considers the object–field and Lagrangian–Eulerian dichotomies, and the SNAP/SPAN ontologies of the dynamic world, and classifies the variety of informatic structures according to these frameworks. A major challenge is transitioning between paradigms. Usually data is captured with respect to one paradigm but can usefully be represented in another. We discuss this process in formal terms and then describe experiments that we performed to show feasibility. It emerges that observational granularity plays a crucial role in these transitions

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

Solubility of Tris(hydroxymethyl)aminomethane in Water + Methanol +1-Propanol Mixtures at Various Temperatures

Article on the solubility of tris(hydroxymethyl)aminomethane in water + methanol + 1-propanol mixtures at various temperatures

Solubility of Sodium Acetate in Ternary Mixtures of Methanol, 1-Propanol, Acetonitrile, and Water at 298.2 K

Article on the solubility of sodium acetate in ternary mixtures of methanol, 1-propanol, acetonitrile, and water at 298.2 K

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity