【摘要】：目前純電動汽車剩余續(xù)駛里程估算方法較為簡單,其估算結果與真實行駛里程存在較大差距,這樣直接造成用戶時刻擔心現(xiàn)有電量不能支持車輛到達預期地點,從而導致“里程焦慮”,降低了用戶購買電動汽車的信心。因此,為了提高電動汽車普及率及使用方便性,在一定程度上提高剩余續(xù)駛里程估算精度是解決該問題重要方法之一,也是本課題研究的目的,其核心內容如下:(1)基于MATLAB/Simulink建立整車動力學、駕駛員、電機、電池以及整車能耗模型,該模型的建立是為后續(xù)章節(jié)對剩余續(xù)駛里程估算模型仿真作鋪墊。(2)詳細研究了三種剩余續(xù)駛里程估算方法:首先將傳統(tǒng)燃油車估算剩余續(xù)駛里程方法應用在純電動汽車上,即平均能耗法,該方法靠獲取電池電壓、電流來進行能耗計算,比較粗糙;然后引出工況識別法,該方法根據(jù)當前行駛工況自動判別當前能耗,且在傳統(tǒng)工況識別方法基礎上新增下述三點:(1)建立工況特征參數(shù)與能耗之間模糊規(guī)則庫,實現(xiàn)能耗預測。(2)單位能耗行駛里程優(yōu)化,根據(jù)實際經驗建立單位能耗與剩余能耗之間的線性關系,使其剩余里程呈遞減優(yōu)化趨勢。(3)對輸出的剩余續(xù)駛里程進行卡爾曼濾波,使其更加真實;最后在工況識別基礎上新增工況預測法,該方法將BP神經網(wǎng)絡和馬爾科夫結合在一起,實現(xiàn)在一定時間范圍的工況預測,從而再次提高剩余續(xù)駛里程估算精度。(3)因空調對剩余續(xù)駛里程影響較大,針對空調的開啟與關閉又單獨采用了另一種平均能耗估算方案:基于空調功率及開啟時間的平均能耗法。(4)為了能詳細對比三種方案的效果,分別從已行駛里程和能量消耗兩方面進行剩余續(xù)駛里程優(yōu)劣評估,仿真結果驗證了工況預測方法的優(yōu)勢。(5)實驗論證部分采用快速原型開發(fā)平臺,首先基于D2P-Motohawk及HIL平臺搭建硬件在環(huán)仿真測試環(huán)境,論證所提出算法模型的可行性及實時性;然后在轉鼓實驗臺進行循環(huán)跟蹤NEDC工況測試剩余續(xù)駛里程;最后通過已行駛里程和能量消耗對比三種方案的優(yōu)劣性,實驗結果表明將工況識別與預測相結合的方法滿足實際控制需求,并提高剩余續(xù)駛里程估算精度。
[Abstract]:At present, the method of estimating the remaining driving range of pure electric vehicle is relatively simple, and there is a big gap between the estimation result and the real driving mileage, which directly causes the user to worry that the existing electric quantity can not support the vehicle to reach the expected location. This led to "mileage anxiety", reducing the confidence of users to buy electric cars. Therefore, in order to improve the popularity and ease of use of electric vehicles, to a certain extent, improving the estimation accuracy of the remaining driving range is one of the important methods to solve this problem, and is also the purpose of this research. The main contents are as follows: (1) based on MATLAB/Simulink, the vehicle dynamics, driver, motor, battery and vehicle energy consumption model are established. The establishment of the model is to pave the way for the simulation of the residual range estimation model in the following chapters. (2) three methods for estimating the residual continuous mileage are studied in detail: firstly, the traditional fuel vehicle is applied to estimate the residual continued mileage. In pure electric cars, That is, the average energy consumption method, which obtains the battery voltage and current to calculate the energy consumption, is relatively rough; Then the working condition identification method is introduced. The method automatically discriminates the current energy consumption according to the current driving condition, and adds the following three points on the basis of the traditional working condition identification method: (1) establishing the fuzzy rule base between the operating condition characteristic parameters and the energy consumption, (2) Optimization of driving mileage per unit energy consumption. The linear relationship between unit energy consumption and residual energy consumption is established according to practical experience. The residual mileage is reduced and optimized. (3) Kalman filter is applied to the output residual driving mileage to make it more real; Finally, a new condition prediction method is added on the basis of condition recognition. The method combines BP neural network with Markov to realize the condition prediction in a certain time range. Therefore, the estimation accuracy of residual range is improved again. (3) because the air conditioning has a great influence on the residual range, In order to compare the effects of the three schemes in detail, another method of estimating the average energy consumption is used separately: the average energy consumption method based on the air conditioning power and the opening time. (4) in order to compare the effects of the three schemes in detail, The advantages and disadvantages of the remaining driving range are evaluated from the aspects of driving mileage and energy consumption respectively. The simulation results verify the advantages of the working condition prediction method. (5) in the experimental demonstration part, the rapid prototyping development platform is used. Firstly, based on D2P-Motohawk and HIL platform, the hardware in loop test environment is built to demonstrate the feasibility and real-time performance of the proposed algorithm model. Then the remaining driving mileage is measured on the rotary drum test bench by circulating tracking the NEDC working condition. Finally, by comparing the advantages and disadvantages of the three schemes, the experimental results show that the combined method of condition identification and prediction can meet the actual control requirements and improve the estimation accuracy of the remaining driving range.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U469.72

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