Patent Application
20140121956
The present disclosure relates generally to a vehicle, and more particularly to a system and method for calculating the range at which the stored energy in a vehicle will be depleted as well as the total range of the vehicle.
Vehicles, such as motor vehicles, utilize an energy source in order to provide power to operate the vehicle. While petroleum based products, such as gasoline, dominate as an energy source in traditional combustion engines, alternative energy sources are available, such as methanol, ethanol, natural gas, hydrogen, electricity, solar or the like. A hybrid powered vehicle, referred to as a “hybrid vehicle,” utilizes a combination of energy sources in order to power the vehicle. For example, a battery may be utilized in combination with the traditional combustion engine to provide power to operate the vehicle. Such vehicles are desirable since they take advantage of the benefits of multiple fuel sources in order to enhance performance and range characteristics of the hybrid vehicle relative to a comparable gasoline powered vehicle. An example of a hybrid vehicle is a vehicle that utilizes a combination of electric and gasoline engine as a power source.
In such vehicles, a display provides information to indicate various conditions relating to operation of the vehicle, such as state of charge, energy consumption, or range or the like. For example, a range indicator provides information regarding the remaining amount of stored energy available, such as electric charge or a hydrocarbon based fuel, solar energy or the like. The range indicator may be employed in vehicles having one or more combinations of energy sources, .e. electric, fossil fuel, or a combination thereof. As a vehicle is driven, the energy stored in the vehicle is consumed and converted into kinetic energy, such as forward motion. In a predictable driving condition of perfectly constant speed and constant motion resistance, the rate of energy depletion can be a straight line passing through all the data points of energy consumption versus time. Using this data, it is possible to algorithmically estimate the time and or distance remaining, after which the stored energy will be completely depleted. This information can then be displayed to the operator of a vehicle via an indicator located within the vehicle, such as a display device. In order to improve accuracy usefulness to the operator of the vehicle, the algorithm can take into account other variables, such as external noise, drivers' behaviors, adaptation to changing driving conditions, or like. These variables, however, are inherently unpredictable and thus pose a problem in accurately estimating algorithmically the operating range of a vehicle. Moreover, increasing the number of variables inevitably increases the difficulty of calculating operating range, defining relatively accurate mathematical model and/or prediction.
Predictive range techniques, also referred to as “range-to-empty systems” are known and vary among different automakers. Known range-to-empty systems and methods utilize filtering algorithms which attempt to predict, or extrapolate, the fuel and/or energy consumption based on past energy usage. The consequence of such an approach is that the algorithms must be heavily damped in order to avoid instability in the display devices, such as a gauge. However, heavy damping of the drive display output may result in unsatisfactory performance and inaccurate driving range predictions. In addition, the accuracy of existing techniques may be compromised when the vehicle utilizes more than one energy source.
Thus, there is a need in the art for a system and method for more accurately calculating the range-to-empty values of a vehicle operating in an electric-only mode, combined fuel-electric mode, and/or a gasoline only mode.
Accordingly, the present disclosure relates to a system for calculating the operating distance range remaining for a vehicle. The system includes: (a) a driver input sensor for sensing predetermined vehicle operating condition data; (b) an energy storage sensor for sensing energy storage capacity data of a corresponding energy supply mechanism; (c) a controller in communication with the driver input sensor and the energy storage sensor, wherein the controller includes a memory and a processor; (d) an executable range calculation software program stored in the memory of the controller, wherein the range calculation software program uses sensed vehicle operating condition data from the driver input sensor and sensed energy storage capacity data from the energy storage sensor to determine range by determining a mean of energy storage capacity data, determining a slope of the energy storage capacity data, determining an intercept of the energy storage capacity data, and applying a least square linear regression to the determined mean, determined slope and determined intercept to find the remaining range; and (e) a display device in communication with the controller, wherein the display device receives the determined remaining range and displays the remaining range on the display device for use by a user.
The present disclosure further provides for a method of calculating a distance range at which the energy used by a vehicle will be depleted. The method includes the steps of (a) sensing a predetermined vehicle operating condition using a driver input sensor for sensing predetermined vehicle operating condition data; (h) sensing energy storage capacity data of an energy supply mechanism using a corresponding energy storage capacity sensor; (c) calculating a distance range remaining for the vehicle by the energy supply mechanism in a vehicle controller using an executable range calculation software program stored in a memory of the controller which applies a least square linear regression to the sensed energy storage capacity; and (d) displaying the calculated distance range remaining on a display associated with the vehicle for use by the operator of the vehicle.
