TWI653458B - Method and device for estimating battery and battery management system - Google Patents
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Abstract
一種電池估測方法，包含：設定第一參數資料的參數初始值與狀態資料的狀態初始值，其中第一參數資料包含電池的可用電量，狀態資料包含電池的電量狀態；根據第一參數資料、電流量測訊號與狀態修正量對狀態資料進行迭代運算以更新狀態資料；當狀態資料之更新次數大於預定值時，根據參數修正量與第一參數資料以計算第二參數資料，重置更新次數並以第二參數資料作為新的第一參數資料以更新狀態資料；以及輸出電池的可用電量與電量狀態。 A battery estimation method includes: setting an initial value of a parameter of a first parameter data and a state initial value of a state data, wherein the first parameter data includes an available power of the battery, and the state data includes a state of charge of the battery; according to the first parameter data, The electric current test signal and the state correction amount perform an iterative operation on the state data to update the state data; when the update number of the state data is greater than the predetermined value, the second parameter data is calculated according to the parameter correction amount and the first parameter data, and the number of update times is reset. The second parameter data is used as the new first parameter data to update the status data; and the available battery power and power status are output.
Description
本揭示內容係關於一種電池估測方法，且特別是關於估測電池電量狀態與健康狀態的估測方法。 The present disclosure is directed to a battery estimation method, and more particularly to an estimation method for estimating battery state and health.
改善傳統以開迴路方法(例如：庫倫積分法與開路電壓法)估測電池之電量狀態的缺失。 Improvements are traditionally based on open loop methods (eg, Coulomb integration and open circuit voltage methods) to estimate the lack of state of charge on the battery.
庫倫積分法必須以高精準度的電流電壓感知器與正確的電量狀態初始值，否則誤差量會隨著時間被放大。 The Coulomb integration method must use a high-precision current-voltage sensor with the correct initial state of the state of charge, otherwise the amount of error will be amplified over time.
開路電壓法是先以實驗建立電池開路電壓與電量狀態查表，但是當電池開路電壓過於平坦時，可能會因為電壓變化太小而導致估測電量狀態不準確。 The open circuit voltage method is to first establish an open circuit voltage and power status check table by experiment. However, when the open circuit voltage of the battery is too flat, the estimated state of the power may be inaccurate because the voltage change is too small.
本揭示文件的一態樣係關於一種電池估測方法，包含：設定第一參數資料的參數初始值與狀態資料的狀態初始值，其中第一參數資料包含電池的可用電量，狀態資料包 含電池的電量狀態；根據第一參數資料、電流量測訊號與狀態修正量對狀態資料進行迭代運算以更新狀態資料；當狀態資料之更新次數大於預定值時，根據參數修正量與第一參數資料以計算第二參數資料，並以第二參數資料作為新的第一參數資料以更新狀態資料且重置更新次數；以及輸出電池的可用電量與電量狀態。 An aspect of the present disclosure relates to a battery estimation method, including: setting an initial value of a parameter of a first parameter data and a state initial value of a state data, wherein the first parameter data includes available power of the battery, and the status data package The state of charge of the battery; according to the first parameter data, the current measurement signal and the state correction amount, the state data is iteratively operated to update the state data; when the update number of the state data is greater than the predetermined value, according to the parameter correction amount and the first parameter The data is used to calculate the second parameter data, and the second parameter data is used as the new first parameter data to update the status data and reset the number of updates; and output the available power and state of the battery.
本揭示內容的另一態樣係關於一種電池估測裝置，包含：狀態器及參數器。狀態器用以取得電池的狀態資料，並根據電池的參數資料、電流量測訊號與狀態修正量對狀態資料進行迭代運算以更新狀態資料，並輸出更新後的狀態資料的電量狀態。參數器電性耦接於該狀態器，用以取得電池的參數資料，當狀態器的狀態資料之更新次數大於預定值時，以參數修正量修正參數資料後輸出參數資料的可用電量。 Another aspect of the present disclosure is directed to a battery estimation apparatus comprising: a stater and a parameterizer. The state device is used to obtain the state data of the battery, and iteratively calculates the state data according to the parameter data of the battery, the current measuring signal and the state correction amount to update the state data, and outputs the state of the power of the updated state data. The parameter device is electrically coupled to the state device for obtaining parameter data of the battery. When the update status of the state data of the state device is greater than a predetermined value, the parameter data is corrected by the parameter correction amount, and the available power of the parameter data is output.
本揭示內容的另一態樣係關於一種電池管理系統，包含：量測裝置、估測裝置及管理裝置。量測裝置用以偵測電池以取得電流量測訊號、電壓量測訊號及溫度量測訊號並輸出。估測裝置電性耦接於量測裝置，用以接收電流量測訊號、電壓量測訊號及溫度量測訊號。估測裝置包含狀態器及參數器。狀態器用以取得電池的狀態資料，並根據電池的參數資料、電流量測訊號與狀態修正量對狀態資料進行迭代運算以更新狀態資料，並輸出更新後的狀態資料的電量狀態。參數器電性耦接於狀態器，用以取得電池的參數資料，當狀態器的狀態資料之更新次數大於預定值時，以參數修正量修正參數資料後輸出參數資料的可用電量。管理裝置電性耦接於估測裝置，用 以接收電量狀態與可用電量以進行電池的運作管理。 Another aspect of the present disclosure is directed to a battery management system including: a measuring device, an estimating device, and a management device. The measuring device is configured to detect the battery to obtain the current measuring signal, the voltage measuring signal and the temperature measuring signal and output the signal. The estimating device is electrically coupled to the measuring device for receiving the current measuring signal, the voltage measuring signal and the temperature measuring signal. The estimation device includes a stater and a parameterizer. The state device is used to obtain the state data of the battery, and iteratively calculates the state data according to the parameter data of the battery, the current measuring signal and the state correction amount to update the state data, and outputs the state of the power of the updated state data. The parameter device is electrically coupled to the state device for obtaining parameter data of the battery. When the update status of the status data of the state device is greater than a predetermined value, the parameter data is corrected by the parameter correction amount, and the available power of the parameter data is output. The management device is electrically coupled to the estimation device and used In order to manage the operation of the battery by receiving the state of charge and the amount of power available.
