Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell

Venugopal, Prakash; Shankar, Sukrit; Jebakumar, C Phillip; Agarwal, Reena; Alhelou, Hassan Haes; Reka, S. Sofana; Golshan, Mohamad Esmail Hamedani

doi:10.1109/access.2021.3130994

Cited by 22 publications

(7 citation statements)

References 39 publications

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“…The results obtained from the neural network algorithm shows the error rate of ±5%. The LSTM algorithm works better in predicting RUL with ±10 cycles of accuracy [2].…”

Section: Background Workmentioning

confidence: 98%

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

Harippriya,

Jayanthy,

Esakki vigneswaran

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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A hybrid energy storage system (HESS) by integrating Lithium-Ion Battery and Wind Turbine System for Electric Vehicle is designed and implemented. An advanced model of lithium ion/wind turbine HESS model is developed to improve the battery’s charging capacity. The behavior of this model is tested by using Machine Learning Algorithms to identify remaining battery capacity of the energy storage components. The Machine learning Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) Decision Tree (DT) and Linear Regression (LR) are implemented which helps in predicting the State of Charge (SoC) in the electric vehicle to estimate the battery’s lifetime. The SoC of the EV without HESS is calculated and compared with the SoC of HESS implemented EV model. Sensors are used to measure the current and voltage parameters of the Lithium-Ion Battery setup (Internal Battery) and Storage Battery (External Battery). The measured data is sent to the LPC2148 ARM microcontroller Board. The data that is obtained from LPC2148 is given as input to the machine learning algorithms which are executed in the Computer. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square (RMSE), R-Square Error (R2E) and Mean Square Error (MSE) are calculated and the SoC values are predicted. The Random Forest algorithm produces better results for the HESS model as the Error values produced are less than 2.5%. The battery capacity of the electric vehicle has been improved by using external battery that is charged by wind turbine. Thus, the driving range of the electric vehicle implemented with HESS is increased twice when compared to conventional electric vehicle [1].

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“…The results obtained from the neural network algorithm shows the error rate of ±5%. The LSTM algorithm works better in predicting RUL with ±10 cycles of accuracy [2].…”

Section: Background Workmentioning

confidence: 98%

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

Harippriya,

Jayanthy,

Esakki vigneswaran

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…It can be seen that the indirect HI extracted from the external data of the battery can well characterize the aging degree of the battery. At the same time, it also avoids the defect that direct HI cannot be obtained online and predicted in time [86,87], considering that the external data obtained in the battery discharge stage is greatly affected by external factors. Therefore, the indirect HI extracted based on the battery-charging stage is more suitable for practical applications.…”

Section: Fusion Of Hismentioning

confidence: 99%

Review on the Selection of Health Indicator for Lithium Ion Batteries

Zhou

Zheng

2022

Machines

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Scientifically and accurately predicting the state of health (SOH) and remaining useful life (RUL) of batteries is the key technology of automotive battery management systems. The selection of the health indicator (HI) that characterizes battery aging affects the accuracy of the prediction model construction, which in turn affects the accuracy of SOH and RUL estimation. Therefore, this paper analyzes the current status of HI selection for lithium-ion batteries by systematically reviewing the existing literature on the selection of HIs. According to the relationship between HI and battery aging, battery HI can be divided into two categories: direct HI and indirect HI. The capacity and internal resistance of the battery can directly represent the aging degree of the battery and are the direct HIs of the battery. Indirect HIs refer to characteristic parameters extracted from battery charge and discharge data that can characterize the degree of battery aging. This paper analyzes and summarizes the advantages and disadvantages of various HIs and indirect HIs commonly used in current research, providing useful support and reference for future researchers in selecting HIs to characterize battery aging. Finally, in view of the capacity regeneration phenomenon in the aging process of the battery, the selection direction of future HI is proposed.

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“…The use of ML methods in BMS was extensively examined in this study to generate an appropriate battery model. SOH and RUL for Li-Ion 18650 cells could be estimated using the approach outlined in the publication [6], which considers various variables, such as the cell's current SOC, internal resistance, discharge voltage, and capacity. Various battery mathematical techniques are developed and deployed on a stand-alone hardware device to determine an optimal SOH and RUL ML-based prediction model.…”

Section: Introductionmentioning

confidence: 99%

Battery life time prediction of electric vehicle using artificial intelligence

Ch.Sreedevi¹,

Shrimali

Leela³

et al. 2022

ijhs

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The widespread use of electric vehicles (EVs) is viewed as a turning point for lower emissions of co2 and much more advanced driver assistance systems. It is time-consuming to predict the battery capacity of the electric vehicle using the endurance and dependability of battery systems. Because battery deterioration is typically non-linear, predicting the capacity of the battery of charge estimation with significantly less deterioration is incredibly time-consuming. As a result of this complexity, real-time control of battery storage has proven difficult. Nevertheless, using the latest advancements in battery deterioration comprehension, simulation techniques, and testing, there is a potential to combine this expertise with new machine learning (ML) approaches to find a potential solution for this complexity. In this study, the battery life is predicted using three different regression models Linear Regression, Ridge Regression, and Ensemble Regression. The model's error analysis is conducted to improve the battery's performance characteristics. Finally, the three ML algorithms are compared using performance parameters. Out of the three regression models, the Ensemble regression model is found to be the best model which has an accuracy of 94.8%.

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Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 22 publications

References 39 publications

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

Review on the Selection of Health Indicator for Lithium Ion Batteries

Battery life time prediction of electric vehicle using artificial intelligence

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