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

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.

State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra RC branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

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

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

Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering

Li-Ion Battery SOC Estimation Method Based on the Reduced Order Extended Kalman Filtering

This approach investigates with an equivalent circuit modeling a polymer electrolyte membrane (PEM) fuel cell degradation combined with a low-frequency ripple current (LFRC) that may shorten the fuel cell life and worsen the fuel efficiency. Through the immediate measurement of the impedance curves on single cells obtained after cycling for hours and operating in cathode flooding and membrane dehydration modes at variable frequencies, it has been shown that the impedance magnitude of a fuel cell injecting a LFRC increased when compared with that of a high-frequency ripple current. This study develops these investigations one step further by designing equivalent circuit elements like resistance and capacitance by electrochemical impedance spectroscopy. Impedance-based model is able to describe the dynamic behavior of the PEM fuel cell. For validation of the proposed fuel cell model, an experimental study has been carried out for a 1.2-kW Nexa module.

Equivalent Circuit Modeling of PEM Fuel Cell Degradation Combined With a LFRC

In this paper, a real time estimate of a battery model is proposed. The existing impedance model is accurate in predicting the behavior of a battery. However, it is complex and not applicable for real time applications. Also, it requires much time and cost to obtain the parameters of the model. A simple lumped circuit model is presented. The parameters and states (overvoltages) of the battery are estimated with a filtering technique and system identification theory. It is verified through simulations and experiments that the proposed method is in good agreement with the dynamic characteristics of the battery, regardless of the conditions of the battery, and the estimated overvoltages are given physical meanings through a comparison with an impedance model with complex RC ladder circuits.

Jaemoon Lee

Papers

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

Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering

Li-Ion Battery SOC Estimation Method Based on the Reduced Order Extended Kalman Filtering

Equivalent Circuit Modeling of PEM Fuel Cell Degradation Combined With a LFRC

Modeling and Real Time Estimation of Lumped Equivalent Circuit Model of a Lithium Ion Battery