Urban Dynamometer Driving Schedule Test Research Articles

A comparative study of state of charge estimation methods of ultracapacitors for electric vehicles considering temperature characteristics

This paper systematically compares and evaluates three types SOC estimation methods for ultracapacitors (UCs) under different ambient temperatures. Firstly, based on the UC dynamic test data, the key parameters of Thevenin model are identified by genetic algorithm. Next, the polynomial fitting is employed to determine the relationship between parameters and ambient temperatures, and the temperature-varied model is established. At the same time, according to the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF) and unscented Kalman filter algorithm (UKF), three kinds UC SOC estimation methods are designed, which are suitable for different ambient temperatures. Then, the accuracy, robustness and self-correction ability of these SOC estimation methods are evaluated by using the urban dynamometer driving schedule test data at −10 °C, 10 °C, 25 °C and 40 °C. Finally, the results indicate that the AEKF algorithm is superior to the EKF and UKF, and its average absolute error and root mean square error are within 0.227 % and 0.318 % at different temperatures, respectively. In addition, the proposed temperature-varied model has a wider application.

Early stage internal short circuit fault diagnosis for lithium-ion batteries based on local-outlier detection

Internal short circuit (ISC) is considered to be one of the main causes of battery thermal runaway, which is a critical obstacle to the application of lithium-ion batteries for energy storage. Aiming at inconspicuous characteristics and slow detection speed of early stage ISC faults, this paper proposes a fast diagnostic method for ISC based on local-gravitation outlier detection. In the serial battery module, the cell terminal voltages are normalized to characterize voltage trends that are more sensitive to faults than voltage magnitudes, which improves the speed of fault diagnosis. The normalized voltages are then evaluated by a local gravity outlier detection algorithm to detect faults, by which the anomalies caused by the faults are amplified to achieve the ability to diagnose early ISC faults. The performance of the method is validated under the Urban Dynamometer Driving Schedule test, where several sets of experimental results for ISC faults of varying severity showed that the proposed method could detect them accurately and rapidly even when the fault characteristics are not obvious.

State of charge estimation for electric vehicle lithium-ion batteries based on model parameter adaptation

Accurate state of charge (SOC) estimation is the basis of the battery management system in electric vehicles. In order to reduce the influence of time-varying model parameters on SOC estimation accuracy for lithium-ion batteries under complex operating conditions, this paper proposes an improved method for online model parameter identification using variable forgetting factor recursive least squares (VFFRLS), which is based on the sliding window time-varying forgetting factor theory. The VFFRLS was used for online parameter identification at the macroscopic timescale, and extended Kalman filter (EKF) was used for estimating the battery SOC at the microscopic timescale. The accuracy of parameter identification was verified using pulsed discharging and urban dynamometer driving schedule (UDDS) tests, and recursive least squares (RLS) was used to identify the parameters of lithium-ion batteries under UDDS test and estimate the SOC of lithium-ion batteries in combination with EKF, and the experimental results verify the accuracy and robustness of VFFRLS-EKF.

A low-complexity state of charge estimation method for series-connected lithium-ion battery pack used in electric vehicles

Practical and accurate state of charge estimation for battery pack is a challenging task due to the inconsistency among in-pack cells. In this paper, we propose a low-complexity state of charge estimation method for series-connected battery pack. Firstly, to reduce computation cost, the capacity and state of charge calculations of battery pack are effectively simplified based on the probability theory. Secondly, we propose a selection method for the representative cells, which is validated by the simulation under Dynamic Stress Test. Subsequently, the state of charge of each representative cell is estimated by the recursive least squares-adaptive extend Kalman filter algorithm successively. Finally, the aging experiments for LiNCM and LiFePO4 battery packs are carried out to validate the feasibility of the simplified method, and the complexity of the three estimation methods is compared. Experimental results indicate that the capacity of battery pack depends only on the representative cells at different cycles, and the proposed “representative cell” method can estimate the state of charge of battery pack with high accuracy and low complexity. The mean absolute errors and root mean square errors for state of charge estimation are less than 3% under Urban Dynamometer Driving Schedule test at different cycles.

Peak power prediction for series-connected LiNCM battery pack based on representative cells

Accurate power prediction for a battery pack is of great importance for efficient and safe operation of electric vehicles. However, a battery pack typically consists of dozens of cells connected in series, and how to accurately predict its peak power in practical applications has always been a challenging issue. This paper proposes a low-complexity peak power prediction method for a series-connected battery pack, where the peak power of battery pack is predicted depending only on representative cells. Firstly, considering state of charge and voltage limits for power prediction, the representative cells are selected by using two easily available variables, namely characteristic voltage and ohmic resistance. Secondly, the state of charge and voltage of the representative cell are estimated accurately by the dual adaptive extended Kalman filter algorithm. Finally, the multi-parameter limited method for battery pack is developed to predict the peak power under Urban Dynamometer Driving Schedule test at different temperatures. The experimental results verify the feasibility and robustness of the proposed selection method, and the peak power of battery pack is predicted with low complexity and high accuracy.

State‐of‐charge estimation of the lithium‐ion battery system with time‐varying parameter for hybrid electric vehicles

This study addresses the state-of-charge (SOC) estimation problem for a lithium-ion battery system with time-varying parameter. Although many methods have been proposed for the SOC estimation, the time-varying parameter problem has not been solved perfectly up to now. In this study, by introducing a novel physical meaning for that the battery terminal current is ℒ 2 -norm bounded and solving one linear matrix inequality, a robust H ∞ filter is proposed for the first time to estimate the SOC of the lithium-ion battery of hybrid electric vehicles. The proposed method is evaluated by the sequence of urban dynamometer driving schedule test. The experimental results show that the filter proposed here is more accurate and reliable than that without considering the time-varying parameter.

Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery

A new method for battery state of charge estimation using a sliding mode observer has been developed. A nonlinear battery dynamic modeling technique is established and design methodology with the sliding mode observer is presented. Contrary to the conventional methods using complicated battery modeling, a simple resistor-capacitor battery model was used in this work. The modeling errors caused by the simple model are compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The convergence of the proposed observer has been proved by the equivalent control method. The performance of the system has been verified by the sequence of urban dynamometer driving schedule test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

The novel state of charge estimation method for lithium battery using sliding mode observer

In this paper, a new state of charge estimation method for lithium battery has been presented. Contrary to the conventional methods which use complicated battery modeling, a simple resistor–capacitor battery model was used in order to reduce calculation time and system resource. Modeling errors caused by the simple model are compensated by the sliding mode observer. The structure of the proposed system is simple, but it shows robust control property against modeling errors and uncertainties. The state equation for battery model and the systematic design approach for sliding mode observer also have been presented. The convergence of proposed observer has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has robust tracking performance with reduced calculation time under the real driving environments.