Energy efficient random sleep-awake schedule design

This letter presents a simple model for determining energy efficient random sleep-awake schedules. Random sleepawake schedules are more appropriate for sensor networks, where the time of occurrence of an event being monitored, e.g., the detection of an intruder, is unknown a priori, and the coordination among nodes is costly. We model the random sleepawake schedule as a two state Markov process, and maximize the probability of the transmission of sensed data by a given deadline. Our results indicate that for a given duty cycle, the optimal policy is to have infrequent transitions between sleep and awake modes, if the average number of packets sent is greater than the mean number of slots the node is awake

Battery Modeling

The use of mobile devices is often limited by the capacity of the employed batteries. The battery lifetime determines how long one can use a device. Battery modeling can help to predict, and possibly extend this lifetime. Many different battery models have been developed over the years. However, with these models one can only compute lifetimes for specific discharge profiles, and not for workloads in general. In this paper, we give an overview of the different battery models that are available, and evaluate these models in their suitability to combine them with a workload model to create a more powerful battery model. \u

Systematic Literature Review on Battery Management Systems and predicting Solar Big Data

Penelitian ini bertujuan untuk menganalisis sistem manajemen baterai dengan memprediksi tenaga surya melalui bigdata ditinjau dari kajian literatur. Dengan adanya pertumbuhan biaya integrasi, pengelolaan limbah yang semakin rumit, variabilitas daya listrik yang berdampak sosio-lingkungan sehingga membutuhkan model sektor listrik baru dengan memanfaatkan tenaga surya. Oleh karenanya penelitian ini merupakan hasil tinjauan literature review dengan prinsip systematic literature review untuk memprediksi tenaga surya dalam pengelolaan listrik dengan sistem baterai.  Metode Systematic Literature Review (SLR) digunakan untuk mendefinisikan dan mengevaluasi literatur dalam rangkaian makalah. Pencarian menggunakan 41 makalah untuk evaluasi  sebelumnya, menunjukkan bahwa model yang digunakan untuk memprediksi tenaga surya adalah eksperimen akademik jangka panjang. Algoritma ELM (Extreme Learning Machine) menjadi pilihan dalam pengelolaan listrik dengan tenaga surya melalui system baterai dibandingkan dengan algoritma JST (Jaringan Syaraf Tiruan)

System-level modeling and thermal simulations of large battery packs for electric trucks

Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi’s expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices

Enhancing road safety behaviour using a psychological and spiritual approaches

Main causes of accident is due to driver itself that is influenced by their bad attitude while driving. Human attitude is closely related to the human psychology. Apart from that, spiritual aspect also influence human attitude. Hence, this study carried out to improve driver safety using a new approach through psychology and spiritual factors. Objectives of this study are to identify then analyze factors of psychological and spiritual that contribute towards safety driving. A self-administered questionnaire were distributed among 256 respondents from various type of background. An analysis descriptive statistics show demographic and experience of respondents. Chi-square analysis showed only education level and traffic summon are significant to safety driving. Furthermore, correlation analysis shows psychological factors has strong linear relationship on attitude of drivers towards safety driving while spiritual factor, the perception of the spiritual and practices, both have a strong relationship to safety driving. Regression analysis demonstrates boths psychological and spiritual factors have strong evidence and significant relationship with safety driving. Thus, it can be identified that spiritual psychological factors encourage drivers to drive more safely and reduce road accidents. Therefore, this study propose useful guidelines to related agencies in order to enhance safety among drivers to be able drive safely on the road

A new equivalent circuit model parametrization methodology based on current pulse tests for different battery technologies

With growing global commitment to renewable energy generation, the role of energy storage systems has become a central issue in traction power applications, such as electric vehicles, trains, and elevators. To achieve the optimal integration of batteries in such applications, without unnecessary oversizing, improvements in the process of battery selection are needed. Specifically, it is necessary to develop models able to predict battery performance for each particular application. In this paper, a methodology for the parametrization of a battery equivalent circuit model (ECM) based on capacity and pulse tests is presented. The model can be extrapolated to different battery technologies, and was validated by comparing simulations and experimental tests with lead-acid and lithium-ion batteries

Characterization and modeling of a hybrid electric vehicle lithium-ion battery pack at low temperatures

Although lithium-ion batteries have penetrated hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), they suffer from significant power capability losses and reduced energy at low temperatures. To evaluate those losses and to make an efficient design, good models are required for system simulation. Subzero battery operation involves nonclassical thermal behavior. Consequently, simple electrical models are not sufficient to predict bad performance or damage to systems involving batteries at subzero temperatures. This paper presents the development of an electrical and thermal model of an HEV lithium-ion battery pack. This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries over the selected operating range of currents and battery capacities. In addition, a thermal modeling method has been developed for this model so that it can predict the battery core and crust temperature by including the effect of internal resistance. First, various discharge tests on one cell are carried out, and then, cell's parameters and thermal characteristics are obtained. The single-cell model proposed is shown to be accurate by analyzing the simulation data and test results. Next, real working conditions tests are performed, and simulation calculations on one cell are presented. In the end, the simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV. © 2015 IEEE

Equivalent Circuit Model Generation for Batteries Using Non-ideal Test Data

Modeling is a key component in the development of battery products. While there are multiple levels of complexity which may be achieved in model development, equivalent circuit modeling is able to quickly produce reliable and accurate predictions for battery behavior. While the use of equivalent circuit models has been described in great detail for lithium ion batteries, it is also desirable to use this methodology regardless of chemistry, specifically with respect to lead-acid technology. When developing battery models for predicting battery behavior in a vehicle, the testing methods meant to mimic vehicle applications often cause non-ideal data for model generation. Specifically, periods of constant voltage charging can limit the model’s capabilities and accuracy. This is due to the imposed voltage limit required for constant voltage charging which is not an inherent battery behavior. By thoroughly examining equivalent circuit models of increasing complexity, it is shown that lead-acid and lithium ion batteries behave similarly so that minimal impact is had on model development. Additionally, three methods are considered for modifying the fitting process so that test data which contains voltage limits may still be considered useful for model development