Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Shrivastava, Prashant; Soon, Tey Kok; Idris, Mohd Yamani Idna; Mekhilef, Saad

doi:10.1016/j.rser.2019.06.040

Cited by 434 publications

(148 citation statements)

References 159 publications

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“…The successful application of the Kalman filter in SOC estimation depends on the accurate model of the battery. Many studies [16][17][18] have shown that the ECM can be successfully used in SOC estimation because it can accurately reflect the physical and chemical changes of the battery, and the computing cost is low, which conforms to the requirements of a BMS. Considering the accuracy and computational complexity of the battery model, a second-order ECM is selected and plotted in Figure 1.…”

Section: Battery Of the Equivalent Circuit Modelmentioning

confidence: 82%

A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

2019

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State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.

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Section: Battery Of the Equivalent Circuit Modelmentioning

confidence: 82%

A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

2019

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“…The filtering algorithm are widely used in ECM methods, including Kalman filter (KF) series, information filter (IF), particle filtering (PF) and others. 141,142 The KF family methods are commonly used due to the moderate calculation cost and high precision, and have satisfying abilities to correct errors. Table 3 shows the comparison between different SOC estimation methods.…”

Section: Soc-based Equalization Strategy (Sbes)mentioning

confidence: 99%

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

et al. 2020

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The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium-ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium-ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies

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“…The overall research idea of calculating SOC of the battery on the basis of reduced‐order electrochemical model is shown in Figure . The ambient temperature, voltage, and current measurement signals are inputs, and the EKF algorithm is compiled based on the simplified electrochemical‐thermal coupling model . The algorithm estimates the concentration of lithium ion in the cathode solid phase, and then the SOC of the battery is calculated.…”

Section: Lithium Battery Modeling and Soc Estimationmentioning

confidence: 99%

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

Lin

Wang

et al. 2020

Int J Energy Res

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Summary Aiming at solving the problem of poor battery cell consistency caused by excessive decay of cell capacity or increased internal resistance during the operation of lithium‐ion battery packs for vehicles, the paper proposes an active equalization control with 12‐V power supply as an equalization energy source, which achieves efficient energy replenishment of individual cells with low power. The electrochemical‐thermal coupling model of lithium‐ion battery is built, and the order reduction of large‐scale system theory ensures that the model had higher accuracy and lower amount of calculation, which is suitable for vehicle battery management system (BMS). Then the extended Kalman filter algorithm is used to calculate the real‐time state of charge (SOC) of each cell and set as an equalization variable. The equalization simulation circuit is built with MATLAB/Simulink, the experimental platform of active equalization system for battery packs is constructed, and the battery packs are tested for equalization in static state. The simulation and experimental results show that the proposed active equalization control strategy can rapidly improve the voltage inconsistency between single cells, and the energy transfer efficiency can reach about 85% during the equalization process.

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Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Cited by 434 publications

References 159 publications

A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

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