Abstract:
A controller for controlling braking of a wheel of a vehicle. The controller includes a first connection to a friction brake, a second connection to a motor/generator, a third connection to a plurality of sensors, and a fuzzy logic module. The motor/generator is configured to drive the wheel in a driving mode and to brake the wheel in a regenerative braking mode. Operating parameters of the vehicle are sensed by the plurality of sensors. The fuzzy logic module is configured to determine a stability of the vehicle and the wheel based on data from the plurality of sensors. The fuzzy logic module allocates braking force between the friction brake and the motor/generator operating in the regenerative braking mode based on the stability of the vehicle and the wheel.

Description:
BACKGROUND 
       [0001]    The invention relates to controlling the ratio of regenerative braking to friction braking in hybrid vehicles. Specifically, fuzzy logic is used to determine the amount of regenerative and friction braking to use based on a variety of sensed parameters. 
         [0002]    Hybrid vehicles generally use regenerative braking to decelerate the vehicle and recharge the batteries. However, in certain circumstances (e.g., during a dynamic maneuver such as skid correction) the vehicle uses friction braking because of the greater braking control provided by friction breaking. 
       SUMMARY 
       [0003]    The invention uses fuzzy logic to determine a ratio of regenerative braking to friction breaking for each wheel of a vehicle, enabling greater use of regenerative braking, and, thus, greater recapture of energy from the vehicle. 
         [0004]    In one embodiment, the invention provides a controller for controlling braking of a wheel of a vehicle. The controller includes a first connection to a friction brake, a second connection to a motor/generator, a third connection to a plurality of sensors, and a fuzzy logic module. The motor/generator is configured to drive the wheel in a driving mode and to brake the wheel in a regenerative braking mode. Operating parameters of the vehicle are sensed by the plurality of sensors. The fuzzy logic module is configured to determine a stability of the vehicle and the wheel based on data from the plurality of sensors. The fuzzy logic module allocates braking force between the friction brake and the motor/generator operating in the regenerative braking mode based on the stability of the vehicle and the wheel. 
         [0005]    In another embodiment the invention provides a method of allocating braking force in a vehicle between a regenerative brake and a friction brake. The method includes receiving a sensed speed of a wheel, a yaw rate of the vehicle, and lateral acceleration of the vehicle, determining an acceleration/deceleration of the wheel, a slip of the wheel, and a jerk of the wheel, performing a first fuzzy operation on the jerk, the slip, the yaw rate, the lateral acceleration, and the acceleration/deceleration of the wheel, the first fuzzy operation returning a value indicative of a stability of the respective wheel parameter, performing a second fuzzy operation on a vehicle speed, the second fuzzy operation returning a value indicative of an impact the vehicle speed has on the stability of the vehicle, determining via a third fuzzy operation an amount of braking power to be applied via regenerative braking versus friction braking, and providing an indication of the amount of braking power to be applied via regenerative braking to a regenerative brake. 
         [0006]    In another embodiment the invention provides a vehicle, including a wheel, a wheel speed sensor, a friction brake configured to brake the wheel, a motor/generator configured to drive the wheel in a driving mode and to brake the wheel in a regenerative braking mode, a throttle sensor configured to sense a position of a throttle of the vehicle, a brake pedal sensor configured to sense a position of a brake pedal of the vehicle, a plurality of sensors sensing operating parameters of the vehicle, and a controller coupled to the wheel speed sensor, the friction brake, the motor/generator, the throttle sensor, the brake pedal sensor, and the plurality of sensors. The controller includes a fuzzy logic module configured to determine a stability of the vehicle based on data from the plurality of sensors and to allocate braking force between the friction brake and the motor/generator operating in the regenerative braking mode based on the stability of the vehicle. 
         [0007]    Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  is a block diagram of a vehicle. 
           [0009]      FIG. 2  is a model of a fuzzy logic based system for allocating braking force between regenerative and friction brakes. 
           [0010]      FIG. 3  is a fuzzy logic graph for determining a weighting factor based on vehicle speed. 
