Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

• Main Authors: Xiaodong Liu, Jian Ma, Xuan Zhao, Yixi Zhang, Kai Zhang, Yilin He
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Processes
• Subjects: optimization; real-time; energy management; NSGA-II; plug-in hybrid electric vehicle; model-free adaptive control
• Online Access: https://www.mdpi.com/2227-9717/7/8/477

The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin’s minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.