Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Xia, Bizhong; Lao, Zizhou; Zhang, Ruifeng; Tian, Yong; Chen, Guanghao; Sun, Zhen; Wang, Wei; Sun, Wei; Lai, Yongzhi; Wang, Mingwang; Wang, Huawen

doi:10.3390/en11010003

Cited by 89 publications

(52 citation statements)

References 37 publications

(9 reference statements)

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“…For example, some studies consider changes in model parameters and capacity after battery aging so as to synchronously estimate the SOC of the battery and capacity/model parameters by applying combined the KF or double KFs algorithm [26]. By utilizing recursive least squares, Xia et al [27] realized the online identification of model parameters and applied the identification results to an SOC estimation. Table 1 compares several methods that are commonly used at present.…”

Section: Methodsmentioning

confidence: 99%

State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

2019

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The lack of endurance is an important reason restricting further development of unmanned aerial vehicles (UAVs). Accurately estimating the state of charge (SOC) of the Li-Po battery can maximize the battery energy utilization and improve the endurance of UAVs. In this paper, the main current methods for estimating the SOC of vehicles were explored and discussed to unveil their advantages and disadvantages. In addition, the extended Kalman filter algorithm based on an equivalent circuit model was used to estimate SOC of power-type Li-Po batteries at different temperatures. The result showed that the closed-loop control method can effectively improve the battery life of small-sized electric UAVs.

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Section: Methodsmentioning

confidence: 99%

State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

2019

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“…The RLS and its variants have been used widely to determine the parameters of Li-ion battery model [124][125][126][127][128][129][130][131][132][133]. The RLS has been applied to identify the characteristic of the battery for a 1 st order ECM [124,129,132].…”

Section: Recursive Least Square (Rls)mentioning

confidence: 99%

“…The fading KF (FKF) was also implemented for SOC estimation [126]. The exponential and variable forgetting factor RLS methods were also used to estimate the SOC [127,128,130]. Table 11 lists the performance of the RLS SOC estimation method.…”

Section: Recursive Least Square (Rls)mentioning

confidence: 99%

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

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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“…In previous work [26], FFRLS was used to identify the parameters of the Thevenin model online. During battery operation, FFRLS-UKF estimates the SOC with model parameters constantly updated.…”

Section: Variable Forgetting Factor Strategy Considering Errorsmentioning

confidence: 99%

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

Lao

Xia

Wang³

et al. 2018

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Abstract:For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Cited by 89 publications

References 37 publications

State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

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

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

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