Abstract
For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.
Highlights
As energy storage systems, lithium-ion batteries have significant advantages in terms of power density [1], self-discharge rate [2], energy density [3, 4], and cycle life compared to other types of batteries [5, 6]
In battery management systems (BMS). The former has strict requirements for measuring open circuit voltage (OCV), so it is difficult to achieve effective state of charge (SOC) estimation, while the latter is affected by the initial SOC value and current measurement error, so it is hard to guarantee the accuracy of SOC estimation. e data-driven methods require large datasets to train algorithms, but the enough training datasets are difficult to obtain
To develop an SOC estimation algorithm with both high accuracy and low computational cost that can be applied in on-board BMS, the alternate algorithm combining the ampere-hour integral (AHI) method and H-infinity filter (HIF) is proposed
Summary
Lithium-ion batteries have significant advantages in terms of power density [1], self-discharge rate [2], energy density [3, 4], and cycle life compared to other types of batteries [5, 6]. The above SOC estimation algorithms possess high accuracy, fast convergence, excellent robustness, and adaptability, yet they consume a large amount of computing resources. The most challenging problem in the development of the SOC estimation algorithm is how to obtain an algorithm that have both high accuracy and low computational cost. In the study by Liu et al [11], an alternate algorithm combining adaptive extended Kalman filter and the ampere-hour counting method is proposed to improve the accuracy and reduce the computational cost. The BCLS-ALT SOC joint estimation algorithm is proposed in this article where the BCLS and ALT algorithm will be applied to identify battery model parameters and SOC, respectively. E experimental results show that the proposed BCLS-ALT SOC joint estimation algorithm can provide excellent performance under different operation conditions.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
