Study of Tools Interoperability

Interoperability of tools usually refers to a combination of methods and techniques that address the problem of making a collection of tools to work together. In this study we survey different notions that are used in this context: interoperability, interaction and integration. We point out relation between these notions, and how it maps to the interoperability problem. We narrow the problem area to the tools development in academia. Tools developed in such environment have a small basis for development, documentation and maintenance. We scrutinise some of the problems and potential solutions related with tools interoperability in such environment. Moreover, we look at two tools developed in the Formal Methods and Tools group1, and analyse the use of different integration techniques

Design and realization of a smart battery management system

Battery management system (BMS) emerges a decisive system component in battery-powered applications, such as (hybrid) electric vehicles and portable devices. However, due to the inaccurate parameter estimation of aged battery cells and multi-cell batteries, current BMSs cannot control batteries optimally, and therefore affect the usability of products. In this paper, we proposed a smart management system for multi-cell batteries, and discussed the development of our research study in three directions: i) improving the effectiveness of battery monitoring and current sensing, ii) modeling the battery aging process, and iii) designing a self-healing circuit system to compensate performance variations due to aging and other variations.published_or_final_versio

Prediction of Hydrate and Solvate Formation Using Statistical Models

Novel, knowledge based models for the prediction of hydrate and solvate formation are introduced, which require only the molecular formula as input. A data set of more than 19 000 organic, nonionic, and nonpolymeric molecules was extracted from the Cambridge Structural Database. Molecules that formed solvates were compared with those that did not using molecular descriptors and statistical methods, which allowed the identification of chemical properties that contribute to solvate formation. The study was conducted for five types of solvates: ethanol, methanol, dichloromethane, chloroform, and water solvates. The identified properties were all related to the size and branching of the molecules and to the hydrogen bonding ability of the molecules. The corresponding molecular descriptors were used to fit logistic regression models to predict the probability of any given molecule to form a solvate. The established models were able to predict the behavior of ∼80% of the data correctly using only two descriptors in the predictive model

Artificial intelligence approach to SoC estimation for smart BMS

One of the most important and indispensable parameters of a Battery Management Systems (BMS) is accurate estimates of the State of Charge (SoC) of the battery. It can prevent battery from damage or premature aging by avoiding over charge/discharge. Due to the limited capacity of a battery, advanced methods must be used to estimate precisely the SoC in order to keep battery safely being charged and discharged at a suitable level and to prolong its life cycle. In this paper, we review several effective approaches: Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation; then we propose Artificial Intelligence (AI) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using our proposed approach, a more accurate SoC measurement will be obtained for the smart battery management systemTaikomosios informatikos katedraVytauto Didžiojo universiteta

Towards a hybrid approach to SoC estimation for a smart battery management system (BMS) and battery supported Cyber-physical systems (CPS)

One of the most important and indispensable parameters of a Battery Management System (BMS) is to accurately estimate the State of Charge (SoC) of battery. Precise estimation of SoC can prevent battery from damage or premature aging by avoiding over charge or discharge. Due to the limited capacity of a battery, advanced methods must be used to estimate precisely the SoC in order to keep battery safely being charged and discharged at a suitable level and to prolong its life cycle. We review several existing effective approaches such as Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation. Then we investigate both Artificial Intelligence (AI) approach and Formal Methods (FM) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using presented approach, a more accurate SoC measurement can be obtained for the smart battery management system and battery supported Cyber-Physical Systems (CPS)Taikomosios informatikos katedraVytauto Didžiojo universiteta

Design and realization of a smart battery management system

Abstract—Battery management system (BMS) emerges a decisive system component in battery-powered applications, such as (hybrid) electric vehicles and portable devices. However, due to the inaccurate parameter estimation of aged battery cells and multi-cell batteries, current BMSs cannot control batteries optimally, and therefore affect the usability of products. In this paper, we proposed a smart management system for multi-cell batteries, and discussed the development of our research study in three directions: i) improving the effectiveness of battery monitoring and current sensing, ii) modeling the battery aging process, and iii) designing a self-healing circuit system to compensate performance variations due to aging and other variationsTaikomosios informatikos katedraVytauto Didžiojo universiteta

Specification and analysis of null convention logic (NCL) circuits using PAFSV

In this work, a process algebraic framework known as PAFSV is applied to the formal specication and analysis of IEEE 1800TM SystemVerilog design. The formal semantics of PAFSV is defined by means of deduction rules that associate a time transition system with a PAFSV process. In addition, a set of properties of PAFSV is defined for a notion of bisimilarity; and PAFSV may be regarded as the formal language of a significant subset of IEEE 1800TM SystemVerilog. The main aim of this paper is to demonstrate that PAFSV is effective and useful for the formal specification and analysis of IEEE 1800TM SystemVerilog design. To achieve the aim of this approach, we apply PAFSV to model and analyse classical circuits such as the Null Convention Logic (NCL) circuit

Improving power-conversion efficiency via a hybrid MPPT approach for photovoltaic systems

This paper presents a hybrid Maximum Power Point Tracking (MPPT) method for improving the power -conversion efficiency of Photovoltaic (PV) generators. By detecting the output power changes caused by environmental reasons, the proposed method performs variable-step online search process with an accurate estimation of the Maximum Power Point (MPP) locus. A PV generator with a Single Ended Primary Inductance Converter (SEPIC) is developed in PSIM to verify the feasibility and suitability of the proposed method. Simulation results show that it can not only deliver a stable reference operating voltage for MPPs at steady state, but also can speed up the searching process under rapidly changing environment conditions.link_to_subscribed_fulltex