100‧‧‧電池管理系統 100‧‧‧Battery Management System
120‧‧‧量測裝置 120‧‧‧Measurement device
140‧‧‧估測裝置 140‧‧‧ Estimation device
142‧‧‧狀態器 142‧‧‧Stater
144‧‧‧參數器 144‧‧‧Parameter
146‧‧‧開路電壓查表 146‧‧‧Open circuit voltage checklist
148‧‧‧更新次數判斷器 148‧‧‧Update count determiner
160‧‧‧管理裝置 160‧‧‧Management device
900‧‧‧電池 900‧‧‧Battery
300‧‧‧電池估測方法 300‧‧‧Battery estimation method
S320~S360‧‧‧操作 S320~S360‧‧‧ operation
S341~S344‧‧‧操作 S341~S344‧‧‧ operation
AS‧‧‧類比量測訊號 AS‧‧‧ analog measurement signal
DS‧‧‧數位量測訊號 DS‧‧‧ digital measurement signal
DS_V‧‧‧電壓量測訊號 DS_V‧‧‧Voltage measurement signal
DS_I‧‧‧電流量測訊號 DS_I‧‧‧current measurement signal
DS_T‧‧‧溫度量測訊號 DS_T‧‧‧ Temperature measurement signal
PD‧‧‧參數資料 PD‧‧‧Parameter data
SD‧‧‧狀態資料 SD‧‧‧Status Information
SOC‧‧‧電量狀態 SOC‧‧‧Charge status
C cap ‧‧‧可用電量 C cap ‧‧‧Available power
V oc ‧‧‧開路電壓值 V oc ‧‧‧ open circuit voltage value
第1圖係根據本揭示文件之部分實施例繪示一種電池管理系統之示意圖。 1 is a schematic diagram of a battery management system in accordance with some embodiments of the present disclosure.
第2圖係根據本揭示文件之其他部分實施例繪示一種電池估測裝置之示意圖。 2 is a schematic diagram showing a battery estimating device according to other embodiments of the present disclosure.
第3圖係根據本揭示文件之部分實施例繪示一種電池估測方法之流程圖。 FIG. 3 is a flow chart showing a battery estimation method according to some embodiments of the present disclosure.
第4圖係根據本揭示文件之其他部分實施例繪示一種電池估測方法之流程圖。 Figure 4 is a flow chart showing a battery estimation method according to other embodiments of the present disclosure.
第5圖係根據本揭示文件之部分實施例繪示參數器雜訊矩陣與電量狀態之查表。 FIG. 5 is a table showing a parametric noise matrix and a state of charge according to some embodiments of the present disclosure.
第6圖係根據本揭示文件之部分實施例繪示估測全新電池電量狀態與可用電量之結果圖。 Figure 6 is a graph showing the results of estimating the state of the new battery and the amount of available power in accordance with some embodiments of the present disclosure.
第7圖係根據本揭示文件之部分實施例繪示估測老化電池電量狀態與可用電量之結果圖。 Figure 7 is a graph showing the results of estimating the state of charge of an aged battery and the amount of available power in accordance with some embodiments of the present disclosure.
下文係舉實施例配合所附圖式作詳細說明，但所描述的具體實施例僅用以解釋本案，並不用來限定本案，而結構操作之描述非用以限制其執行之順序，任何由元件重新組合之結構，所產生具有均等功效的裝置，皆為本揭示內容所涵蓋 的範圍。 The embodiments are described in detail below with reference to the drawings, but the specific embodiments described are only used to explain the present invention and are not intended to limit the present invention, and the description of structural operations is not intended to limit the order of execution thereof, and any components. Recombined structures that produce equal devices are covered by this disclosure The scope.
電池900之電量狀態SOC是透過電池之可用電量C cap 估算而得，然而，當電池900老化時，其內部化學材料變異，造成電池900之可用電量C cap 衰減。若使用全新的電池初始可用電量C cap0估算電池900之電量狀態，會導致估測誤差增加。 The state of charge SOC of the battery 900 is estimated by the available power C cap of the battery. However, when the battery 900 ages, its internal chemical material mutates, causing the available power C cap of the battery 900 to decay. Estimating the state of charge of battery 900 using the new initial battery power C cap 0 will result in an increase in estimation error.
