Prediction-Assisted Control of an Electric-Vehicle CO2 Heat Pump with Secondary Throttling Based on a Bidirectional Feedforward Neural Network

To improve the low-temperature heating performance of electric-vehicle CO2 heat pumps, their system configurations and control strategies have become increasingly complex. This has made coordinated regulation among multiple components more difficult and can lead to delayed supply-air temperature response, operating fluctuations, and other problems. Accordingly, this study proposes a shared-parameter bidirectional feedforward neural network (BFNN)-assisted regulation method. The method jointly learns the forward and inverse relationships between controllable components and the prediction variable. The inverse path generates candidate operating parameters, which are verified and corrected by the forward path to improve regulation efficiency. Under a −20 °C cold-start condition, the BFNN-assisted strategy achieved a supply-air temperature stabilization time of 8.2 min, 45.6% and 56.3% shorter than those of unidirectional FNN-assisted control and conventional rule-based feedback control, respectively. Compared with rule-based feedback control, the time-averaged heating COP over the 30 min test increased by 8.9%, while compressor energy consumption decreased by 19.0%. When cabin-side airflow increased from 300 to 550 m3/h, the BFNN-assisted strategy limited the maximum temperature drop to 2.50 °C and the recovery time to 1.67 min. Under the tested conditions, the integrated BFNN-assisted control strategy improved the CO2 heat pump system temperature-response and energy-performance metrics.