To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error.

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Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach

Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

Power train electrification is promoted as a potential alternative to reduce carbon intensity of transportation. Lithium-ion batteries are found to be suitable for hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), and temperature control on lithium batteries is vital for long-term performance and durability. Unfortunately, battery thermal management (BTM) has not been paid close attention partly due to poor understanding of battery thermal behaviour. Cell performance change dramatically with temperature, but it improves with temperature if a suitable operating temperature window is sustained. This paper provides a review on two aspects that are battery thermal model development and thermal management strategies. Thermal effects of lithium-ion batteries in terms of thermal runaway and response under cold temperatures will be studied, and heat generation methods are discussed with aim of performing accurate battery thermal analysis. In addition, current BTM strategies utilised by automotive suppliers will be reviewed to identify the imposing challenges and critical gaps between research and practice. Optimising existing BTMs and exploring new technologies to mitigate battery thermal impacts are required, and efforts in prioritising BTM should be made to improve the temperature uniformity across the battery pack, prolong battery lifespan, and enhance the safety of large packs.

/pdf/a-critical-review-of-thermal-management-models-and-solutions-4ckn8yrgfu.pdf

A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles

A review on prognostics and health monitoring of Li-ion battery

State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures

State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge

Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation

The conventional test to obtain the direct current internal resistance (DCIR) has only experimented with a duration time of 5 seconds in the discharge region[3]~[5]. To obtain the DCIR, the duration time, Deltat and the region condition are important for the hybrid electric vehicle (HEV). In this paper, a new measurement method to obtain a direct current internal resistance (DCIR) is proposed. The proposed approach is performed during 10 seconds in the charge and discharge regions in order to obtain the new relationship between the DCIR and the state of charge (SOC). Thus, this obtained data can be used to estimate the battery pulse power using the previous SOC algorithm, extended Kalman filter (EKF)[6], which includes the DCIR-SOC relationship. The experiments are achieved using a fresh 1.3 Ah 18650 type Li-ion battery at 25degC.

A new direct current internal resistance and state of charge relationship for the Li-ion battery pulse power estimation

The State of charge (SOC) estimation algorithm uses the model parameter, which includes capacity, open circuit voltage (OCV), resistance and capacitance. These model parameter values are subject to be changed at varying temperatures[1–4]. In order to estimate SOC for temperature variation, it is indispensable to include the temperature effect on battery's performance. In this paper, the state of charge estimation employing empirical parameters measurements is introduced for SOC estimation for various temperatures. The internal parameters are measured on Li-Ion batteries from 10°C through 50°C at an interval of 10°C. Especially, the modified parameters are applied to estimate SOC at below room temperature and low SOC. The measured results are incorporated in the extend kalman filter (EKF) algorithm[5,6] and verified by comparison of Ah-counting and EKF result. The estimation and the results are shown to be under 3%.

The State of Charge estimation employing empirical parameters measurements for various temperatures

In this report, a method for estimating pulse power performance according to pulse duration is proposed. This approach can be used for power control logic in an environmentally friendly power generation system such as electric vehicles and an energy storage system (ESS). Although there have been studies on pulse power capability, we are unaware of any publications on the estimation of the magnitude of pulse power according to the power usage time, and the verification of the estimation result. Therefore, we propose a method to predict power performance according to the pulse duration of batteries and supercapacitors that are used in eco-friendly power generation systems. The proposed method is systematically presented using both a lithium-ion battery module with a nominal voltage of 44 V, 11 Ah, and a supercapacitor module with a maximum voltage of 36 V and a capacitance of 30 F.

https://www.mdpi.com/2079-9292/8/12/1395/pdf

Seongjun Lee

Papers

State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge

Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation

A new direct current internal resistance and state of charge relationship for the Li-ion battery pulse power estimation

The State of Charge estimation employing empirical parameters measurements for various temperatures

Power Capability Analysis of Lithium Battery and Supercapacitor by Pulse Duration