Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System

Kim, Min-Joon; Chae, Sung-Hun; Moon, Yeon-Kug

doi:10.1109/isocc50952.2020.9332950

Cited by 36 publications

(9 citation statements)

References 5 publications

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“…Algorithms are generally classified as regression and probabilistic, time series, and hybrid algorithms. These algorithms are grouped as coulomb counting and voltagebased estimation [3,6,8]. The Coulomb count-based method uses the integral function of the measured current, while the voltage-based estimation method uses the open circuit voltage of the battery pack.…”

Section: Soc Estimationmentioning

confidence: 99%

“…Information such as the battery's open circuit voltage and discharge current is used for the estimation process. Providing an accurate SOC estimation prevents the battery from being overcharged or over-discharged, increasing the lifetime of the battery pack [5][6][7]. At the same time, it is one of the critical parameters that help to keep the battery within appropriate limits and provides safe use.…”

Section: Introductionmentioning

confidence: 99%

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FPGA-Based battery management system for real-time monitoring and instantaneous SOC prediction

Saday

Özkan

Sarıtaş

2023

International Journal of Applied Mathematics Electronics and Computers

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Battery management systems (BMS) are becoming essential for all types of electric vehicles using battery packs. Various factors, such as battery temperature and balance, directly affect the life, safety, and efficiency of batteries used in vehicles. For security and robustness, these factors should be monitored and adjusted instantly. Today, battery management systems are constantly being developed using different production methods and algorithms. In the studies, calculations are made by measuring parameters such as temperature, current, current balance, load status, and health status of the battery cells, and the control of the battery group is provided with these calculations. Instant and continuous measurement and processing of all these data and the creation of a control algorithm according to the calculation result are possible with the use of powerful processors. FPGA is a processor that can provide the speed and functionality required for BMS. In the battery management system, the FPGA is responsible for receiving and processing all signals from the battery cells and producing results. It instantly processes the data from temperature, current, and voltage sensors and applies the control stage required for balancing. In addition, the charge and discharge capacity of the battery is calculated by instantly measuring the state of charge (SOC). SOC is of great importance in the battery management system to ensure the safety of the battery pack. Therefore, the SOC needs to be estimated accurately and in real-time. Thanks to its parallel processing capability, the FPGA can simultaneously read data from the sensors and perform related calculations. In this study, a versatile system design with real-time, high computational speed for BMS was carried out on FPGA. The voltage and current of an experimental battery based on the embedded system were monitored in real time in a simulation environment. Experimental results show that the instantaneous SOC estimation is successful, and the system returns instant results to the incoming sensor data. The use of FPGA as a management unit will provide significant advantages in BMS with its high operating speed, real-time monitoring, low power consumption, and re-programmability.

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Section: Soc Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPGA-Based battery management system for real-time monitoring and instantaneous SOC prediction

Saday

Özkan

Sarıtaş

2023

International Journal of Applied Mathematics Electronics and Computers

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“…In this paper to predict SoC a Kalman-filter (EKF) based method is used. Conventional Coulomb counting method and EKF is combined to predict SoC which shows less than 2% error in predicting SoC [6].…”

Section: Background Workmentioning

confidence: 99%

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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“…An adaptive nonlinear observer design that corrects for nonlinearity and improves estimation accuracy is also effective. It is shown that a fixed feedback gain cannot sufficiently tolerate wide ranges of SOC fluctuations during charge/discharge operations [55][56][57][58]. SOC estimation accuracy can be significantly improved by using the calculated SOC to alter the feedback gain.…”

Section: Category Model Characteristicsmentioning

confidence: 99%

Effect of Regenerative Braking on the Life of Battery

Kumar¹,

Chatterjee²,

Barman³

et al. 2023

Preprint

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The automobile industry is often believed to be moving towards electric vehicles. Hybrid electric vehicles (HEVs) and electric vehicles (EVs) are anticipated to dominate the automotive industry. Regenerative braking is one of the modern approaches that recharge the battery by extracting the kinetic energy from the braking thus improving the vehicle range. However, the rate of current supplied by regenerative braking to the battery is a major concern as lithium-ion (Li-ion) is susceptible to failure due to the intensity of the charging-discharging rate, and operating temperature. So, this review article focuses on the effect of regenerative braking on the life of the battery and measures being taken to protect the battery from higher charging during regenerative braking. Various research articles are considered for observing the effect of regenerative braking on battery life. It is found that longer duration charging current obtained from regenerative braking that is irrespective of current intensity is the prominent factor of battery deterioration. If the SOC and the temperature are not in the optimal range, the Lithium plating rate increases. However, a higher level of regenerative braking increases the battery life even at high SOC and temperature by reducing the Depth of Discharge (DOD) and by using shorter recharging periods.

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Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System

Cited by 36 publications

References 5 publications

FPGA-Based battery management system for real-time monitoring and instantaneous SOC prediction

FPGA-Based battery management system for real-time monitoring and instantaneous SOC prediction

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

Effect of Regenerative Braking on the Life of Battery

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