Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles

Meng, Jinhao; Ricco, Mattia; Acharya, Anirudh Budnar; Luo, Guangzhao; Świerczyński, Maciej; Stroe, Daniel‐Ioan; Teodorescu, Remus

doi:10.1016/j.jpowsour.2018.05.082

Cited by 55 publications

(28 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed methods show good robustness with a dynamic current at different temperatures. Also in another study [160], the two PIO were used to compensate for the inaccuracy of the SOC and current sensor error. Furthermore, they compared their proposed method with the EKF.…”

Section: Reference Mae (%)mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Reference Mae (%)mentioning

confidence: 99%

“…Xu et al 2014 [154,155] ME ≤ ± 2% Tang et al 2015 [26] ME ≤ ± 2.5% Zheng et al 2016 [158] ME ≤ ± 3.58% Wei et al 2017 [159] ME ≤ ± 5% Meng et al 2018 [160] ME ≤ ± 1.86%…”

Section: Reference Mae (%)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

View full text Add to dashboard Cite

show abstract

“…The corresponding profile of current and voltage on the low-current OCV test is shown in Figure 2, and the curve of SOC and OCV is shown in Figure 3. The relationship between SOC and OCV is expressed by Equation (3). The errors between the real OCV and the measured OCV are given in Figure 4, and the values of i a are shown in Table 2.…”

Section: Ocv-soc Mapping Testmentioning

confidence: 99%

“…The state of charge (SOC) is one of the most significant parameters for the lithium-ion battery because the SOC of the cell represents the fuel gauge in an EV application. It is not only a crucial parameter for drivers but also an index in the battery management system (BMS) [3]. Without an accurate SOC knowledge, EVs would not meet the requirements for a safe and reliable operation, as the BMS would not manage to predict the driving range accurately nor to balance the battery cells [4].…”

Section: Introductionmentioning

confidence: 99%

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Liu

Zheng

et al. 2020

Energies

Self Cite

View full text Add to dashboard Cite

In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

show abstract

“…Citation information: DOI 10.1109/TIE.2018.2880668, IEEE Transactions on Industrial Electronics IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS RLS[35] is used to identify the parameters (i.e., the resistance R0, R1 and R2, the capacitance C1 and C2) in the two RC ECM. Eq.…”

mentioning

confidence: 99%

A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Meng

Stroe

Ricco

et al. 2019

IEEE Trans. Ind. Electron.

Self Cite

149

View full text Add to dashboard Cite

The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM). Index Terms-State-of-charge estimation, partial least squares regression, Kalman filter, Lithium-ion battery. I. INTRODUCTION  ith the significant progress of the battery technology, Lithium-ion (Li-ion) batteries have become a promising choice for Electrical Vehicle (EV) [1] and Battery Energy Storage System (BESS) [2], [3]. The extensive usage of the Li-ion batteries is mainly because of their superior properties including long lifespan, high energy density, low self-discharge 

show abstract

Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles

Cited by 55 publications

References 38 publications

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

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

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Contact Info

Product

Resources

About