TWI461718B - Battery power test method - Google Patents
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Description
本發明是有關於一種測試方法,特別是指一種電池電量測試方法。The invention relates to a test method, in particular to a battery power test method.
目前電池電量測試方法,主要有開路電壓法及改良式庫侖法。開路電壓法利用電池在放電的時候,端電壓值會隨著放出電量而下降的原理,藉由量測電池開路電壓而得知電池電量,但對於動態變化的電池殘餘電量無法有效估測。改良式庫倫法由於電池在持續放電下,電化學反應總釋放量會減少,故無法在放電過程中提供準確的電池電量。At present, the battery power test method mainly includes an open circuit voltage method and an improved coulomb method. The open circuit voltage method uses the principle that the terminal voltage value will decrease with the discharge of the battery when discharging, and the battery power is known by measuring the open circuit voltage of the battery, but the dynamic residual battery capacity cannot be effectively estimated. The improved Coulometric method, because the battery is continuously discharged, the total amount of electrochemical reaction is reduced, so it is impossible to provide accurate battery power during the discharge process.
另外還有安培小時積分累計法,以單晶片系統定時量取電池的端電壓和負載電流,計算累積之消耗電流量,以估算殘餘的電池電量。再依據電池的充放電特性,推算還可使用之電池電量相對值。但由於電池內部電化學反應非常複雜,使得電池於放電過程中,電池端電壓與電池電量可能呈現非線性關係,故安培小時積分累計法,無法有效測試電池剩餘能量。In addition, there is an ampere-hour integral method, which takes the terminal voltage and load current of the battery in a single-chip system timing, and calculates the accumulated consumption current to estimate the residual battery power. According to the charge and discharge characteristics of the battery, the relative value of the battery power that can be used is estimated. However, due to the complicated internal electrochemical reaction of the battery, the battery terminal voltage and the battery power may have a nonlinear relationship during the discharge process. Therefore, the ampere-hour integral method cannot effectively test the remaining energy of the battery.
因此,本發明之目的,即在提供一種電池電量測試方法。Accordingly, it is an object of the present invention to provide a battery power test method.
於是,本發明電池電量測試方法,適用於一具有一供測試電池參數組的電池,包含一第一修正步驟、一訓練步 驟及一測試步驟。該第一修正步驟修正多個供訓練電池參數及電量對應組,以得到多個修正後供訓練電池參數及電量對應組。該訓練步驟根據該等修正後供訓練電池參數及電量對應組,訓練一分類器。該測試步驟將該供測試電池參數組輸入該分類器中,以得到該電池之測試電池電量。Therefore, the battery power test method of the present invention is applicable to a battery having a test battery parameter set, including a first correction step and a training step. And a test step. The first correcting step corrects a plurality of training battery parameters and power corresponding groups to obtain a plurality of corrected training battery parameters and power corresponding groups. The training step trains a classifier according to the modified battery parameters and the power corresponding group. The test step inputs the test battery parameter set into the classifier to obtain the test battery power of the battery.
本發明之功效在於,藉由修正步驟及該分類器的分析能力,在完成訓練過程之後,將電池的參數輸入該分類器,以得到一準確的測試電池電量。The effect of the present invention is that, by the correction step and the analysis capability of the classifier, after the training process is completed, the parameters of the battery are input to the classifier to obtain an accurate test battery power.
有關本發明之前述及其他技術內容、特點與功效,在以下配合參考圖式之二個較佳實施例的詳細說明中,將可清楚的呈現。The above and other technical contents, features and advantages of the present invention will be apparent from the following detailed description of the preferred embodiments of the invention.
參閱圖1及圖2,本發明電池電量測試方法之第一較佳實施例適用於一具有一供測試電池參數組的電池9,且係以如圖1所示之電池電量測試系統2來實施,其中該電池9例如可為一電動機車之充電電池等。如圖1所示,該電池電量測試系統2之第一較佳實施例包含一感應裝置201、一第一修正單元202、一分類器203、一感應裝置211、一分類器203及一顯示裝置213。其中上述元件201、202、203涉及一訓練階段之運作流程,而元件203、211、213則是涉及一測試階段之運作流程。Referring to FIG. 1 and FIG. 2, the first preferred embodiment of the battery power test method of the present invention is applicable to a battery 9 having a test battery parameter set, and is implemented by the battery power test system 2 as shown in FIG. The battery 9 can be, for example, a rechargeable battery of an electric motor vehicle or the like. As shown in FIG. 1 , the first preferred embodiment of the battery power test system 2 includes a sensing device 201 , a first correcting unit 202 , a classifier 203 , a sensing device 211 , a classifier 203 , and a display device . 213. The above components 201, 202, and 203 relate to the operation flow of a training phase, and the components 203, 211, and 213 are related to the operational flow of a test phase.