An advantage of the present disclosure is that a system and method of calculating the range-to-empty of a vehicle is provided that is more efficient and more accurate than other techniques. Another advantage of the present disclosure is the application of statistical methods and calculations using a least squares linear regression may be utilized in determining the range-to-empty t. Yet another advantage of the present disclosure is that the methodology used can be more responsive and adaptive to varying vehicle operating conditions, such as driving styles, grade angles, accelerations, decelerations, loads, energy regeneration events, and other input disturbances. An even further advantage is that the methodology can learn vehicle behavior and adapt accordingly to improve accuracy of prediction. A yet even further advantage of the present disclosure is that the methodology provides for quick and accurate calibration of the system. Still yet an even further advantage of the methodology is a significant reduction in the systems propensity for errors in estimation of range-to-empty.
Other features and advantages of the present disclosure will be readily appreciated, as the same becomes better understood after reading the subsequent description taken in conjunction with the accompanying drawings.
Referring generally to
The vehicle 10 includes a power train 11 that controls the operation of the vehicle. In this example, the power train is a plug-in hybrid, and includes an electrically powered motor 19 coupled to a motor controller. The vehicle includes a gasoline powered engine 17 that supplements the electric motor when required under certain operating conditions. The electrical energy is stored in an energy storage device, such as the battery 15. The battery 15 may be a single unit, or a plurality of modules arranged in a predetermined manner, such as in series to be described in more detail below. Various types of batteries are available, such as lead acid, or lithium-ion or the like. The vehicle 10 may include more than one type of battery 15 or energy storage device. The battery 15 supplies the power in the form of electricity to operate various vehicle components. In this example, there is a low voltage battery (not shown) that provides electrical power to vehicle components such as the various auxiliary systems and a high voltage battery 15 (i.e. 400 V traction battery) that provides electrical power to an electric drive motor 19. The battery may be in communication with a control system that regulates the distribution of power within the vehicle, such as to the electric drive motor, or a vehicle component or other accessories or the like. In this example, the high voltage battery receives electrical energy from a plug-in source, and the low voltage battery receives electrical energy from a solar source and from the higher voltage battery as needed. The energy storage capacity of the engine 17 and battery 15, and depletion of energy from both the engine and battery, determine the operating range of the vehicle.
The interior 12 includes an instrument panel (IP) 14 which can be defined as a dashboard or an instrument cluster (IC) 14. A display device such as a human machine interface (HMI) 16 is shown at a relatively center location of the front area of the interior of the vehicle.
The instrument panel 14 extends laterally in the front portion of the vehicle 10 from one side of the vehicle 10 to the other side of the vehicle 10, as shown in
A display 180, as shown to
In this example, cluster 18C includes a generally circular speedometer 111 positioned on a left side portion 110 of cluster 18a Speedometer 111 includes a speedometer bar 112. A gear mode indicator 113 is shown in the center of the speedometer 111. An odometer 114 is also shown adjacent the central gear mode indicator.
In an example, cluster 13C further includes an upper cluster 120 that defines several measurable indicators such as, compass 121, driving mode 122, clock 123, and external temperature 124. In a right side portion 130 of cluster 18C, an example circular cluster of energy consumption is shown including a battery energy consumption indicator 131, fuel level bar 132, and charge level bar 133. A trip A/B indicator 134 is also provided within the right side portion 130.
In a central portion 140 of cluster 18C, a distance or range indicator is provided. In this example, the range indicator includes a total range indicator 141 and an electric distance range indicator 142. The vehicle 10 is able to travel until the energy available is depleted, otherwise known as the range-to-empty. The range-to-empty feature and values are displayed in indicators 141 and 142, as provided by the distance calculation system 22.
