Not Applicable.
Not Applicable.
The present invention relates in general to battery state-of-charge determination in electric vehicles, and, more specifically, to battery age monitoring to track changes in the relationship between state-of-charge and open circuit voltage. The DC power source (e.g., a battery) and other elements of electric drives for electrified vehicles (e.g., full electric and hybrids) require monitoring in order to maximize efficiency and performance as well as to determine a battery state-of-charge (SOC) to predict remaining driving range under battery power. Common battery types such as lithium ion (Li-Ion) use a large number of individual battery cells stacked together (connected in series and/or parallel) into a battery pack. Besides monitoring the total voltage output by a battery pack, each cell is typically monitored individually to determine their voltage production, current, and other parameters. The temperature of each cell is typically monitored in order to protect against overheating.
It is very challenging to reliably monitor the various battery conditions because of the high-voltage levels involved, the range of intermediate voltages at which respective cells operate within the stack, and the high levels of accuracy required. Various battery monitoring integrated circuit devices have been developed commercially for use in the vehicle environment. Examples of a commercially available battery monitoring IC device include the AD7280A device available from Analog Devices, Inc., of Norwood, Mass., the LTC6804 devices available from Linear Technology Corporation of Milpitas, Calif., and the ISL94212 Multi-Cell Li-Ion Battery Manager available from Intersil Corporation of Milpitas, Calif. A typical component in an electric drive is a Battery Energy Controller Module (BECM) that includes or can be programmed to include various battery management and communication functions in addition to the monitoring functions.
The SOC in particular is a critical parameter to be monitored because it is used to estimate remaining capacity, power capability, and other battery status. Although current measurements can be used to track the value of the SOC, a more accurate method is based on measuring battery cell open circuit voltage (OCV) which correlates to the SOC via a known relationship which is characteristic of each particular battery design. With a Li-ion battery especially, this SOC-OCV curve changes (i.e., drifts) as a result of battery aging and usage. Use of an inaccurate SOC-OCV curve impairs accurate SOC estimation.
The present invention uses a piecewise linear model obtained by measuring a charging voltage-vs-SOC curve for comparison with a family of predetermined SOC-vs-OCV aging curves, and picks the one with a best fit as the one most accurately representing the aged condition of the battery or cell.
In one aspect of the invention, a method is provided for monitoring battery cell state-of-charge (SOC) using open circuit voltage (OCV). A charging current is applied to the battery cell. A charging condition is detected in response to a predetermined charging current. A charging slope vector is compiled during the charging condition comprising a plurality of slope values over respective state-of-charge increments. A plurality of SOC-OCV slope vectors are determined corresponding to a plurality of stored SOC-OCV aging curves, each SOC-OCV slope vector comprising a plurality of slope values over equivalent state-of-charge increments. One of the stored SOC-OCV aging curves is selected having an SOC-OCV slope vector best fitting the charging slope vector for use in converting measured OCV values to battery cell SOC values.
The term “electrified vehicle” as used herein includes vehicles having an electric motor for vehicle propulsion, such as battery electric vehicles (BEV), hybrid electric vehicles (HEV), and plug-in hybrid electric vehicles (PHEV). A BEV includes an electric motor, wherein the energy source for the motor is a battery that is re-chargeable from an external electric grid. In a BEV, the battery is the source of energy for vehicle propulsion. A HEV includes an internal combustion engine and an electric motor, wherein the energy source for the engine is fuel and the energy source for the motor is a battery. In a HEV, the engine is the main source of energy for vehicle propulsion with the battery providing supplemental energy for vehicle propulsion (e.g., the battery buffers fuel energy and recovers kinematic energy in electric form). A PHEV is like a HEV, but the PHEV has a larger capacity battery that is rechargeable from the external electric grid. In a PHEV, the battery is the main source of energy for vehicle propulsion until the battery depletes to a low energy level, at which time the PHEV operates like a HEV for vehicle propulsion.
Cell voltage can be modeled using a simple R model (especially when current is constant) according to the formula:
v
t(t)=voc(t)+i(t)R(T,SOC)
where R(T,SOC) is the internal resistance, which is a function of temperature and SOC. In the equation, the charging current is positive and discharge current is negative. During charging of the battery, the increase in charge as measured in Amp-Hours (e.g., measured by integrating the charging current, ∫t
The value of index i increases as long as total charge accumulation continues to increase by the threshold amount. Each successive increment is measured from its beginning at a respective time t0 to its respective completion at a time t1, wherein time t1 is detected as the time when the accumulating charge defined by the integral of i·dt reaches the Amp-Hour threshold (i.e., at a time t0+t). The Amp-Hour threshold varies with the battery chemistries, and can be determined via laboratory testing. The Amp-Hour threshold should be big enough that increase of cell voltages respectively measured at time (t0+t) and time t0 is noticeable. For example, the Amp-Hour threshold could be be about 0.1 of the battery cell capacity. One criterion to select the Amp-Hour threshold is making sure that the internal resistance will not change significantly when the SOC change within the Amp-Hour threshold. Thus, each slope value is calculated from the cell voltages at the beginning and ending of an SOC increment and the Amp-Hour threshold (AH) as follows:
In some embodiments, multiple Amp-Hour thresholds could be used according to different SOC ranges. For example, a small Amp-Hour threshold can be defined for a low SOC range from 0 to 0.2 of the battery cell capacity; a relatively big Amp-Hour threshold can be defined for a medium SOC range from 0.2 to 0.7 of the battery cell capacity; and a medium Amp-Hour threshold can be defined for a high SOC range from 0.8 to 1 of the battery cell capacity.
