JP5073601B2 - Battery state estimation method and power supply device - Google Patents

Battery state estimation method and power supply device Download PDF

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JP5073601B2
JP5073601B2 JP2008180687A JP2008180687A JP5073601B2 JP 5073601 B2 JP5073601 B2 JP 5073601B2 JP 2008180687 A JP2008180687 A JP 2008180687A JP 2008180687 A JP2008180687 A JP 2008180687A JP 5073601 B2 JP5073601 B2 JP 5073601B2
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battery
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voltage value
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kalman filter
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JP2010019705A (en
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勝也 生田
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住友電気工業株式会社
住友電装株式会社
株式会社オートネットワーク技術研究所
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Description

本発明は、車両等に使用されるバッテリの内部抵抗値を、バッテリの電流値及び電圧値を時系列的にサンプリングして推定するバッテリの状態推定方法及び電源装置に関するものである。   The present invention relates to a battery state estimation method and a power supply apparatus that estimate an internal resistance value of a battery used in a vehicle or the like by sampling the current value and voltage value of the battery in time series.

特許文献1には、車両用エンジンの始動期間中のクランキング期間に電圧値・電流値のペアを複数採取し、採取した電圧値・電流値のペア群から回帰直線を求め、その傾きより内部抵抗値を算出し、擬似開放電圧値を求め、エンジン始動前後の擬似開放電圧値の差を算出し、この差に基づき内部抵抗値を補正するバッテリの状態量演算装置が開示されている。
特開2007−223530号公報
特許文献1には、車両用エンジンの始動期間中のクランキング期間に電圧値・電流値のペアを複数採取し、採取した電圧値・電流値のペア群から回帰直線を求め、その傾きより内部抵抗値を算出し、擬似開放電圧値を求め、エンジン始動前後の擬似開放電圧値の差を算出し、この差に基づき内部抵抗値を補正するバッテリの状態量演算装置が開示されている。
特開2007−223530号公報
In Patent Document 1, a plurality of voltage / current value pairs are sampled during a cranking period during the start-up period of the vehicle engine, a regression line is obtained from the collected voltage / current value pairs, and an internal value is obtained from the slope. There is disclosed a battery state quantity computing device that calculates a resistance value, obtains a pseudo open voltage value, calculates a difference between pseudo open voltage values before and after engine start, and corrects an internal resistance value based on the difference. In Patent Document 1, a plurality of voltage / current value pairs are sampled during a cranking period during the start-up period of the vehicle engine, a regression line is obtained from the collected voltage / current value pairs, and an internal value is obtained from the slope. There is disclosed a battery state quantity computing device that calculates a resistance value, obtains a pseudo open voltage value, calculates a difference between pseudo open voltage values ​​before and after engine start, and corrects an internal resistance value based on the difference ..
JP 2007-223530 A JP 2007-223530 A

特許文献1に開示されたバッテリの状態量演算装置では、バッテリ電圧をV=Vo −RI(Vo ;開放電圧値、R;内部抵抗値、I;電流値)の式で表しているが、この式では時間的変動を考慮したバッテリの特性を表すことはできないという問題がある。
本発明は、上述したような事情に鑑みてなされたものであり、第1発明では、時間的変動を考慮したバッテリの特性を知ることができるバッテリの状態推定方法を提供することを目的とする。
第2発明では、時間的変動を考慮したバッテリの特性を知ることができる電源装置を提供することを目的とする。 It is an object of the second invention to provide a power supply device capable of knowing the characteristics of a battery in consideration of time fluctuations. In the battery state quantity computing device disclosed in Patent Document 1, the battery voltage is expressed by an equation of V = Vo−RI (Vo: open-circuit voltage value, R: internal resistance value, I: current value). There is a problem that the battery characteristics cannot be expressed in consideration of temporal variation in the formula. In the battery state quantity computing device disclosed in Patent Document 1, the battery voltage is expressed by an equation of V = Vo−RI (Vo: open-circuit voltage value, R: internal resistance value, I: current value). There is a problem that the battery characteristics cannot be expressed in consideration of temporal variation in the formula.
The present invention has been made in view of the above-described circumstances, and an object of the first invention is to provide a battery state estimation method capable of knowing battery characteristics in consideration of temporal variation. . The present invention has been made in view of the above-described circumstances, and an object of the first invention is to provide a battery state estimation method capable of knowing battery characteristics in consideration of temporal variation.
It is an object of the second invention to provide a power supply apparatus capable of knowing battery characteristics in consideration of temporal variations. It is an object of the second invention to provide a power supply apparatus capable of knowing battery characteristics in consideration of temporal variations.

第1発明に係るバッテリの状態推定方法は、バッテリの内部抵抗値を、前記バッテリの電流値及び電圧値を時系列的にサンプリングして推定するバッテリの状態推定方法において、相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数を作成し、作成した関数に基づくカルマンフィルタを作成しておき、サンプリングしたバッテリの電圧値及び電流値を前記カルマンフィルタに逐次適用させることにより、前記関数の各係数を逐次推定し、逐次推定された各係数に基づき、前記バッテリの内部抵抗値を逐次推定することを特徴とする。   A battery state estimation method according to a first aspect of the present invention is a battery state estimation method for estimating an internal resistance value of a battery by sampling the current value and voltage value of the battery in a time series. Of the sampling, the previous voltage value and current value and the subsequent current value are used as variables, a function representing the subsequent voltage value is created, a Kalman filter based on the created function is created, and the sampled battery voltage By sequentially applying a value and a current value to the Kalman filter, each coefficient of the function is sequentially estimated, and the internal resistance value of the battery is sequentially estimated based on each sequentially estimated coefficient.

第2発明に係る電源装置は、バッテリの内部抵抗値を、前記バッテリの電流値及び電圧値を時系列的にサンプリングして推定する電源装置において、相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数を記憶する手段と、該手段が記憶する関数に基づき作成されたカルマンフィルタを記憶する手段と、サンプリングしたバッテリの電圧値及び電流値を前記カルマンフィルタに逐次適用して、前記関数の各係数を逐次推定する手段と、該手段が逐次推定した各係数により、前記バッテリの内部抵抗値を逐次推定する手段とを備えることを特徴とする。   According to a second aspect of the present invention, there is provided a power supply apparatus that estimates an internal resistance value of a battery by sampling the current value and voltage value of the battery in a time series, and the previous one of two successive samplings. A means for storing a function representing the subsequent voltage value, a means for storing a Kalman filter created based on the function stored by the means, and a sampled battery Are sequentially applied to the Kalman filter to sequentially estimate each coefficient of the function, and each means sequentially estimates the internal resistance value of the battery by each coefficient estimated by the means. It is characterized by providing.

第1発明に係るバッテリの状態推定方法及び第2発明に係る電源装置では、バッテリの内部抵抗値を、バッテリの電流値及び電圧値を時系列的にサンプリングして推定する。相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数を記憶し、記憶する関数に基づき作成されたカルマンフィルタを記憶しておく。サンプリングしたバッテリの電圧値及び電流値を、記憶しているカルマンフィルタに逐次適用して、記憶している関数の各係数を逐次推定し、逐次推定した各係数により、バッテリの内部抵抗値を逐次推定する。   In the battery state estimation method according to the first invention and the power supply device according to the second invention, the internal resistance value of the battery is estimated by sampling the current value and voltage value of the battery in time series. Of the two samplings before and after each other, the previous voltage value, current value, and subsequent current value are variables, and a function that represents the subsequent voltage value is stored, and a Kalman filter created based on the stored function is stored. Keep it. The sampled voltage and current values of the battery are sequentially applied to the stored Kalman filter, each coefficient of the stored function is sequentially estimated, and the internal resistance value of the battery is sequentially estimated based on each estimated coefficient. To do.

第1発明に係るバッテリの状態推定方法によれば、時間的変動を考慮したバッテリ特性を逐次知ることができ、バッテリ特性に基づきバッテリの内部抵抗値を逐次知ることができるバッテリの状態推定方法を実現することができる。   According to the battery state estimation method according to the first aspect of the present invention, there is provided a battery state estimation method capable of sequentially knowing battery characteristics in consideration of temporal fluctuations and successively knowing internal battery resistance values based on the battery characteristics. Can be realized.

第2発明に係る電源装置によれば、時間的変動を考慮したバッテリ特性を逐次知ることができ、バッテリ特性に基づきバッテリの内部抵抗値を逐次知ることができる電源装置を実現することができる。 According to the power supply device according to the second aspect of the present invention, it is possible to realize a power supply device that can sequentially know the battery characteristics in consideration of temporal fluctuations and can sequentially know the internal resistance value of the battery based on the battery characteristics.

以下に、本発明をその実施の形態を示す図面に基づき説明する。
図1は、本発明に係るバッテリの状態推定方法及び電源装置の実施の形態の概略構成を示すブロック図である。 FIG. 1 is a block diagram showing a schematic configuration of a battery state estimation method and an embodiment of a power supply device according to the present invention.
この電源装置が内部抵抗値を推定する対象は車両用のバッテリ2である。 The target for which this power supply device estimates the internal resistance value is the vehicle battery 2. バッテリ2及びオルタネータ(車載発電機、交流発電機)3は、電気負荷であるエアコン(エアコンディショナ)/ヒータ10、ヘッドライト11、フォグランプ12及びデフォガ13等に、イグニッションスイッチ7(ここでは、アクセサリスイッチも含むものとする)を通じて電力を供給する。 The battery 2 and the alternator (vehicle-mounted generator, alternator) 3 are electric loads such as an air conditioner (air conditioner) / heater 10, a headlight 11, a fog lamp 12, a defogger 13, and an ignition switch 7 (here, an accessory). Power is supplied through the switch). Hereinafter, the present invention will be described with reference to the drawings illustrating embodiments thereof. Embodied, the present invention will be described with reference to the drawings illustrating embodiments thereof.
FIG. 1 is a block diagram showing a schematic configuration of an embodiment of a battery state estimation method and a power supply device according to the present invention. FIG. 1 is a block diagram showing a schematic configuration of an embodiment of a battery state estimation method and a power supply device according to the present invention.
The object for which the power supply device estimates the internal resistance value is a vehicle battery 2. A battery 2 and an alternator (on-vehicle generator, AC generator) 3 are connected to an air conditioner (air conditioner) / heater 10, which is an electric load, a headlight 11, a fog lamp 12, a defogger 13, and the like, and an ignition switch 7 (here, an accessory) (Including switches). The object for which the power supply device estimates the internal resistance value is a vehicle battery 2. A battery 2 and an alternator (on-vehicle generator, AC generator) 3 are connected to an air conditioner (air conditioner) / heater 10, which is an electric load, a headlight 11, a fog lamp 12, a defogger 13, and the like, and an ignition switch 7 (here, an accessory) (Including switches).

バッテリ2は、例えば鉛蓄電池であって、一方の端子はイグニッションスイッチ7に接続され、他方の端子は接地されている。オルタネータ3は、車両のエンジン4に連動して、バッテリ2を適宜充電する。
バッテリ状態推定部1は、マイクロコンピュータで構成されており、電圧検出部6が検出したバッテリ2の両極間の電圧値V、及び電流検出部5が検出したバッテリ2を流れる電流値Iが与えられ、時系列的にサンプリングする。 The battery state estimation unit 1 is composed of a microcomputer, and is given a voltage value V between both poles of the battery 2 detected by the voltage detection unit 6 and a current value I flowing through the battery 2 detected by the current detection unit 5. , Sampling in chronological order.
バッテリ状態推定部1が推定した内部抵抗値は、バッテリ2の残容量の指標として表示部9に表示される。 The internal resistance value estimated by the battery state estimation unit 1 is displayed on the display unit 9 as an index of the remaining capacity of the battery 2. The battery 2 is, for example, a lead storage battery, and one terminal is connected to the ignition switch 7 and the other terminal is grounded. The alternator 3 charges the battery 2 as appropriate in conjunction with the engine 4 of the vehicle. The alternator 3 charges the battery 2 as appropriate in conjunction with the engine 4 of the vehicle. The battery 2 is, for example, a lead storage battery, and one terminal is connected to the ignition switch 7 and the other terminal is grounded.
The battery state estimation unit 1 is configured by a microcomputer, and is given a voltage value V between both electrodes of the battery 2 detected by the voltage detection unit 6 and a current value I flowing through the battery 2 detected by the current detection unit 5. Sampling in time series. The battery state estimation unit 1 is configured by a microcomputer, and is given a voltage value V between both electrodes of the battery 2 detected by the voltage detection unit 6 and a current value I flowing through the battery 2 detected by the current detection unit 5 . Sampling in time series.
The internal resistance value estimated by the battery state estimation unit 1 is displayed on the display unit 9 as an indicator of the remaining capacity of the battery 2. The internal resistance value estimated by the battery state estimation unit 1 is displayed on the display unit 9 as an indicator of the remaining capacity of the battery 2.

