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An electrochemical model-based particle filter approach for Lithium-ion battery estimation | IEEE Conference Publication | IEEE Xplore

An electrochemical model-based particle filter approach for Lithium-ion battery estimation

Publisher: IEEE

Abstract:

Lithium-ion batteries are currently amongst the leading technologies for electrical energy storage. In automotive industry they are recognized as the most promising alter...View more

Abstract:

Lithium-ion batteries are currently amongst the leading technologies for electrical energy storage. In automotive industry they are recognized as the most promising alternative to gasoline powered engines. State estimation of the state of the battery can provide useful information regarding the state of charge (SOC) and state of health (SOH) of the battery which play a crucial role in optimal and safe utilization of the battery. Although the electrochemical dynamics of the battery are described by nonlinear system of PDAEs, most works in the area of condition monitoring of the battery resort to empirical or equivalent electrical circuit models. These models don't provide any physical insight into the battery and lack insight into physical limitations of the battery. This work presents a particle filter algorithm for state estimation and condition monitoring of the Li-ion battery. This filter can effectively deal with the nonlinear and complex nature of the PDAEs describing the dynamics of the battery. It provides accurate estimation of the average as well as spatial distribution of concentration in the battery. The simulation results demonstrate the effectiveness of the proposed estimation algorithm.
Date of Conference: 10-13 December 2012
Date Added to IEEE Xplore: 04 February 2013
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Publisher: IEEE
Conference Location: Maui, HI, USA

References

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