該訓練階段係指針對某一特定型號電池8,車廠需於研發生產過程中,先利用感應裝置201感測多個供訓練電池參數及電量對應組,再利用第一修正單元202修正該等供 訓練電池參數及電量對應組(第一修正步驟S1),以得到多個修正後供訓練電池參數及電量對應組,繼而根據該等修正後供訓練電池參數及電量對應組,訓練分類器203(訓練步驟S2)。至於,該測試階段係指,機車騎士將電動機車騎乘上路時,電動機車之感應裝置211取得電動機車內的電池9之供測試電池參數組,並輸入安裝於車上的分類器203中。於是分類器203接著進行測試步驟S3,以運算出測試電池電量顯示於電動機車之顯示裝置213上。The training stage is directed to a specific type of battery 8. The vehicle manufacturer needs to sense a plurality of training battery parameters and power corresponding groups by using the sensing device 201 in the R&D and production process, and then correct the supply by using the first correcting unit 202. Training the battery parameter and the power corresponding group (first correction step S1) to obtain a plurality of modified training battery parameters and power corresponding groups, and then training the classifier 203 according to the modified training battery parameters and the power corresponding group ( Training step S2). As for the test phase, when the locomotive rider rides the electric motor vehicle on the road, the induction device 211 of the electric motor vehicle obtains the test battery parameter set of the battery 9 in the electric motor vehicle, and inputs it into the classifier 203 installed in the vehicle. The classifier 203 then proceeds to a test step S3 to calculate the test battery power displayed on the display device 213 of the electric motor vehicle.
如圖2訓練階段之第一修正步驟S1所示,以該第一修正單元202修正該等供訓練電池參數及電量對應組,以得到該等修正後供訓練電池參數及電量對應組。其中每一供訓練電池參數及電量對應組包括一供訓練電流I train 、一供訓練電壓V train 及一對應的供訓練電池電量SOC train ,每一修正後供訓練電池參數及電量對應組包括該供訓練電流I train 、一修正後供訓練電壓V train ’ 及該對應的供訓練電池電量SOC train 。As shown in the first modification step S1 of the training phase of FIG. 2, the first correction unit 202 corrects the training battery parameters and the power corresponding group to obtain the corrected training battery parameters and the power corresponding group. Each of the training battery parameters and the power corresponding group includes a training current I train , a training voltage V train and a corresponding training battery power SOC train , and each modified training battery parameter and power corresponding group includes the For training current I train , a modified training voltage V train ' and the corresponding training battery SOC train .
在本較佳實施例中,該等供訓練電池參數及電量對應組共有500組。每一組供訓練電池參數及電量對應組中的該供訓練電流I train 及該供訓練電壓V train ,為在CNS-D3029驅動週期標準下,每間隔一定時間由該感應裝置201所接收到的電流及電壓,而該對應的供訓練電池電量SOC train 是根據該電池之額定容量減去當時已放電的電量之後剩餘電量所佔的百分比。In the preferred embodiment, the training battery parameters and the power corresponding group have a total of 500 groups. The training current I train and the training voltage V train in each group for the training battery parameter and the power corresponding group are received by the sensing device 201 at intervals of the CNS-D3029 driving cycle standard. Current and voltage, and the corresponding SOC train for training battery is a percentage of the remaining capacity after subtracting the amount of electricity discharged at that time from the rated capacity of the battery.
