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

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)

Development of battery management system for hybrid electric propulsion system.

Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.PhD in Aerospac

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Programmable Battery Management System

Lithium batteries provide excellent energy storage capabilities at a relatively high density; however, precautions must be taken with these high energy devices to ensure safe operation. A battery management system (BMS) provides protection by monitoring cell and pack voltage levels and maintaining them in a specific range. They limit the output current and disable the output in extreme conditions. Most devices in the targeted power range (\u3c1000W) do not allow the user to manipulate the values for maximum current, cut-off voltage, or other limits. This project introduces the Programmable BMS (PBMS), which instead allows the user to select these values through a physical interface. The interface displays measurements including pack voltage and output current, and it reports additional characteristics of interest such as the battery’s temperature, state of charge, and cumulative number of charge cycles. This level of access and control permits users to receive the maximum performance and safety from common lithium battery packs

Energy and quality of service management in wireless multimedia sensor networks

Sensor networks are composed of resource constrained nodes that capture data from the environment, preprocess it and then transmit it to a sink node. This paper presents a scenario for monitoring an electricity distribution network, an energy analysis of the used sensor nodes and an intelligent energy and quality of service (QoS) manager. This manager continuously adapts the provided QoS according to the energy level of the nodes