2,378 research outputs found

    State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

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    State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

    Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

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    The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

    E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle

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    Electric vehicles (EVs) are a promising solution to reduce the transportation dependency on oil, as well as the environmental concerns. Realization of E-transportation relies on providing electrical energy to the EVs in an effective way. Energy storage system (ESS) technologies, including batteries and ultra-capacitors, have been significantly improved in terms of stored energy and power. Beside technology advancements, a battery management system is necessary to enhance safety, reliability and efficiency of the battery. Moreover, charging infrastructure is crucial to transfer electrical energy from the grid to the EV in an effective and reliable way. Every aspect of E-transportation is permeated by the presence of an intelligent hardware platform, which is embedded in the vehicle components, provided with the proper interfaces to address the communication, control and sensing needs. This embedded system controls the power electronics devices, negotiates with the partners in multi-agent scenarios, and performs fundamental tasks such as power flow control and battery management. The aim of this paper is to give an overview of the open challenges in E-transportation and to show the fundamental role played by embedded systems. The conclusion is that transportation electrification cannot fully be realized without the inclusion of the recent advancements in embedded systems

    A Review of Hybrid Battery Management System (H-BMS) for EV

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    Significant to a major pollution contributor in passenger vehicles, electric vehicles are more acceptable to use on the road. Electric Vehicles (EVs) burn energy based on the usage of the battery. The usage of the battery in EVs is monitored and controlled by Battery Management System (BMS). A few factors monitor and control Battery Management System (BMS). This paper reviewed the battery charging technology and Remote Terminal Unit (RTU) development as a Hybrid Battery Management System (H-BMS) for Electric Vehicle (EV)

    Evaluation of lightweight battery management system with field test of electric bus in campus transit system

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    A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than one year

    Zips Racing Electric Battery Management System

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    Zips Racing Electric currently uses a bulky, off-the-shelf battery management system to monitor and manage the voltage, temperature, and state-of-charge of an electric formula-style racecar battery pack (accumulator). The objective of this project is to research current battery management methodologies and apply said research to design and create a lightweight, compact, custom battery management system that is integrated with existing vehicle systems. This will allow for cleaner accumulator packaging and improved communication between the battery management system and the rest of the vehicle

    Hardware platform design of small energy storage system using second life batteries

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    As the number of electric vehicles (EV) increases rapidly, the reclamation and repurposing of used EV batteries into energy storage systems (ESSs) becomes a promising way to extend battery life and to generate extra revenues. This research project aims to design, build and experimentally verify an ESS that is based on the integration of mixed technology and mixed state of health battery modules, and this paper presents the hardware platform construction process of an ESS using second life battery modules. Details on system design, key components selection and commissioning are described. The developed ESS is compatible with a variety of battery modules and forms the foundation of test data collection and algorithm verification for further research on new battery management algorithms specifically for second life applications
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