           [0011]      FIG. 4  is a first fuzzy logic graph for determining an output based on an input and a pair of variables. 
           [0012]      FIG. 5  is a second fuzzy logic graph for determining an output based on an input and a pair of variables. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. 
         [0014]      FIG. 1  shows a hybrid vehicle  100 . The vehicle  100  includes a left front wheel  105 , a right front wheel  110 , a left rear wheel  115 , and a right rear wheel  120 . Each of the wheels  105 - 120  has an associated motor/generator  125 - 140 . The wheels are capable of being driven by a combustion engine  145  and/or their electric motors  125 - 140  (which in the embodiment shown are position directly adjacent each wheel). The vehicle  100  also includes a plurality of sensors including wheel speed sensors  150 - 165  (each associated with one of the wheels  105 - 120 ), a steering angle sensor  170 , a lateral acceleration sensor  180 , a longitudinal acceleration sensor  185 , a yaw rate sensor  190 , a throttle position sensor  195 , and a brake pedal position sensor  200 . The sensors  170 - 200  provide indications of the various parameters they sense to an engine control unit (ECU)  205  which includes electronic stability control functionality. In some embodiments, one or more of the sensors are not used. Instead the information that would be provided by the sensor is developed using data from one or more other sensors. 
         [0015]      FIG. 2  shows a block diagram of a model  250  of the operation of a system using fuzzy logic to allocate braking control between regenerative braking and friction braking for a wheel of the vehicle  100 . The model  250  can be implemented in hardware, software, or a combination of hardware and software. In addition, modules described below can be implemented in hardware, software, or a combination of hardware and software, and can be integrated or distributed. The wheel speed sensors  150 - 165 , the steering angle sensor  170 , the lateral acceleration sensor  180 , the longitudinal acceleration sensor  185 , the yaw rate sensor  190 , the throttle position sensor  195 , and the brake pedal position sensor  200  provide signals indicative of their respective sensed parameters to the ECU  205 . An electronic stability control (ESC) module  255  of the ECU  205  provides information on the brake pedal and throttle positions to an acceleration/deceleration module  260 . The module  260  determines a desired acceleration/deceleration (e.g., in meters per second squared—m/s2). The module provides the desired acceleration/deceleration to a subtractor  265 . The ESC module  255  also provides an indication of actual acceleration/deceleration  267  (in m/s2) to the subtractor  265 . The actual acceleration/deceleration is obtained from the longitudinal acceleration sensor  185 . In some embodiments, the acceleration/deceleration is determined using data from sensors other than the longitudinal acceleration sensor  185  (e.g., using wheel speed sensors). The subtractor  265  generates an error signal  270  indicative of the difference between the desired acceleration/deceleration and the actual acceleration/deceleration. The error signal  270  is provided to a proportional-integral-derivative (PID) controller  275 . The PID controller  275  is a closed-loop controller which generates a braking signal  280  indicative of an amount of braking force that should be applied based on present and past desired and actual vehicle acceleration/deceleration. The braking signal  280  is indicative of an amount of braking force that should be applied to an individual wheel. 
         [0016]    The braking signal  280  is fed to a fuzzy logic controller  285 . The fuzzy logic controller  285  also receives a plurality of signals  290  from the ESC module  255 . The plurality of signals  290  include data on the wheel speed, wheel acceleration/deceleration, wheel jerk, wheel slip, vehicle lateral acceleration, and vehicle yaw rate. Using the plurality of signals  290 , the fuzzy logic controller  285  allocates braking force between regenerative and friction braking. The fuzzy logic controller  285  determines a stability of the vehicle  100  and of the individual wheel, assigning values between zero (i.e., very unstable) and one (very stable). The greater the stability, the more of the braking force that is allocated to regenerative braking. The fuzzy logic controller  285  produces a signal  295  indicative of the force to be applied by regenerative braking, and a signal  300  indicative of the force to be applied by friction braking. 
