Application of an Adaptive Digital Filter for Estimation of Internal Battery Conditions

Hisafumi Asai,Hiroyuki Ashizawa,Yoshimasa Ochi,Daijiro Yumoto,Hideo Nakamura

doi:10.4271/2005-01-0807

Hisafumi Asai, Hiroyuki Ashizawa + Show 3 more

https://doi.org/10.4271/2005-01-0807

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This paper proposes an innovative and accurate method of estimating the internal conditions of rechargeable batteries for vehicles powered by electric motors, such as electric vehicles (EVs) and hybrid electric vehicles (HEVs). The proposed method is necessary to utilize battery power fully on vehicles powered by electric motors (especially HEVs) and thereby improve fuel economy or reduce the battery size. As the first step in this study, the relationship between the current and terminal voltage of a rechargeable lithium-ion battery was described using a linear parameter varying (LPV) model. That made it possible to reduce the problem of estimating the internal battery conditions (internal resistance, time constant, and so on) to a problem of recursively estimating the model parameters with an adaptive digital filter. An up-to-date parameter identification algorithm has been applied in order to estimate the model parameters recursively with good accuracy at all times, since they vary greatly depending on the operating conditions (state of charge (SOC), temperature, degree of battery degradation, etc.). As the second step, the calculated model parameters (internal resistance, time constant) and another type of LPV battery model were used to derive the open voltage (one of the internal states). The calculated model parameters and open voltage facilitated accurate estimations of the SOC, available output power and acceptable input power using an inherent battery characteristic (the steady-state correlation between open voltage and SOC) and maximum power definitions regardless of the operating conditions. This paper describes an example of the application of this method to a lithium-ion battery and presents the simulation and experimental results. Bench test results verified that SOC estimation accuracy was within ±4% and that of the available output power and acceptable input power was within ±10%.

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