バッテリ状態推定部1は、記憶部1aを備えており、記憶部1aは、バッテリ2の電圧値及び電流値の相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数(バッテリの特性式)を記憶している。この関数は、例えば式(1)のように表される。
t =aV t-1 +bI t +cI t-1 +d (1) V t = aV t-1 + bI t + cI t-1 + d (1)
尚、2回のサンプリングは、時系列的に隣合う必要はなく、例えば、3サンプリング周期離れた状態で、1サンプリング周期ずつ移動して行くような形態も含むものとする。 It should be noted that the two samplings do not have to be adjacent to each other in chronological order, and include, for example, a form in which the sampling cycles are separated by three sampling cycles and moved by one sampling cycle. The battery state estimation unit 1 includes a storage unit 1a, and the storage unit 1a includes a previous voltage value, a current value, and a subsequent current among two samplings before and after the voltage value and current value of the battery 2. Each value is a variable, and a function (battery characteristic equation) representing a subsequent voltage value is stored. This function is expressed, for example, as in equation (1). The battery state estimation unit 1 includes a storage unit 1a, and the storage unit 1a includes a previous voltage value, a current value, and a subsequent current among two samplings before and after the voltage value and current value of the battery 2. Each value is a variable, and a function (battery characteristic equation) representing a subsequent voltage value is stored. This function is expressed, for example, as in equation (1).
V t = aV t-1 + bI t + cI t-1 + d (1) V t = aV t-1 + bI t + cI t-1 + d (1)
Note that the two samplings do not need to be adjacent in chronological order, and include, for example, a mode in which they are moved one sampling period at a time separated by three sampling periods. Note that the two samplings do not need to be adjacent in chronological order, and include, for example, a mode in which they are moved one sampling period at a time separated by three sampling periods.

また、式(1)の係数から下記により内部抵抗値を求められることが分かっている。
動的内部抵抗値=b (電流通流時)
静的内部抵抗値=−c/a (電流非通流時)
Further, it has been found that the internal resistance value can be obtained from the coefficient of the equation (1) by the following.
Dynamic internal resistance value = b (when current flows)
Static internal resistance value = -c / a (when no current flows)

記憶部1aは、また、式(1)に示す関数に基づき作成されたカルマンフィルタを記憶している。
式(1)に示す関数は、式(2)に示す行列Ht と式(3)に示す行列xt との乗算により、式(4)に示すように表現することができる。
t =(Vt-1 ,It ,It-1,1) (2)
t =(a,b,c,d)T (3)
(T;転置行列)
The storage unit 1a also stores a Kalman filter created based on the function shown in Expression (1).
The function shown in Expression (1) can be expressed as shown in Expression (4) by multiplication of the matrix H t shown in Expression (2) and the matrix x t shown in Expression (3).
H t = (V t−1 , I t , I t−1 , 1) (2) H t = (V t−1 , I t , I t−1 , 1) (2)
x t = (a, b, c, d) T x t = (a, b, c, d) T (3) (3)
(T: transposed matrix) (T: transposed matrix)

ここで、観測量(検出したバッテリ2の電圧値V t )をy tとすると、記憶部1aが記憶しているカルマンフィルタの観測方程式は、式(5)で示される。
t =H tt +v t (5)

式(5)に示すv tは、共分散行列R及び0平均のガウス分布に従う雑音である。 V t shown in the equation (5) is a noise according to the Gaussian distribution of the covariance matrix R and the 0 average. Rは、時間により変化する行列を示す。 R indicates a matrix that changes with time. Here, when the observation amount (the detected voltage value V t of the battery 2) is y t , the Kalman filter observation equation stored in the storage unit 1 a is expressed by Equation (5). Here, when the observation amount (the detected voltage value V t of the battery 2) is y t , the Kalman filter observation equation stored in the storage unit 1 a is expressed by Equation (5).
y t = H t x t + v t (5) y t = H t x t + v t (5)
V t shown in Equation (5) is noise according to the covariance matrix R and the zero-mean Gaussian distribution. R represents a matrix that changes with time. V t shown in Equation (5) is noise according to the covariance matrix R and the zero-mean Gaussian distribution. R represents a matrix that changes with time.

このカルマンフィルタの、時点tにおける状態x tを1サンプリング前の時点t−1の状態x t-1により表現する状態方程式は、式(6)で示される。
t =Fx t-1 +Gw t (6) x t = Fx t-1 + Gw t (6)
式(6)に示すw tは、共分散行列Q及び0平均のガウス分布に従う雑音であり、F,G,Qは、時間により変化する行列を示す。 W t shown in equation (6) is a noise according to a Gaussian distribution of a covariance matrix Q and 0 mean, F, G, Q represents a matrix varies with time. State equations that represent the state x t-1 of the Kalman filter, a state x t at time t 1 before sampling time t-1 is represented by the formula (6). State equations that represent the state x t-1 of the Kalman filter, a state x t at time t 1 before sampling time t-1 is represented by the formula (6).
x t = Fx t-1 + Gw t (6) x t = Fx t-1 + Gw t (6)
W t shown in Expression (6) is noise according to the covariance matrix Q and the zero-mean Gaussian distribution, and F, G, and Q indicate matrices that change with time. W t shown in Expression (6) is noise according to the covariance matrix Q and the zero-mean Gaussian distribution, and F, G, and Q indicate matrices that change with time.

ここで、式(3)のxt が収束するとき、xt ,xt-1の各最尤値は等しく、G=0であるので、式(6)からFは式(7)に示すような単位行列になる。 Here, when x t in equation (3) converges, the maximum likelihood values of x t and x t−1 are equal and G = 0, so equations (6) through F are expressed in equation (7). It becomes such a unit matrix.