         [0017]    There is a limit to the amount of braking force regenerative braking can provide. This is referred to as the regenerative braking saturation point. The fuzzy logic controller  285  provides the signal  295  to a saturation module  305 . If the braking force to be applied by regenerative braking exceeds a saturation point, the saturation module  305  provides a signal to the regenerative brake to apply its maximum braking force, and also provides a signal to an adder  310  indicative of the amount of braking force that exceeds the saturation point. The adder  310  combines the amount of force that exceeds the saturation point with the amount of friction braking force received from the fuzzy logic controller  285  (signal  300 ), and provides a signal to the friction braking system indicating the combined braking force the friction braking system should provide. 
         [0018]    In some embodiments, the ECU  205  and/or other modules include a processor (e.g., a microprocessor, microcontroller, ASIC, DSP, etc.) and memory (e.g., flash, ROM, RAM, EEPROM, etc.; i.e., a non-transitory computer readable medium), which can be internal to the processor, external to the processor, or both. 
       Operation of the Fuzzy Logic Controller  285   
       [0019]    The fuzzy logic controller  285  uses a plurality of process variables and sensed parameters. The list below shows the variables and parameters used by the fuzzy logic controller: 
         [0020]    V=vehicle speed (m/s). 
         [0021]    ψ=yaw input from the yaw rate sensor  190  in radians per second (rad/s). 
         [0022]    γ=fuzzy based weighting factor based on vehicle speed. 
         [0023]    λ=wheel slip (%). 
         [0024]    V″=wheel jerk (m/s 3 ). 
         [0025]    V′=wheel acceleration/deceleration (m/s 2 ). 
         [0026]    Ay=lateral acceleration (m/s 2 ) (from the lateral acceleration sensor  180 ). 
         [0027]    axF=longitudinal acceleration (m/s 2 ) (from the longitudinal acceleration sensor  185 ). 
         [0028]    X 1  is an output of a first fuzzy logic operation based on V′. 
         [0029]    X 2  is an output of a second fuzzy logic operation based on V″. 
         [0030]    X 3  is an output of a third fuzzy logic operation based on λ. 
         [0031]    X 4  is an output of a fourth fuzzy logic operation based on Ay. 
         [0032]    X 5  is an output of a fifth fuzzy logic operation based on ψ. 
         [0033]    Y 1 , Y 2 , Y 3  are temporary variables. 
         [0034]    C 1  and C 2  are parameters that are preset based on the fuzzy logic operation. 
         [0035]    RB is the regenerative braking portion of the total braking force. 
         [0036]    FB is the friction braking portion of the total braking force. 
         [0037]    Each of the fuzzy logic operations returns a value between zero and one inclusive. In some embodiments, γ is determined based on the speed of the vehicle  100  using the chart shown in  FIG. 3 . When the vehicle  100  is traveling at less than 5 m/s, γ=1. When the vehicle  100  is traveling at greater than 20 m/s, γ=0.5. When the vehicle  100  is traveling at a speed between 5 and 20 m/s, γ is determined by the equation γ=1−(V−5)/30 as shown in  FIG. 3 . 
         [0038]    In some embodiments, X1, X2, X4, and X5 are determined using the graph shown in  FIG. 4 . X3 is determined using the graph shown in  FIG. 5  when the vehicle is accelerating, and using the graph in  FIG. 4  when the vehicle is decelerating. In one embodiment, when the vehicle  100  is taking off (accelerating from a stop) or accelerating: 
         [0039]    X 1  is determined using input |V′| and parameters C 1 =4.2 m/s 2  and C 2 =6.0 m/s 2 . 
         [0040]    X 2  is determined using input |V″| and parameters C 1 =2 m/s 3  and C 2 =20 m/s 3 . 
         [0041]    X 3  is determined using input λ and parameters C 1 =f(V) and C 2 =f(V). 
         [0042]    X 4  is determined using input |Ay| and parameters C 1 =3.0 m/s 2  and C 2 =9.0 m/s 2 . 
