基于卡爾曼濾波的動力電池SOC估計算法設(shè)計
發(fā)布時間：2018-12-06 09:43
【摘要】：電池荷電狀態(tài)(State Of Charge, SOC)的準確估計是電動汽車電池充放電控制和動力優(yōu)化管理的重要依據(jù),直接影響電池的使用壽命和汽車性能。本文針對電動汽車動力電池的SOC估計問題,主要進行了以下幾個方面的研究工作： 1.采用映射近似對模型進行線性化,引入環(huán)境溫度比例系數(shù)和充放電倍率比例系數(shù)來確定折算庫倫效率,設(shè)計了一種基于復(fù)合模型的卡爾曼濾波算法。仿真結(jié)果表明,所設(shè)計的算法具有更好的修正累計誤差和初值誤差的能力。 2.采用加權(quán)統(tǒng)計線性回歸法來實現(xiàn)模型函數(shù)線性化,基于電池復(fù)合模型狀態(tài)方程線性的特性,通過將標(biāo)準卡爾曼濾波算法和基于加權(quán)統(tǒng)計線性回歸法的卡爾曼濾波算法組合,并引入奇異值分解,設(shè)計了一種基于奇異值分解的卡爾曼濾波算法。仿真結(jié)果表明,所設(shè)計的算法具有比基于復(fù)合模型的卡爾曼濾波算法更好的運算效率,以及更好的收斂速度和估計精度。 3.為了實現(xiàn)算法具有應(yīng)對突變狀態(tài)的強跟蹤能力和應(yīng)對模型不準確的魯棒性,基于強跟蹤原理,引入次優(yōu)漸消因子,設(shè)計了一種基于強跟蹤的卡爾曼濾波算法。仿真結(jié)果表明,所設(shè)計的算法具有比基于復(fù)合模型的卡爾曼濾波算法和基于奇異值分解的卡爾曼濾波算法更高的估計精度和更快的收斂速度。
[Abstract]:The accurate estimation of battery charge state (State Of Charge, SOC) is an important basis for battery charge and discharge control and dynamic optimization management of electric vehicle, which directly affects the battery service life and vehicle performance. In this paper, the SOC estimation of electric vehicle battery is studied in the following aspects: 1. Using mapping approximation to linearize the model and introducing environmental temperature ratio coefficient and charge-discharge ratio coefficient to determine the conversion Coulomb efficiency, a Kalman filter algorithm based on composite model is designed. Simulation results show that the proposed algorithm has better ability to correct cumulative error and initial error. 2. The weighted statistical linear regression method is used to linearize the model function. Based on the linear characteristic of the state equation of the battery composite model, the standard Kalman filter algorithm and the Kalman filter algorithm based on weighted statistical linear regression method are combined. A Kalman filter algorithm based on singular value decomposition is designed by introducing singular value decomposition. Simulation results show that the proposed algorithm has better computational efficiency, better convergence speed and better estimation accuracy than the Kalman filter algorithm based on composite model. 3. In order to realize the strong tracking ability of the algorithm to deal with the mutation state and the robustness of the model inaccuracy, a Kalman filter algorithm based on strong tracking is designed based on the strong tracking principle and the suboptimal fading factor. The simulation results show that the proposed algorithm has higher estimation accuracy and faster convergence speed than the Kalman filtering algorithm based on composite model and singular value decomposition.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912;U469.72
本文編號：2365840
[Abstract]:The accurate estimation of battery charge state (State Of Charge, SOC) is an important basis for battery charge and discharge control and dynamic optimization management of electric vehicle, which directly affects the battery service life and vehicle performance. In this paper, the SOC estimation of electric vehicle battery is studied in the following aspects: 1. Using mapping approximation to linearize the model and introducing environmental temperature ratio coefficient and charge-discharge ratio coefficient to determine the conversion Coulomb efficiency, a Kalman filter algorithm based on composite model is designed. Simulation results show that the proposed algorithm has better ability to correct cumulative error and initial error. 2. The weighted statistical linear regression method is used to linearize the model function. Based on the linear characteristic of the state equation of the battery composite model, the standard Kalman filter algorithm and the Kalman filter algorithm based on weighted statistical linear regression method are combined. A Kalman filter algorithm based on singular value decomposition is designed by introducing singular value decomposition. Simulation results show that the proposed algorithm has better computational efficiency, better convergence speed and better estimation accuracy than the Kalman filter algorithm based on composite model. 3. In order to realize the strong tracking ability of the algorithm to deal with the mutation state and the robustness of the model inaccuracy, a Kalman filter algorithm based on strong tracking is designed based on the strong tracking principle and the suboptimal fading factor. The simulation results show that the proposed algorithm has higher estimation accuracy and faster convergence speed than the Kalman filtering algorithm based on composite model and singular value decomposition.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912;U469.72
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