At the end of a charging cycle or anytime after sufficient slope values have been compiled for the charging slope vector, the resulting charging slope vector is compared to the stored family of SOC-OCV curves in a piecewise manner. Since storing both the SOC-OCV curves and all the potential slope values for all the starting and stopping cell voltage values would be impractical, slope vectors for all the SOC-OCV aging curves may preferably be calculated on the fly.
Then the slope values for curve 12 are determined for increments 27 until the corresponding end of the charging slope vector at point 28. Subsequently, each remaining SOC-OCV aging curve is processed to obtain their respective SOC-OCV slope vectors and then each one is compared with the charging slope vector to find a best fit as described in greater detail below.
Vehicle 30 includes a battery system 35 including a main battery pack 36 and a battery energy controller module (BECM) 37. An output of battery pack 36 is connected to an inverter 38 which converts the direct current (DC) power supplied by the battery to alternating current (AC) power for operating motor 31 in accordance with commands from a traction control module (TCM) 40. TCM 40 monitors, among other things, the position, speed, and power consumption of motor 31 and provides output signals corresponding to this information to other vehicle systems including a main vehicle controller 41 (which may be a powertrain control module, or PCM, for example).
An AC charger 42 is provided for charging main battery 36 from an external power supply (not shown), such as the AC power grid. A current sensor 43 measures the charging current and provides the resulting current measurement to BECM 37. Although vehicle 30 is shown as a BEV, the present invention is applicable to any electric vehicles using a multi-cell battery pack including HEVs and PHEVs.
Throughout vehicle service, the present invention repeatedly monitors battery performance during charging in order to identify the appropriate aging curve. Battery charging is initiated in step 52. In step 53, an initial open circuit voltage for a battery cell is measured and stored. Since all battery cells can often be reasonably expected to perform in a similar manner, testing of just one battery cell may usually be sufficient to identify the appropriate aging curve. Otherwise, the method described herein can be employed with a plurality of battery cells as necessary.
During charging, the change in SOC is monitored in step 54 according to the accumulation of the amp-hour charging. In step 55, a check is made to determine whether desired optimal charging conditions are present. The desired charging condition preferably corresponds to the existence of a quasi-steady-state cell charging current (i.e., that remains stable within a predetermined calibrated range). For example, the quasi-steady-state current is defined as follows:
For a time>a calibrated time (e.g., 100 seconds), it is true that
abs(i)+Δi>abs(i)>abs(i)−Δi,
where Δi is a calibratable offset. In addition, the desired charging condition may include the requirement that the quasi-steady-state current remain in a preferred measurement range which includes a peak accuracy in the operation of the current sensor being used. As a fourth condition, the desired charging conditions may include a requirement that a cell temperature is within a predetermined range (e.g. a range that avoids undesirable cell conditions such as freezing). If desired charging conditions are not detected in step 55 then the conditions are periodically rechecked until the desired charging condition is obtained.
In step 56, a charging slope vector is compiled once the desired charging condition is present. Compilation of the charging slope vector may preferably be performed in accordance with a preferred method as shown in
Returning to
where vj,i,1OC is the OCV based on jth SOC-OCV curve corresponding to the beginning of the ith linear piece, and vj,i,2OC is the OCV based on jth SOC-OCV curve corresponding to the end of the ith linear piece. The vj,i,1OC and vj,i,1OC are calculated by
V
j,i,1
OC
=V
S
OC
V
j,i,1
OC
=V
j,i-1,2
OC
i>2
where Q is the battery capacity, and fj(•) is the jth SOC-OCV curve stored in the SOC-OCV curves bank. Once all the stored aging curves have been processed to provide respective SOC-OCV slope vectors, they are each compared to the charging slope vector in order to select a best fit in step 59. The comparison may preferably be performed using a squared Euclidian distance of the respective slope values as follows:
The SOC-OCV curve with the best fit is one with the minimal distance, e.g.
It should be noted that some other similarity measures can also be used to compare the charging slope vector and the SOC-OCV slope vectors. For example, it is usual that every slope in the slope vector has different significance to compare the charging curve and the SOC-OCV curve. Thus, the weighted squared Euclidian distance is a nice choice for comparison, e.g.,
where wj are some significance factor. The slope, which has great significance in comparison, has a greater weight. The SOC-OCV curve with the best fit is the one which can minimize following objective:
The SOC-OCV slope vector for j satisfying the minimum becomes the selected SOC-OCV curve. The selected curve is then used for battery monitoring and control in step 60. The monitoring of the battery includes the ability to obtain a more accurate estimate of the actual SOC of the battery. The selected SOC-OCV curve also enables better estimation of the battery capacity and battery power capability as it ages.
As is apparent from