An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles (original) (raw)
Related papers
State of Charge Estimation of Lithium-Ion Batteries by Extended Kalman Filtering
Advanced Science Letters, 2012
With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.
State of charge estimation of a Li-ion battery based on extended Kalman filtering and sensor bias
The growing usage of electric vehicles (EVs) has led to significant advancements in batteries' technology. State of charge (SOC) estimation is an essential function of the battery management system-the heart of EVs and Kalman filtering is a standard SOC estimation method. Because of the non-uniformities in tuning and testing scenarios, it is challenging to quantify SOC estimation algorithms' performance. A SOC estimation algorithm is developed in this work, extended Kalman filter (EKF), and tested for variable scenarios like adding sensor noise and bias to terminal voltage and current and varying state and parameter initializations. Also, a dual EKF is implemented to estimate the sensor voltage and current bias and compared it against the state EKF to estimate SOC. Finally, a comparative study has been introduced to decide which algorithm represents the most accurate estimation for the battery parameters, and it was found that the dual EKF gave the best results. K E Y W O R D S dual extended Kalman filter, extended Kalman filter, Kalman filter, lithium-ion batteries, sensor bias, state of charge, unsupervised learning tools
Estimation of lithium-ion battery state-of-charge using an extended kalman filter
Bulletin of Electrical Engineering and Informatics, 2021
The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESS Energies 2015, 8 5917
State of charge estimation based on a modified extended Kalman filter
International Journal of Electrical and Computer Engineering (IJECE), 2023
The global transition from fossil-based automobile systems to their electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modified priori estimate and a compensating proportional gain, an improved extended Kalman filter (IEKF) for the estimation task due to its nonlinear application and adaptiveness to noise. The improvement was achieved by incorporating the residuals of the previous state matrices to the current state predictor and introducing an attenuating factor in the Kalman gain, which was chosen to counteract the effect of the measurement and process noise resulting in better accuracy performance than the conventional SOC curve fitting-based estimation and ampere hour methods. Simulation results show that the standard EKF estimator results in performance with an error bound of 12.9% due to an unstable start, while the modified EKF reduces the maximum error to within 2.05% demonstrating the quality of the estimator.
Energies, 2012
In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.
2023
In recent years, the energy crisis has become more and more serious. Li-ion batteries are used in grids because of their benefits such as contributing to the intermittent generation of renewable energy sources and stabilizing the grid. In addition, li-ion batteries are widely used in electric vehicles due to their long cycle life and high energy density. Li-ion battery state of charge (SoC) is an important indicator for safety. Therefore, the SoC estimation of li-ion batteries is important. Today, there are different methods to determine the state of the SoC in many applications. The traditional estimation method, the ampere-hour integration method and the coulomb counting method, has a cumulative error and cannot achieve good results in a working environment with Gaussian noise. For this purpose, in this study, firstly, the Thevenin equivalent model was created for battery SOC estimation, and then the Kalman filter algorithm was applied. Thus, the estimation error caused by Gaussian noise is eliminated. SoC estimation was simulated for the battery model created in the MATLAB/Simulink program using this method. Using these simulation results, the charge/discharge characteristics of the battery were obtained. However, the SoC estimation has been made for the charging and discharging processes of the battery. In the simulation, the charge value was recorded for 6 hours. The data recorded every 10 minutes gave results very close to the true value.
Inventions, 2019
The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.
Batteries
The Li-Ion battery state-of-charge estimation is an essential task in a continuous dynamic automotive industry for large-scale and successful marketing of hybrid electric vehicles. Also, the state-of-charge of any rechargeable battery, regardless of its chemistry, is an essential condition parameter for battery management systems of hybrid electric vehicles. In this study, we share from our accumulated experience in the control system applications field some preliminary results, especially in modeling, control and state estimation techniques. We investigate the design and effectiveness of two state-of-charge estimators, namely an extended Kalman filter and a proportional integral observer, implemented in a real-time MATLAB environment for a particular Li-Ion battery. Definitely, the aim of this work is to find the most suitable estimator in terms of estimation accuracy and robustness to changes in initial conditions (i.e., the initial guess value of battery state-of-charge) and changes in process and measurement noise levels. By a rigorous performance analysis of MATLAB simulation results, the potential estimator choice is revealed. The performance comparison can be done visually on similar graphs if the information gathered provides a good insight, otherwise, it can be done statistically based on the calculus of statistic errors, in terms of root mean square error, mean absolute error and mean square error.