Support Vector Machines Used to Estimate the Battery State of Charge

Anton, J.C.; Nieto, P.J. Garcı́a; Viejo, Cecilio Blanco; Vilán, José Antonio Vilán

doi:10.1109/tpel.2013.2243918

Cited by 355 publications

(82 citation statements)

References 19 publications

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“…However, it is not suitable for online estimation because long rest time is required to measure the true OCV. Machine learning approaches, including artificial neural networks [4,5] and support vector machines [6,7], generally apply a set of data to train the model. However, this kind of method is reliant on the reliability of the training data.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

et al. 2017

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Accurate estimation of the state of charge (SOC) of batteries is crucial in a battery management system. Many studies on battery SOC estimation have been investigated recently. Temperature is an important factor that affects the SOC estimation accuracy while it is still not adequately addressed at present. This paper proposes a SOC estimator based on a new temperature-compensated model with extended Kalman Filter (EKF). The open circuit voltage (OCV), capacity, and resistance and capacitance (RC) parameters in the estimator are temperature dependent so that the estimator can maintain high accuracy at various temperatures. The estimation accuracy decreases when applied in high current continuous discharge, because the equivalent polarization resistance decreases as the discharge current increases. Therefore, a polarization resistance correction coefficient is proposed to tackle this problem. The estimator also demonstrates a good performance in dynamic operating conditions. However, the equivalent circuit model shows huge uncertainty in the low SOC region, so measurement noise variation is proposed to improve the estimation accuracy there.

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Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

et al. 2017

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“…However, it suffers accumulated errors caused by current measurement drift and cannot deal with the initial SOC error problem. The ANNs [5][6][7] and SVM [8,9] methods can be used to estimate the SOC for all kinds of batteries because they do not require the details of batteries. However, they require a large number of sample data to train the networks.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles

et al. 2014

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Abstract:The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, it is difficult to get an accurate value of SOC, because the SOC cannot be directly measured by a sensor. In this paper, an adaptive gain nonlinear observer (AGNO) for SOC estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs) is proposed. The second-order resistor-capacitor (2RC) equivalent circuit model is used to simulate the dynamic behaviors of a LIB, based on which the state equations are derived to design the AGNO for SOC estimation. The model parameters are identified using the exponential-function fitting method. The sixth-order polynomial function is used to describe the highly nonlinear relationship between the open circuit voltage (OCV) and the SOC. The convergence of the proposed AGNO is proved using the Lyapunov stability theory. Two typical driving cycles, including the New European Driving Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) are adopted to evaluate the performance of the AGNO by comparing with the unscented Kalman filter (UKF) algorithm. The experimental results show that the AGNO has better performance than the UKF algorithm in terms of reducing the computation cost, improving the estimation accuracy and enhancing the convergence ability. OPEN ACCESSEnergies 2014, 7 5996 Keywords: state of charge (SOC); adaptive gain nonlinear observer (AGNO); lithium-ion battery (LIB); electric vehicles (EVs)

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“…According to the research on vehicle battery life detection technology, detection system development life battery, realize real-time monitoring and prediction of the performance of the battery life of the battery, and protect the safety of railway operation, optimization of maintenance procedures, reduce the replacement frequency, is very important to strengthen the effective management of the battery, but also has very important social and economic benefit [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

The Research of the Detecting System for the Battery Cycle Life of the Train

Cao¹,

Feng²

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract. The train storage batteries are mainly providing power for train control system and emergency ventilation system. The performance of the vehicle battery status directly affects the safety of the train. Because the cell of batteries is difference, leading to a sharp drop in battery performance and cycle life shortened. Based on the hardware and software platforms of American national instruments(NI), application of artificial neural network algorithm, researching and developing the online detection system of the train battery life. Implementation on the performance of battery state detection and battery life estimates. When using the system on one storage battery, the result shows that the system has some characteristics of simple operation, accurate testing, stability and friendly interface.

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Support Vector Machines Used to Estimate the Battery State of Charge

Cited by 355 publications

References 19 publications

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles

The Research of the Detecting System for the Battery Cycle Life of the Train

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