Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Zhang, Ming; Wang, Kai; Zhou, Yanting

doi:10.1155/2020/8231243

Cited by 31 publications

(24 citation statements)

References 56 publications

(51 reference statements)

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“…Although ultracapacitors are affected by voltage, current, temperature, and electrode materials [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44], their cycle life is still long. With high energy density and high average output voltage, the aging of lithium batteries is a long-term gradual process [45][46][47], and their life is affected by temperature, current ratio, cutoff voltage, and other factors [48]. e evaluation of the parameters such as the state of charge and the remaining useful life has guiding significance for the use, maintenance, and economic analysis of lithium batteries.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Wang

Liu

Sun³

et al. 2021

Complexity

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This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.

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Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Wang

Liu

Sun³

et al. 2021

Complexity

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“…However, accurate OCV value can be obtained only after shelving the battery for quite long time [17], making it difficult to implement in practice [18]. The electrochemical impedance based method can estimate SOC via measuring the internal impedance of batteries; which, however, is highly affected by the external environment, and is difficult to measure online but more suitable for offline calibration [19]. Model based filtering methods usually establish observation models to bridge the implicit SOC with the measured current, voltage and other related variables; and on this basis, the SOC estimation is conducted by different filter/observer algorithms [20], involving Kalman filter (KF), extended KF (EKF) [21], unscented particle filter (UPF) [22], H-infinity filter (HIF) [23], adaptive unscented KF (AUKF) [24], etc.…”

Section: Introductionmentioning

confidence: 99%

Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter

Chen

Zhao

Shu

et al. 2021

Energy

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Accurate state of charge estimation is essential to improve operation safety and service life of lithiumion batteries. This paper proposes a synthetic state of charge estimation method for lithium-ion batteries based on long short-term memory network modeling and adaptive H-infinity filter. Firstly, the long short-term memory network is exploited to roughly estimate state of charge with the input of voltage, current, operating temperature and state of health. Then, to mitigate the output fluctuation and improve the estimation robustness of long shortterm memory network, the adaptive H-infinity filter is employed to flatten the estimation results and further improve the estimation accuracy. A main advantage of the proposed synthetic method lies in that precise battery modeling and burdensome model parameter identification tasks that are imperative in traditional observers or filters can be omitted, thus improving the application efficiency of the proposed algorithm. The proposed method is verified effective on two types of lithium-ion batteries under dynamic working scenarios including the varying temperature and aged conditions. The experimental results highlight that the estimation error of state of charge can be restricted within 2.1% in wide temperature range and different aging states, manifesting its high-precision estimation capacity and strong robustness.

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“…When the SC is directly connected to the battery, stabilizing the output voltage and controlling the energy distribution is difficult. In our research, we adopted the half-bridge of the control topology, wherein a bidirectional DC/DC converter connects the SC to the battery [27][28][29][30]. To enhance the discharge persistence of the battery current, the battery current is reduced to a minimum.…”

Section: Introductionmentioning

confidence: 99%

An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems

Wang

et al. 2020

Energies

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In this paper, we propose an optimized power distribution method for hybrid electric energy storage systems for electric vehicles (EVs). The hybrid energy storage system (HESS) uses two isolated soft-switching symmetrical half-bridge bidirectional converters connected to the battery and supercapacitor (SC) as a composite structure of the protection structure. The bidirectional converter can precisely control the charge and discharge of the SC and battery. Spiral wound SCs with mesoporous carbon electrodes are used as the energy storage units of EVs. Under the 1050 operating conditions of the EV driving cycle, the SC acts as a “peak load transfer” with a charge and discharge current of 2isc~3ibat. An improved energy allocation strategy under state of charge (SOC) control is proposed, that enables SC to charge and discharge with a peak current of approximately 4ibat. Compared with the pure battery mode, the acceleration performance of the EV is improved by approximately 50%, and the energy loss is reduced by approximately 69%. This strategy accommodates different types of load curves, and helps improve the energy utilization rate and reduce the battery aging effect.

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Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Cited by 31 publications

References 56 publications

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter

An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems

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