Trade-offs between fuel economy and NOx emissions using fuzzy logic control. Page: 4 of 11
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Figure 3 presents a simplified overview of the power controller. The first block converts the driver inputs from the brake
and accelerator pedals to a driver power command. The signals from the pedals are normalized to a value between zero
and one (zero: pedal is not pressed, one: pedal fully pressed). The braking pedal signal is then subtracted from the
accelerating pedal signal, so that the driver input takes a value between -1 and +1.
Vehicle Driver
Speed Power 1
Driver Driver Command ICE Power
Comma Interpreter Power ICE and
- InerprterEM
LOFogic Scaling Power EM Power
Controller Factor
EM Speed
Figure 3: Fuzzy Logic Controller Block Diagram
The fuzzy energy management strategy described below has been implemented using a Takagi-Sugeno fuzzy logic
controller [10]. A fuzzy logic controller relates the controller outputs to the inputs using a list of if-then statements called
rules (example in Table 2). The if-part of the rules refers to adjectives that describe regions (fuzzy sets) of the input
variable. A particular input value belongs to these regions to a certain degree, represented by the degree of membership
(see Figure 4 for examples of membership functions that define the degree of membership). The then-part of the rules of a
Takagi-Sugeno controller refers to values of the output variable. To obtain the output of the controller, the degrees of
membership of the if-parts of all rules are evaluated, and the then-parts of all rules are averaged, weighted by these
degrees of membership.
1 If SOC is too high then Pgen is 0 kW N 1 normal high
2 If SOC is normal and Pdriver is normal and OEM is optimal then Pgen is 10 kW i
3 If SOC is normal and OEM is not optimal then Pgen is 0 kW
4 If SOC is low and Pdriver is normal and OEM is low then Pgen is 5 kW
5 If SOC is low and Pdriver is normal and OEM is not low then Pgen is 15 kW 0 20 40 60 80
6 If SOC is too low then Pgen Is Pgen,max Driver Power Command (kW)
7 If SOC is too low then scale factor is 0
8 If SOC is not too low and Pdriver is high then Pgen is 0 kW too low low normal too high
9 If SOC is not too low then scale factor is 1
0.s
Table 2: Example Rules of the Fuzzy Logic Controller
0 0.2 0.4 0.6 0.8 1
State of Charge
Figure 4: Example of
Membership Functions
If the SOC is too high (rule 1) the desired generator power (Pgen) will be zero, to prevent overcharging the battery. If the
SOC is normal (rules 2 and 3), the battery will only be charged when both the EM speed is optimal and the driver power
is normal. If the SOC drops too low, the battery will be charged at a higher power level. This will result in a relatively
fast recovery to a normal SOC. If the SOC drops to too low (rules 6 and 7), the SOC is increased as fast as possible to
prevent battery damage. To achieve this, the generator power is maximized and the scaling factor is decreased from one
to zero. Rule 8 prevents battery charging when the driver power demand is high and the SOC is not too low. Charging in
this situation moves the engine power outside the optimum range (25-50 kW). Finally, when the SOC is not too low (rule
9), the scaling factor is one.
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Rousseau, Aymeric; Saglini, Sylvain; Jakov, Michael; Gray, Donald & Hardy, Keith. Trade-offs between fuel economy and NOx emissions using fuzzy logic control., article, August 19, 2002; Illinois. (https://digital.library.unt.edu/ark:/67531/metadc737798/m1/4/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.