首页|Optimal thermal management of lithium-ion battery packs using a fuzzy neural network controller
Optimal thermal management of lithium-ion battery packs using a fuzzy neural network controller
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NETL
NSTL
Elsevier
The emergence of electric vehicles has reduced tailpipe emissions and improved living environments. However, battery thermal management systems (BTMS) still face challenges of short driving range and thermal safety in extremely low and high-temperature conditions. To address these challenges, a fuzzy neural network (FNN) approach is proposed. The performance of the BTMS under environmental temperatures of -20 degrees C and 40 degrees C, as well as 1C charging, NEDC, and US06 conditions, was investigated. The results show that the thermoelectric coupled model developed in this study has a model error of <1 degrees C compared to the experimental model, indicating a high level of accuracy. Compared with the fuzzy strategy, under these three operating conditions, the heating rate of FNN increased by 1 %, 1.1 %, and 23.3 %, respectively. Compared with fuzzy control strategy and switch control strategy, FNN exhibits superior temperature control stability. In terms of actuator energy consumption, under 1C charging, NEDC, and US06 conditions, the FNN algorithm reduced the energy consumption of the PTC, pump, and fan by up to 8.1 %, 63.8 %, and 54.4 %, respectively. The application of the FNN algorithm is expected to promote wider adoption in electric vehicles, extending driving range through stable temperature control and excellent energy-saving effects, while enhancing system safety under complex environmental conditions.
NETL
NSTL
Elsevier
