This paper proposes a low-complexity online state of charge estimation method for LiFePO4 battery in electrical vehicles. The proposed method is able to achieve accurate state of charge with less computational efforts in comparison with the nonlinear Kalman filters, and also can provide state of health information for battery management system. According to the error analysis of equivalent circuit model with two resistance and capacitance, two proportional-integral filters are designed to compensate the errors from inaccurate state of charge and current measurements, respectively. An error dividing process is proposed to tune the contribution of each filter to the finial estimation results, which enhances the validation and accuracy of the proposed method.
- Battery SOC is estimated with a low-complexity online method.
- Errors in the battery two RC equivalent circuit model are analyzed.
- Two PI filters are designed to compensate the errors in the battery model.
- An adaptively adjusted process is proposed to form the fusing weights of the PI filters.
- The execution time of the algorithms is measured on the MicroZed development board.
Authors: Jinhao Meng(a), Mattia Ricco(b), Anirudh Budnar Acharya(c), Guangzhao Luo(a), Maciej Swierczynski(d), Daniel-Ioan Stroe(b), Remus Teodorescu(b)