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

Zheng, Yongliang; Feng, He; Wang, Wenliang

doi:10.3390/electronics8121391

Cited by 21 publications

(10 citation statements)

References 18 publications

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“…Although the ECM is perfectly accessible, identifying the model parameters is complicated. Numerous investigations have demonstrated that the ECM is of high accuracy and robustness in SOC estimation, since in this approach, chemical and physical changes of the battery can be precisely monitored [5,6].…”

Section: Introductionmentioning

confidence: 99%

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State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

Mahboubi

Gavzan

Saidi

et al. 2022

IET Electrical Syst in Trans

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The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real‐time of SOC in lithium‐ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square‐Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non‐linear least square method that acquired the second‐order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium‐ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions.

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Section: Introductionmentioning

confidence: 99%

“…Some of these algorithms are reviewed in Ref. [6][7][8][9][10][11][12]. Xu et al [26] employed an EKF for the SOC estimation after modelling the battery with a stochastic fuzzy NN.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

Mahboubi

Gavzan

Saidi

et al. 2022

IET Electrical Syst in Trans

View full text Add to dashboard Cite

show abstract

“…In addition, the unbalance of the battery module may increase replacement and maintenance cost, etc. [10][11][12]. To solve the above issues, the usage of an equalizer could keep the battery module balance, improve energy utilization, and further prolong the life span of the battery module [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development of an Active Equalizer for Lithium-Ion Batteries

Yang

2022

Electronics

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In this paper, a bi-directional-buck-boost-converter-based active equalizer is developed. The energy between adjacent cells can be transferred bi-directionally by manipulating the balancing current to solve the unbalanced problem in a battery module. It is noted that the conduction time of the main switch in the conventional buck-boost equalizer is fixed. Thus, the balancing current will diminish as the voltage difference of the adjacent cells decreases, which results in a prolonged equilibrium period. This paper has proposed two methods, namely, the varied-on-time (VOT) method and the voltage ratio modulation (VRM) method, to shorten the equilibrium period. In the VOT method, the conduction time of the main switch is determined according to high state-of-charge (SOC) cell voltage. In this way, the balancing current is able to be kept at the desired level rather than reduced during the balancing process. On the other hand, the VRM method computes the proportion of the conduction time and the cut-off time in a switching cycle based on the voltages of adjacent cells. Hence, the equalizer can deliver the maximum energy in a switching period and shorten the equilibrium period. The simulation platform and experiments with four batteries connected in serial are carried out to verify the proposed control methods. According to the experimental results, the VOT method saves 10.3%, 11.7%, and 16% of the equilibrium time compared with the fixed duty cycle (FDC) method. The VRM method can shorten 35.9%, 36.6%, and 37.3% of the equilibrium time compared with the FDC method.

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“…Rather than wasting energy to improve efficiency, active balancing may be preferable when the number of cells is small, although the equalization becomes less efficient and slower when the number increases. Efficient use of balancing methods not only increases battery usage time, but also extends battery life [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Low Voltage Battery Management System with Internal Adaptive Charger and Fuzzy Logic Controller

2020

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Lithium ion (Li-Ion) and lithium polymer (Li-Po) batteries need to be used within certain voltage/current limits. Failure to observe these limits may result in damage to the battery. In this work, we propose a low voltage battery management system (LV-BMS) that balances the processes of the battery cells in the battery pack and the activating-deactivating of cells by guaranteeing that the operation is within these limits. The system operates autonomously and provides energy from the internal battery. It has a modular structure and the software is designed to control the charging and discharging of eight battery cells at most. A STM32F103 microcontroller is used for system control. The fuzzy logic controller (FLC) is used to set the discharge voltage limit to prevent damage to the battery cells, shorten the settlement time and create a specialized design for charge control. The proposed structure enables solar panel or power supplies with different voltage values between 5 V and 8 V to be used for charging. The experimental results show there was a 42% increase in usage time and the voltage difference between the batteries was limited to a maximum of 65 mV. Moreover, the charge current settles at about 20 ms, which is a much faster response when compared to a PID controller.

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

Cited by 21 publications

References 18 publications

State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

Development of an Active Equalizer for Lithium-Ion Batteries

Low Voltage Battery Management System with Internal Adaptive Charger and Fuzzy Logic Controller

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