接下來,該感應裝置201將接收到的每一組該供訓練 電流I train 、該供訓練電壓V train 及該對應的供訓練電池電量SOC train ,傳送到該第一修正單元202。接著,該第一修正單元202根據每一供訓練電流I train ,將同一組的該供訓練電壓V train 加上一修正函數f(I train ) ,以得到修正後訓練電壓V train ’ 。在本較佳實施例中,該函數為供訓練電流I train 乘以一常數c,可以下列方程式表示:V train ’=V train +f(I train )=V train +c‧I train Next, the sensing device 201 transmits each of the received training currents I train , the training voltage V train and the corresponding training battery power SOC train to the first correcting unit 202 . Then, the first correcting unit 202 adds a correction function f(I train ) to the training voltage V train of the same group according to each training current I train to obtain the corrected training voltage V train ' . In the preferred embodiment, the function is for multiplying the training current I train by a constant c, which can be expressed by the following equation: V train '=V train +f(I train )=V train +c‧I train
其中該常數c 的最佳數值會因電池型號不同而有所不同,但因為通常供訓練資料會預先進行正規化處理,所以該常數c 的最佳數值通常介於0.1至0.9之間;此外,經實驗觀察,在本較佳實施例中所使用的該電池8在該常數c 為0.525時有最佳的訓練效果。The optimum value of the constant c may vary depending on the type of the battery, but since the training data is usually pre-normalized, the optimum value of the constant c is usually between 0.1 and 0.9; It has been experimentally observed that the battery 8 used in the preferred embodiment has the best training effect when the constant c is 0.525.
再者,該等供訓練電池參數及電量對應組中的修正後供訓練電壓V train ’ 之間,因為所對應的該供訓練電壓V train 是間隔一定時間由該感應裝置201所接收,故具有一時間上的先後次序關係。每一修正後供訓練電壓V train ’ 會再以前後至少二相鄰的修正後供訓練電壓V train ’ 的值再修正該修正後供訓練電壓V train ’ 本身的值。Furthermore, between the training battery parameters and the corrected training voltage V train ' in the power supply corresponding group, since the corresponding training voltage V train is received by the sensing device 201 at a certain interval, A prioritized relationship over time. Value of each corrected value for training voltage V train 'will again after the previous correction of at least two adjacent training supply voltage V train' re-training for correcting the correction voltage V train 'itself.
舉例來說,該供訓練電壓V train 為51.53V,此時供訓練電流I train 為1.7A,則相對應的修正後供訓練電壓V train ’ 為52.4225V。For example, the supply voltage V train training is 51.53V, this time for training is 1.7A current I train, after the training for the correction corresponding voltage V train 'is 52.4225V.
在本實施例中,參考區間設定為5筆資料,再考慮時序上前二個及後二個修正後供訓練電壓V train ’ ,依序是52.55825V、52.2905V、52.4225V、52.4815V、52.17375V ,取其平均值可得到52.3853V,故最後得到的該供訓練電池參數及電量對應組為(1.7,51.53;81.52%),相對應的修正後供訓練電池參數及電量對應組為(1.7,52.3853;81.52%)。In this embodiment, the reference interval is set to 5 data, and then the training voltage V train ' is applied to the first two and the last two corrections in the sequence, which are 52.55825V, 52.2905V, 52.4225V, 52.4815V, 52.17375. V, taking the average value can get 52.3853V, so the last set of training battery parameters and power supply corresponding group is (1.7, 51.53; 81.52%), the corresponding corrected training battery parameters and power corresponding group are (1.7 , 52.3853; 81.52%).
參閱圖1、圖2及圖3,如訓練階段之訓練步驟S2所示,根據該等修正後供訓練電池參數及電量對應組,訓練分類器203。在本較佳實施例中,該分類器203為一機率類神經網路模型,包括一輸入層、一隱藏層及一輸出層,該隱藏層包括多個神經元(以五個神經元為例),每一神經元對應一採一簡單代數運算方式直接決定的權重。Referring to FIG. 1, FIG. 2 and FIG. 3, as shown in the training step S2 of the training phase, the classifier 203 is trained according to the modified training battery parameters and the power corresponding group. In the preferred embodiment, the classifier 203 is a probability-like neural network model, including an input layer, a hidden layer, and an output layer. The hidden layer includes a plurality of neurons (taking five neurons as an example). ), each neuron corresponds to a weight directly determined by a simple algebraic operation.
