Geodesy

Energy consumption

Fuel efficiency

Quantum mechanics

Algorithm

Benchmark (surveying)

Process (computing)

Engineering

Dynamic programming

Statistics

Convergence (economics)

Energy (signal processing)

Operating system

Economics

Power (physics)

Computer science

Electric vehicle

Energy management

Physics

Reinforcement learning

Mathematics

Geography

Automotive engineering

Artificial intelligence

Structural engineering

Deep learning

Economic growth

Reinforcement

Q-learning

Electrical engineering

Applied Energy

An energy management strategy, based on double deep Q-learning algorithm, is proposed for a dual-motor driven hybrid electric tracked-vehicle. Typical model framework of tracked-vehicle is established where the lateral dynamic can be taken into consideration. For the propose of optimizing the fuel consumption performance, a double deep Q-learning-based control structure is put forward. Compared to conventional deep Q-learning, the proposed strategy prevents training process falling into the overoptimistic estimate of policy value and highlights its significant advantages in terms of the iterative convergence rate and optimization performance. Unique observation states are selected as input variables of reinforcement learning algorithm in view of revealing tracked-vehicles characteristic. The conventional deep Q-learning and dynamic programming are also employed and compared with the proposed strategy for different driving schedules. Simulation results demonstrate the fuel economy of proposed methodology achieves 7.1% better than that of conventional deep Q learning-based strategy and reaches 93.2% level of Dynamic programing benchmark. Moreover, the designed algorithm has a good performance in battery SOC retention with different initial values.

Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle