Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

André, Dave; Appel, Christian; Soczka‐Guth, Thomas; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2012.10.001

Cited by 366 publications

(154 citation statements)

References 12 publications

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“…Currently, the least square method is widely employed to identify the model parameters [36][37][38][39]. This method is easy to implement and has acceptable local identification performance, though the overall identification precision will be compromised as the identified data quantity becomes large [40].…”

Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

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Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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“…As a result, data-driven techniques draw more and more attention in SOH prognostics. In the literature, statistical, computational and artificial intelligence algorithms, such as autoregressive model [12], particle filter (PF) [13,14], Gaussian process regression [15], Wiener process [16], relevance vector machine (RVM) [17], Bayesian approach [18], support vector machine (SVM) [19] and neural networks [20,21] have been used for battery SOH and remaining useful life (RUL) prognostics in various applications. Capacity fade and impedance increase are the two most used health indicators of batteries.…”

Section: Introductionmentioning

confidence: 99%

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

et al. 2016

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Abstract:State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

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“…The limits of the SoH are Begin of Life (BoL) and End of Life (EoL) values. BoL (SoH=100%) represents the state when the battery is new, and EoL (SoH=0%) is defined as the condition when the battery cannot meet the performance specification for the particular application for which it was designed [2]. For automotive applications, the EoL has been defined in previous publications [1], [2] and procedures [3], [4] and are provided in Equation (1) and Equation (2):…”

Section: Introductionmentioning

confidence: 99%

“…SoHE and SoHP are often calculated as a percentage with respect to the difference between BoL and EoL in either capacity (Equation (3)) or resistance (Equation (4)) [2].…”

Section: Introductionmentioning

confidence: 99%

A SoH Diagnosis and Prognosis Method to Identify and Quantify Degradation Modes in Li-ion Batteries using the IC/DV technique

Pastor-Fernández¹,

Widanage²,

Chouchelamane³

et al. 2016

6th Hybrid and Electric Vehicles Conference (HEVC 2016)

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Accurate State of Health (SoH) diagnosis and prognosis of Lithium-ion batteries (LIBs) may become an important estimate for the Battery Management System (BMS) if it can be acted upon to de-rate the demands placed on the battery in order to reduce the rate of ageing and to extend battery life. The BMS often quantifies SoH based on capacity (SoHE) and power fade (SoHP) without diagnosing the root causes. In line with this, Incremental Capacity (IC) and Differential Voltage (DV) techniques are used to identify and quantify degradation modes as well as to estimate and to predict the SoHE. These techniques were applied to four parallelised LIBs loaded with a constant current profile for 500 cycles. Loss of active material and loss of lithium ions were identified to be the most relevant degradation modes for this experiment. Moreover, a linear relationship between the intensity of the peaks of the IC and DV curves were identified with respect to the SoHE. This result may enable the future estimation and prediction of SoHE in a simple way. Overall, the outcomes of this work may support novel strategies to control SoHE within the BMS, so that the rate of battery degradation can be reduced.

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Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

Cited by 366 publications

References 12 publications

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

A SoH Diagnosis and Prognosis Method to Identify and Quantify Degradation Modes in Li-ion Batteries using the IC/DV technique

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