為提高估測電池健康狀態及電量狀態的準確度，本揭示內容提出一種電池管理系統。請參考第1圖。第1圖係為根據本揭示內容部分實施例所繪示之電池管理系統之示意圖。如第1圖所示，在部分實施例中，電池管理系統100電性耦接於電池900，用以估測電池900之電量狀態SOC及代表電池900健康狀態的可用電量C cap 。電池管理系統100包含量測裝置120、估測裝置140和管理裝置160。結構上，量測裝置120電性耦接於電池900及估測裝置140，估測裝置140電性耦接於量測裝置120與管理裝置160。 To improve the accuracy of estimating battery health and state of charge, the present disclosure proposes a battery management system. Please refer to Figure 1. 1 is a schematic diagram of a battery management system in accordance with some embodiments of the present disclosure. As shown in FIG. 1 , in some embodiments, the battery management system 100 is electrically coupled to the battery 900 for estimating the state of charge SOC of the battery 900 and the available power C cap representing the health of the battery 900 . The battery management system 100 includes a metrology device 120, an estimation device 140, and a management device 160. Structurally, the measuring device 120 is electrically coupled to the battery 900 and the estimating device 140. The estimating device 140 is electrically coupled to the measuring device 120 and the management device 160.
操作上，量測裝置120用於量測電池900的電流、電壓及溫度並轉換為數位量測訊號DS輸出至估測裝置140。估測裝置140用以設定參數資料PD及狀態資料SD，並根據接收到的數位量測訊號DS進行迭代運算，以估測並輸出電池900的電量狀態SOC與可用電量C cap 。管理裝置160用以接收自估測裝置140輸出之電量狀態SOC與可用電量C cap ，以進行電池900的運作管理。 In operation, the measuring device 120 is configured to measure the current, voltage and temperature of the battery 900 and convert it into a digital measuring signal DS for output to the estimating device 140. The estimating device 140 is configured to set the parameter data PD and the state data SD, and perform an iterative operation according to the received digital measuring signal DS to estimate and output the state of charge SOC and the available power C cap of the battery 900. The management device 160 is configured to receive the power state SOC and the available power C cap output from the estimation device 140 for performing operation management of the battery 900.
請參考第2圖。第2圖係為根據本揭示內容部分實施例所繪示的估測裝置140之示意圖。在部分實施例中，如第 2圖所示，估測裝置140包含狀態器142和參數器144。狀態器142和參數器144彼此電性耦接。狀態器142用於取得電池900的一個狀態資料SD。狀態資料SD包含電量狀態SOC和擴散電壓V p 。參數器144用於取得電池900的一個參數資料PD。參數資料PD包含可用電量C cap 、擴散電容C p 、擴散電阻R p 及內電阻R i 。 Please refer to Figure 2. 2 is a schematic diagram of an estimation device 140, depicted in accordance with some embodiments of the present disclosure. In some embodiments, as shown in FIG. 2, the estimation device 140 includes a state machine 142 and a parameterizer 144. The state 142 and the parameter 144 are electrically coupled to each other. The state machine 142 is used to retrieve a status data SD of the battery 900. State data SD contain state of charge SOC and the diffusion voltage V p. The parameter 144 is used to obtain a parameter data PD of the battery 900. The parameter data PD includes the available power C cap , the diffusion capacitance C p , the diffusion resistance R p , and the internal resistance R i .
在其他部分實施例中，如第2圖所示，估測裝置140更包含開路電壓查表146和/或更新次數判斷器148。結構上，開路電壓查表146電性耦接於狀態器142和參數器144。更新次數判斷器148亦電性耦接於狀態器142和參數器144。操作上，開路電壓查表146用以根據狀態器142中狀態資料SD的電量狀態SOC和參數器144中參數資料PD的可用電量C cap ，以取得開路電壓值V oc ，開路電壓值V oc 用以計算狀態修正量及參數修正量。更新次數判斷器148用以判斷狀態器142中狀態資料SD的更新次數N是否大於預定值K。 In other partial embodiments, as shown in FIG. 2, the estimation device 140 further includes an open circuit voltage lookup table 146 and/or an update count determiner 148. Structurally, the open circuit voltage look-up table 146 is electrically coupled to the state 142 and the parameterizer 144. The update count determiner 148 is also electrically coupled to the state 142 and the parameterizer 144. In operation, the open circuit voltage lookup table 146 is used to obtain the open circuit voltage value V oc and the open circuit voltage value V oc according to the state of charge SOC of the state data SD in the state 142 and the available power C cap of the parameter data PD in the parameter 144. Calculate the state correction amount and the parameter correction amount. The update count determiner 148 is configured to determine whether the update count N of the status data SD in the status 142 is greater than a predetermined value K.
為便於說明起見，電池管理系統100及估測裝置140當中各個元件的具體操作將於以下段落中搭配圖式進行說明。請參考第3圖。第3圖係為根據本揭示內容部分實施例所繪示的電池估測方法之流程圖。如第3圖所示，電池估測方法300包含操作S320~S360。 For ease of explanation, the specific operation of each component of the battery management system 100 and the estimation device 140 will be described in conjunction with the drawings in the following paragraphs. Please refer to Figure 3. FIG. 3 is a flow chart of a battery estimation method according to some embodiments of the present disclosure. As shown in FIG. 3, the battery estimation method 300 includes operations S320~S360.
