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20 pages, 5380 KiB

Open AccessArticle

Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications

by Mina Naguib, Aashit Rathore, Nathan Emery, Shiva Ghasemi and Ryan Ahmed

Energies 2023, 16(16), 5887; https://doi.org/10.3390/en16165887 - 9 Aug 2023

Viewed by 1047

Abstract

Lithium-ion battery (LIBs) packs represent the most expensive and safety-critical components in any electric vehicle, requiring accurate real-time thermal management. This task falls under the battery management system (BMS), which plays a crucial role in ensuring the longevity, safety, and optimal performance of [...] Read more.

Lithium-ion battery (LIBs) packs represent the most expensive and safety-critical components in any electric vehicle, requiring accurate real-time thermal management. This task falls under the battery management system (BMS), which plays a crucial role in ensuring the longevity, safety, and optimal performance of batteries. The BMS accurately monitors cell temperatures and prevents thermal runaway by leveraging multiple temperature sensors; however, adding a temperature sensor to each individual cell is not practical and increases the total cost of the EV. This paper provides three key original contributions: (1) the development and optimization of a new efficient electro-thermal battery model that accurately estimates the LIB voltage and temperature, which reduces the required number of temperature sensors; (2) the investigation of the ECM parameters’ dependency on the state of charge (SOC) at a wide range of ambient temperatures, including cold temperatures; (3) the testing and validation of the proposed electro-thermal model using real-world dynamic drive cycles and temperature ranges from −20 to 25 °C. Results indicate the effectiveness of the proposed electro-thermal model, which shows good estimation accuracy with an average error of 50 mV and 0.5 °C for the battery voltage and surface temperature estimation, respectively. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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16 pages, 2716 KiB

Open AccessArticle

Health Monitoring of Lithium-Ion Batteries Using Dual Filters

by Richard Bustos, Stephen Andrew Gadsden, Pawel Malysz, Mohammad Al-Shabi and Shohel Mahmud

Energies 2022, 15(6), 2230; https://doi.org/10.3390/en15062230 - 18 Mar 2022

Cited by 19 | Viewed by 1794

Abstract

Accurate estimation of a battery’s capacity is critical for determining its state of health (SOH) and retirement, as well as to ensure its reliable operation. In this paper, a dual filter architecture using the Kalman filter (KF) and the novel sliding innovation filter [...] Read more.

Accurate estimation of a battery’s capacity is critical for determining its state of health (SOH) and retirement, as well as to ensure its reliable operation. In this paper, a dual filter architecture using the Kalman filter (KF) and the novel sliding innovation filter (SIF) was implemented to estimate the capacity and state of charge (SOC) of a lithium-ion battery. NASA’s Prognostic Center of Excellence (PCOE) B005 battery data set was selected for this experiment based on its wide use in academia and industry. This dataset contains cycling data of a 2 Ah lithium-ion battery until its capacity was measured at 1.3 Ah or less. The dual polarity equivalent circuit model (DP-ECM) was selected for modeling. The model parameter values were estimated using the least squares (LS) algorithm. Under normal operating conditions, both the dual-KF and dual-SIF performed similarly in terms of estimation accuracy. However, an uncertainty case was considered where the filters were subjected to rapid changing dynamics by cutting the data by 300 cycles. In this case, the battery capacity root-mean-square error (RMSE) for the dual-KF and the proposed dual-SIF were 0.1233 and 0.0675, respectively. Under rapidly changing dynamics and faulty conditions, the dual-SIF shows better convergence and robustness to disturbances. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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15 pages, 3987 KiB

Open AccessArticle

A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles

by Ephrem Chemali, Phillip J. Kollmeyer, Matthias Preindl, Youssef Fahmy and Ali Emadi

Energies 2022, 15(3), 1185; https://doi.org/10.3390/en15031185 - 6 Feb 2022

Cited by 37 | Viewed by 2979

Abstract

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a [...] Read more.

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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17 pages, 1788 KiB

Open AccessArticle

Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model

by Yinfeng Jiang, Wenxiang Song, Hao Zhu, Yun Zhu, Yongzhi Du and Huichun Yin

Energies 2022, 15(3), 963; https://doi.org/10.3390/en15030963 - 28 Jan 2022

Cited by 3 | Viewed by 1689

Abstract

The state of charge (SOC) of a lithium battery system is critical since it indicates the remaining operating hours, full charge time, and peak power of the battery. This paper recommends an extended Rauch–Tung–Striebel smoother (ERTSS) for estimating SOC. It is implemented based [...] Read more.

The state of charge (SOC) of a lithium battery system is critical since it indicates the remaining operating hours, full charge time, and peak power of the battery. This paper recommends an extended Rauch–Tung–Striebel smoother (ERTSS) for estimating SOC. It is implemented based on an improved equivalent circuit model with hysteresis voltage. The smoothing step of ERTSS will reduce the estimation error further. Additionally, the genetic algorithm (GA) is employed for searching the optimal ERTSS’s smoothing time interval. Various dynamic cell tests are conducted to verify the model’s accuracy and error estimation deviation. The test results demonstrate that ERTSS’s SOC estimation error is limited to

4 %

with an initial error between −25

^{\circ}

C and 45

^{\circ}

C and that the root mean square error (RMSE) of ERTSS’s SOC estimation is approximately 5% lower than that of extended Kalman filter (EKF). The ERTSS improves the SOC estimation accuracy at all operating temperatures of batteries. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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19 pages, 1452 KiB

Open AccessArticle

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

by Sara Rahimifard, Saeid Habibi, Gillian Goward and Jimi Tjong

Energies 2021, 14(24), 8560; https://doi.org/10.3390/en14248560 - 19 Dec 2021

Cited by 7 | Viewed by 2041

Abstract

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of [...] Read more.

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than

2 %

over the full operating range of SoC along with an accurate estimation of SoH. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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16 pages, 691 KiB

Open AccessArticle

Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

by Sungwoo Jo, Sunkyu Jung and Taemoon Roh

Energies 2021, 14(21), 7206; https://doi.org/10.3390/en14217206 - 2 Nov 2021

Cited by 33 | Viewed by 4666

Abstract

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a [...] Read more.

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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13 pages, 18837 KiB

Open AccessArticle

A Multi-Frequency Electrical Impedance Spectroscopy Technique of Artificial Neural Network-Based for the Static State of Charge

by Yi-Feng Luo

Energies 2021, 14(9), 2526; https://doi.org/10.3390/en14092526 - 28 Apr 2021

Cited by 8 | Viewed by 2615

Abstract

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC [...] Read more.

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed ANN-based multi-frequency EIS technique can be utilized to measure the static SOC of random discharge of Li-ion batteries. Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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Review

Jump to: Research

16 pages, 2042 KiB

Open AccessReview

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

by Maryam Ghalkhani and Saeid Habibi

Energies 2023, 16(1), 185; https://doi.org/10.3390/en16010185 - 24 Dec 2022

Cited by 22 | Viewed by 6338

Abstract

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of [...] Read more.

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV). Full article

(This article belongs to the Special Issue State-of-the-Art in Electric Vehicle Battery State of Charge, Health and Power Estimation)

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