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基于布谷鳥(niǎo)算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的鋰電池健康狀態(tài)預測
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常州大學(xué)機械工程學(xué)院

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Estimation of SOH for battery based on CS-BP neural network
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    摘要:

    鋰電池健康狀態(tài)(SOH)的預測是電動(dòng)汽車(chē)鋰電池管理系統的最重要的關(guān)鍵技術(shù)之一。傳統的誤差逆向傳播(BP)神經(jīng)網(wǎng)絡(luò )容易使權值和閾值陷入局部最優(yōu),從而導致預測結果不精確。結合布谷鳥(niǎo)搜索算法(CS)的全局優(yōu)化能力,提出一種基于CS算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的鋰電池SOH預測方法,該方法的核心在于優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的初始權值和閾值,從而減小算法對初始值的依賴(lài)。為了驗證算法的泛化性,利用美國國家航空航天局開(kāi)源鋰電池數據集6號電池和7號電池進(jìn)行仿真實(shí)驗,仿真得到該算法預測SOH的均方根誤差(RMSE)分別為0.2658和0.2620,平均絕對百分比誤差(MAPE)分別為0.3319%和0.2605%。通過(guò)與BP神經(jīng)網(wǎng)絡(luò )、粒子群優(yōu)化的BP神經(jīng)網(wǎng)絡(luò )(PSO-BP)、遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò )(GA-BP)對比,布谷鳥(niǎo)算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò )(CS-BP)具有更小的預測誤差。

    Abstract:

    Estimating the state of health(SOH) of lithium battery is one of the most important key techniques of lithium electric vehicle battery management system. The traditional error back propagation(BP) neural network is easy to bring the weight fall into local optimal solutions, which can lead to inaccurate prediction results. Combined with the cuckoo search algorithm(CS) which has global optimization ability. A method based on cuckoo search algorithm optimized BP neural network model for predicting the SOH of lithium ion battery is proposed, the core of the method is optimizing the BP neural network's initial weights and thresholds. This method can reduce the dependence of the algorithm on the initial value. At the same time, in order to verify the generalization performance of the algorithm, use the NASA open source lithium battery data set No. 6 battery and No. 7 battery for simulation experiments, and the CS-BP algorithm is simulated to predict the root mean square error (RMSE) of SOH. They are 0.2658 and 0.2620, and the mean absolute percentage error (MAPE) is 0.3319% and 0.2605%, respectively. Compared with BP algorithm, particle swarm optimization BP neural network (PSO-BP), genetic algorithm optimized BP neural network (GA-BP), cuckoo search algorithm optimized BP neural network(CS-BP) has smaller prediction error.

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魏新堯,佘世剛,容偉,劉愛(ài)琦.基于布谷鳥(niǎo)算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的鋰電池健康狀態(tài)預測計算機測量與控制[J].,2021,29(4):65-69.

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  • 收稿日期:2020-09-21
  • 最后修改日期:2020-10-15
  • 錄用日期:2020-10-16
  • 在線(xiàn)發(fā)布日期: 2021-04-25
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