Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

Waag, Wladislaw; Fleischer, Christian; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2014.02.064

Cited by 835 publications

(449 citation statements)

References 174 publications

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“…The H ∞ performance of the proposed nonlinear observer is the main reason why the proposed observer is superior to the EKF. Notice that the modeling error rather than the measurement noises is the main factor affecting the SOC estimation [4]. Furthermore, the modeling error is non-Gaussian.…”

Section: Soc Estimation Results and Evaluationmentioning

confidence: 99%

“…However, the SOC cannot be measured directly by on-board sensors. Therefore, the SOC estimation is a problem of considerable importance in both theory and application in EVs [4].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the accumulation of measurement errors and the inaccurate initial SOC, the ampere-hour counting method cannot be utilized directly to estimate the SOC. Accordingly, various kinds of model-based SOC estimation algorithms are proposed [4] that are usually established by combining the ampere-hour counting and the battery model.…”

Section: Introductionmentioning

confidence: 99%

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State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H ∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

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Section: Soc Estimation Results and Evaluationmentioning

confidence: 99%

“…However, the SOC cannot be measured directly by on-board sensors. Therefore, the SOC estimation is a problem of considerable importance in both theory and application in EVs [4].…”

Section: Introductionmentioning

confidence: 99%

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State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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“…Based on the simplification processes shown in Equations (1) and (2), respectively, the determination of the battery SOHE and SOHP can be reduced to the estimation of the capacity and ohmic resistance [2]. Wherein …”

Section: Introductionmentioning

confidence: 99%

“…For a joint estimation for SOHP and SOHE, the internal resistance can be taken as the health indicator, and the SOHE will be estimated by establishing a correlation between the capacity loss and resistance increase [2]. However, the resistance values are very sensitive to temperature changes and the effects of temperature on the resistances are not sufficiently discussed in the existing methods.…”

Section: Introductionmentioning

confidence: 99%

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Wang¹,

Pei²,

Wang

et al. 2015

Energies

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Abstract:A state-of-health (SOH) estimation method for electric vehicles (EVs) is presented with three main advantages: (1) it provides joint estimation of cell's aging states in terms of power and energy (i.e., SOHP and SOHE)-because the determination of SOHP and SOHE can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2) it has wide applicability to various ambient temperatures-to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3) it needs low computational efforts for on-board application-based on a linear equation of cell's dynamic behaviors, the recursive least-squares (RLS) algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

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Battery State of Health Estimation

2018

Advanced Battery Management Technologies for Electric Vehicles

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Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

Cited by 835 publications

References 174 publications

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Battery State of Health Estimation

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