Open Access

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
Search for this author on
Sciendo|Google Scholar
Han, Lei
  
Sep 26, 2025
Capacity estimation and energy allocation model of new energy vehicle battery management system based on optimization algorithm's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Published Online: Sep 26, 2025
Received: Jan 20, 2025
Accepted: Apr 20, 2025
DOI: https://doi.org/10.2478/amns-2025-1064
Keywords
© 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
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
Journal RSS Feed