In an example of
Referring to
External inputs (e.g., sensors, etc.) 26 are operable to detect and provide a variety of inputs, such as driver inputs 28 and road load inputs 30, or the like, to the system controller 24. The driver inputs 28 may include an accelerator pedal position sensor, regenerative braking pedal position sensor, powertrain mode switching, or the like. The road load inputs 30 may include characteristics such as aerodynamic resistance, tire rolling resistance, or the like. The data received from the external inputs 26 are likewise transmitted to the vehicle system controller 24.
The system control computer 24 utilizes a variety of algorithms stored in a memory associated therewith that enable the system to perform various tasks, such as calculations, estimations, and monitoring, or the like. Examples of algorithms include a distance calculation 32, a SOC estimator 34, and a battery protection monitor 36, or the like. The distance calculation 32 is calculated using data such as wheel speed sensor data, wheel pulses per revolution calculations, and wheel rotation time resolution data. The SOC estimator 34 uses data such as voltage sensor data, current integrator data, and SOC calculations. The battery protection monitor monitors battery conditions such as the minimum SOC limit, the maximum SOC limit, and the normalized SOC output. The calculated results, i.e. in the form of data or estimations, etc. are transmitted to a display of the vehicle such as display 18C.
The system is in communication with a display device, such as a human-machine interface (HMI) 16. The human machine interface may have an integral controller and processor. Data from the vehicle system control computer 24 is received by the HMI 16 and the HMI algorithms 38 perform various functions. Although the HMI 16 can have its own controller to perform these calculations, the HMI 16 can merely be a display device and the calculations performed in another device (e.g., vehicle controller, etc.) associated with the vehicle. These calculations are then used to display information, such as, electric range, total range, average fuel consumption, or the like, on the display screens of the HMI 16. The range calculations of this example may be performed in any one of the controllers associated with the vehicle. In this example, the calculation is performed by a controller associated with the HMI.
In an example, a vehicle includes several dedicated controllers often referred to as control units or control modules. Referring to
Referring now to
In box 220, data from the various inputs are transferred to the controller. Examples of input data include wheel speed from the wheel speed sensor, state of charge from the voltage sensor or the like. The collection of the initial data can be referred to as a moving window since the gathering points are continuously advancing, leaving the earliest data point out of the calculation. The methodology advances to box 230.
In block 230, electrical range (EV Range) and total range is determined. For example, the electrical range and total range is determined using the methodology described with respect to
The display may be discrete or continuous, thus, the methodology then advances to decision box 250 where it loops back to the initialization step of box 210 and continues or ends resulting from turning off the vehicle in box 260.
Referring now to
Referring to
The methodology advances to decision box 211. In decision box 311 it is determined whether there is sufficient data to determine the range. For example a counter may be utilized to determine if sufficient data points have been acquired and transmitted to the controller. The variable “k” is the inner loop counter for consecutive loops and “n” is the data sample size which is a calibrated point which is predetermined for desired adaptability. If sufficient data points have not been gathered, then the methodology advances to decision box 321.
In decision box 321, it is determined if k is equal to a predetermined value, such as (n−1). If it is equal to the predetermined value, then the methodology advances to box 323 where the latest data point is loaded. The methodology advances to decision box 324 and it is determined whether to continue gathering data. If determined to continue gathering data, the methodology advances to decision box 325, and the sum of the desired values, for example SOC data, are calculated. Referring back to decision box 321, if the k value is other than (n−1) than the methodology advances to box 322, in box 322, old data arrays which are stored in a memory component of the system are advanced. Accordingly, this allows the system to generate data at initiation to begin learning behavior of the vehicle. The methodology advances to box 324 and then continues to the sum calculation of box 325, as previously described. The methodology advances to box 326 and the old arrays are then updated. The methodology advances to box 327 to add to the counter (k) count represented by “k++” representing the number of data points. The methodology then returns to decision block 311 and continues. It should be appreciated that the data gathering steps described with respect to the moving window of step 220 is continuous after the system is initiated.
Returning back to decision block 311, the methodology advances to box 331 if determined that there is sufficient data to calculate the range using the linear regression algorithm to be described. In block 331, the mean of the data points associated with the state of charge, or energy capacity, or fuel tank level are calculated. The methodology advances then to box 332 where the slope of the previously collected data points associated with the state of charge, energy capacity or fuel tank level are calculated. The methodology advances to block 333 and uses the means and slopes to calculate the intercept values for the data.
