Layered control strategies for hybrid electric vehicles based on optimal control

Abstract

Dynamic programming is known to provide the optimal solution to the energy management problem. However, it is not implementable online because it requires complete a-priori knowledge of the driving cycle and high computational requirements. This article presents a methodology to extract an implementable rule-based strategy from the dynamic programming results and thus build a near-optimal controller. The case study discussed in this paper focused on mode switching in a series/parallel hybrid vehicle, in which a clutch may be used to change the powertrain topology. Because of the complexity of the system, the controller is divided in two layers: the supervisory controller, which decides the powertrain configuration, and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described in detail. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by dynamic programming, which functions as a benchmark. Then another comparison is performed with respect to the equivalent consumption minimisation strategy (ECMS) which, if optimally tuned, can achieve optimal performance as close to DP as possible with the advantage of being implementable.

Real World Operation of a Complex Plug-in Hybrid Electric Vehicle: Analysis of Its CO2 Emissions and Operating Costs

“…It is based on Bellman's principle of optimality [11] and is able to manage dynamic models of the system; since DP is commonly used to solve time-continuous control problems, the model has to be discretized in a sequence of time steps for which DP is capable of determining the optimal control laws. Even though the need for a backward procedure means that the solution can be obtained only offline, for a driving cycle known a priori, and therefore it is not implementable on a real vehicle, the optimal control law can be used to gather information for the development of simpler and implementable strategies and to benchmark their performance [16,17].…”

Energy Management of Hybrid Electric Vehicles: 15 years of development at the Ohio State University

“…Nonetheless, the behavior obtained by the DP solution could in principle be mimicked and reproduced by means of a set of rules which are of easier implementation. Thus, inspired by Lin et al (2003), Bianchi et al (2010Bianchi et al ( , 2011 and Biasini et al (2012) we went through a re-thinking of a 1 500 2 000 2 500 3 000 3 500…”

“…In particular, two modes of operating a PHEV were considered: 1) EV mode control -the battery energy is used as quickly as possible followed by charge sustaining operation; and 2) Blended Mode control (BM) -the battery is discharged gradually throughout the trip. With respect to these two powertrain modes of operation different strategies (DP and ECMS) were compared and analyzed Hybrid powertrain mode of operation selection as a function of gearbox input torque and speed (Bianchi et al, 2011). Comparison between RB and DP and effect of calibration parameter on the RB strategy over the WVU-suburban cycle.…”

Insight into the HEV/PHEV optimal control solution based on a new tuning method

“…There is a wide literature on HEV/PHEV control that ranges from rule-based control (Baumann et al, 2000;Schouten et al, 2003;Poursamad and Montazeri, 2008) to the application of the optimal control theory (a few examples may be found in Wei et al (2007), Bernard et al (2010), Kermani et al (2012), and van Keulen et al (2012)); in some cases, simple implementable rule-based control may be explicitly obtained from the application of the optimal control theory (Lin et al, 2003;Bianchi et al, 2011). For the optimal control approach, the a priori knowledge of the driving cycle is usually assumed (although stochastic optimisation concepts have also been applied, as for example in Moura et al (2011);Chan-Chiao et al (2004)).…”