A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer

Tang, Xiaopeng; Chen, Zonghai

doi:10.1016/j.jpowsour.2015.07.028

Cited by 121 publications

(52 citation statements)

References 36 publications

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“…(2) Proportional-integral Observer (PIO) PIO has been widely applied to the replacement of feedback control system [125]. The function of a PIO controller is to converge the estimated voltage to the measured voltage in an accurate and fast way.…”

Section: Non-linear Observermentioning

confidence: 99%

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State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Section: Non-linear Observermentioning

confidence: 99%

“…Non-linear observers (NLO), sliding mode observer (SMO), and proportional-integral observer (PIO) theories are proposed to estimate the SOC of lithium-ion batteries [58][59][60][61][62][63]124,125].…”

Section: Non-linear Observermentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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“…Nonetheless, the lithium battery has been considered one of the major viable energy storage media in electric cars [4][5][6][7]. To meet the voltage and power requirements of cars, if we use small-capacity and low-power lithium cells, as in Tesla's Model S, then the cells should be connected in parallel or/and series fashion [8][9][10][11]. A parallel-connected cell pack (PCCP) can be regarded as a larger single cell by omitting the differences among cells.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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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“…An accurate SOC estimation can not only provide the information of the remaining mileage, but also give assistance to active balancing and protection of the battery pack [5]. There are many different kinds of methods that focus on SOC estimation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. However, due to the limited computational ability of the embedded processor and the large amount of batteries used in EVs, the SOC estimation algorithm needs to be simple and reasonably accurate.…”

Section: Introductionmentioning

confidence: 99%

“…It uses fixed gain for state feedback, and the selection of this gain-more precisely, the pole placement of the observer system [12]determines the convergence speed and noise filtering performance. Due to its linear property, the performance of the Luenberger observer deteriorates heavily when dealing with battery systems of strong nonlinearity [13]. The PI observer [14] provides a solution to the problem, using a proportional-integral structure to replace the constant gain.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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A battery's state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.

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A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer

Cited by 121 publications

References 36 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

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

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

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