由該第一修正單元202修正後得到的多組該供訓練電流I train 、該修正後供訓練電壓.V train ’ 及該對應的供訓練電池電量SOC train ,所構成的該等修正後供訓練電池參數及電量對應組,會成為訓練資料集(Training Set),用以輸入該分類器203,以決定該等權重。舉例來說,在第一修正步驟S1中由該第一修正單元202修正後得到五組修正後供訓練電池參數及電量對應組,分別為(I 1 ,V 1 ’;SOC 1 )、(I 2 ,V 2 ’;SOC 2 )、(I 3 ,V 3 ’;SOC 3 )、(I 4 ,V 4 ’;SOC 4 )及(I 5 ,V 5 ’;SOC 5 )。如圖3所示,該隱藏層具有五個對應的神經元,分別具有權重H 1 、H 2 、H 3 、H 4 及H 5 ,假設在該測試步驟S3中要輸入的該供測試電池參數組為(I x ,V x ),得到的對應測試電池電量為SOC x ,經該訓練步驟S2後,五個權重分別被設定為: ,net 1 =(I x -I 1 ) 2 +(V x -V 1 ) 2 The plurality of sets of the training current I train obtained by the first correcting unit 202, the corrected training voltage V train ' and the corresponding training battery power SOC train are configured for training The battery parameter and the power corresponding group will become a training set (Training Set) for inputting the classifier 203 to determine the weights. For example, in the first correcting step S1, the first correcting unit 202 corrects and obtains five sets of corrected training battery parameters and power corresponding groups, respectively (I 1 , V 1 '; SOC 1 ), ( I 2 , V 2 '; SOC 2 ), ( I 3 , V 3 '; SOC 3 ), ( I 4 , V 4 '; SOC 4 ) and ( I 5 , V 5 '; SOC 5 ). As shown in FIG. 3, the hidden layer has five corresponding neurons, having weights H 1 , H 2 , H 3 , H 4 , and H 5 , respectively, assuming the test battery parameters to be input in the test step S3. The group is ( I x , V x ), and the corresponding test battery power is SOC x . After the training step S2, the five weights are respectively set to: , net 1 = (I x -I 1 ) 2 +(V x -V 1 ) 2
,net 2 =(I x -I 2 ) 2 +(V x -V 2 ) 2 , net 2 = (I x -I 2 ) 2 +(V x -V 2 ) 2
,net 3 =(I x -I 3 ) 2 +(V c -V 3 ) 2 , net 3 = (I x -I 3 ) 2 +(V c -V 3 ) 2
,net 4 =(I x -I 4 ) 2 +(V x -V 4 ) 2 , net 4 = (I x -I 4 ) 2 +(V x -V 4 ) 2
,net 5 =(I x -I 5 ) 2 +(V x -V 5 ) 2
所得到的測試電池電量SOC x
為:
其中σ為10-3 ,為一機率類神經網路之預設值,而I x ,V x ,及SOC x 在訓練步驟S2時,為未知變數。Where σ is 10 -3 , which is a preset value of a probability type neural network, and I x , V x , and SOC x are unknown variables in the training step S2.
由於此權重設定以一簡單代數關係式即可完成,故此步驟所需要的運算時間極短,相較於其他種類需要長時間 學習或是迭代過程的類神經網路,本發明所使用之機率類神經網路不但訓練時間短,且方法單純可以微處理控制晶片實作,所需成本較低且更有效率。Since this weight setting can be completed in a simple algebraic relationship, the calculation time required for this step is extremely short, and it takes a long time compared to other types. The neural network of the learning or iterative process, the probability-like neural network used in the present invention not only has a short training time, but also the method can be used to control the wafer implementation by the micro-processing, and the cost is lower and more efficient.
接下來,如測試階段之測試步驟S3所示,將該供測試電池參數組輸入該分類器中,以得到該電池之測試電池電量。由於在該訓練步驟S2之後,該分類器203已建構完成,此時將由該感應裝置211接收到的即時電流I test 及即時電壓V test 做為該供測試電池參數組,用以輸入該分類器203後,即可得到該測試電池電量SOC test 並輸出至該顯示裝置213。Next, as shown in the test step S3 of the test phase, the test battery parameter set is input into the classifier to obtain the test battery power of the battery. Since the classifier 203 has been constructed after the training step S2, the instantaneous current I test and the instantaneous voltage V test received by the sensing device 211 are used as the test battery parameter group for inputting the classifier. After 203, the test battery power SOC test is obtained and output to the display device 213.
