Patent Application
20080097684
These and other objects, features and advantages of the present invention will become apparent from the following detailed description and the appended drawings in which:
Input membership functions associated with the input variables x1(n), x2(n), and x3(n) are shown in
Generally provided is a system and method to improve vehicle performance by controlling engine power and speed behavior in a power-split HEV through use of a fuzzy controller. In one embodiment of the invention, fuzzy gain-scheduling is used to schedule an appropriate gain for a proportional-integral (PI) controller based on the system's operating conditions. The system and method of the present invention eliminates the overshoots as well as faster response and settling times in comparison with a conventional linear PI control approach. The fuzzy controller is operated in accordance with fuzzy rules designed by utilizing human control knowledge and experience for intuitively constructing the fuzzy controller to achieve desired control behavior for engine power and speed.
Fuzzy control provides a way to cope with the limitations of conventional controllers. The fuzzy gain-scheduling control system and method operates to control desired engine power and speed in a power-split hybrid electric vehicle, thereby resulting in an improved engine speed behavior. Fuzzy gain-scheduling is used to determine an appropriate gain for an associated PI controller based on the system's operating conditions.
A conventional PI based control system may prove effective for linear or nearly linear control issues, however, nonlinear PI controllers such as the fuzzy gain-scheduling controller of the present invention are needed to satisfactorily control nonlinear plants, time-varying plants, or plants with significantly large time delays.
The nonlinear PI controller of the present invention provides an anti-windup scheme that may resolve integrator windup problems typically associated with conventional PI controllers. The fuzzy logic control system of the present invention does not require a mathematical model of the system to be controlled, and allows for the development of a knowledge-based nonlinear controller. Thus, fuzzy logic based PI controllers may be used for nonlinear control of such plants.
The fuzzy logic system control of the present invention provides a power-split HEV closed-loop system with enhanced response and controllability. Without compromising the stability of the system, the control system provides significant reduction in anti-windup to help reduce engine speed and power overshoots. The fuzzy gain-scheduling based engine power control system provides a smoother desired engine speed output, thereby providing for an improvement in customer satisfaction.
Fuzzy Controller Design
The engine power control system is responsible for determining a desired engine speed (ωeng
During the hybrid mode of operation of the vehicle, when the engine is running, a desired engine power (Peng
Conventional techniques use conventional PI controllers 44 to calculate the HV battery feedback power as shown in
The desired engine power is then calculated as the sum of a desired feed-forward engine power and the HV battery feedback power (Pbatt
A schematic of the fuzzy gain-scheduling PI controller 46 is shown in
In one embodiment of the invention, the fuzzy controller is defined by a multiple-input single-output (MISO) Mamdani fuzzy gain-scheduling PI controller 46. The fuzzy gain-scheduling PI controller 46 includes a MISO fuzzy logic gain-scheduler 48 and a PI controller 50. The terms and output of the fuzzy gain-scheduling PI controller 46 as shown in
wherein βf defines a fuzzy logic gain-scheduler output, βfKp defines a proportional gain, βfKi defines an integral gain, e(n) defines an error between a desired HV battery power and an actual HV battery power and Ts defines the sampling time. Both the proportional gain and the integral gain are dynamically modified by the fuzzy gain-scheduler output βf, and the final output u(n) is the sum of the proportional term, P(n), and the integral term, I(n).
The fuzzy gain-scheduling PI controller 46 controls the fuzzy logic gain-scheduler output βf to a first multiplier 70 associated with a proportional portion of the controller 4-6 and also to a second multiplier 72 associated with an integral portion of the controller 46.
If a conventional PI controller was only used instead of the MISO fuzzy logic gain-scheduler, then the fuzzy gain-scheduling PI controller shown in
To design the fuzzy logic gain-scheduler 46, input variables, output variables, and input and output fuzzy sets need to be defined. It is important to correctly select the desired input variables for the fuzzy logic gain-scheduler so that improved engine behavior can be achieved.
Since the conventional PI controller used the error e(n) between the desired HV battery power and the actual HV battery power as an input, shown in
Similarly, to control engine behavior during transient events, the magnitude of the rate of change of the error r(n) is used as a second input variable X2(n), shown in
Finally, an absolute difference between commanded engine speed (actual engine speed) and the target engine speed is used as a third input variable X3(n), because it can be used as a predictor of undesired behavior and hence, can improve and control engine behavior.