このカルマンフィルタの初期条件は、状態x tについては式(8)のように設定され、誤差の共分散行列Σ t|t-1については式(9)のように設定されている。 The initial conditions of the Kalman filter are set as shown in Equation (8) for the state x t and as shown in Equation (9) for the error covariance matrix Σ t | t−1 .

以下に、このような構成の電源装置のバッテリ状態推定部1の動作を、それを示す図2のフローチャートを参照しながら説明する。
バッテリ状態推定部1は、先ず、所定時間(例えば数百ミリ秒間)待機した(S1)後、電圧検出部6で検出したバッテリ2の電圧値V(=観測値y t )と、電流検出部5で検出したバッテリ2を流れる電流値I(I t )とを読込む(S3)。 The battery state estimation unit 1 first waits for a predetermined time (for example, several hundred milliseconds) (S1), and then the voltage value V (= observed value y t ) of the battery 2 detected by the voltage detection unit 6 and the current detection unit 1. 5 current flowing through the battery 2 detected by the I (I t) and loads the (S3). 次いで、このサンプリング(S3)が初回であるなら(S4)、再度、所定時間待機した(S1)後、バッテリ2の電圧値V(=観測値y t )と電流値I(I t )とを読込む(S3)。 Then, if the sampling (S3) is the first time (S4), again after waiting a predetermined time (S1), the voltage value of the battery 2 V (= observed value y t) and the current value I and (I t) Read (S3). Hereinafter, the operation of the battery state estimation unit 1 of the power supply apparatus having such a configuration will be described with reference to the flowchart of FIG. Efficiently, the operation of the battery state estimation unit 1 of the power supply apparatus having such a configuration will be described with reference to the flowchart of FIG.
Battery state estimating unit 1, first, a predetermined time (example, several hundred milliseconds) after waiting (S1), the voltage value of the battery 2 detected by the voltage detection unit 6 V (= observed value y t), the current detection unit The current value I (I t ) flowing through the battery 2 detected in 5 is read (S3). Next, if this sampling (S3) is the first time (S4), after waiting again for a predetermined time (S1), the voltage value V (= observed value y t ) and current value I (I t ) of the battery 2 are obtained. Read (S3). Battery state estimating unit 1, first, a predetermined time (example, several hundred milliseconds) after waiting (S1), the voltage value of the battery 2 detected by the voltage detection unit 6 V (= observed value y t), the current detection unit The current value I (I t ) flowing through the battery 2 detected in 5 is read (S3). Next, if this sampling (S3) is the first time (S4), after waiting again for a predetermined time (S1), the voltage value V (= observed value y t) and current value I (I t) of the battery 2 are obtained. Read (S3).

バッテリ状態推定部1は、このサンプリング(S3)が初回でなければ(S4)、直近の2回のサンプリングの内、前の電圧値Vt-1 及び電流値It-1 と後の電流値It(HIt-1 )を使用して、式(10)により最適カルマンゲインKt を算出して、記憶部1aに記憶する(S5)。 If this sampling (S3) is not the first time (S4), the battery state estimation unit 1 determines that the previous voltage value V t-1, current value I t-1, and the subsequent current value among the two most recent samplings Using I t (HI t−1 ), the optimum Kalman gain K t is calculated by Equation (10) and stored in the storage unit 1a (S5).

尚、式(10)の式(11)に示す部分は、観測残差(イノベーション)の共分散行列である。   In addition, the part shown to Formula (11) of Formula (10) is a covariance matrix of an observation residual (innovation).

バッテリ状態推定部1は、次に、読込んだ観測値yt (S3)、及び算出した最適カルマンゲイン(S5)を使用して式(12)を演算し、式(1)に示す関数の各係数a,b,c,dの推定値を算出して、記憶部1aに記憶する(S7)。 Next, the battery state estimation unit 1 calculates Expression (12) using the read observation value y t (S3) and the calculated optimum Kalman gain (S5), and the function of the function shown in Expression (1) is calculated. Estimated values of the coefficients a, b, c, d are calculated and stored in the storage unit 1a (S7).

バッテリ状態推定部1は、次に、算出した最適カルマンゲイン(S5)を使用して式(13)を演算し、誤差行列Σt|t を算出して、記憶部1aに記憶する(S9)。 Next, the battery state estimation unit 1 calculates Equation (13) using the calculated optimal Kalman gain (S5), calculates the error matrix Σ t | t , and stores it in the storage unit 1a (S9). .

バッテリ状態推定部1は、次に、算出した誤差行列Σt|t (S9)を使用して式(14)を演算し、更新された誤差行列Σt+1|tを算出して、記憶部1aに記憶する(S11)。更新された誤差行列Σt+1|tは、次回のサンプリング時に、式(10)による最適カルマンゲインKtの算出(S5)に使用される。
尚、式(3)のx tが収束するとき、式(14)において、F=F T =1,G=G T =0である。 Incidentally, when x t of formula (3) converges, in the equation (14), is F = F T = 1, G = G T = 0. Next, the battery state estimation unit 1 calculates Equation (14) using the calculated error matrix Σ t | t (S9), calculates an updated error matrix Σ t + 1 | t , and stores it. Store in the unit 1a (S11). The updated error matrix Σ t + 1 | t is used for the calculation (S5) of the optimum Kalman gain K t according to the equation (10) at the next sampling. Next, the battery state estimation unit 1 calculates Equation (14) using the calculated error matrix Σ t | t (S9), calculates an updated error matrix Σ t + 1 | t , and stores it. Store in the unit 1a (S11) The updated error matrix Σ t + 1 | t is used for the calculation (S5) of the optimum Kalman gain K t according to the equation (10) at the next sampling.
Note that when x t in equation (3) converges, in equation (14), F = F T = 1 and G = G T = 0. Note that when x t in equation (3) converges, in equation (14), F = F T = 1 and G = G T = 0.

バッテリ状態推定部1は、次に、式(1)に示す関数の算出した各係数a,b,c,dの推定値(S7)を使用して、動的内部抵抗値=b、静的内部抵抗値=−c/aを算出し、記憶部1aに記憶する(S13)。次いで、所定時間待機して(S1)、上述した処理(S3〜13)を繰返す。   Next, the battery state estimation unit 1 uses the estimated values (S7) of the coefficients a, b, c, and d calculated by the function shown in the equation (1), and the dynamic internal resistance value = b, static The internal resistance value = −c / a is calculated and stored in the storage unit 1a (S13). Next, after waiting for a predetermined time (S1), the above-described processing (S3 to 13) is repeated.

