Predictive energy management (PEM) of a CAEV
Problem to solve
- Developing an optimal control strategy for the powertrain of a CAEV to achieve maximum energy efficiency
Figure 1. Concept of optimal energy management
Technical challenges
- Incorporating proactive and predictive considerations of future operating conditions
- Addressing uncertainties associated with future operating conditions
- Tackling nonconvexity and the large-scale nature of the optimization problem
Specific research topics
- Developing a real-time PEM strategy for CAEVs that ensures global optimality
- Designing a prediction model to accurately forecast future operating conditions
- Validating the effectiveness of the combined real-time PEM strategy and prediction model in real-world driving scenarios
Experimental setup
Figure 2. Experimental setup
Control and AI techniques used in research
- Optimal control principles
- Model predictive control (MPC) techniques
- Numerical optimization algorithms
- Sequence prediction methods
Expected results
- The developed PEM strategy will enable CAEVs to achieve the highest level of energy efficiency across various driving scenarios.
Relevant references
- K. Choi and W. Kim*, “Real-time Predictive Energy Management Strategy for Fuel Cell-powered Unmanned Aerial Vehicles Based on the Control-oriented Battery Model,” IEEE Control Systems Letters, vol. 7, pp. 943-948, 2023.
- K. Choi, J. Byun, S. Lee, and I. G. Jang*, “Adaptive equivalent consumption minimization strategy (A-ECMS) for the HEVs with a near-optimal equivalent factor considering driving conditions”, IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2538-2549, 2022.
- J. Byun and K. Choi*, “Effects analysis of light-duty diesel truck hybrid conversion depending on driving style,” Transportation Research Part D: Transport and Environment, vol. 97, pp. 102958, 2021.
Relevant research projects or grants
- 소형 하이브리드 전기 트럭의 실시간 연비 최적화 기술 개발 (KAIST, 2020 - 2021)
- 택배차량용 디젤 트럭의 하이브리드 개조기술 개발 및 실용화 연구 개발 (국토교통과학기술진흥원, 2017 - 2021)
