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Dynamic model for estimating SoC and peak power capability in PHEVs
24 December 2012
Researchers at the Beijing Institue of Technology have developed a battery model to estimate the state of charge (SoC) and peak power capability of a Li-ion battery in plug-in hybrid electric vehicles (PHEVs). According to their report in the Journal of Power Sources, their approach can not only achieve an accurate SoC estimate with an error below 0.02, but also give a reliable and robust peak power capability estimate.
The team used an adaptive extended Kalman filter (AEKF)-based method to jointly estimate the State of Charge (SoC) and peak power capability of a lithium-ion battery. (A Kalman filter is used to produce a statistically optimal estimate of an underlying system state from noisy data; an extended Kalman filter works with non-linear functions of the the state.)
To strengthen the links of the model’s performance with battery’s SoC, they used a dynamic electrochemical polarization battery model for the state estimations. To get accurate parameters, they used four different charge-discharge current to improve the hybrid power pulse characteristic test.
The AEKF-based method is employed to achieve a robust SoC estimation.
They proposed an approach to continuous peak power capability estimation due to the PHEV’s requirement for continuous peak power estimates for acceleration, regenerative braking and gradient climbing.
To improve its applicability, they suggested a general framework for six-step joint estimation approach for SoC and peak power capability.
Rui Xiong, Hongwen He, Fengchun Sun, Xinlei Liu, Zhentong Liu (2012) Model-based State of Charge and peak power capability joint estimation of Lithium-Ion battery in plug-in hybrid electric vehicles, Journal of Power Sources, doi: 10.1016/j.jpowsour.2012.12.003
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