Efficient energy management strategies for hybrid electric vehicles using shuffled frog-leaping algorithm

Figures & data

Figure 1 ANN training using SFLA.

Table 1 Simulation parameters.

GA		PSO		SFLA
Parameter	Value	Parameter	Value	Parameter	Value
Number of iterations	1000	Number of iterations	1000	Number of iterations	1000
Number of individuals	50	Number of particles	50	Population size	100
Mutation ratio	0.03	Coefficient C1	0.7	Memeplexes	10
Crossover ratio	0.9	Coefficient C2	0.7	Memes in memeplexes	10
Mutation type	Uniform			Memes in sub-memeplexes	08
Crossover type	Single point			Memetic evolutions in each sub-memeplex	10

Table 2 Fuel consumption.

Pl	500 W	1000 W	2000 W
Strategy	Fuel use (g)	Fuel use (g)	Fuel use (g)
GA	557.261	590.442	662.141
PSO	556.157	589.862	661.464
SFLA	551.112	585.242	659.101
ANN-BP	547.126	583.151	657.112
ANN-SFLA	541.224	577.726	652.181

Table 3 Simulation runtime(s) using MATLAB.

Strategy	GA	PSO	SFLA	ANN-BP	ANN-SFLA
Runtime (s)	22	23	25	26	26

Figure 2 Battery SOE.

Figure A1 Model of the vehicle used in the paper.

Table A1 Vehicle parameters used in simulations.

Quantity	Symbol	Value	Unit
Mass	M	1400	kg
Frontal area	Ad	2	m^2
Air drag coefficient	Cd	0.3	–
Rolling resistance	Cr	0.015	–
Wheel radius	wr	0.3	m
Final drive ratio	fr	4.0	–
Gear ratio	gr	3.4–2.1–1.4–1.0–0.77	–
Idle speed		73.3	rad/s
Air density		1.2	kg/m^3
Gravity	g	9.8	m/s^2