The methodology advances to box 334 and determines the remaining electric energy range once the mean, slopes, and intercepts are calculated. Linear regression modeling includes the step of assuming that in most cases the overall trend and rate of energy depletion over time is linear. The “best” fitting straight line to a set of data points is determined. For example, a, least squares linear regression analysis is employed to find the “best” fitting straight line to a set of data points. The mathematical expression for a straight line is determined using:
y=a
0
+a
1
x (1)
where a0 and a1 coefficients representing the intercept and the slope respectively. The slope of the energy depletion is determined. For example, the least square linear regression algorithm determines the slope of the energy depletion based on the following formula:
a
1
=nΣx
i
y
i
−Σx
i
y
i
/nΣx
i
2−(Σxi)2 (2)
where the value of i is the sample size.
The means of x and y and the intercept value are then determined. For example, after solving for the slope a1, based on the formula above, the means of x and y are calculated, and the intercept value is solved for as follows:
a
0
=
1
(3)
A future moment in time is then determined when the energy will be depleted to a predetermined value, such as zero. For example, the moving sample of the most recent data, and the linear trend defined by the coefficients a1 and a0, may be utilized by the algorithm to extrapolates a future moment in time when the energy will become depleted to zero such as by using:
x
extrap
=−a
0
/a
1 (4)
The methodology determines the remaining extrapolated time to deplete a predetermined amount of the stored energy. For example as the vehicle is consuming the available energy, the SOC data samples are constantly being consumed by the algorithm and the old SOC data samples are being replaced with new samples. Therefore, the remaining extrapolated time to deplete all of the energy, at a time when the sample i is acquired, is calculated as follows:
timeremain=xextrap−xi (5)
The average vehicle speed (“vavg”) is simply the distance traveled, divided by the time expired.
Finally, the remaining range is:
Rangeremain=timeremain*vavg (6)
The driving range can be predicted using statistical analytical techniques such as linear regression. For example, utilizing statistical methods in real-time, such as the least squares linear regression as in the present disclosure, a suitable and relatively stable prediction of the driving range in either electric or fuel modes may be determined. This algorithm also adapts better than the traditional algorithms employed to varying driver behaviors and road load. During traveling, the lesser the remaining amount of onboard fuel energy), the more accurately the algorithm will converge on the remaining range value. The calculated range to energy depletion is displayed on a display device, which in this example is referred to as a human machine interface.
The methodology advances to block 335 and calculates the remaining fuel range based on fuel tank remaining values. For example, the linear regression technique previously described with respect to the state of charge may be utilized.
The methodology advances to block 336 and determined the total range remaining. For example, the EV remaining range is summed together with the fuel remaining range to obtain the total range remaining calculation.
The methodology advances to decision box 350 and determines if a predetermined condition is met to continue calculating the range. An example of a predetermined operating condition is whether the vehicle is still operating. Another example of a predetermined operating condition is whether the vehicle is keyed on. If the predetermined operating condition to continue calculating the range is met, the methodology returns to clock 310 and continues. If the predetermined condition is not met, of the methodology advances to circle 360 and ends. It should be appreciated that the order of implementation of the steps may be varied.
a)-8(c) illustrate example graphs of data points of vehicle operation over a fixed amount of time. In this example, the time is 2 hours and 15 minutes. The data shows (a) normalized SOC over time decreasing from 100 to zero and thus complete depletion of the high voltage battery charge. It is noted that in vehicle operation, typically the battery is not depleted below a certain threshold such as 10% to avoid destroying the battery. Vehicle speed in (b) is shown over the same time period and varying randomly in a range from zero to 60. In (c), the remaining range shown in distance value is shown as decreasing but not necessarily linearly as a result in speed and SOC depletion. Accordingly, as shown by 8(c), the range remaining can vary in a significantly nonlinear graphical model and thus providing a more accurate prediction than traditional linear calculations.
Many modifications and variations of the present disclosure are possible in light of the above teachings. Therefore, within the scope of the appended claim, the present disclosure may be practiced other than as specifically described.
This application claims the benefit of U.S. Provisional Patent Application No. 61/319,553, filed Mar. 31, 2010, the disclosure of which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61319553 | Mar 2010 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13638393 | US | |
| Child | 13895570 | US |
