Algorithms for Advanced Battery-Management Systems

Chaturvedi, N.A.; Klein, Reinhardt; Christensen, Jake; Ahmed, Jasim; Kojić, Aleksandar

doi:10.1109/mcs.2010.936293

Cited by 494 publications

(131 citation statements)

References 26 publications

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“…This error feedback is maintained in such a way that all of the variables being estimated converge to their true values [21]. The PDAE observer equations are written for the volume averaged concentrations in the individual electrodes as follows:…”

Section: Pdae Observermentioning

confidence: 99%

“…Among the stated fault scenarios in a Li-ion battery, OD and OC are critical for maintaining the health of the battery. While over-charging can cause overheating that can lead to the vaporization of the active material and possibly explosion, over-discharge can short circuit the battery cell [19][20][21][22][23]. However, if these faults can be detected quickly according to the described methodology, steps can be taken to solve the issues before the faults can deteriorate to their extreme conditions.…”

Section: Introductionmentioning

confidence: 99%

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Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Anwar

Izadian

2017

Energies

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Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO 2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

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Section: Pdae Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Anwar

Izadian

2017

Energies

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“…The dynamic model of batteries has been the topic of many recent publications. Some of these publications [1][2][3][4] studied the electrochemical modeling technique (ECM), which depends on the internal structure of the battery. Since the structure of the battery is difficult to be obtained, it is hardly used in the modeling.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Safwat

2017

Energies

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State-of-charge (SOC) estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model's parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC) of the Li-ion battery based on Newton's method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea. Keywords: dynamic model of Li-ion battery; variable forgetting factor recursive least square (RLS) estimator; multiple forgetting factors RLS estimator; state-of-charge (SOC) estimation using Newton's method

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“…Various battery models have been approximated within the framework of a BSE. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] Coulomb counting and rule-based BSEs are the most rudimentary and are still used in many battery powered devices due to simplicity, low cost, and minimal computing demands. 3 The Coulomb counting technique uses the integration of current over time to estimate the battery SOC.…”

mentioning

confidence: 99%

“…Various attempts have been made to significantly simplify and reduce the complexity of the models. [11][12][13][14][15] However, even with complexity reduction, these models are still computationally intensive relative to what resources are available with respect to automotive controllers.…”

mentioning

confidence: 99%

Battery State Estimator Based on a Finite Impulse Response Filter

Wang

Baker

et al. 2013

J. Electrochem. Soc.

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We have developed a battery state estimator based on a finite impulse response filter. Simulation results indicate that the estimator gives accurate prediction and numerically-stable performance in the regression of filter coefficients and the open-circuit potential, which yields the battery state of charge. The estimator is also able to predict battery power capabilities. Comparison of the measured and predicted state of charge (SOC) and the charge and discharge power capabilities (state of power, SOP) of a Li-ion battery are provided. Predictions for the SOC and SOP agree well with experimental measurements, demonstrating the estimator's application in battery management systems. In particular, this new approach appears to be more flexible than previous models; we show that it can capture the behavior of batteries governed by various physicochemical phenomena.In many battery-powered systems, such as electric vehicles (EV) and hybrid electric vehicles (HEV), the performance of the electric traction system can be greatly enhanced by intelligent management of the electrochemical energy storage device. An adaptive battery state estimator (BSE) can provide accurate and timely estimation of battery states: the state of charge (SOC), the charge and the discharge power capabilities (SOP), and the state of health (SOH). 1 Battery modeling for BSE application is challenging because, particularly at high rates of operations, the battery system is nonlinear. In addition, the system is non-stationary in the sense that the battery is aging. Last, for vehicle applications where cost of controllers is important, the model for the BSE must be fast in terms of computation and storage requirements must be minimal. Hence, BSEs are usually based on a zero-dimensional equivalent circuit. Actual batteries have concentration, current, potential, and temperature distributions. Various battery models have been approximated within the framework of a BSE. 2-17 Coulomb counting and rule-based BSEs are the most rudimentary and are still used in many battery powered devices due to simplicity, low cost, and minimal computing demands. 3 The Coulomb counting technique uses the integration of current over time to estimate the battery SOC. The main drawback of this method is its susceptibility to large errors in long-term usage, as it is not adaptive. Rule-based battery controls are used to restrict the range of battery power and voltage in an attempt to ensure battery abuse tolerance and lifetime. However, these rules ignore the battery usage history and aging, and are therefore less reliable. 4 In contrast, advanced battery control and integration requires accurate battery SOC and SOP at any given moment."Black-box" type battery models (machine learning) 5-8 can be based on neural network and machine learning techniques that train the models with the test data of the battery. Despite some success in describing the capacities of lead-acid, nickel-metal hydride, 5 and recently lithium ion batteries, 6 these approaches are computationally intensiv...

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Algorithms for Advanced Battery-Management Systems

Cited by 494 publications

References 26 publications

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Battery State Estimator Based on a Finite Impulse Response Filter

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