The input variables X1(n), X2(n), and X3(n) for the controller are determined in accordance's with Equations (7)-(9):
wherein Pbatt
Input membership functions associated with the input variables x1(n), x2(n), and x3(n) are shown in
The fuzzy rules are laid out in a manner such that they may distinguish between various HEV powertrain behaviors and make decisions based on current and future states of the powertrain. The fuzzy rules operate to define conditions where PI controller will not windup, and conditions where the PI controller will windup. In addition, the fuzzy rules provide the ability for the controller to anticipate conditions where PI controller may windup, thereby providing a mechanism to avoid possible undesirable behaviors.
The description of the fuzzy rules is also shown in Table 54. Among the fuzzy rules, some of the rules are intended to cover steady state and transient conditions, and the other fuzzy rules are used for special conditions, such as conditions where the PI controller may possibly windup.
The first part of the rules, which are called antecedents, specifies the condition for the specific rule. The antecedents contain linguistic terms, such as low, medium and high, which reflect human knowledge of the system behavior. The antecedents shown in columns 58, 60, and 62 are defined as a combination of logical and operators. The language following the antecedent is the consequent, the output Uf(n) or the action of the controller.
The rule 1, where x1(n), x2(n), x3(n) are all low, depicts a steady state condition and hence allows for scheduling of a high value (hH) for the multiplier, βf(n).
In rule 2, even though x3(n) is medium, the other inputs, x1(n) and x2(n), that are low imply that the system is in a condition close to steady state and hence allows for scheduling of a high value (h3) for βf(n).
The rule 3, where x1(n) and x2(n), are low, but, x3(n) is high, predicts that a condition exist, due to the huge difference of desired and actual engine speed, where the PI controller may windup, and hence corrects for this issue by lowering the value of βf(n) to medium (hM).
The rule 4, where x2(n) is medium, meaning that the magnitude of the rate of change of error between the desired and actual HV battery power is relatively larger than an ideal small value, but since the other inputs, x1(n) and x3(n), are low indicates that the system is in a condition that more closely resembles a steady state condition and hence allows for scheduling of a high value (hH) for βf(n).
The rule 5, where x1(n) is low, and the other inputs, x2(n) and x3(n), are medium indicates that the system is in a condition that somewhat resembles a steady state condition and hence allows for scheduling of a high value (hH) for βf(n).
The rule 6, where x1(n) is low, and x2(n) is medium, but, x3(n) is high, predicts that conditions exist, due to the huge difference of desired and actual engine speed, and a medium rate of change of error magnitude, that the PI controller may windup, and hence corrects for this issue by lowering the value of βf(n) to medium (hM).
The rule 7, where x1(n) and x3(n) is low, and x2(n) is high, indicates that the system's response is quick, as the magnitude of rate of change of error between desired and actual HV battery power is large but the magnitude of this error is still small, and therefore the PI controller will not windup. Under this condition, a high value (hH) of βf(n) is scheduled.
The rule 8, where x1(n) is low, and x2(n) is high, and x3(n) is medium indicates that the system's response may not be as quick as than in rule 7 because x3(n) is relatively greater, and therefore predicts that conditions exist where the PI controller may windup. Under this condition, a medium value (hM) of βf(n) is scheduled. The rule 9, where x1(n) is low, and x2(n) is high, and x3(n) is high clearly predicts that conditions exist where the PI controller may windup, and hence corrects for this issue by scheduling the value of βf(n) to medium (hM).
The rule 10, where x1(n) is medium, but x2(n), and x3(n) are both low, clearly indicates a steady state condition where for some reason the powertrain is not generating enough power and hence resulting is a relatively larger error between the desired and the actual HV battery power. Therefore a high value (hH) of βf(n) is scheduled under this condition to increase the desired power from the powertrain to minimize this error as quickly as possible.
Similarly, in rule 11, even though x2(n) is low, the other inputs, x1(n) and x3(n), that are medium imply that the system is in a condition close to steady state and the powertrain may not be producing enough power hence allows for scheduling of a high value (hH) for βf(n).
The rule 12, where x1(n) is medium, x2(n) is low, but x3(n) is high, predicts that conditions exist, due to the huge difference of desired and actual engine speed, where the PI controller may windup, and hence corrects for this issue by lowering the value of βf(n) to medium (hM).