其中該H 1 、H 2 、H 3 、H 4 及H 5 分別為: ,net 1 =(I test -I 1 ) 2 +(V test -V 1 ) 2 Wherein H 1 , H 2 , H 3 , H 4 and H 5 are: , net 1 = (I test -I 1 ) 2 +(V test -V 1 ) 2
,net 2 =(I test -I 2 ) 2 +(V test -V 2 ) 2 , net 2 = (I test -I 2 ) 2 +(V test -V 2 ) 2
,net 3 =(I test -I 3 ) 2 +(V test -V 3 ) 2 , net 3 = (I test -I 3 ) 2 +(V test -V 3 ) 2
,net 4 =(I test -I 4 ) 2 +(V test -V 4 ) 2 , net 4 = (I test -I 4 ) 2 +(V test -V 4 ) 2
,net 5 =(I test -I 5 ) 2 +(V test -V 5 ) 2 , net 5 = (I test -I 5 ) 2 +(V test -V 5 ) 2
由於機率類神經網路的特性,在測試步驟S3時,僅需 將該即時電流I test 及即時電壓V test 分別代入訓練步驟S2中的I x 及V x ,即可即時地得到當時的測試電池電量SOC test ,非常適合在放電過程中立即取得當時電池的殘餘電量。Due to the characteristics of the probability-like neural network, in the test step S3, only the instant current I test and the instantaneous voltage V test need to be substituted into I x and V x in the training step S2, respectively, and the test battery at that time can be obtained instantly. The SOC test is very suitable for immediately obtaining the residual power of the battery at the time of discharge.
參閱圖4及圖5,本發明電池電量測試方法之第二較佳實施例,還包含在該訓練步驟S2以及該測試步驟S3之間的一第二修正步驟S4,且該系統2還包含一第二修正單元212。如圖5所示,該第二修正單元212用以修正由該感應裝置211接收到的該電池9之一即時電池參數組,做為該供測試電池參數組。其中該即時電池參數組包括該即時電流I test 及該即時電壓V test ,該供測試電池參數組包括該即時電流I test 及一供測試電壓V test ’ 。該第二修正單元212先根據該即時電流I test ,將即時電壓V test 加上一修正函數f(I test ) ,以得到該供測試電壓V test ’ ,繼而輸入該分類器203。在本較佳實施例中,該函數為該即時電流I test 乘以一常數d ,可以下列方程式表示:V test ’=V test +f(I test )=V test +d‧I test Referring to FIG. 4 and FIG. 5, a second preferred embodiment of the battery power testing method of the present invention further includes a second correcting step S4 between the training step S2 and the testing step S3, and the system 2 further includes a Second correction unit 212. As shown in FIG. 5, the second correcting unit 212 is configured to correct one of the battery parameter groups of the battery 9 received by the sensing device 211 as the test battery parameter set. The instant battery parameter set includes the instantaneous current I test and the instantaneous voltage V test , and the test battery parameter set includes the immediate current I test and a test voltage V test ' . The second correcting unit 212 first adds a correction function f(I test ) to the instantaneous voltage V test according to the instantaneous current I test to obtain the test voltage V test ' , and then inputs the classifier 203. In the preferred embodiment, the function multiplies the instantaneous current I test by a constant d , which can be expressed by the following equation: V test '=V test +f(I test )=V test +d‧I test
其中該常數d 依電池型號而有所不同,但通常介於0.1至0.9之間,在本較佳實施例中該常數為0.525。由於該第一修正單元202及該第二修正單元212的功能,使得該測試電池電量更進一步地接近真實情況。The constant d varies depending on the battery model, but is usually between 0.1 and 0.9, which is 0.525 in the preferred embodiment. Due to the functions of the first correcting unit 202 and the second correcting unit 212, the test battery power is further closer to the real situation.
