Control of hybrid electric vehicles

TLDR

Dynamic programming and other global optimization methods benchmark fuel‑economy potential of powertrain configurations, but real‑time controllers must remain simple and automatically tuned to be implementable with limited resources. The study compares feedback controllers and the equivalent consumption minimization strategy (ECMS) for optimal energy management in hybrid electric vehicles, addressing challenges of advanced architectures and multi‑objective performance indices. Both feedback controllers and ECMS can achieve near‑optimal vehicle performance, with feedback controllers derived from dynamic programming.

Abstract

Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort