Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales

Li, Peiqing; Wang, Huile; Xing, Zixiao; Ye, Kanglong; Li, Qipeng

doi:10.1108/jimse-09-2020-0008

Cited by 9 publications

(9 citation statements)

References 35 publications

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“…It has been found that the optimal temperature should be kept within a narrow variation of 20–45°C. Therefore, battery thermal management system (BTMS) must be well designed to control the battery temperature within the safe range (Li et al , 2020; Shi et al , 2021; Teng et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

Numerical study on thermal performance of cold plates with leaf type channels for lithium-ion batteries

Wang

Shen

et al. 2023

HFF

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Purpose The purpose of this study is to design a new type of cold plate to improve the thermal performance of liquid-cooled thermal management system of lithium-ion batteries. Design/methodology/approach A cold plate with leaf type channels is proposed to enhance the cooling performance. Effects of the leaf type channel parameters (i.e. channel angle 20°, 40°, 60°, 80°; coolant mass flow rate 0.25 × 10–3, 0.50 × 10–3, 0.75 × 10–3, 1.00 × 10–3, 1.25 × 10–3 kg·s−1; channel number 1, 3, 5, 7) on the performance are numerically investigated by using a 3D mathematical model. Findings Compared to the traditional I type channels, the leaf type channels have better cooling performance. It is found that the battery temperature variation and channel pressure drop are decreased with decreasing channel angle and increasing channel number. In addition, the cooling performance can be improved by increasing the coolant mass flow rate. Practical implications This study can provide guidance for the development of novel effective cold plates. Originality/value The design of cold plates with leaf type channels can be used in liquid-cooled thermal management system to reduce the battery temperature difference.

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Section: Introductionmentioning

confidence: 99%

Numerical study on thermal performance of cold plates with leaf type channels for lithium-ion batteries

Wang

Shen

et al. 2023

HFF

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“…7 In addition, various methods, such as the total leastsquares algorithm for capacity estimation, 8 hybrid neural networks, 9 and improved long short-term memory (LSTM) algorithms, 10 have been studied to estimate the SOH. 11,12 Accordingly, original equipment manufacturers manufacture eco-friendly vehicles that demand certain accuracies in terms of the SOC and SOH, that is, typically within 5% and 10% for the SOC and SOH, respectively. Several studies and patents related to SOC and SOH estimation algorithms exist; however, accuracy standards have not been stipulated.…”

Section: Introductionmentioning

confidence: 99%

“…These include OCV, model‐based, data‐driven, hybrid, 6 and combined methods 7 . In addition, various methods, such as the total least‐squares algorithm for capacity estimation, 8 hybrid neural networks, 9 and improved long short‐term memory (LSTM) algorithms, 10 have been studied to estimate the SOH 11,12 . Accordingly, original equipment manufacturers manufacture eco‐friendly vehicles that demand certain accuracies in terms of the SOC and SOH, that is, typically within 5% and 10% for the SOC and SOH, respectively.…”

Section: Introductionmentioning

confidence: 99%

Method for evaluating the accuracy of state‐of‐charge (SOC)/state‐of‐health (SOH) estimation of BMSs

Song,

Shin

2023

Energy Science & Engineering

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The battery management system (BMS), which is widely adopted in various eco‐friendly automobiles that use batteries for propulsion, increases energy efficiency and extends the usable life by monitoring and optimally managing batteries. Eco‐friendly automobile original equipment manufacturers demand improvements in state‐of‐charge (SOC) and state‐of‐health (SOH) accuracies. However, because standards for evaluating accuracy do not exist, evaluations are performed using methods suggested by BMS developers or researchers, which differ from each other. Thus, the reliability of the SOC and SOH accuracy results is low. To accurately evaluate the SOC/SOH, it is necessary to reflect various conditions such as the power of the battery during actual vehicle operation and the temperature inside and outside the battery at the time. Therefore, a method is proposed herein that individually or simultaneously evaluates the SOC and SOH estimation accuracies of the BMS while considering an eco‐friendly vehicle's actual driving environment, instead of simple charge and discharge test conditions and room temperature. The reference values for evaluating the accuracy of SOC/SOH estimation in a battery can be defined as follows: Full charge and full discharge are conducted before applying the evaluation pattern to calculate the precise available capacity of the battery. Subsequently, it is fully charged and discharged to its initial SOC. A load pattern reflecting the load conditions of the subsequent application is repeatedly applied. After concluding the evaluation pattern, the remaining capacity is discharged, thereby determining the reference value. The proposed method is applied to two types of BMSs to evaluate its accuracy in SOC/SOH estimation and the validity of the proposed method was verified.

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“…Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are introduced to expand their applications into nonlinear systems. [15][16][17] These methods effectively eliminated the process noise and measurement noise, and robust SOC information is provided based on the battery state-space analysis. However, the implementation of the algorithm is complex and complicated for the use of the mass of the matrix.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional KF method is only suitable for linear systems. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are introduced to expand their applications into nonlinear systems 15‐17 . These methods effectively eliminated the process noise and measurement noise, and robust SOC information is provided based on the battery state‐space analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Sakile

Sinha

2021

Energy Storage

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The state of charge (SOC) estimation of lithium-ion batteries is complex due to the various nonlinear uncertainties present in the battery. However, in this paper, a new nonlinear state observer (NSO) is proposed to be designed for the estimation of accurate and robust SOC. This proposed observer is suitable for both continuous and discrete-time nonlinear systems. To design the nonlinear observer, two-RC equivalent circuit model state equations are simulated for the dynamic behavior of the lithium-ion battery. The seventh-order polynomial fitting approach is assumed for the nonlinear relationship between opencircuit voltage (OCV) and SOC, and the exponential fitting method is used to estimate the battery's offline parameters. Lyapunov's stability criterion achieves the stability and convergence capability of the proposed method. An urban dynamometer driving schedule (UDDS) cycle was adopted to estimate the performance of the proposed observer by comparing it with the wellestablished methods like unscented Kalman filter (UKF) and sliding mode observer (SMO) algorithms, and it was found that the proposed observer achieved better performance like accurate SOC, high convergence capability, and less SOC error. K E Y W O R D S lithium-ion battery, nonlinear state observer, open-circuit voltage and equivalent circuit model, SOC

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Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales

Cited by 9 publications

References 35 publications

Numerical study on thermal performance of cold plates with leaf type channels for lithium-ion batteries

Numerical study on thermal performance of cold plates with leaf type channels for lithium-ion batteries

Method for evaluating the accuracy of state‐of‐charge (SOC)/state‐of‐health (SOH) estimation of BMSs

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

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