Are you interested in something like the one discussed in these works?

János Kontos (0000-0002-9676-0797) - ORCID

Recent advances in predictive control techniques applied to hybrid vehicle control focus on improving energy efficiency, reducing emissions, and optimizing overall vehicle performance.

Here are some of the most relevant advances:

1. Model-Based Predictive Control (MPC) with Driving Condition Forecasting: Control strategies have been developed that incorporate predictions of future driving conditions, such as terrain topography, traffic, and the desired speed profile. This allows the operation of the engine and hybrid system to be adjusted more efficiently, anticipating energy demands and optimizing the use of the battery and combustion engine.

2. Distributed Predictive Control: In complex hybrid systems, where multiple subsystems such as the internal combustion engine, electric motors, and batteries must work in a coordinated manner, distributed predictive control has been implemented. This approach allows each subsystem to optimize its operation independently, but in a coordinated manner, improving the overall efficiency of the hybrid system.

3. Incorporation of Machine Learning in MPC: The use of machine learning techniques, such as neural networks and reinforcement learning algorithms, has enabled improvements in the predictive models used in MPC. This helps improve the accuracy of vehicle behavior predictions and allows the controller to better adapt to different driving conditions and vehicle wear and tear.

4. Real-Time Optimization: Progress has been made in implementing real-time optimization algorithms, which allow predictive control to run efficiently even on computationally limited hardware. This is crucial for in-vehicle applications where processing power may be limited.

5. Integration with Smart Infrastructure and Connected Vehicles: Developments in smart infrastructure and vehicle-to-everything (V2X) communication have enabled predictive control to extend beyond the individual vehicle. For example, predictive control can leverage information from traffic lights, road signs, and other vehicles to further optimize energy consumption and hybrid system operation.

6. MPC with Battery Aging Consideration: MPC techniques have been developed that take battery aging into account, adjusting control strategies to maximize battery life without compromising vehicle performance. This is especially relevant given that batteries are one of the most critical and expensive components in hybrid vehicles.

These advances reflect an increasingly sophisticated and multidisciplinary approach to optimizing hybrid vehicle performance, combining advanced control theory with emerging technologies such as machine learning and vehicle connectivity.