         [0043]    X 5  is determined using input |ψ| and parameters C 1 =0.4 rad/s and C 2 =0.7 rad/s. 
         [0044]    And, when the vehicle  100  is decelerating: 
         [0045]    X 1  is determined using input |V′| and parameters C 1 =8.4 m/s 2  and C 2 =14.0 m/s 2 . 
         [0046]    X 2  is determined using input |V″| and parameters C 1 =15 m/s 3  and C 2 =150 m/s 3 . 
         [0047]    X 3  is determined using input X and parameters C 1 =0.03 and C 2 =0.07. 
         [0048]    X 4  is determined using input |ay| and parameters C 1 =2.0 m/s 2  and C 2 =8.0 m/s 2 . 
         [0049]    X 5  is determined using input |ψ| and parameters C 1 =0.3 rad/s and C 2 =0.6 rad/s. 
         [0050]    Once X1 through X5 are determined, they are used to solve the following equations: 
         [0000]        Y   1 =γ*MIN( X   1   ,X   2 )+(1−γ)*( X   1   +X   2 )/2
 
         [0000]        Y   2 =γ*MIN( Y   1   ,X   3 )+(1−γ)*( Y   1   +X   3 )/2
 
         [0000]        Y   3 =γ*MIN( Y   2   ,X   4 )+(1−γ)*( Y   2   +X   4 )/2
 
         [0051]    Then the portion of braking force to be applied to regenerative braking PR is determined by: 
         [0000]        P   R =γ*MIN( Y   3   ,X   5 )+(1−γ)*( Y   3   +X   5 )/2
 
         [0052]    Finally, the actual regenerative braking force BR is determined by multiplying the portion by the output of the PID controller  285 : 
         [0000]        B   R   =P   R   *PID OUT 
         [0053]    And, the actual friction braking force BF is determined by multiplying the portion to be applied to friction braking (1−PR) by the output of the PID controller  285 : 
         [0000]        B   F =(1 −P   R )* PID OUT 
         [0054]    Again, any BR that exceeds a predetermined saturation threshold is added to BF. The process is performed for each of the four wheels. 
         [0055]    For example, for a situation where the vehicle  100  is traveling at 10 m/s (γ=0.83) and is accelerating at a slow rate (V′&lt;4.2 m/s 2 ), wheel jerk is small (V″&lt;2 m/s 3 ), wheel slip is small, vehicle yaw rate is small (ψ&lt;0.4 rad/s), and vehicle lateral acceleration is small (Ay&lt;3.0 m/s2). In addition, X1 through X5 are all 1.0 (very stable). Solving the equations above results in RB being one. This means that all braking force (up to saturation) is applied via regenerative braking. 
         [0056]    As a second example, consider a situation where the vehicle  100  is braking in a turn, and the vehicle  100  is decelerating from 20 m/s (γ=0.5) at a relatively rapid wheel deceleration (V′·12 m/s 2 ), wheel jerk is moderate (V″˜82 m/s3), wheel slip is moderate, vehicle yaw rate is relatively large (Φ˜5.4 rad/s), and vehicle lateral acceleration is large (Ay˜7.7 m/s2), using the fuzzy operations, X1=0.3, X2=0.5, X3=0.6, X4=0.1, X5=0.2, and solving the equations above, yields Y1=0.35, Y2=0.4125, Y3=0.1781, and RB=0.1836. Therefore, FB=0.8164. Because the sensed parameters indicate that the vehicle  100  and the wheel are relatively unstable, 82% of the braking force is applied using friction braking and 18% is applied via regenerative braking. However, this 18% of regenerative braking is greater than prior-art systems, which go to 100% friction braking, and 0% regenerative braking, whenever an unstable condition is encountered. 
         [0057]    The variables used above are for example only, and are not intended to be limiting. Variables can be chosen based on actual vehicle testing, and can vary between different vehicles. 
         [0058]    Thus, the invention provides, among other things, a fuzzy logic based brake control system. Various features and advantages of the invention are set forth in the following claims.