綜上所述,本發明使用的機率類神經網路主要的理論基礎建立在於貝氏決策上,運用機率類神經網路的多層網路架構,即使面臨稀疏的樣本空間,對於錯誤的資訊還是 具有相當的容忍性,故即使電池之放電為一非線性模型,以機率類神經網路配合本發明之第一修正步驟S1及第二修正步驟S4,不但有效率且成本較低,又可在電池放電過程中正確測試當時的電池電量,故確實能達成本發明之目的。In summary, the main theoretical basis of the probability-like neural network used in the present invention is based on the Bayesian decision-making, using a multi-layer network architecture of a probability-like neural network, even for a sparse sample space, for the wrong information. It is quite tolerant, so even if the discharge of the battery is a non-linear model, the probability-like neural network cooperates with the first modification step S1 and the second correction step S4 of the present invention, which is not only efficient but also low in cost, and The battery power at the time is correctly tested during the discharge of the battery, so that the object of the present invention can be achieved.
惟以上所述者,僅為本發明之較佳實施例而已,當不能以此限定本發明實施之範圍,即大凡依本發明申請專利範圍及發明說明內容所作之簡單的等效變化與修飾,皆仍屬本發明專利涵蓋之範圍內。The above is only the preferred embodiment of the present invention, and the scope of the invention is not limited thereto, that is, the simple equivalent changes and modifications made by the scope of the invention and the description of the invention are All remain within the scope of the invention patent.
S1‧‧‧第一修正步驟S1‧‧‧First Amendment Step
S2‧‧‧訓練步驟S2‧‧‧ training steps
S3‧‧‧測試步驟S3‧‧‧ test procedure
S4‧‧‧第二修正步驟S4‧‧‧ second amendment step
2‧‧‧電池電量測試系統2‧‧‧Battery power test system
201‧‧‧感應裝置201‧‧‧Induction device
202‧‧‧第一修正單元202‧‧‧First Correction Unit
203‧‧‧分類器203‧‧‧ classifier
211‧‧‧感應裝置211‧‧‧Induction device
212‧‧‧第二修正單元212‧‧‧Second correction unit
213‧‧‧顯示裝置213‧‧‧ display device
8‧‧‧電池8‧‧‧Battery
9‧‧‧電池9‧‧‧Battery
I1 至I5 ‧‧‧供訓練電流I 1 to I 5 ‧‧‧ for training current
V1 ’至V5 ’‧‧‧修正後供訓練電壓V 1 ' to V 5 '‧‧‧corrected for training voltage
H1 至H5 ‧‧‧權重H 1 to H 5 ‧‧ ‧ weights
SOC1 至SOC5 ‧‧‧供訓練電池電量SOC 1 to SOC 5 ‧‧‧ for training battery power
Ix ,Vx ‧‧‧供測試電池參數組I x , V x ‧‧‧ for test battery parameter set
SOCx ‧‧‧測試電池電量SOC x ‧‧‧Test battery power
圖1是一系統方塊圖,說明用以實作本發明電池電量測試方法之第一較佳實施例之電池電量測試系統;圖2是一流程圖,說明本發明方法之第一較佳實施例;圖3是一示意圖,說明機率類神經網路;圖4是一流程圖,說明本發明方法之第二較佳實施例;及圖5是一系統圖,說明用以實作本發明電池電量測試方法之第二較佳實施例之電池電量測試系統。1 is a system block diagram showing a battery power test system for implementing the first preferred embodiment of the battery power test method of the present invention; FIG. 2 is a flow chart illustrating a first preferred embodiment of the method of the present invention Figure 3 is a schematic diagram showing a probability-like neural network; Figure 4 is a flow chart illustrating a second preferred embodiment of the method of the present invention; and Figure 5 is a system diagram illustrating the operation of the battery of the present invention A battery power test system of a second preferred embodiment of the test method.
S1‧‧‧第一修正步驟S1‧‧‧First Amendment Step
S2‧‧‧訓練步驟S2‧‧‧ training steps
S3‧‧‧測試步驟S3‧‧‧ test procedure
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EP1081499B1 (en) * | 1998-05-28 | 2003-07-09 | Toyota Jidosha Kabushiki Kaisha | Means for estimating charged state of battery and method for estimating degraded state of battery |
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EP1081499B1 (en) * | 1998-05-28 | 2003-07-09 | Toyota Jidosha Kabushiki Kaisha | Means for estimating charged state of battery and method for estimating degraded state of battery |
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