The rules 13 and 14, where x1(n) and x2(n) are both medium, meaning that the magnitude of the error and rate of change of error between the desired and actual HV battery power is relatively larger than an ideal small value, and x3(n) is either low or medium, indicates that system is in a relatively slow transient event and predicts that conditions exist where the PI controller may windup and hence schedules of a medium value (hM) for βf(n).
The rule 15, where x1(n) and x2(n) are both medium, and x3(n) is high indicates that system is currently in a relatively slower transient event which may become a fast transient event due to the fact that x3(n) is high. Hence the PI controller may windup, and therefore a low value (hL) of βf(n) is scheduled.
The rule 16, where x1(n) is medium, x2(n) is high, and x3(n) is low, indicates that the system is in a fast transient event, and therefore the PI controller may windup. But since x3(n) is low, the amount of windup may not be large, hence under this condition, a medium value (hM) of βf(n) is scheduled.
The rules 17 and 18, where x1(n) is medium, and x2(n) is high, and x3(n) is either medium or low indicates that the system is in a fast transient event, and the PI controller may windup. Hence a low value (hL) of βf(n) is scheduled.
The rule 19, where x1(n) is high, but x2(n), and x3(n) are both low, clearly indicates a steady state condition where for some reason the powertrain is producing low power resulting in a large error between the desired and the actual HV battery power. Therefore a high value (hH) of βf(n) is scheduled under this condition to increase the desired powertrain (engine) power to reduce this error as quickly as possible.
Similarly, in rule 20, where x1(n) is high, x2(n) is low, and x3(n) is medium, indicates that even though the powertrain may not be producing enough power, but at the same time there exist a condition that the PI controller may windup due to relatively larger x3(n). Hence, a medium value (hM) for βf(n) is scheduled for this condition.
The rule 21, where x1(n) is high, x2(n) is low, but x3(n) is high, predicts that conditions exist, due to the huge difference of desired and actual engine speed, where the PI controller may windup, and hence corrects for this issue by lowering the value of βf(n) to medium (hM).
The rules 22 and 23, where x1(n) is high, x2(n) is medium, meaning that the magnitude of the error and rate of change of error between the desired and actual HV battery power is relatively larger than an ideal small value, and x3(n) is either low or medium, indicates that system is in a relatively slow transient event and predicts that conditions exist where the PI controller may windup and hence schedules of a medium value (hM) for βf(n).
The rule 24, where x1(n) is high, x2(n) is medium, and x3(n) is high indicates that system is currently in a relatively slower transient event which may become a fast transient event due to the fact that x3(n) is high. Hence the PI controller may windup, and therefore schedules a low value (hL) of βf(n).
The rules 25, 26 and 27, where x1(n) is high, x2(n) is high, and x3(n) is either low, medium or high indicates that the system is in a fast transient event, and therefore the PI controller will windup. Hence under this condition, a low value (hL) of βf(n) is scheduled.
If Ω represents the total number of fuzzy rules (Ω=27 in our case) and μj(xi,Ãi,j) represents the combined membership value from the antecedent of the jth rule, the output, uf(n), of the fuzzy scheduler can be written as follows when the centroid defuzzifier is employed,
where xi represents all the inputs (i=1 . . . 3)and Ãi,j is a vector involving all the input fuzzy sets and {tilde over (h)}j represents the output fuzzy set for the j-th rule.
Using (1) to (3), the complete fuzzy controller system for engine power control can be described by the following equation:
Controlling engine power in a power-split hybrid electric vehicle requires development of sophisticated control systems and algorithms. The present invention provides an approach that uses the rule-based fuzzy gain-scheduling PI controller to control desired engine power and speed behavior in a power-split HEV. Traditionally, a conventional controller with a fast rise-time and settling-time can result in engine speed and power overshoots in a power-split HEV, as the conventional approaches use linear control methods. However the developed fuzzy gain-scheduling PI controller may improve on the control of engine power and speed in a power-split HEV versus using the conventional PI control methods. The use of fuzzy gain-scheduling controller of the present invention is effective in significantly improving the engine power and speed behavior of a power-split HEV by eliminating overshoots, and by providing enhanced and uncompromised rise-time and settling-time.
While several aspects have been presented in the foregoing detailed description, it should be understood that a vast number of variations exist and these aspects are merely an example, and it is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the foregoing detailed description provides those of ordinary skill in the art with a convenient guide for implementing a desired aspect of the invention and various changes can be made in the function and arrangements of the aspects of the technology without departing from the spirit and scope of the appended claims.