また、バッテリ状態推定部1は、算出した動的内部抵抗値b(S13)を使用して、CCA(Cold Cranking Ampere)により下記のようにバッテリ2の劣化度(健全度)を判定することができる。CCAは、バッテリが満充電の状態で−18℃のとき、放電開始から30秒後の最終電圧値が7.2Vとなるような放電電流値である。
先ず、バッテリ状態推定部1は、次式により、動的内部抵抗値bを−18℃のときの動的内部抵抗値に換算する。
r(−18)=b+f(T) (Tは現状の測定温度) Moreover, the battery state estimation part 1 determines the deterioration degree (health degree) of the battery 2 as follows by CCA (Cold Cranking Ampere) using the calculated dynamic internal resistance value b (S13). it can. CCA is a discharge current value such that when the battery is at −18 ° C. in a fully charged state, the final voltage value 30 seconds after the start of discharge is 7.2V. r (-18) = b + f (T) (T is the current measured temperature) Moreover, the battery state estimation part 1 determines the deterioration degree (health degree) of the battery 2 as follows by CCA (Cold Cranking Ampere) using the calculated dynamic internal resistance value b (S13). It can. CCA is a discharge current value such that when the battery is at -18 ° C. in a fully charged state, the final voltage value 30 seconds after the start of discharge is 7.2V ..
First, the battery state estimation unit 1 converts the dynamic internal resistance value b into a dynamic internal resistance value at −18 ° C. by the following equation. First, the battery state estimation unit 1 converts the dynamic internal resistance value b into a dynamic internal resistance value at −18 ° C. by the following equation.
r (−18) = b + f (T) (T is the current measured temperature) r (-18) = b + f (T) (T is the current measured temperature)

次に、バッテリ状態推定部1は、次式により現状(但し−18℃と仮定)のバッテリ2のCCA値であるCCAnow を算出する。
CCAnow =(aVnow +cInow+d−7.2)/r(−18)
バッテリ状態推定部1は、算出した現状のバッテリ2のCCA now値と基準のCCA値との割合CCA now /CCAを算出し、算出した割合がCCA now /CCA>0.7を満たすか否かを判定する。 Battery state estimating unit 1, calculated by calculating the ratio CCA now / CCA and CCA now value and the reference CCA value of the battery 2 of the present situation, the calculated ratio is whether they meet the CCA now /CCA>0.7 To judge. バッテリ状態推定部1は、判定した結果がCCA now /CCA>0.7を満たしていれば、バッテリ2の健全度は良好とし、満たしていなければ、バッテリ2への充電を促進する充電制御、使用する負荷を制限する負荷制御等を実行する。 If the result of the determination satisfies CCA now / CCA> 0.7, the battery state estimation unit 1 considers the soundness of the battery 2 to be good, and if not, the charge control that promotes charging of the battery 2. Execute load control, etc. to limit the load to be used. Next, the battery state estimation unit 1 calculates CCA now , which is the CCA value of the current battery 2 (assuming −18 ° C.) by the following equation. Next, the battery state estimation unit 1 calculates CCA now , which is the CCA value of the current battery 2 (assuming −18 ° C.) by the following equation.
CCA now = (aV now + cI now + d-7.2) / r (-18) CCA now = (aV now + cI now + d-7.2) / r (-18)
The battery state estimation unit 1 calculates a ratio CCA now / CCA between the calculated current CCA now value of the battery 2 and the reference CCA value, and whether or not the calculated ratio satisfies CCA now /CCA>0.7. Determine. The battery state estimation unit 1 determines that the soundness of the battery 2 is good if the determined result satisfies CCA now / CCA> 0.7, and if not, charging control that promotes charging of the battery 2; Execute load control to limit the load to be used. The battery state estimation unit 1 calculates a ratio CCA now / CCA between the calculated current CCA now value of the battery 2 and the reference CCA value, and whether or not the calculated ratio satisfies CCA now / CCA> 0.7. Determine. The battery state estimation unit 1 determines that the soundness of the battery 2 is good if the determined result satisfies CCA now / CCA> 0.7, and if not, charging control that promotes charging of the battery 2; Execute load control to limit the load to be used.

また、式(1)の各係数を推定することにより、現在の状態で大電流負荷△Iが掛かったときのバッテリ2の電圧値を推定することができる。現在のバッテリ2の電圧値及び電流値をV m ,I mとすれば、大電流負荷時のバッテリ2の推定電圧値は、
n =aV m +b(I m +△I)+cI m +d V n = aV m + b (I m + ΔI) + cI m + d
で算出でき、大電流作動時に負荷を制限する負荷制御に使用することができる。 It can be calculated with, and can be used for load control that limits the load when operating with a large current.
また、バッテリ2の内部抵抗値を推定できれば、既知であるハーネス抵抗等を使用して、バッテリの現状でエンジンを始動させることができるか否かを推定することができる。 Further, if the internal resistance value of the battery 2 can be estimated, it is possible to estimate whether or not the engine can be started in the current state of the battery by using a known harness resistance or the like. Further, by estimating each coefficient of the equation (1), it is possible to estimate the voltage value of the battery 2 when the large current load ΔI is applied in the current state. If the current voltage value and current value of the battery 2 are V m and I m , the estimated voltage value of the battery 2 at the time of a large current load is Further, by estimating each coefficient of the equation (1), it is possible to estimate the voltage value of the battery 2 when the large current load ΔI is applied in the current state. If the current voltage value and current value of the battery 2 are V m and I m, the estimated voltage value of the battery 2 at the time of a large current load is
V n = aV m + b (I m + ΔI) + cI m + d V n = aV m + b (I m + ΔI) + cI m + d
And can be used for load control to limit the load during large current operation. And can be used for load control to limit the load during large current operation.
Moreover, if the internal resistance value of the battery 2 can be estimated, it is possible to estimate whether the engine can be started with the current state of the battery using a known harness resistance or the like. Moreover, if the internal resistance value of the battery 2 can be estimated, it is possible to estimate whether the engine can be started with the current state of the battery using a known harness resistance or the like.

