The development and real-time application of DP-based optimized A-ECMS algorithm for multi-mode series-parallel hybrid electric vehicles

The hybrid electric drive system shows its great potential in the energy sustainability of the automotive industry. This paper investigates the improved adaptive equivalent consumption minimization strategy (A-ECMS) for a multi-mode series-parallel hybrid electric vehicle. First, a basic ECMS algorithm for the series-parallel vehicle is established, which considers the instantaneous optimal torque matching in the electric, serial hybrid, and engine driving modes. Then, Genetic Algorithms (GA) are used for offline global optimization of the equivalent factor and penalty function exponent. Under the condition that the future traffic information scenario is known, it is desired to realize the global optimal planning based on the combination of dynamic programming (DP) and ECMS. Therefore, the SOC, engine speed, and torque results calculated by the DP strategy are used as benchmarks to develop the improved SOC-AECMS and S-AECMS strategies, which better incorporate the advantages of the global optimization results. Finally, a hardware-in-the-loop simulation platform is set up to validate the real-time application of the proposed algorithms. A comparative analysis of the engine operating point distribution and fuel consumption levels is performed. The results show that the basic A-ECMS is adaptable to different driving cycles, but has limited fuel saving effects. Both the optimized SOC-AECMS and S-AECMS can effectively improve fuel economy while avoiding the computational burden of DP algorithms. The S-AECMS approaches 91.6% of the total fuel consumption level of the globally optimized DP, which provides a promising method for integrating the driving scenario information with the advanced energy management techniques.