A Comparative Analysis of Route‐Based Energy Management Systems for Phevs

Abstract

Plug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

“…In the aspect of DPREMS, some researchers applied prediction algorithm to the future driving patterns, which mainly included Markov chain-based stochastic predictive algorithm, combined with model predictive control and dynamic programming. [18][19][20] Although these strategies can achieve approximate optimal results in theory, they are poorly adaptable to different driving conditions and have disadvantages, such as complex calculation and ordinary realtime performance. Besides, fuzzy controller, 21 artificial neural network, 22 and support vector machine 23 were also utilized for the driving pattern recognition, which improved the economic performance but with inaccurate classification of driving patterns, because the number of selected characteristic parameters of driving patterns was limited.…”

“…DPREMS analyzes the existing information of driving patterns to predict future driving conditions and then adjusts the EMS parameters accordingly to provide adaptive control. In the aspect of DPREMS, some researchers applied prediction algorithm to the future driving patterns, which mainly included Markov chain–based stochastic predictive algorithm, combined with model predictive control and dynamic programming . Although these strategies can achieve approximate optimal results in theory, they are poorly adaptable to different driving conditions and have disadvantages, such as complex calculation and ordinary real‐time performance.…”

Control‐relevant parameter estimation application to a model‐based PHEV power management system

“…Other advantageous MPC features are the capability of dealing with time delays, of taking advantage of future information and of rejecting measured and unmeasured disturbances . It is noteworthy that MPC embodies both (receding horizon) optimization and feedback adjustment …”

“…9 It is noteworthy that MPC embodies both (receding horizon) optimization and feedback adjustment. 10 Model predictive control has been applied to diesel engine control, 11 catalyst control, 12 transmission control, 13 and HEV 14,15 /PHEV 16,17 power management system design. The MPC approach has been applied to vehicle suspension control, 18 ducted fan in a thrust-vectored flight-control experiment, 19 magnetically actuated mass spring damper system, 20 power converters, 21 and autonomous vehicles 8 as well.…”

Special Issue on “Recent Developments on Modeling and Control of Hybrid Electric Vehicles”

“…Then, the problem of performance analysis of an eco‐driving model predictive control system is studied in for HEVs during car following. Moreover, the authors in present a comparative analysis of route‐based energy management systems for HEVs. The coordinate control strategy of torque recovery is studied in for parallel HEVs.…”