図3は、車両の走行パターン2(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図4(a)(b)(c)(d)は、車両の走行パターン2の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。 4 (a), (b), (c), and (d) show the transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic formula (1) in the case of the traveling pattern 2 of the vehicle. It is a graph. 図3のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。 The values ​​of a, b, c, and d in FIG. 3 indicate the convergence values ​​of the coefficients a, b, c, and d after each transition.
図3,4によれば、例えば18秒付近の電圧値及び電流値の急変に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 3 and 4, for example, the coefficients a, b, c, and d change in response to a sudden change in the voltage value and the current value around 18 seconds, and by applying the Kalman filter, the characteristic equation (1) It can be seen that the voltage value according to the above can accurately reproduce the voltage value of the battery 2. FIG. 3 is a graph showing each transition of the voltage value of the battery 2 and the voltage value of the battery 2 according to the characteristic formula (1) to which the Kalman filter is applied in the traveling pattern 2 (specific description is omitted) of the vehicle. is there. FIG. 3 is a graph showing each transition of the voltage value of the battery 2 and the voltage value of the battery 2 according to the characteristic formula (1) to which the Kalman filter is applied in the traveling pattern 2 (specific description is omitted ) Of the vehicle. Is there.
4A, 4B, 4C, and 4D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 2 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 3 indicates a convergence value after each transition of the coefficients a, b, c, and d. 4A, 4B, 4C, and 4D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 2 of the vehicle. It is a graph .Each value of a, b, c, and d in FIG. 3 indicates a convergence value after each transition of the coefficients a, b, c, and d.
According to FIGS. 3 and 4, for example, the coefficients a, b, c and d change corresponding to a sudden change in voltage value and current value in the vicinity of 18 seconds, and by applying the Kalman filter, the characteristic formula (1) It can be seen that the voltage value of can accurately reproduce the voltage value of the battery 2. According to FIGS. 3 and 4, for example, the coefficients a, b, c and d change corresponding to a sudden change in voltage value and current value in the vicinity of 18 seconds, and by applying the Kalman filter, the characteristic formula ( 1) It can be seen that the voltage value of can accurately reproduce the voltage value of the battery 2.

図5は、車両の走行パターン4(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図6(a)(b)(c)(d)は、車両の走行パターン4の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。図5のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。
図5,6によれば、例えば58秒付近の電圧値及び電流値の急変に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 5 and 6, for example, the coefficients a, b, c, and d change in response to a sudden change in the voltage value and the current value around 58 seconds, and by applying the Kalman filter, the characteristic equation (1) It can be seen that the voltage value according to the above can accurately reproduce the voltage value of the battery 2. FIG. 5 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 4 (specific description is omitted). is there. FIG. 5 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 4 (specific description is omitted). Is there.
FIGS. 6A, 6B, 6C, and 6D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 4 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 5 indicates the convergence value after each transition of the coefficients a, b, c, and d. FIGS. 6A, 6B, 6C, and 6D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 4 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 5 indicates the convergence value after each transition of the coefficients a, b, c, and d.
According to FIGS. 5 and 6, for example, coefficients a, b, c, and d change corresponding to a sudden change in the voltage value and current value in the vicinity of 58 seconds. By applying the Kalman filter, the characteristic formula (1) It can be seen that the voltage value of can accurately reproduce the voltage value of the battery 2. According to FIGS. 5 and 6, for example, coefficients a, b, c, and d change corresponding to a sudden change in the voltage value and current value in the vicinity of 58 seconds. By applying the Kalman filter, the characteristic formula ( 1) It can be seen that the voltage value of can accurately reproduce the voltage value of the battery 2.

図7は、車両の走行パターン52(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図8(a)(b)(c)(d)は、車両の走行パターン52の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。 8 (a), (b), (c), and (d) show the transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic formula (1) in the case of the traveling pattern 52 of the vehicle. It is a graph. 図7のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。 Each value of a, b, c, and d in FIG. 7 indicates a convergence value after each transition of the coefficients a, b, c, and d.
図7,8によれば、例えば18秒付近の電圧値の急変及び50秒付近の電流値の変化に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 7 and 8, for example, the coefficients a, b, c, and d change in response to a sudden change in the voltage value around 18 seconds and a change in the current value around 50 seconds, and by applying the Kalman filter. It can be seen that the voltage value according to the characteristic formula (1) can accurately reproduce the voltage value of the battery 2. FIG. 7 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 52 (specific description is omitted). is there. FIG. 7 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 52 (specific description is omitted). Is there.
8A, 8B, 8C, and 8D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 52 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 7 indicates a convergence value after each transition of the coefficients a, b, c, and d. 8A, 8B, 8C, and 8D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 52 of the vehicle. It is a graph .Each value of a, b, c, and d in FIG. 7 indicates a convergence value after each transition of the coefficients a, b, c, and d.
According to FIGS. 7 and 8, for example, the coefficients a, b, c, and d change corresponding to a sudden change in the voltage value around 18 seconds and a change in the current value around 50 seconds, and by applying the Kalman filter, It can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2. According to FIGS. 7 and 8, for example, the coefficients a, b, c, and d change corresponding to a sudden change in the voltage value around 18 seconds and a change in the current value around 50 seconds, and by applying the Kalman filter, It can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2.

図9は、車両の走行パターン53(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図10(a)(b)(c)(d)は、車両の走行パターン53の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。図9のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。
図9,10によれば、例えば18秒付近の電圧値及び電流値の急変及び60秒付近の電流値の変化に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 9 and 10, for example, the coefficients a, b, c, and d change in response to a sudden change in the voltage value and the current value around 18 seconds and a change in the current value around 60 seconds, and the Kalman filter is applied. By doing so, it can be seen that the voltage value according to the characteristic formula (1) can accurately reproduce the voltage value of the battery 2. FIG. 9 is a graph showing each transition of the voltage value and current value of the battery 2 and the voltage value of the battery 2 according to the characteristic expression (1) to which the Kalman filter is applied in the vehicle running pattern 53 (specific description is omitted). is there. FIG. 9 is a graph showing each transition of the voltage value and current value of the battery 2 and the voltage value of the battery 2 according to the characteristic expression (1) to which the Kalman filter is applied in the vehicle running pattern 53 ( specific description is omitted). is there.
FIGS. 10A, 10B, 10C, and 10D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 53 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 9 indicates a convergence value after each transition of the coefficients a, b, c, and d. FIGS. 10A, 10B, 10C, and 10D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 53 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 9 indicates a convergence value after each transition of the coefficients a, b, c, and d.
According to FIGS. 9 and 10, for example, the coefficients a, b, c, and d change corresponding to a sudden change in voltage value and current value around 18 seconds and a change in current value around 60 seconds, and the Kalman filter is applied. Thus, it can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2. According to FIGS. 9 and 10, for example, the coefficients a, b, c, and d change corresponding to a sudden change in voltage value and current value around 18 seconds and a change in current value around 60 seconds, and the Kalman filter is applied. Thus, it can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2.

