Neural Computing and Applications

, Volume 31, Issue 12, pp 8171–8183

# Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter

Machine Learning - Applications & Techniques in Cyber Intelligence

## Abstract

The state of charge (SOC) is a significant part of energy management for electric vehicle power battery, which has important influence on the safe operation of power battery and the judgment of driver’s operation. Because the battery SOC cannot be measured directly, many researchers use various estimation methods to obtain accurate SOC values. But the SOC is affected by the temperature, current, cycle life and other time-varying nonlinear factors, which make difficult to construct prediction model. The key problem of battery SOC estimation is the change rule of battery capacity. The Peukert equation is a good method for calculating the battery capacity. The traditional Peukert equation without considering the influence of temperature, but the differences of temperature lead to changes in the constants n and K of the Peukert equations. In this paper, the Peukert equation based on temperature, current change and cycle life is established to estimate the battery capacity. And the battery model state equation is established for estimation and measurement equations of charge and discharge parameters $$\left\{ {C_{\text{e}} ,R_{\text{e}} ,C_{\text{d}} ,R_{\text{d}} ,R_{0} } \right\}$$ and $$V_{\text{OC}}$$ by using the ampere-hour method and the second-order RC model. And the dynamic estimation of charge state of battery is realized by AUKF. The results show that the accuracy of the lithium battery SOC estimation algorithm based on the temperature, current and cycle life of the modified Peukert equation is about 8% higher than that of the traditional KF ampere-hour method.

## Keywords

State of charge Peukert equation AUKF Electric vehicle

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