Electricity: battery or capacitor charging or discharging – Battery or cell discharging – With charging
Reexamination Certificate
2002-01-10
2003-03-18
Tso, Edward H. (Department: 2838)
Electricity: battery or capacitor charging or discharging
Battery or cell discharging
With charging
C324S433000
Reexamination Certificate
active
06534954
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to the implementation of a battery State of Charge (SOC) estimator.
2. Background Art
Batteries are used in a wide variety of electronic and electrical devices. In each application, it is often useful and necessary to measure how much charge is left in the battery. Such a measurement is called the State of Charge (SOC). It is useful, for example, for a cell phone user to know how much longer he can talk on his phone. On the other hand, recharging devices need to know how much charge is in a battery to prevent overcharging. Many types of battery are sensitive to overcharging as well as undercharging. Overcharging and undercharging can erode the effectiveness of batteries and even damage them.
Currently there are many techniques that measure the remaining charge of a battery. Each of these SOC determination techniques has drawbacks. Some such as Ampere-hour counting are sensitive to measurement errors. Others such as Coup de fouet work for only One type of battery. Still other techniques such as Impedance Spectroscopy are constrained by battery conditions such as rapidly changing temperature. Also many do not give an uncertainty range in their estimation of the SOC. In applications such as HEV and EV batteries, the uncertainty range associated with the SOC measurement is very critical. Vehicles can lose power on the road and cause danger if the uncertainty range is unknown and the battery is erroneously undercharged. Knowing the uncertainty range can prevent this. For example if the battery SOC is determined to be within 10% of the minimum charge threshold and the uncertainty range is known to be 15%, the system will know to charge the battery because the uncertainty range is greater than the distance to the threshold.
Existing Techniques
Presented here is an overview of the existing techniques and some of their shortcomings. One technique called the discharge test is an accurate form of testing. It involves completely discharging the battery to determine the SOC under controlled conditions. However, the complete discharge requirement renders this test impractical for real-life application. It is too time consuming to be useful and interrupts system function while the test is being performed.
Another SOC determination technique is called Ampere-hour counting. This is the most common technique for determining the SOC because of its ease of implementation. It measures the current of the battery and uses the measurement to determine what the SOC is. Ampere-hour counting uses the following:
SOC
=
SOC
0
+
1
C
N
∫
0
(
I
batt
-
I
loss
)
ⅆ
t
(
1
)
where C
N
is the rated capacity of the battery, I
batt
is the battery current, and I
loss
is the current consumed by the loss reactions. The equation determines the SOC based on an initial SOC
0
starting point. Ampere-hour counting is essentially an “open loop” method that is easily confused. Measurement error accumulates over time to degrade the accuracy of SOC determination. There are methods to improve current measurement but they are expensive.
Electrolyte Measurement is another common technique. In lead-acid batteries, for example, the electrolyte takes part in reactions during charge and discharge. Thus, a linear relationship exists between the change in acid density and the SOC. Therefore measuring the electrolyte density can yield an estimation of the SOC. The density is measured directly or indirectly by ion-concentration, conductivity, refractive index, viscosity, etc. However, this technique is only feasible for vented lead-acid batteries. Furthermore it is susceptible to acid stratification in the battery, water loss and long term instability of the sensors.
An open-circuit voltage measurement may be performed to test the SOC of the battery. Although the relationship between the open circuit voltage and the SOC is non-linear, it may be determined via lab testing. Once the relationship is determined, the SOC can be determined by measuring the open circuit voltage. However the measurement and estimation are accurate only when the battery is at a steady state, which can be achieved only after a long period of inactivity. This makes the open-circuit voltage technique impractical for dynamic real time application.
Impedance Spectroscopy is another technique used to determine the SOC. Impedance spectroscopy has a wide variety of applications in determining the various characteristics of batteries. Impedance Spectroscopy exploits a relationship between battery model parameters derived from impedance spectroscopy measurements and the SOC. However the drawback of this technique is that impedance curves are strongly influenced by temperature effects. Thus its application is limited to applications where temperature is stable.
Internal resistance is a technique related to impedance spectroscopy. Internal resistance is calculated as the voltage drop divided by the current change during the same time interval. The time interval chosen is critical because any time interval longer than 10 ms will result in a more complex resistance measurement. Measurement of internal resistance is very sensitive to measurement accuracy. This requirement is especially difficult to achieve in Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) applications.
Some techniques use non-linear modeling to estimate SOC directly from measurements. An example is artificial neural networks. Artificial neural networks operate on any system and predict the relationship between input and output. The networks have to be trained repeatedly so that it can improve its estimation. Because the accuracy of the data is based on the training program for the networks, it is difficult to determine the error associated with the SOC prediction given by artificial neural networks.
There is another group of SOC estimation techniques called the interpretive techniques. Interpretive techniques do not give SOC directly. Instead they use electrical discharge and charge characteristics to determine the SOC. As such, the SOC must be inferred from the calculated values. One of these techniques is called the Coup de fouet. Coup de fouet describes the short voltage drop region occurring at the beginning of discharge following a full charge of lead-acid battery. Using a special correlation between the voltage parameters occurring in this Coup de fouet region, the SOC can be inferred. One limitation of the Coup de fouet technique is that it works for lead-acid batteries only. Moreover it is effective only in cases where full charge is frequently reached during battery operations.
The Kalman Filter
One SOC determination technique involves mathematically modeling the behavior of the battery and predicting the SOC based on the model. One such model is the Kalman filter. It has mathematical basis in statistics, probabilities and system modeling. The main purpose of the Kalman filter is to predict recursively the internal states of a dynamic system using only the system's outputs. In many instances this is very useful because the internal states of the system are unknown or cannot be directly measured. As such, the Kalman filter can work on all types of batteries and addresses a limitation of many aforementioned techniques.
The Kalman filter has been widely used in fields such as aerospace and computer graphics because it has several advantages over many other similar mathematical system models. In particular, the Kalman filter takes into account both measurement uncertainty and estimation uncertainty when it updates its estimation in successive steps. The Kalman filter corrects both uncertainties based on new measurements received from sensors. This is very important for two reasons. First, sensors often have a noise factor, or uncertainty, associated with its measurement. Over time, if uncorrected, the measurement uncertainty can accumulate. Second, in any modeling system the estimation itself has inherent uncertainty because the internal
Compact Power Inc.
Coudert Brothers LLP
Harriman II, Esq. J. D.
Tso Edward H.
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