Browsing by Subject "Lithium Ion"

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    Charge optimization of lithium-ion batteries for electric-vehicle application
    (2015-03-02) Pramanik, Sourav; Anwar, Sohel; Wasfy, Tamar; Li, Lingxi
    In recent years Lithium-Ion battery as an alternate energy source has gathered lot of importance in all forms of energy requiring applications. Due to its overwhelming benefits over a few disadvantages Lithium Ion is more sought of than any other Battery types. Any battery pack alone cannot perform or achieve its maximum capacity unless there is some robust, efficient and advanced controls developed around it. This control strategy is called Battery Management System or BMS. Most BMS performs the following activity if not all Battery Health Monitoring, Temperature Monitoring, Regeneration Tactics, Discharge Profiles, History logging, etc. One of the major key contributor in a better BMS design and subsequently maintaining a better battery performance and EUL is Regeneration Tactics. In this work, emphasis is laid on understanding the prevalent methods of regeneration and designing a new strategy that better suits the battery performance. A performance index is chosen which aims at minimizing the effort of regeneration along with a minimum deviation from the rated maximum thresholds for cell temperature and regeneration current. Tuning capability is provided for both temperature deviation and current deviation so that it can be tuned based on requirement and battery chemistry and parameters. To solve the optimization problem, Pontryagin's principle is used which is very effective for constraint optimization with both state and input constraints. Simulation results with different sets of tuning shows that the proposed method has a lot of potential and is capable of introducing a new dynamic regeneration tactic for Lithium Ion cells. With the current optimistic results from this work, it is strongly recommended to bring in more battery constraints into the optimization boundary to better understand and incorporate battery chemistry into the regeneration process.
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    Fault diagnosis of lithium ion battery using multiple model adaptive estimation
    (2013-12) Sidhu, Amardeep Singh; Anwar, Sohel; Izadian, Afshin; Xie, Jian
    Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.