Adopting combined strategies to make state of charge (SOC) estimation for practical use

Abstract

The estimation of state of charge (SOC) requires the tradeoff between high accuracy and robustness in the design of the battery management system. There are varieties of studies being carried out around this issue, aiming to balance the model complication, algorithm complexity, estimation accuracy, as well as robustness. In this work, in order to solve the SOC estimation problem under real complex working conditions, we introduce a strategy that combines battery modeling tactics and algorithm developing techniques to make it. In detail, we employ a combined model and build discrete state-space equations based on it. For improving the estimation accuracy, we use the recursive least squares method with forgetting factor to identify the parameters of the model. The particle filter embedded genetic algorithm is employed for SOC estimation, which overcomes the particle degradation and diversity loss for further enhancing the accuracy and robustness of estimation. Finally, real road test data is applied to investigate the estimation performance of the developed SOC estimation strategy.

Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review

“…These algorithms utilize a battery ECM or electrochemical model as part of the estimation process. The Kalman filter [10]- [14], particle filter [15], [16], least squares filter [17], [18], and adaptive Luenberger observer (ALBO) [19] are all commonly used to estimate battery SOC. The family of Kalman filters includes the extended Kalman filter (EKF) [10], fuzzy-based EKF [11], adaptive Kalman filter (AKF) [12], sigma point Kalman filter (SPKF) [13], and unscented Kalman filter (UKF) [14].…”

“…Adaptive filters and observers estimate SOC using a battery model combined with measured physical quantities. Examples of these algorithms include the family of Kalman filters [10]- [14] and the particle filter [15], [16], least squares filter [17], [18], and adaptive Luenberger observer [19]. Data-driven algorithms, which are based on machine learning models, are often referred to as black-box models because they model LIB input-output relationships without the need for models representing the underlying physics or chemistry.…”

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…Different researchers used the PF to SOC estimation [114][115][116][117]. The PF was merged with other techniques to improve its efficiency [118][119][120]. Fuzzy rules were used to model the battery and the PF was utilized to provide a co-estimation of the state of maximum power available (SoMPA) and SOC [118].…”

“…Fuzzy rules were used to model the battery and the PF was utilized to provide a co-estimation of the state of maximum power available (SoMPA) and SOC [118]. Furthermore, the forgetting factor RLS method was also used to determine the battery parameters with the PF as a SOC estimator [120]. Different variants of the PF have also been reported [121,122].…”

“…Gao et al 2011 [114] Unspecified Schwunk et al 2013 [115] Unspecified He et al 2013 [117] ≤± 3.5% Burgos-Mellado et al 2016 [118] Unspecified Zhou et al 2016 [119] ME ≤ ± 1.61% Xia et al 2017 [121] ≤± 0.5% Li et al 2018 [122] ≤± 1% Du et al 2018 [120] ME ≤ ± 3.5% Ye et al 2018 [123] ≤± 1%…”