首先，在操作S320中，由量測裝置120取得電池900的數位量測訊號DS並傳送至估測裝置140。具體而言，在部分實施例中，量測裝置120用以偵測電池900的電流、電壓及溫度以取得類比量測訊號AS。類比量測訊號AS包含電流類 比訊號AS_I、電壓類比訊號AS_V及溫度類比訊號AS_T。接著，量測裝置120將類比量測訊號AS轉換成數位量測訊號DS。數位量測訊號DS包含電流量測訊號DS_I、電壓量測訊號DS_V及溫度量測訊號DS_T。再者，量測裝置120用以將數位量測訊號DS傳送至估測裝置140。 First, in operation S320, the digital measuring signal DS of the battery 900 is acquired by the measuring device 120 and transmitted to the estimating device 140. Specifically, in some embodiments, the measuring device 120 is configured to detect the current, voltage, and temperature of the battery 900 to obtain the analog measuring signal AS. Analog measurement signal AS contains current class The signal AS_I, the voltage analog signal AS_V and the temperature analog signal AS_T. Next, the measuring device 120 converts the analog measuring signal AS into a digital measuring signal DS. The digital measurement signal DS includes a current measurement signal DS_I, a voltage measurement signal DS_V, and a temperature measurement signal DS_T. Furthermore, the measuring device 120 is configured to transmit the digital measurement signal DS to the estimating device 140.
接著，在操作S340中，由估測裝置140根據數位量測訊號DS、參數資料PD及狀態資料SD以取得電池900的電量狀態SOC及可用電量C cap ，並將電量狀態SOC及可用電量C cap 輸出至管理裝置160。進一步的詳細操作將配合第4圖在以下段落說明。 Next, in operation S340, the estimating device 140 obtains the state of charge SOC and the available power C cap of the battery 900 based on the digital measurement signal DS, the parameter data PD, and the status data SD, and the state of charge SOC and the available power C cap Output to the management device 160. Further detailed operations will be described in conjunction with Figure 4 in the following paragraphs.
接著，在操作S360中，由管理裝置160自估測裝置140接收電量狀態SOC及可用電量C cap 以進行電池900的運作管理。 Next, in operation S360, the management device 160 receives the power state SOC and the available power C cap from the estimation device 140 to perform operation management of the battery 900.
請參考第4圖。如第4圖所示，操作S340包含操作S341~S344。首先，在操作S341中，由估測裝置140設定第一參數資料PD1的參數初始值與狀態資料SD的狀態初始值。具體而言，在部分實施例中，由估測裝置140中的參數器144設定第一參數資料PD1的參數初始值，由估測裝置140中的狀態器142設定狀態資料SD的狀態初始值。參數資料PD係一個1x4矩陣，包含內電阻R i 、擴散電阻R p 、擴散電容C p 及可用電量C cap 。狀態資料SD係一個1x2矩陣，包含擴散電壓V p 和電量狀態SOC。換句話說，參數資料PD與狀態資料SD可分別由下列矩陣表示：θ k =[R i,k R p,k C p,k C cap,k ] T Please refer to Figure 4. As shown in FIG. 4, operation S340 includes operations S341 to S344. First, in operation S341, the parameter initial value of the first parameter data PD1 and the state initial value of the state data SD are set by the estimating device 140. Specifically, in some embodiments, the parameter initial value of the first parameter data PD1 is set by the parameterizer 144 in the estimating device 140, and the state initial value of the state data SD is set by the state device 142 in the estimating device 140. The parameter data PD is a 1x4 matrix containing an internal resistance R i , a diffusion resistance R p , a diffusion capacitance C p , and an available power C cap . A state data SD strain 1x2 matrix, comprising the diffusion voltage V p and the state of charge SOC. In other words, the parameter data PD and the state data SD can be represented by the following matrix, respectively: θ k =[ R i,k R p,k C p,k C cap,k ] T
x l =[SOC l V p,l ] T x l =[ SOC l V p,l ] T
其中，θ k 代表第k次遞迴時之參數資料PD，R i,k 、R p,k 、C p,k 、C cap,k 分別代表第k次遞迴時之內電阻R i 、擴散電阻R p 、擴散電容C p 及可用電量C cap 。x l 代表第1次遞迴時之狀態資料SD，SOC l 、V p,l 分別代表第1次遞迴時之電量狀態SOC和擴散電壓V p 。 Where θ k represents the parameter data PD, R i,k , R p,k , C p,k , C cap,k at the kth recursion , respectively representing the internal resistance R i and diffusion at the kth recursion Resistor R p , diffusion capacitor C p and available power C cap . x l represents the state data SD at the time of the first recursion, and SOC l , V p,l represent the state of charge SOC and the diffusion voltage V p at the time of the first reentry, respectively.
接著，在操作S342中，由狀態器142根據第一參數資料PD1、電流量測訊號DS_I與狀態修正量對狀態資料SD進行運算以更新狀態資料SD。具體而言，由狀態器142根據參數器144的第一參數資料PD1與量測裝置120所取得的電流量測訊號DS_I以更新狀態資料SD，接著，再根據狀態修正量對於更新後的狀態資料SD進行修正。此外，每當進行一次操作S342，狀態器142中狀態資料SD的更新次數N即增加一次。 Next, in operation S342, the state device 142 operates the state data SD based on the first parameter data PD1, the current measurement signal DS_I, and the state correction amount to update the state data SD. Specifically, the state device 142 updates the state data SD according to the current parameter DS_I obtained by the first parameter data PD1 of the parameterizer 144 and the measuring device 120, and then, according to the state correction amount, the updated state data. SD is corrected. Further, each time the operation S342 is performed, the number N of updates of the status data SD in the state 142 is incremented once.
