Energy management in hybrid electric vehicles using optimized radial basis function neural network

Figures & data

Table 1 Simulation parameters for GA.

Parameter	Value	Parameter	Value
Number of iterations	1000	Number of individuals	50
Selection type	Roulette	Mutation type	Uniform
Mutation ratio	0.03	Crossover type	Two point
Crossover ratio	0.9

Table 2 Simulation parameters for PSO.

Parameter	Number of iterations	Number of particles	Coefficient C1	Coefficient C2
Value	1000	50	0.7	0.7

Table 3 Fuel consumption.

$P_{\text{l}}$	500 W	1000 W	2000 W
Strategy	Fuel use (g m)	Fuel use (g m)	Fuel use (g m)
GA	557.261	590.442	662.141
PSO	556.157	589.862	661.464
RBF	546.524	581.140	655.460
GRBF	545.26	580.510	655.112
PRBF	544.913	579.821	654.002

Figure 1 Electrical alternator power for $P_{\text{l}}$ = 1000 W.

Figure 2 Battery SOE.

Figure 3 Adapted $\lambda$ for $P_{\text{l}}$ = 1000 W.

Table 4 Simulation runtime using MATLAB.

Strategy	GA	PSO	RBF	GRBF	PRBF
Runtime (s)	22	23	24	25s	28s

Table A1 Vehicle parameters used in simulations.

Quantity	Symbol	Value	Unit
Mass	M	1400	kg
Frontal area	A d	2	m^2
Air drag coefficient	C d	0.3	–
Rolling resistance	C r	0.015	–
Wheel radius	w r	0.3	m
Final drive ratio	f r	4.0	–
Gear ratio	g r	3.4–2.1–1.4–1.0–0.77	–
Idle speed	${\omega }_i$	73.3	°/s
Air density	$\rho$	1.2	kg/m^3
Gravity	g	9.8	m/s^2

Figure A1 Model of the vehicle used in the paper.