Electrical engineering

Physics

Automotive engineering

Energy (signal processing)

Mathematics

Dynamic programming

Agronomy

Biology

Markov chain

Algorithm

Air conditioning

Statistics

Geodesy

Machine learning

Engineering

Model predictive control

Benchmark (surveying)

Artificial intelligence

Power (physics)

Controller (irrigation)

Geography

Control (management)

Electric vehicle

Efficient energy use

Electric energy consumption

Energy consumption

Computer science

Quantum mechanics

Electric energy

Simulation

Control theory (sociology)

Mechanical engineering

DEStech Transactions on Environment Energy and Earth Science

A stochastic model predictive controller based on combined conditions of air conditioning system is proposed to improve the energy efficiency of electric vehicles. According to the 25 groups of on-road tests, the velocity, solar radiation and temperature of driving conditions are recorded to construct combined conditions. A Markov chain-based combined conditions predictor is adopted to provide a prediction of the future conditions. In each predicted horizon, dynamic programming algorithm is applied to determine the best control strategy to balance the energy consumption and the cooling effect. The differences of three control strategies are compared in energy consumption and cooling effect, which are (i) the proposed SMPC method, (ii)a rule-based bang-bang controller and (iii) dynamicprogramming as the benchmark. Comparison resultsillustrate that, SMPC as an online method, theperformance is close to DP and is able to improve theAC energy economy by 7.86% than rule-basedcontroller.

A Stochastic Model Predictive Controller Based on Combined Conditions of Air Conditioning System for Electric Vehicles