scholarly journals Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

2020 ◽  
Vol 11 (3) ◽  
pp. 50 ◽  
Author(s):  
Zachary Bosire Omariba ◽  
Lijun Zhang ◽  
Hanwen Kang ◽  
Dongbai Sun
Keyword(s):  
Parameter Identification ◽  
State Estimation ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Model ◽  
Temperature Dynamics

There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

scholarly journals A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 394 ◽  
Author(s):  
Qi Zhang ◽  
Yan Li ◽  
Yunlong Shang ◽  
Bin Duan ◽  
Naxin Cui ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Lithium Ion ◽  
Absolute Error ◽  
Battery Management System ◽  
Order Kinetic ◽  
Battery Management ◽  
Nonlinear Capacity ◽  
Battery Model ◽  
Parameter Identifications

Accurate battery models are integral to the battery management system and safe operation of electric vehicles. Few investigations have been conducted on the influence of current rate (C-rate) on the available capacity of the battery, for example, the kinetic battery model (KiBaM). However, the nonlinear characteristics of lithium-ion batteries (LIBs) are closer to a fractional-order dynamic system because of their electrochemical materials and properties. The application of fractional-order models to represent physical systems is timely and interesting. In this paper, a novel fractional-order KiBaM (FO-KiBaM) is proposed. The available capacity of a ternary LIB module is tested at different C-rates, and its parameter identifications are achieved by the experimental data. The results showed that the estimated errors of available capacity in the proposed FO-KiBaM were low over a wide applied current range, specifically, the mean absolute error was only 1.91%.

Modeling and Simulation Research on Lithium-Ion Battery in Electric Vehicles Based on Genetic Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 246-249
Author(s):  
Cheng Lin ◽  
Xiao Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Lithium Ion ◽  
Model Parameters ◽  
Test Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Simulation Research ◽  
Battery Model

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

scholarly journals Advanced Lithium-Ion Battery Model for Power System Performance Analysis

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2411 ◽  
Author(s):  
Szymon Potrykus ◽  
Filip Kutt ◽  
Janusz Nieznański ◽  
Francisco Jesús Fernández Morales
Keyword(s):  
Lithium Ion Battery ◽  
System Performance ◽  
System Development ◽  
Storage System ◽  
Lithium Ion ◽  
Model Parameters ◽  
Diffusion Resistance ◽  
System Modelling ◽  
Battery Management System ◽  
Battery Model

The paper describes a novel approach in battery storage system modelling. Different types of lithium-ion batteries exhibit differences in performance due to the battery anode and cathode materials being the determining factors in the storage system performance. Because of this, the influence of model parameters on the model accuracy can be different for different battery types. These models are used in battery management system development for increasing the accuracy of SoC and SoH estimation. The model proposed in this work is based on Tremblay model of the lithium-ion battery. The novelty of the model lies in the approach used for parameter estimation as a function of battery physical properties. To make the model perform more accurately, the diffusion resistance dependency on the battery current and the Peukert effect were also included in the model. The proposed battery model was validated using laboratory measurements with a LG JP 1.5 lithium-ion battery. Additionally, the proposed model incorporates the influence of the battery charge and discharge current level on battery performance.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

scholarly journals Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5265
Author(s):  
Longxing Wu ◽  
Kai Liu ◽  
Hui Pang ◽  
Jiamin Jin
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Current Sensor ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Electrochemical Model ◽  
The Impact

State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals In-Operando Impedance Spectroscopy and Ultrasonic Measurements during High-Temperature Abuse Experiments on Lithium-Ion Batteries

Batteries ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 25 ◽  
Author(s):  
Hendrik Zappen ◽  
Georg Fuchs ◽  
Alexander Gitis ◽  
Dirk Sauer
Keyword(s):  
High Temperature ◽  
Impedance Spectroscopy ◽  
Lithium Ion Batteries ◽  
Measurement Techniques ◽  
Lithium Ion ◽  
Ultrasonic Waves ◽  
Operating Conditions ◽  
Battery Management System ◽  
Battery Cell ◽  
In Operando

Lithium-Ion batteries are used in ever more demanding applications regarding operating range and safety requirements. This work presents a series of high-temperature abuse experiments on a nickel-manganese-cobalt oxide (NMC)/graphite lithium-ion battery cell, using advanced in-operando measurement techniques like fast impedance spectroscopy and ultrasonic waves, as well as strain-gauges. the presented results show, that by using these methods degradation effects at elevated temperature can be observed in real-time. These methods have the potential to be integrated into a battery management system in the future. Therefore they make it possible to achieve higher battery safety even under the most demanding operating conditions.

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