What does this research mean for the field?

A reinforcement learning-based control strategy for R290 electric vehicle heat pump systems significantly improves the coefficient of performance, reduces energy consumption, and minimizes temperature overshoot compared to traditional PID and MPC algorithms. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the thermodynamic efficiency of an R290 heat pump air conditioning system in electric vehicles and develop a dynamic reinforcement learning control strategy to optimize cabin climate management.

September 2, 2026Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering

Performance evaluation and intelligent control of electric vehicle R290 heat pump/air conditioning system based on reinforcement learning

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To evaluate the thermodynamic efficiency of an R290 heat pump air conditioning system in electric vehicles and develop a dynamic reinforcement learning control strategy to optimize cabin climate management.
Constructed a dynamic thermal simulation model of an automotive R290 heat pump air conditioning system and validated the model through experimental bench testing.
Compared optimal refrigerant charge volume and heating/cooling coefficient of performance between R290 and R134a across varying operating speeds.
Evaluated dynamic response, temperature overshoot, and power consumption across reinforcement learning, proportional-integral-derivative, and model predictive control strategies under 45 °C and -10 °C conditions.
R290 required an optimal refrigerant charge volume equal to only 39% of R134a and achieved a superior coefficient of performance across heating and cooling modes, especially at lower compressor speeds.
Under 45 °C cooling, reinforcement learning reduced peak temperature overshoot by 70% compared to PID and 51% compared to MPC, while improving COP by 3.9% and reducing energy use by 4% relative to PID.
Under -10 °C heating, reinforcement learning reduced overshoot by 80% versus PID and 63% versus MPC, increased COP by 5.8% (vs PID) and 3% (vs MPC), and decreased energy consumption by 4.7% (vs PID) and 1.6% (vs MPC).

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Implication

Simulation and experimental study demonstrates enhanced efficiency of R290 heat pumps in electric vehicles, indicating reinforcement learning optimizes thermal control and saves energy.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a97e2f6c562ede874ec77d3 https://doi.org/10.1177/09544070261469974

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Implication

Simulation and experimental study demonstrates enhanced efficiency of R290 heat pumps in electric vehicles, indicating reinforcement learning optimizes thermal control and saves energy.

Performance evaluation and intelligent control of electric vehicle R290 heat pump/air conditioning system based on reinforcement learning

Key Points

Discussion

Implication

Cite This Study

Performance evaluation and intelligent control of electric vehicle R290 heat pump/air conditioning system based on reinforcement learning

Key Points

Discussion

Implication

Cite This Study

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