Abstract
Currently, lithium-ion batteries are mainly used as central power components in electric vehicles. Usually, accurate battery cell modeling and state of charge estimation are required in order to effectively use the lithium-ion batteries in electrified vehicles. For an elaborate battery cell modeling on a wide temperature range, we select a temperature-dependent battery cell modeling approach, which relies on one resistance plus second-order resistance-capacitance equivalent circuit model. In order to corporate some temperature effects into the modeling, we derive the sixth-order polynomial functions using the curve fitting algorithm. The functions contribute to the accurate battery cell modeling on the wide temperature range. For a practical usage of the functions in real-time embedded systems, we propose an offset-based lookup table technique. In addition, we propose a novel hysteresis voltage unit model for more accurate parameter estimation of hysteresis voltages. This also leads to more accurate battery cell modeling. Based on the battery cell modeling, we propose a temperature-dependent estimation method for state of charge. This approach exploits the extended Kalman filter, which is suitable for nonlinear characteristics such as the hysteresis effect. Experimental evaluation exhibits that the proposed estimation method outperforms the conventional approaches in the wide temperature range.
Highlights
Most fuel economy regulations increasingly require the automotive industry to reduce the carbon dioxide (CO2) emission, which is considered one of the most hostile behaviors to natural environments
In order to satisfy the state of charge (SoC) estimation error requirements, we propose a novel hysteresis voltage unit model, where the maximum hysteresis voltage adaptively changes according to the SoC range
In order to overcome the limitation of the conventional model, we propose the novel hysteresis voltage unit model, where the maximum hysteresis voltage (M ) adaptively changes according to the SoC range
Summary
Most fuel economy regulations increasingly require the automotive industry to reduce the carbon dioxide (CO2) emission, which is considered one of the most hostile behaviors to natural environments. The temperature-dependent SoC estimation method (which relies on the offset-based lookup table technique and the hysteresis voltage unit model) suitable for lithium-ion batteries in electrified vehicles. Based on the sixth-order polynomial functions of Table 2, the temperature-dependent ECM can model the lithium-ion battery on a wide temperature range. PROPOSED HYSTERESIS VOLTAGE UNIT MODEL Figure 9 exhibits a comparison of the OCV curves at the temperature of 20◦C in the cases of charge and discharge under the same current value. In order to estimate the SoC accurately, the EKF repeats the prediction phase and the update phase
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