Physics

Automotive engineering

Energy (signal processing)

Mathematics

Learning vector quantization

Energy management

Operating system

Algorithm

Statistics

Engineering

Embedded system

Artificial intelligence

Thermostat

Power (physics)

Test bench

Electric vehicle

Vector quantization

Computer science

Mode (computer interface)

Quantum mechanics

Artificial neural network

Mechanical engineering

Journal of Power Sources

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm