Electrochemical model based condition monitoring of a Li-ion battery using fuzzy logic
dc.contributor.advisor | Anwar, Sohel | |
dc.contributor.author | Shimoga Muddappa, Vinay Kumar | |
dc.contributor.other | Wasfy, Tamer | |
dc.contributor.other | Li, Lingxi | |
dc.date.accessioned | 2014-12-19T13:36:53Z | |
dc.date.available | 2014-12-19T13:36:53Z | |
dc.date.issued | 2014 | |
dc.degree.date | 2014 | en_US |
dc.degree.discipline | Mechanical Engineering | en |
dc.degree.grantor | Purdue University | en_US |
dc.degree.level | M.S.M.E. | en_US |
dc.description | Indiana University-Purdue University Indianapolis (IUPUI) | en_US |
dc.description.abstract | There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/5588 | |
dc.identifier.uri | http://dx.doi.org/10.7912/C2/2661 | |
dc.language.iso | en_US | en_US |
dc.subject | Li-ion battery | en_US |
dc.subject | Diagnosis | en_US |
dc.subject | Model based diagnosis | en_US |
dc.subject | Electrochemical model | en_US |
dc.subject | Fuzzy logic | en_US |
dc.subject.lcsh | Lithium ion batteries -- Research -- Testing -- Analysis | en_US |
dc.subject.lcsh | Lithium cells | en_US |
dc.subject.lcsh | Electric vehicles -- Research | en_US |
dc.subject.lcsh | Electricity in transportation | en_US |
dc.subject.lcsh | Electric automobiles -- Technological innovations -- Research | en_US |
dc.subject.lcsh | Hybrid electric vehicles -- Technological innovations -- Research | en_US |
dc.subject.lcsh | Fuzzy algorithms -- Research -- Analysis | en_US |
dc.subject.lcsh | Intelligent control systems | en_US |
dc.subject.lcsh | Electronic circuits -- Testing -- Analysis | en_US |
dc.subject.lcsh | Electric circuit analysis | en_US |
dc.subject.lcsh | Real-time control -- Experiments -- Research | en_US |
dc.subject.lcsh | Reliability (Engineering) -- Mathematical models -- Research | en_US |
dc.subject.lcsh | Electrochemical analysis -- Experiments | en_US |
dc.subject.lcsh | Battery chargers -- Research | en_US |
dc.subject.lcsh | Electric batteries -- Safety measures | en_US |
dc.title | Electrochemical model based condition monitoring of a Li-ion battery using fuzzy logic | en_US |
dc.type | Thesis | en |
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