進一步詳細說明，在部分實施例中，更新狀態資料SD的步驟係包含以下動作：由狀態器142根據第一參數資料PD1及電流量測訊號DS_I，透過系統動態方程式F更新狀態資料SD。舉例來說，系統動態方程式F可為如下式之一非線性方程式：
其中，u l 代表電流，η代表庫倫係數，T代表時間。 Where u l represents current, η represents Coulomb coefficient, and T represents time.
具體來說，一階RC等效電路模型之電器特性方程式以及計算電池電量狀態方程式可分別表示為下列之式
(1)、(2)以及式(3)：
V t =V oc +V p +I L R i 式(2) V t = V oc + V p + I L R i (2)
其中，I L 代表電池的電流，V t 代表電池的端電壓，代表C avbl 電池的可用電量。 Wherein, on behalf of the battery current I L, V t that represents the battery terminal voltage, representative of C avbl available battery power.
第l+1次遞迴之狀態資料SDx l+1，第k+1次遞迴之參數資料PD θ k+1以及電壓量測訊號DS_Vy l 可分別由式(4)、式(5)與式(6)表示：x l+1=F(x l , θ k ,u l )+w l 式(4) The l-state data SD x l +1 +1 times of recursion, the k + 1 recursively parameter data PD θ k +1 and the voltage measuring signal DS_Vy l respectively by the formula (4), (5) And equation (6) means: x l +1 = F ( x l , θ k , u l )+ w l (4)
θ k+1=θ k +r k 式(5) θ k +1 = θ k + r k (5)
y l =G(x l , θ k ,u l )+v l 式(6) y l =G( x l , θ k ,u l )+ v l (6)
其中，w l 代表狀態器142之過程雜訊，r k 代表參數器144之過程雜訊，v l 代表量測雜訊，G代表量測方程式。為方便說明起見，關於y l 及G的具體內容將於後續段落中進行說明。 Wherein, w l represents the process noise of the state 142, r k represents the process noise of the parameter 144, v l represents the measurement noise, and G represents the measurement equation. For the sake of explanation, the specific content of y l and G will be explained in the following paragraphs.
如此一來，根據上述式(1)到式(6)，便可由狀態器142將系統動態方程式F對狀態資料SD進行偏微分以取得第一偏導矩陣A1(即：下式中的)。 In this way, according to the above equations (1) to (6), the state dynamics F can be differentiated by the state machine 142 to the state data SD to obtain the first partial matrix A1 (ie: in the following formula) ).
由狀態器142根據第一偏導矩陣A1計算狀態誤差協方差矩陣Psd：
其中代表該次計算狀態誤差協方差矩陣Psd，Q x代表過程雜訊之協方差矩陣。 among them Representing the state error covariance matrix Psd, Q x represents the covariance matrix of the process noise.
另一方面，在部分實施例中，對於更新後的狀態資料SD進行修正的步驟係包含以下動作：由開路電壓查表146根據量測裝置120所取得的溫度量測訊號DS_T取得一個開路電壓表。開路電壓查表146再根據狀態器142中狀態資料SD的電量狀態SOC及參數器144中第一參數資料PD1的可用電量C cap ，透過開路電壓表取得對應的開路電壓值V oc 。開路電壓查表146將開路電壓值V oc 傳送至狀態器142。狀態器142根據開路電壓值V oc 以量測方程式G計算狀態預測之電池端電壓。舉例來說，量測方程式G可為如下式之一非線性方程式：G(x l , θ k ,u l )=V oc (SOC l ,C cap,k )+Vp,l +R i,k ．u l On the other hand, in some embodiments, the step of correcting the updated status data SD includes the following actions: the open circuit voltage look-up table 146 obtains an open circuit voltage table according to the temperature measurement signal DS_T obtained by the measuring device 120. . The open circuit voltage lookup table 146 then obtains the corresponding open circuit voltage value V oc through the open circuit voltage meter according to the state of charge SOC of the state data SD in the state 142 and the available power C cap of the first parameter data PD1 of the parameterizer 144. The look-up table 146 open-circuit voltage V oc open-circuit voltage value transferred to state machine 142. The state 142 calculates the state-predicted battery terminal voltage by measuring the equation G based on the open circuit voltage value V oc . For example, the measurement equation G can be a nonlinear equation of the following equation: G( x l , θ k , u l )= V oc (SOC l , C cap,k )+V p ,l + R i, k . u l
再者，由狀態器142將量測方程式G對狀態資料SD進行偏微分以取得第二偏導矩陣C2(即：下式中的)。 Furthermore, the state equation 142 differentially differentiates the state data SD by the state 142 to obtain the second partial matrix C2 (ie: in the following formula) ).
其中代表該次計算狀態最佳卡爾曼增益值Lsd，R x代表量測雜訊之協方差矩陣。 among them Representing the best calculated Kalman gain value Lsd for this time, R x represents the covariance matrix of the measured noise.
狀態器142再根據狀態預測之電池端電壓與量測裝置120所取得之電壓量測訊號DS_V，將兩者的誤差乘上狀態最佳卡爾曼增益值Lsd，以取得狀態修正量。由狀態器142 根據狀態修正量加上更新後的狀態資料SD以計算出修正後的狀態資料SD(即：下式中的)。 The state device 142 multiplies the error of the two by the battery terminal voltage predicted by the state and the voltage measurement signal DS_V obtained by the measuring device 120 by the state optimum Kalman gain value Lsd to obtain the state correction amount. The state machine 142 adds the updated state data SD according to the state correction amount to calculate the corrected state data SD (ie, in the following formula) ).
