State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

Abstract:Inconsistencies that are associated with parallel-connected cells used in electric vehicles induce varied states of charge (SOCs) in each cell. Thus, loop current in the battery pack is inevitable, and this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method is available to address loop current. To reduce loop current and the resulting battery inconsistency, a parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel Simscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of parallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC) strategy, which automatically adjusts the number of cells in a circuit in accordance with the load demand, and turns on the first N switches in the corresponding SOC order. The New European Driving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably reduces loop current and improves the consistency of battery performance and the utilization rate of battery power.

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“…Thus, the battery SOC must be accurate under all conditions [22]. Hence, constructing a battery model to estimate the SOC is advisable.…”

Section: First-order Rc Equivalent Circuit Modelmentioning

confidence: 99%

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

et al. 2017

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“…The open-circuit voltage (OCV) measurement method is straightforward but needs a long relaxation time to obtain an accurate OCV, which is unrealistic under continuous and dynamic load conditions. The model-based observers enjoy the merits of high accuracy and robustness; thus, they have been widely studied for online SOC estimation [11,12]. An accurate battery serves as the prerequisite for this category of methods.…”

Section: Introductionmentioning

confidence: 99%

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Wei

Feng

et al. 2018

Energies

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The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation's alertness and numerical stability so as to achieve an accurate online adaption of model parameters.Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data-model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.

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“…The model-based observers enjoy the merits of high accuracy and robustness, thus have been widely studied for online SOC estimation in the literature [3]. An accurate battery serves as the prerequisite of this category of methods.…”

Section: Introductionmentioning

confidence: 99%

Online State of Charge and State of Health Estimation for Lithium-Ion Battery Based on a Data-Model Fusion Method

Wei¹,

Feng²,

He³

et al. 2018

Preprint

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The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, the online model identification is scrutinized to achieve high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation alertness and numerical stability, so as to achieve accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of LIB. Simulation and experimental studies are performed to evaluate the performance of the proposed data-model fusion method. Results suggest that the proposed method can effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high accuracy and high stability.

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State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

Cited by 26 publications

References 31 publications

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Online State of Charge and State of Health Estimation for Lithium-Ion Battery Based on a Data-Model Fusion Method

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