Accesso libero

Capacity estimation and energy allocation model of new energy vehicle battery management system based on optimization algorithm

Lei Han

Lei Han

Department of Automotive Engineering, Sichuan Vocational and Technical College of CommunicationsChengdu, China
Cerca questo autore su
Sciendo|Google Scholar
Han, Lei
  
26 set 2025
Capacity estimation and energy allocation model of new energy vehicle battery management system based on optimization algorithm's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Pubblicato online: 26 set 2025
Ricevuto: 20 gen 2025
Accettato: 20 apr 2025
DOI: https://doi.org/10.2478/amns-2025-1064
Parole chiave
© 2025 Lei Han, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

MOF algorithm iterative process
MOF algorithm iterative process

Figure 2.

Different methods for the estimate of battery 2
Different methods for the estimate of battery 2

Figure 3.

Different methods for the estimate of battery 5
Different methods for the estimate of battery 5

Figure 4.

The capacity estimation error of the different methods of cell 2 battery
The capacity estimation error of the different methods of cell 2 battery

Figure 5.

Multi-objective optimization mapping relation
Multi-objective optimization mapping relation

Figure 6.

Brake area division
Brake area division

Figure 7.

Experimental results of the original mixing degree under NEDC condition
Experimental results of the original mixing degree under NEDC condition

Figure 8.

Experimental results of the new mixing degree under NEDC condition
Experimental results of the new mixing degree under NEDC condition

Comparison of performance indexes before and after optimization

Survey content Categories Parametric performance Numerical value
Performance indicators for new mixtures Power 0-100km/h acceleration (ms) 1088
Maximum speed (km/h) 125.01
Economy 100 kilometers of hydrogen consumption (L) 83.26
Comparison of performance indexes before and after optimization Categories Parametric performance Preoptimize After optimization Contrast
Power 0-100km/h acceleration (ms) 1088 1088 —
Maximum speed (km/h) 125.01 125.01 —
Economy 100 kilometers of hydrogen consumption (L) 83.26 80.94 ↓

The parameters of the car

Parameter name Numerical value
Half load/kg 2000
Full load/kg 2350
Windward area/m2 2.16
Wind resistance coefficient/cd 0.33
Rolling radius/m 0.308
Rolling resistance coefficient 0.0014
Main speed ratio 8.8
Transmission efficiency 0.95
Minimum power/kW 40
Maximum power/kW 75
Rated power (maximum power)/kW 45(90)
Rated torque (maximum torque)/(N·m) 110(220)
Rated speed (maximum speed)/(r/min) 4100(12500)

The particle swarm algorithm parameter Settings

Parameter Iteration number(M) Population scale(N) Acceleration constant(C)
Set value 200 100 C = 2, C = 2
Parameter Inertia weight Decision vector dimension(D) Maximum speed (V)
Set value w 3 50
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
Feed RSS della rivista