An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots

Partovibakhsh, Maral; Liu, Guangjun

doi:10.1109/tcst.2014.2317781

Cited by 245 publications

(104 citation statements)

References 24 publications

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“…The following section describe detail of optimizing the running parameters using the parameter online estimation method of Forgetting Factor Recursive Least Square (FFRLS) and Extended Kalman Filter (EKF), which makes the SoC run in a reasonable interval [38][39][40].…”

Section: Soc Online Calibration and Operation Range Optimization Methodsmentioning

confidence: 99%

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

et al. 2018

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This paper discusses a design of a Battery Management System (BMS) solution for extending the life of Nickel-Metal Hydride (NI-MH) battery. Combined with application of electric boat, a State of Charge (SoC) optimal operation range control method based on high precision energy metering and online SoC correction is proposed. Firstly, a power metering scheme is introduced to reduce the original energy measurement error. Secondly, by establishing a model based parameter identification method and combining with Extended Kalman Filter (EKF) method, the estimation accuracy of SoC is guaranteed. Finally, SoC optimal operation range control method is presented to make battery running in the optimal range. After two years of operation, the battery managed by proposed method has much better status, compared to batteries that use AH integral method and fixed SoC operating range. Considering the SoC estimation of NI-MH battery is more difficult becausing special electrical characteristics, proposed method also would have a very good reference value for other types of battery management.

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Section: Soc Online Calibration and Operation Range Optimization Methodsmentioning

confidence: 99%

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

et al. 2018

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“…Basically, the parameter identification techniques are the recursive least squares (RLS) type or adaptive filtering (AF) type of methods [16][17][18][19][20][21][22][23][24][25], e.g., Xiong et al [23] proposed a data-driven estimation approach which can simultaneously obtain the model parameter and the internal state of the battery. These techniques have been widely proven to be effective in online parameter identification.…”

Section: Online Model Parameter Identificationmentioning

confidence: 99%

“…During the tests, the battery pack is put in an environmental chamber, and the temperature is set to 25 Several tests with different current profiles are designed to thoroughly investigate the performance of the method. During the tests, the battery pack is put in an environmental chamber, and the temperature is set to 25 To investigate the performance of the proposed method in different cell aging conditions and different current profiles, the battery pack is firstly tested with the NEDC current profile, followed by 50 constant full charge/discharge cycles. Then, the battery pack is tested with the J1015 current profile, followed by another 50 full charge/discharge cycles.…”

Section: Experimental Setupsmentioning

confidence: 99%

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

et al. 2017

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Abstract:The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.

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“…In a normal UKF algorithm [17,18], the covariance is a constant and cannot satisfy the real-time dynamic characteristics of the noise, which has a certain impact on the accuracy. In this paper, to eliminate this effect, the normal UKF algorithm is improved by updating the covariance in real-time and thus improving the accuracy of the UKF.…”

Section: Establishment Of the Aukf Algorithmmentioning

confidence: 99%

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

et al. 2016

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An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

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An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots

Cited by 245 publications

References 24 publications

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

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

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