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Item Adaptive Nonlinear Model-Based Fault Diagnosis of Li-ion Batteries(IEEE, 2015-02) Sidhu, Amardeep Singh; Izadian, Afshin; Anwar, Sohel; Department of Mechanical Engineering, School of EngineeringIn this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion LiFePO4 cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.Item Application of Adaptive Estimation Techniques on Battery Fault Diagnosis(Office of the Vice Chancellor for Research, 2013-04-05) Singh, Amardeep; Izadian, Afshin; Anwar, SohelHigh energy storage systems like Li-ion Batteries are one of the most widely used renewable energy sources today. They find applications in everyday electronic gadgetry, critical medical devices, hybrid & electric vehicles to name a few. Our study aims to observe continuously the state of the Li-ion battery and detect Over Charge (OC) and Over Discharge (OD) failures occurring in real time. Both conditions are detrimental to the health of the battery, while over charge can lead to overheating and thus vaporization of active material and hence explosion, over discharge can short the battery cell. However, these types of failures can be detected before they occur and by raising a flag before the system reaches the failure condition such failure modes can be avoided. Different battery models based on equivalent circuit approach are constructed using the impedance spectroscopy data from Li-ion battery cells. Kalman filters are used to estimate the state of each system and subsequent residuals are generated for each model. Multiple model adaptive estimation is then used, where the generated residuals are evaluated and the fault probabilities are generated. Based on these probabilities, the system is classified as normal operation, OC fault or OD fault. Simulation results show that the battery faults can be detected and diagnosed in real time, thereby proving to an effective way of Li-ion battery fault diagnosis.Item 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, JieLi-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.Item Waste-Lithium-Liquid (WLL) Flow Battery for Stationary Energy Storage Applications(Office of the Vice Chancellor for Research, 2013-04-05) Kim, Youngsik; Mahootcheian Asl, NinaWith using a multi-layer electrolyte that consists of one liquid electrolyte and one solid electrolyte, the choices for cathode will be dramatically widened to include solid, liquid, and gas phases. Applying this concept, gas and liquid phases have been used as cathodes to create different battery systems such as the Li-air, Li-sea water, and Li-aqueous liquid batteries. Based on these reports, we hypothesized that, by charging the cell, Li metal could be electrochemically collected from any material containing Li-ions. This idea extended to harvesting Li metal from waste Li-ion batteries, in both solid and liquid phases, that contain Li-ion sources such as the LixC6 anode, LixFePO4 cathode, and LiPF6 in the EC:DEC electrolyte. The harvested Li metal could then be an energy source for Li-Liquid flow batteries by using water as the cathode. This study demonstrates the feasibility of using waste Li-ion batteries and water for the electrodes in a Waste-Lithium-Liquid (WLL) flow battery that can be used in a stationary energy storage application. Li metal was collected electrochemically from a waste Li-ion battery containing Li-ion source materials from the battery’s anode, cathode, and electrolyte, thereby recycling the Li contained in the waste battery. The harvested Li metal in the battery system was discharged to produce the electricity by using water as the cathode. The discharge voltage of the water showed 2.7 V at 0.1 mA/cm2 versus Li metal harvested from waste Li-ion batteries, compared to 2.8 V versus fresh Li metal at the same current rate. Since the energy source for this proposed battery system is provided by waste Li-ion batteries and water, the cost of the battery dramatically decreases, which is an attractive strategy for a large size energy storage application