另外，由狀態器142以狀態最佳卡爾曼增益值Lsd更新狀態誤差協方差矩陣Psd(即：下式中的)。 In addition, the state error covariance matrix Psd is updated by the state machine 142 with the state optimal Kalman gain value Lsd (ie: in the following equation ).
接著，在操作S343中，由更新次數判斷器148判斷狀態器142中狀態資料SD的更新次數N是否大於預定值K。當更新次數N未大於預定值K，則由狀態器142將狀態資料SD中的電量狀態SOC輸出，由參數器144將第一參數資料PD1中的可用電量C cap 輸出，且再次進行操作S342。當更新次數N大於預定值K，即操作S342進行次數超過預定值K時，則進行操作S344。 Next, in operation S343, it is judged by the number-of-updates determiner 148 whether or not the number N of updates of the state data SD in the state 142 is greater than a predetermined value K. When the number of updates N is not greater than the predetermined value K, the state of charge SOC in the state data SD is output by the state 142, the available power C cap in the first parameter data PD1 is output by the parameterizer 144, and operation S342 is performed again. When the number of updates N is greater than the predetermined value K, that is, the number of operations S342 exceeds the predetermined value K, then operation S344 is performed.
換言之，電池估測方法300中，包含操作S342及操作S344兩種不同更新頻率的迭代運算。由於狀態資料SD具有變動較快速之特性，而參數資料PD具有變動較緩慢之特性，因此藉由更新次數判斷器148根據判斷更新次數N是否大於預定值K，以使得狀態器142進行狀態資料SD的更新頻率較高，而參數器144進行參數資料PD的更新頻率較低。藉此達到減少估測裝置140的運算量，進而避免影響管理裝置160進行其他電池900的保護功能之運算。 In other words, the battery estimation method 300 includes an iterative operation of two different update frequencies of operation S342 and operation S344. Since the status data SD has a relatively fast change characteristic, and the parameter data PD has a characteristic of relatively slow change, the update count determiner 148 determines whether the update count N is greater than a predetermined value K by the update count determiner 148, so that the status 142 performs the status data SD. The update frequency is higher, and the parameter 144 performs the update frequency of the parameter data PD is lower. Thereby, the amount of calculation of the estimation device 140 is reduced, and the calculation of the protection function of the other battery 900 by the management device 160 is avoided.
舉例來說，在部分實施例中，更新次數N的初始值為0，而預訂值K為9。進行1次操作S342後，即由狀態器142對狀態資料SD進行運算以更新狀態資料SD一次之後，更新次 數N為1。由於更新次數1未大於預定值9，因此估測裝置140將電量狀態SOC與可用電量C cap 輸出至管理裝置160後，再次進行操作S342。當進行10次操作S342後，更新次數N為10，更新次數10大於預定值9，因此接著進行操作S344，之後將N歸零重新計數。 For example, in some embodiments, the initial value of the number of updates N is zero and the subscription value K is nine. After the operation S342 is performed once, that is, the state data SD is operated by the state machine 142 to update the state data SD once, the number of updates N is 1. Since the number of updates 1 is not greater than the predetermined value 9, the estimating device 140 outputs the state of charge SOC and the available amount of power C cap to the management device 160, and then proceeds to operation S342. When the operation S342 is performed 10 times, the number of updates N is 10, and the number of updates 10 is greater than the predetermined value 9, so that operation S344 is performed next, and then N is reset to zero.
值得注意的是，上述預定值K僅為方便說明起見的示例，並非用以限制本案。本領域具通常知識者可根據實際需求設定預定值K。 It should be noted that the above predetermined value K is merely an example for convenience of explanation, and is not intended to limit the case. Those skilled in the art can set a predetermined value K according to actual needs.
接著，當更新次數N大於預定值K時，則進行操作S344。在操作S344中，由參數器144根據參數修正量與第一參數資料PD1計算第二參數資料PD2，並以第二參數資料PD2作為新的第一參數資料PD1以進行狀態資料SD的更新。 Next, when the number of updates N is greater than the predetermined value K, operation S344 is performed. In operation S344, the second parameter data PD2 is calculated by the parameterizer 144 according to the parameter correction amount and the first parameter data PD1, and the second parameter data PD2 is used as the new first parameter data PD1 to update the status data SD.
具體而言，由狀態器142將最新的狀態資料SID傳送至參數器144。由參數器144根據新的狀態資料SD計算參數最佳卡爾曼增益值Lpd，並根據溫度量測訊號DS_T取得參數預測之電池端電壓。參數器144再基於參數預測之電池端電壓與量測裝置120所取得之電壓量測訊號DS_V，將兩者的誤差乘上參數最佳卡爾曼增益值Lpd，以取得參數修正量。接著，由參數器144根據參數修正量加上第一參數資料PD1以計算出第二參數資料PD2。另外，由參數器144以參數最佳卡爾曼增益值Lpd更新參數誤差協方差矩陣Ppd。 Specifically, the latest status data SID is transmitted by the state machine 142 to the parameterizer 144. The parameter optimal Kalman gain value Lpd is calculated by the parameterizer 144 according to the new state data SD, and the battery terminal voltage predicted by the parameter is obtained according to the temperature measurement signal DS_T. The parameter 144 further multiplies the error of the two by the parameter-predicted battery terminal voltage and the voltage measurement signal DS_V obtained by the measuring device 120 by the parameter optimal Kalman gain value Lpd to obtain the parameter correction amount. Next, the first parameter data PD1 is added by the parameterizer 144 according to the parameter correction amount to calculate the second parameter data PD2. In addition, the parameter error covariance matrix Ppd is updated by the parameterizer 144 with the parameter optimal Kalman gain value Lpd.
