Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

Guo, Xiangwei; Kang, Longyun; Yao, Yuan; Huang, Zhihui; Li, Wenbiao

doi:10.3390/en9020100

Cited by 71 publications

(29 citation statements)

References 20 publications

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“…Huria et al [20] reported that the first-order resistor-capacitor (RC) circuit model is precise for most industrial application. Increasing the order will significantly increase the amount of required calculation, but will not increase accuracy [21]. The first-order equivalent circuit model is supported by Huria et al [20] due to its accuracy and feasibility, and many researchers have adapted this model in their studies [18,[22][23][24][25].…”

Section: Simscape Battery Cell Modelmentioning

confidence: 95%

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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Abstract:To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

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Section: Simscape Battery Cell Modelmentioning

confidence: 95%

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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“…Huria et al [21] reported that the first-order resistance capacitance (RC) circuit model is precise for most industrial applications. Increasing the order will significantly elevate the amount of calculation, but will not increase accuracy greatly [25]. The first-order equivalent circuit model is advocated by Thomas and Huria et al for its accuracy and feasibility, and many researchers [26][27][28][29] have adapted this model in their studies.…”

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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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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“…LS methods are also widely used in other applications, such as the short-term forecasting of wind power [15], power estimation for electric vehicles [16], the design of filters [17], and the forecasting of electricity prices [18][19][20]. According to paper [11], the optimization problem corresponding to the modeling of an area load with multiple boundary buses is a complex non-linear programming problem with multiple local optima.…”

Section: Introductionmentioning

confidence: 99%

Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning

Shang

et al. 2017

Energies

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Abstract:The accuracy of load modeling directly influences power system operation and control. Previous modeling studies have mainly concentrated on the loads connected to a single boundary bus, without thoroughly considering the static load characteristics of the voltage. To remedy this oversight, this paper proposes an accurate modeling approach for area loads with multiple boundary buses and ZIP loads (a combination of constant-impedance, constant-current and constant-power loads) based on Ward equivalence. Furthermore, to satisfy the requirements for real-time monitoring, the model parameters are identified in an online manner using an enhanced reinforcement learning (ERL) algorithm. Parallel tables of value functions are implemented in the ERL algorithm to improve its tracking performance. Three simulation cases are addressed, the first involving a single ZIP load and the second and third involving area loads in the IEEE 57-bus system and in a real 1209-bus power system in China, respectively. The results demonstrate that the ERL algorithm outperforms an existing reinforcement learning algorithm and the improved least-squares method in terms of convergence and the ability to track both step-changing and time-varying loads. Additionally, the results obtained on test cases confirm that the proposed area load model is more accurate than a previously introduced model.

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Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

Cited by 71 publications

References 20 publications

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

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

Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning

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