Browsing by Author "Banvait, Harpreetsingh"

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    Failure Detection for Over-Discharged Li-Ion Batteries
    (Office of the Vice Chancellor for Research, 2012-04-13) Xiong, Jing; Banvait, Harpreetsingh; Li, Lingxi; Chen, Yaobin; Xie, Jian; Liu, Yadong; Wu, Meng; Chen, Jie
    Li-ion batteries are high density, slow loss of charge when not in use and no memory effect. Vast research on Li-ion batteries has been focusing on increasing the energy density, durability, and cost. Due to its advantages it has been widely used in consumer electronics and electric vehicles. Apart from its advantages, safety is a major concern for Li-ion batteries. The Li-ion safety issues have been widely publicized due to devastating incidents with laptop and cell phone batteries. Despite of much research towards the safety of Li-ion battery, it remains as a major concern related to Li-Ion batteries. A failure of Li-ion battery may result in thermal runaway. Li-ion battery failure may be due to overcharge, over-discharge, short circuits, particles poisoning, mechanical or thermal damage [1, 2]. Short circuit, overcharge, and over-discharge are the most common electrical abuses a battery suffers. This poster presents preliminary results for the failure signatures of over-discharged Li-ion batteries, and proposes a rule-based method and a probabilistic method for failure detection. The two methods Rule-based method and Probabilistic method are verified using experimental results for a Li-ion battery. The proposed methods were successfully implemented in a real-time system for failure detection and early warning.
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    OPTIMAL ENERGY MANAGEMENT SYSTEM OF PLUG-IN HYBRID ELECTRIC VEHICLE
    (ProQuest, 2009) Banvait, Harpreetsingh; Anwar, Sohel; Chen, Yaobin; Eberhart, Russell C.
    Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.