進一步詳細說明，在部分實施例中，計算參數最佳卡爾曼增益值Lpd的步驟係包含以下動作：參數器144將由狀態器142更新的狀態資料SD帶入量測方程式G。參數器144 將量測方程式G對參數資料全微分以取得第三矩陣C3(即：下式中的)。 In further detail, in some embodiments, the step of calculating the parameter optimal Kalman gain value Lpd includes the following action: The parameterizer 144 brings the state data SD updated by the state 142 to the measurement equation G. The parameterizer 144 fully differentiates the measurement equation G from the parameter data to obtain the third matrix C3 (ie: in the following formula ).
其中代表該次計算參數最佳卡爾曼增益值Lpd，R θ代表量測雜訊之協方差矩陣。 among them Representing the optimal Kalman gain value Lpd for the calculation of the parameter, R θ represents the covariance matrix of the measured noise.
在其他部分實施例中，計算參數最佳卡爾曼增益值Lpd的步驟更包含：由參數器144根據狀態資料PD中的電量狀態SOC透過查表方式取得對應的雜訊矩陣Rpd，如第5圖所示。並且由參數器144根據雜訊矩陣Rpd調整參數最佳卡爾曼增益值Lpd。換言之，透過此查表的適應性法則，當電量狀態SOC變小時，將取得較大的雜訊矩陣Rpd，使得參數最佳卡爾曼增益值Lpd變小，而達到參數資料PD受到雜訊的影響變小，避免因雜訊導致參數資料PD不穩定或發散。 In other embodiments, the step of calculating the parameter optimal Kalman gain value Lpd further includes: obtaining, by the parameterizer 144, the corresponding noise matrix Rpd according to the state of charge SOC in the state data PD, as shown in FIG. Shown. And the parameter optimal Kalman gain value Lpd is adjusted by the parameterizer 144 according to the noise matrix Rpd. In other words, through the adaptive rule of this look-up table, when the state of charge SOC becomes small, a larger noise matrix Rpd is obtained, so that the parameter optimal Kalman gain value Lpd becomes smaller, and the parameter data PD is affected by the noise. It becomes smaller to avoid instability or divergence of the parameter PD due to noise.
另一方面，在部分實施例中，取得參數預測之電池端電壓的步驟係包含以下動作：由開路電壓查表146根據量測裝置120所取得的溫度量測訊號DS_T取得一個開路電壓表。開路電壓查表146再根據狀態器142中狀態資料SD的電量狀態SOC及參數器144中第一參數資料PD1的可用電量C cap ，透過開路電壓表取得對應的開路電壓值V oc 。開路電壓查表146將開路電壓值V oc 傳送至參數器144。參數器144根據開路電壓值V oc 以量測方程式G計算參數預測之電池端電壓。 On the other hand, in some embodiments, the step of obtaining the parameter predicted battery terminal voltage includes the following operation: the open circuit voltage look-up table 146 obtains an open circuit voltage table according to the temperature measurement signal DS_T obtained by the measuring device 120. The open circuit voltage lookup table 146 then obtains the corresponding open circuit voltage value V oc through the open circuit voltage meter according to the state of charge SOC of the state data SD in the state 142 and the available power C cap of the first parameter data PD1 of the parameterizer 144. The open circuit voltage of the open circuit voltage value of the look-up table 146 is transmitted to the parameter V oc 144. The parameter 144 calculates the parameter predicted battery terminal voltage based on the open circuit voltage value V oc by the measurement equation G.
此外，在部分實施例中，在進行操作S344後且再次進行操作S342前，由狀態器142將狀態資料SD中的電量狀態SOC輸出，由參數器144將第一參數資料PD1中的可用電量C cap 輸出。 Further, in some embodiments, before the operation S344 is performed and before the operation S342 is performed again, the state of charge SOC in the state data SD is output by the state 142, and the available power C in the first parameter data PD1 is output by the parameterizer 144. Cap output.
在其他部分實施例中，在輸出可用電量C cap 的操作，更包含由參數器144根據全新的電池初始可用電量C cap0將參數資料PD中的可用電量C cap 進行正規化運算。舉例來說，將可用電量C cap 除以電池初始可用電量C cap0以取得正規化運算的結果再進行輸出。 In other partial embodiments, the operation of outputting the available power C cap further includes normalizing the available power C cap in the parameter data PD by the parameterizer 144 according to the new battery initial available power C cap 0 . For example, the available power C cap is divided by the initial battery available power C cap 0 to obtain the result of the normalization operation and then output.
所屬技術領域具有通常知識者可直接瞭解此電池估測方法300如何基於上述多個不同實施例中的電池管理系統100或估測裝置140以執行該等操作及功能，故不在此贅述。 Those skilled in the art can directly understand how the battery estimation method 300 is based on the battery management system 100 or the estimation device 140 in the above various embodiments to perform such operations and functions, and thus will not be described herein.