図11は、車両の走行パターン63(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図12(a)(b)(c)(d)は、車両の走行パターン63の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。図11のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。
図11,12によれば、例えば15秒付近の電圧値の急変及び28秒付近の電圧値及び電流値の変化に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 11 and 12, for example, the coefficients a, b, c, and d change in response to a sudden change in the voltage value around 15 seconds and a change in the voltage value and the current value around 28 seconds, and the Kalman filter is applied. By doing so, it can be seen that the voltage value according to the characteristic formula (1) can accurately reproduce the voltage value of the battery 2. FIG. 11 is a graph showing each transition of the voltage value and current value of the battery 2 and the voltage value of the battery 2 according to the characteristic formula (1) to which the Kalman filter is applied in the vehicle running pattern 63 (specific description is omitted). is there. FIG. 11 is a graph showing each transition of the voltage value and current value of the battery 2 and the voltage value of the battery 2 according to the characteristic formula (1) to which the Kalman filter is applied in the vehicle running pattern 63 ( specific description is omitted). is there.
12A, 12B, 12C, and 12D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 63 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 11 indicates a convergence value after each transition of the coefficients a, b, c, and d. 12A, 12B, 12C, and 12D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 63 of the vehicle. It is a graph .Each value of a, b, c, and d in FIG. 11 indicates a convergence value after each transition of the coefficients a, b, c, and d.
11 and 12, for example, the coefficients a, b, c, and d change corresponding to a sudden change in the voltage value near 15 seconds and a change in the voltage value and current value around 28 seconds, and the Kalman filter is applied. Thus, it can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2. 11 and 12, for example, the coefficients a, b, c, and d change corresponding to a sudden change in the voltage value near 15 seconds and a change in the voltage value and current value around 28 seconds, and the Kalman filter is applied Thus, it can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2.

図13は、車両の走行パターン72(具体的な記述は省略)におけるバッテリ2の電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリ2の電圧値の各推移を示すグラフである。
図14(a)(b)(c)(d)は、車両の走行パターン72の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。図13のa,b,c,dの各値は、係数a,b,c,dの各推移後の収束値を示す。
図13,14によれば、例えば5秒付近の電流値の急変、15秒付近の電圧値の急変及び32,3秒付近の電圧値及び電流値の変化に対応して係数a,b,c,dが推移しており、カルマンフィルタを適用することにより、特性式(1)による電圧値がバッテリ2の電圧値を精度良く再現できていることが分かる。 According to FIGS. 13 and 14, for example, the coefficients a, b, and c correspond to a sudden change in the current value around 5 seconds, a sudden change in the voltage value around 15 seconds, and a change in the voltage value and the current value around 32.3 seconds. , D are changing, and it can be seen that the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2 by applying the Kalman filter. FIG. 13 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 72 (specific description is omitted). is there. FIG. 13 is a graph showing each transition of the voltage value of the battery 2, the current value, and the voltage value of the battery 2 according to the characteristic equation (1) to which the Kalman filter is applied in the vehicle running pattern 72 (specific description is omitted). Is there.
14A, 14B, 14C, and 14D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 72 of the vehicle. It is a graph. Each value of a, b, c, and d in FIG. 13 indicates a convergence value after each transition of the coefficients a, b, c, and d. 14A, 14B, 14C, and 14D show transitions of the coefficients a, b, c, and d when the Kalman filter is applied to the characteristic equation (1) in the case of the traveling pattern 72 of the vehicle. It is a graph .Each value of a, b, c, and d in FIG. 13 indicates a convergence value after each transition of the coefficients a, b, c, and d.
According to FIGS. 13 and 14, for example, the coefficients a, b, c correspond to the sudden change of the current value around 5 seconds, the sudden change of the voltage value around 15 seconds, and the change of the voltage value and current value around 32, 3 seconds. , D change, and it can be seen that by applying the Kalman filter, the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2. According to FIGS. 13 and 14, for example, the coefficients a, b, c correspond to the sudden change of the current value around 5 seconds, the sudden change of the voltage value around 15 seconds, and the change of the voltage value and current value around 32, 3 seconds., D change, and it can be seen that by applying the Kalman filter, the voltage value according to the characteristic equation (1) can accurately reproduce the voltage value of the battery 2.

