Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles

State-of-charge (SoC) of an electric vehicle (EV) battery pack is a crucial information for the driver. The accuracy of the SoC estimation algorithms is often limited by the computational resources of the electronic hardware. Ensuring that required performances are maintained between the design stage and the implementation in a low-cost microcontroller is critical. The growing demand for light EVs increases the need for precise and computationally-light algorithms for low-cost Battery Management System (BMS). This paper proposes a novel SoC estimation method that deals with accuracy and simplicity. A disturbance observer-based (DOB) algorithm is developed to offer a simple solution that reduces the computational time while achieving similar performances of other well-known SoC estimators. The new estimator has been implemented into an on-board system to be more representative of the real computational resources. The validation of the algorithm has been done with a customized hardware-in-the-loop system that emulates the battery electrical signals of the BMS sensors. The validation uses a real vehicle speed profile recorded on a three-wheel light EV. The performance of the proposed method has been compared to the other estimators. The results indicate that the DOB-algorithm SoC estimator can achieve a faster and more accurate estimation than the conventional approaches.