Practical Online Estimation of Lithium-Ion Battery Apparent Series Resistance for Mild Hybrid Vehicles

Abstract

In hybrid vehicles, lithium-ion cells constituting a battery pack are frequently used to provide and recover high power to assist the vehicle's internal combustion engine (ICE) powertrain. This usage is more present in mild hybrid applications where the battery does not have long discharge time. Under such conditions, the pack's series resistance R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> proved to be an important parameter to monitor since its evolution depends on the cell's characteristics (manufacturing tolerance, temperature, etc.). This resistance, which is monitored by the battery management system (BMS), reflecting the available power level in the cell can be used as an indicator to enhance the security of the battery pack. Its evolution can be used to quantify its aging (state of health: SoH). This paper presents an online approach to identify the cell's series resistance based on a direct estimation of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> . This parameter can be usually identified through the voltage drop occurring across the cell caused by a high current variation profile (mild hybrid conditions). These estimated values are then filtered with an “exponential moving average” method to limit the measurement noise effect. This approach provides good results for mild hybrid conditions, while minimizing the computing power required.