本発明に係るバッテリの状態推定方法及び電源装置の実施の形態の概略構成を示すブロック図である。 It is a block diagram which shows schematic structure of embodiment of the battery state estimation method and power supply device which concern on this invention. 本発明に係る電源装置の動作の例を示すフローチャートである。 It is a flowchart which shows the example of operation | movement of the power supply device which concerns on this invention. 車両の走行パターン2におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。It is a graph which shows each transition of the voltage value of a battery by the characteristic formula (1) to which the voltage value of a battery in the driving | running | working pattern 2 and an electric current value and a Kalman filter are applied. 車両の走行パターン2の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。It is a graph which shows each transition of coefficient a, b when a Kalman filter is applied to characteristic formula (1) in the case of traveling pattern 2 of a vehicle. 車両の走行パターン4におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。It is a graph which shows each transition of the voltage value of a battery by the characteristic formula (1) to which the voltage value of a battery in the driving | running | working pattern 4 and an electric current value and a Kalman filter are applied. 車両の走行パターン4の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。It is a graph which shows each transition of coefficient a, when a Kalman filter is applied to characteristic formula (1) in the case of driving pattern 4 of a vehicle. 車両の走行パターン52におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。It is a graph which shows each transition of the voltage value of a battery by the characteristic formula (1) to which the voltage value of the driving | running | working pattern 52 of a vehicle, the current value, and the Kalman filter are applied. 車両の走行パターン52の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。 It is a graph which shows each transition of coefficient a, b when a Kalman filter is applied to characteristic formula (1) in the case of driving pattern 52 of vehicles. 車両の走行パターン53におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。 It is a graph which shows each transition of the voltage value of a battery in the driving | running | working pattern 53 of a vehicle, and the voltage value of a battery by the characteristic formula (1) to which a Kalman filter is applied. 車両の走行パターン53の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。It is a graph which shows each transition of coefficient a, b when a Kalman filter is applied to characteristic formula (1) in the case of running pattern 53 of vehicles. 車両の走行パターン63におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。 It is a graph which shows each transition of the voltage value of a battery in the driving | running | working pattern 63 of a vehicle, and the voltage value of a battery by the characteristic formula (1) to which a Kalman filter is applied. 車両の走行パターン63の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。 It is a graph which shows each transition of coefficient a, b when a Kalman filter is applied to characteristic formula (1) in the case of running pattern 63 of vehicles. 車両の走行パターン72におけるバッテリの電圧値、電流値、及びカルマンフィルタを適用した特性式(1)によるバッテリの電圧値の各推移を示すグラフである。It is a graph which shows each transition of the voltage value of a battery in the driving | running | working pattern 72 of a vehicle, and the voltage value of a battery by the characteristic formula (1) to which a Kalman filter is applied. 車両の走行パターン72の場合にカルマンフィルタを特性式(1)に適用したときの、係数a,b,c,dの各推移を示すグラフである。 It is a graph which shows each transition of coefficient a, b when a Kalman filter is applied to characteristic formula (1) in the case of running pattern 72 of vehicles.

符号の説明Explanation of symbols

1 バッテリ状態推定部
1a 記憶部
2 バッテリ
3 オルタネータ
5 電流検出部
6 電圧検出部
7 イグニッションスイッチ
9 表示部
DESCRIPTION OF SYMBOLS 1 Battery state estimation part 1a Memory | storage part 2 Battery 3 Alternator 5 Current detection part 6 Voltage detection part 7 Ignition switch 9 Display part

Claims (2)

  1. バッテリの内部抵抗値を、前記バッテリの電流値及び電圧値を時系列的にサンプリングして推定するバッテリの状態推定方法において、
    相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数を作成し、作成した関数に基づくカルマンフィルタを作成しておき、サンプリングしたバッテリの電圧値及び電流値を前記カルマンフィルタに逐次適用させることにより、前記関数の各係数を逐次推定し、逐次推定された各係数に基づき、前記バッテリの内部抵抗値を逐次推定することを特徴とするバッテリの状態推定方法。 Of the two samplings before and after the phase, the previous voltage value and current value and the later current value are used as variables, a function representing the later voltage value is created, and a Kalman filter based on the created function is created. , The voltage value and the current value of the sampled battery are sequentially applied to the Kalman filter to sequentially estimate each coefficient of the function, and the internal resistance value of the battery is sequentially estimated based on each sequentially estimated coefficient. A method for estimating the state of a battery, which comprises. In the battery state estimation method for estimating the internal resistance value of the battery by sampling the current value and voltage value of the battery in time series, In the battery state estimation method for estimating the internal resistance value of the battery by sampling the current value and voltage value of the battery in time series,
    Of the two samplings that precede and follow, the previous voltage value, current value, and subsequent current value are variables, and a function that represents the subsequent voltage value is created, and a Kalman filter based on the created function is created. , By sequentially applying the sampled voltage value and current value of the battery to the Kalman filter, sequentially estimating each coefficient of the function, and sequentially estimating the internal resistance value of the battery based on each sequentially estimated coefficient The battery state estimation method characterized by these. Of the two samplings that precede and follow, the previous voltage value, current value, and subsequent current value are variables, and a function that represents the subsequent voltage value is created, and a Kalman filter based on the created function is created., By Sequentially applying the sampled voltage value and current value of the battery to the Kalman filter, sequentially estimating each coefficient of the function, and sequentially estimating the internal resistance value of the battery based on each sequentially estimated coefficient The battery state estimation method characterized by these.
  2. バッテリの内部抵抗値を、前記バッテリの電流値及び電圧値を時系列的にサンプリングして推定する電源装置において、
    相前後する2回のサンプリングの内、前の電圧値及び電流値と後の電流値とをそれぞれ変数とし、後の電圧値を表す関数を記憶する手段と、該手段が記憶する関数に基づき作成されたカルマンフィルタを記憶する手段と、サンプリングしたバッテリの電圧値及び電流値を前記カルマンフィルタに逐次適用して、前記関数の各係数を逐次推定する手段と、該手段が逐次推定した各係数により、前記バッテリの内部抵抗値を逐次推定する手段とを備えることを特徴とする電源装置。 Created based on a means for storing a function representing a later voltage value and a function for storing a function in which the previous voltage value and the current value and the subsequent current value are used as variables in the two samplings before and after the phase. The means for storing the Kalman filter, the means for sequentially applying each coefficient of the function by sequentially applying the voltage value and the current value of the sampled battery to the Kalman filter, and the means for sequentially estimating each coefficient of the function, and the above-mentioned A power supply device including a means for sequentially estimating the internal resistance value of a battery. In the power supply device that estimates the internal resistance value of the battery by sampling the current value and voltage value of the battery in time series, In the power supply device that estimates the internal resistance value of the battery by sampling the current value and voltage value of the battery in time series,
    Of the two samplings before and after each other, the previous voltage value, the current value, and the subsequent current value are variables, and a function that stores a function that represents the subsequent voltage value is created based on the function that the means stores. Means for storing the obtained Kalman filter, means for sequentially applying the sampled voltage value and current value of the battery to the Kalman filter, sequentially estimating each coefficient of the function, and each coefficient sequentially estimated by the means, A power supply apparatus comprising: means for sequentially estimating an internal resistance value of the battery. Of the two samplings before and after each other, the previous voltage value, the current value, and the subsequent current value are variables, and a function that stores a function that represents the subsequent voltage value is created based on the function that the means stores Means for storing the obtained Kalman filter, means for sequentially applying the sampled voltage value and current value of the battery to the Kalman filter, sequentially estimating each coefficient of the function, and each coefficient sequentially estimated by the means, A power supply apparatus comprising. : means for sequentially estimating an internal resistance value of the battery.
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