A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

Li, Jiabo; Min, Yong; Jiao, Shengjie; Wei, Meng; Xu, Xinxin

doi:10.1109/access.2020.3030260

Cited by 23 publications

(9 citation statements)

References 33 publications

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“…On this basis, some scholars have proposed filtering methods that are more suitable for strongly nonlinear systems. Some articles [11,12] use UKF instead of EKF to predict the vehicle state and solve the vehicle tracking problem. The results show that the UKF has higher accuracy in trajectory estimation.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Deng,

Li,

Jiang

et al. 2023

Electronics

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Vehicle motion trajectory prediction is the basis of vehicle collision early warning or vehicle conflict resolution. In order to improve the accuracy of trajectory prediction, a vehicle trajectory prediction method based on “current” statistical (CS) model and cubature Kalman filter (CKF) is proposed. This method considers the acceleration variation rules in the actual motion process of the vehicle in the state equation, so that the estimated value of the acceleration can be consistent with the real range. This condition overcomes the limitation of the general trajectory prediction model, which ignores the acceleration change, so it improved prediction accuracy. In addition, this method also avoids the large amount of computational resources required, being that some new methods describe the real acceleration fluctuations. The vehicle trajectory at the intersection that crossed by Yingbin Avenue and Qiche Avenue in Nanchang is selected to verify the tracking performance of Constant Acceleration-Unscented Kalman Filter (CA-UKF), Current Statistical-Unscented Kalman Filter (CS-UKF), and CS-CKF models. The results show that the CS-CKF model has superior prediction effectiveness than the CA-UKF and CS-UKF models, and it improves the accuracy of vehicle motion trajectory prediction.

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Section: Introductionmentioning

confidence: 99%

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Deng,

Li,

Jiang

et al. 2023

Electronics

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“…Nevertheless, an accurate assessment of the SOC is usually of highly challenging, as it is complicated and associated with many factors [ 7 , 11 ], such as the charge-and-discharge efficiency, the charge-and-discharge rate, the temperature [ 12 ], etc. Thus far, various methods, like coulomb counting [ 13 ], artificial intelligence [ 14 , 15 ], the fuzzy logic algorithm, and Kalman filters [ 16 , 17 , 18 ] have been widely investigated and used in SOC estimation. In comparison, the model-based method is relatively popular due to its simplicity [ 7 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters

Gomez

Escalera

et al. 2023

Sensors

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Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data.

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“…Lithium-ion battery (LIB), with its high power density, high energy density, non-pollution, and low selfdischarge rate, has become one of the main energy sources of EVs. 1,2 The accuracy and reliability of battery management system (BMS) can ensure the safety of EVs during driving. As the key parameters of BMS, accurate estimation of SOH and SOC can improve battery life and utilization, which are very important to ensure system performance and reliable operation.…”

Section: Introductionmentioning

confidence: 99%

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Min

Gao

et al. 2021

Int J Energy Res

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Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%. K E Y W O R D S dynamic window, improved dual-adaptive extended Kalman filter (IDAEKF), lithium-ion battery (LIB), state of charge (SOC), state of health (SOH), error model

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A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

Cited by 23 publications

References 33 publications

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

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