State of Charge Estimation of Lithium Batteries in Electric Vehicles Using IndRNN

Venugopal, Prakash; Vigneswaran, T.; Reka, S. Sofana

doi:10.1080/03772063.2021.1906770

Cited by 20 publications

(4 citation statements)

References 20 publications

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“…The SoC of a battery cannot be directly measured because it is an abstract concept that represents the amount of energy stored in the battery at a given moment [18]. Voltage measurement is a commonly used method for estimating the SoC of a Li-ion battery.…”

Section: Soc Estimationmentioning

confidence: 99%

Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review

Kaleem

2023

AI and Autonomous Systems

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Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion (Li-ion) batteries are the most widely used type of batteries in EVs because of their superior performance as compared to their counterparts. The core of EVs is their battery management systems (BMS), which can unarguably improve a battery's performance, operation, safety, and lifespan. Li-ion battery state estimation is one of the most important parts of the implementation of BMS, as it serves an important role in safe and reliable battery operation. Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. The objective of this study is to review, characterize, and compare various ML-based approaches for the state estimation of different Li-ion battery states. Firstly, this study describes and characterizes several Li-ion battery state estimation approaches proposed in recent years. Secondly, the battery state estimation of electric vehicles is discussed. In addition, the challenges and prospects of Li-ion battery state estimation are put forward.

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Section: Soc Estimationmentioning

confidence: 99%

Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review

Kaleem

2023

AI and Autonomous Systems

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“…Machine learning for parameter estimation has proved various applications with reliable and efficient prediction of states associated with it. The state of the art of the machine learning-based approach has been reviewed by researchers [11,12]. Various parameters affect the performance of Li ion due to which the conditions associated with operation such as over discharge and over charge may lead to catastrophic failure.…”

Section: Machine Leaning Technique For Parameter Estimationmentioning

confidence: 99%

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

Surya

Saldanha²,

Williamson

2021

Electronics

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The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

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“…The integration of machine learning models allows EVs to adapt to dynamic driving conditions, account for battery aging effects, and improve overall system robustness. Moreover, machine learning techniques enable EVs to tap into the potential of data-driven insights, further enhancing their role in the future of sustainable transportation [18][19][20][21][22]. In this review, we will delve into the diverse spectrum of machine learning methods and algorithms employed in SoC estimation for EVs.…”

Section: Introductionmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing state of charge management efficiency and effectiveness for electric vehicles, offering critical insights.. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

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State of Charge Estimation of Lithium Batteries in Electric Vehicles Using IndRNN

Cited by 20 publications

References 20 publications

Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review

Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

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