Improved particle filter algorithm-based binary state of charge estimation method

A particle filter algorithm and state-of-charge technology, applied in computing, electrical digital data processing, special data processing applications, etc., can solve the problems of voltage and current model parameters, battery aging, SOC estimation complexity, battery SOC cannot be directly measured, etc.

Inactive Publication Date: 2018-10-12
YANSHAN UNIV +1
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Problems solved by technology

Battery SOC cannot be directly measured, and it shows highly nonlinear changes under different charge and discharge rates
The complexity of SOC estimation is also increa

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  • Improved particle filter algorithm-based binary state of charge estimation method
  • Improved particle filter algorithm-based binary state of charge estimation method
  • Improved particle filter algorithm-based binary state of charge estimation method

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Embodiment Construction

[0075] The present invention will be further described below in conjunction with accompanying drawing:

[0076] 1—Battery comprehensive model establishment

[0077] The establishment of the battery model is based on the classic Thevenin model, and the KiBaM model (double-well model) that clearly analyzes the capacity characteristics of the battery is introduced. Furthermore, the classic Thevenin model is combined with the KiBaM model to form a comprehensive model, such as figure 1 As shown, in the integrated model h max Indicates the maximum height of the double well of the battery; y 1 ,y 2 Equivalent to the area, indicating the electricity in the two wells; h 1 , h 2 Respectively represent the height of the current storage power of available wells and restricted wells; R 0 is the ohmic internal resistance; R 1 is the polarization resistance; C is the polarization capacitance; u c Indicates the polarized capacitor voltage; V is the battery terminal voltage. The compr...

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Abstract

The invention discloses an improved particle filter algorithm-based binary state of charge estimation method. According to the method, a traditional Thevenin model is combined with a KiBaM model capable of correctly reflecting rate capacity characteristics of storage batteries to serve as a binary state of charge comprehensive model. By adoption of a recursive least-squares method, model parameters are online identified, and an improved particle filter algorithm is applied to the comprehensive model so as to realize binary state of charge estimation; on the basis of a standard particle filteralgorithm, related steps of a residual resampling algorithm and a Thompson-Taylor algorithm are further imported, and the resampling algorithm is capable of effectively remitting the particle degeneracy problem that sequential significance sampling method must face. Conventional sampling is easy to intensify particle depletion, and new particles are generated through the Thompson-Taylor algorithmto ensure the diversity of the particles, so that the filter performance of systems is strengthened, nonlinear working characteristics of storage batteries can be better adapted, and then correct andreal-time estimation of state of charge is realized.

Description

technical field [0001] The invention belongs to the field of storage battery management systems, and relates to a method for estimating the storage battery comprehensive model binary state of charge based on an improved particle filter (PF, ParticleFilter) algorithm. Background technique [0002] The battery management system (BMS, Battery Management System), as a bridge connecting the battery pack and the power unit of the electric vehicle, is an important part of the electric vehicle. State of Charge (SOC, State of Charge) estimation is one of the main functions of electric vehicle BMS. Accurately estimating the SOC of the battery pack can accurately feed back the cruising range of the battery pack to the operator. At the same time, the system can automatically Effectively weigh, select and adjust the battery load to maximize the working performance of the battery and prolong the service life of the battery pack. Battery SOC cannot be measured directly, and it shows highl...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 张金龙李端凯佟微张迪漆汉宏林涛梁晓亮
Owner YANSHAN UNIV
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