因此，在部分實施例中，透過電池估測方法300，電池900之電量狀態SOC及代表電池900健康狀態的可用電量C cap 與真實值之比較如第6圖、第7圖所示。第6圖、第7圖分別係根據本揭示文件之部分實施例繪示估測全新、老化電池電量狀態與可用電量之結果圖。 Therefore, in some embodiments, the battery estimation method 300, the battery state SOC of the battery 900 and the available power C cap representing the health state of the battery 900 are compared with the true values as shown in FIGS. 6 and 7. 6 and 7 are graphs showing the results of estimating a new, aged battery state and available power, respectively, according to some embodiments of the present disclosure.
雖然本文將所公開的方法示出和描述為一系列的步驟或事件，但是應當理解，所示出的這些步驟或事件的順序不應解釋為限制意義。例如，部分步驟可以以不同順序發生和/或與除了本文所示和/或所描述之步驟或事件以外的其 他步驟或事件同時發生。另外，實施本文所描述的一個或多個態樣或實施例時，並非所有於此示出的步驟皆為必需。此外，本文中的一個或多個步驟亦可能在一個或多個分離的步驟和/或階段中執行。 While the methods disclosed are shown and described herein as a series of steps or events, it is understood that the order of the steps or events shown should not be construed as limiting. For example, some of the steps can occur in a different order and/or with other than the steps or events shown and/or described herein. His steps or events happen simultaneously. In addition, not all of the steps shown herein are required in the practice of one or more aspects or embodiments described herein. Moreover, one or more steps herein may also be performed in one or more separate steps and/or stages.
需要說明的是，在不衝突的情況下，在本揭示內容各個圖式、實施例及實施例中的特徵與電路可以相互組合。圖式中所繪示的電路僅為示例之用，係簡化以使說明簡潔並便於理解，並非用以限制本案。 It should be noted that the features and circuits in the various drawings, embodiments, and embodiments of the present disclosure may be combined with each other without conflict. The circuits illustrated in the drawings are for illustrative purposes only and are simplified for simplicity and ease of understanding and are not intended to limit the present invention.
綜上所述，本案透過應用上述各個實施例中，以估測裝置基於離散等效電路模型，透過設計不同更新頻率之雙擴展式卡爾曼濾波器，並隨電量狀態調整雜訊矩陣以控制卡爾曼增益，使得電池管理系統能夠經由此電池估測方法將設定的初始值收斂至正確值，避免因電池老化而失去估測電量狀態之精準度。 In summary, in the present application, by applying the above embodiments, the estimation device is based on the discrete equivalent circuit model, and the double-extended Kalman filter with different update frequencies is designed, and the noise matrix is adjusted according to the state of charge to control Karl. Mann gain enables the battery management system to converge the set initial value to the correct value via this battery estimation method, avoiding the loss of accuracy in estimating the state of the battery due to battery aging.
此外，上述各實施例中的各個裝置、單元及元件可以由各種類型的數位或類比電路實現，亦可分別由不同的積體電路晶片實現。各個元件亦可整合至單一的數位控制晶片。各個控制電路亦可由各種處理器或其他積體電路晶片實現。上述僅為例示，本揭示內容並不以此為限。 In addition, each device, unit, and component in the above embodiments may be implemented by various types of digital or analog circuits, or may be implemented by different integrated circuit chips. Individual components can also be integrated into a single digital control chip. The various control circuits can also be implemented by various processors or other integrated circuit chips. The above is only an example, and the disclosure is not limited thereto.
雖然本揭示內容已以實施方式揭露如上，然其並非用以限定本揭示內容，所屬技術領域具有通常知識者在不脫離本揭示內容之精神和範圍內，當可作各種更動與潤飾，因此本揭示內容之保護範圍當視後附之申請專利範圍所界定者為準。 The present disclosure has been disclosed in the above embodiments, but it is not intended to limit the disclosure, and those skilled in the art can make various changes and refinements without departing from the spirit and scope of the disclosure. The scope of protection of the disclosure is subject to the definition of the scope of the patent application.
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| CN102156265B (en) | 2011-03-16 | 2013-07-17 | 深圳市派司德科技有限公司 | Device and method for testing health state of battery |
| CN103675704B (en) | 2013-12-05 | 2016-01-13 | 沈阳君威新能科技有限公司 | battery capacity evaluation method |
| TW201614257A (en) | 2014-10-14 | 2016-04-16 | Univ Nat Sun Yat Sen | Battery SOC/SOH estimation circuit |
| TWM529170U (en) | 2016-06-03 | 2016-09-21 | Whetron Electronic Co Ltd | Battery capacity estimation system |
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| CN102156265B (en) | 2011-03-16 | 2013-07-17 | 深圳市派司德科技有限公司 | Device and method for testing health state of battery |
| CN103675704B (en) | 2013-12-05 | 2016-01-13 | 沈阳君威新能科技有限公司 | battery capacity evaluation method |
| TW201614257A (en) | 2014-10-14 | 2016-04-16 | Univ Nat Sun Yat Sen | Battery SOC/SOH estimation circuit |
| TWM529170U (en) | 2016-06-03 | 2016-09-21 | Whetron Electronic Co Ltd | Battery capacity estimation system |
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