Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Abstract

In this paper a new concept for the layout of hybridelectric-powertrains is developed that includes optimization of the component-sizes as well as control strategies. In contrast to most existing publications, the approach explicitly considers the conflicting goals of low fuel consumption and high vehicle longitudinal dynamics and the trade-off is quantified. Two multiobjective optimization subproblems are solved for one example with a parallelized genetic algorithm (NSGA-II) using the Condor software framework. The analysis of the solutions (Pareto front) shows that combinations exist which improve the fuel consumption with only a slight deterioration of the dynamic performance. So the designers are supported in their decision for a configuration which is attractive for the customers.

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

“…The selection between Modes 1, 2, 3 and 4 is carried out in the second decision layer that incorporates the offline optimized values of the parameters, calculated for each mode. In [25], the rule-based controller is based on five operating modes. The driver torque request, speed of the crankshaft and SoC determine which mode is active.…”

“…These methods are capable of producing globally-optimal solutions, and some more sophisticated ones, such as the Non-dominated Sorting Genetic Algorithm II (NSGA II) [23], are also capable of tackling multiple and conflicting objectives, such as the problem of fuel consumption and emissions minimization in [24], fuel consumption and component sizing in [25] and fuel cost and battery health degradation minimization in [26]. The other category of power management optimization techniques includes those that can be implemented in real-time, such as [27], where along with a real-time controller, two optimization goals of fuel consumption and battery SoC deviation minimization were considered.…”

“…Battery SoC was taken as the state variable. Similarly, rule-based strategies were also considered in combination with ECMS, where optimization is carried out offline with DP in [35] and with more sophisticated multi-objective techniques, such as NSGA II in [25].…”

“…Optimization in HEVs often involves multi-objectives [14], and for a multi-objective optimization problem where the objectives are conflicting, multi-objective genetic algorithms are considered suitable [25], as they search for a Pareto optimal set. In [25], the two conflicting objectives of fuel consumption minimization and sizing were considered.…”

“…In [25], the two conflicting objectives of fuel consumption minimization and sizing were considered. When the configuration was evaluated, the parameters of the control strategy were optimized, whereas while evaluating the control strategy, it was manually tuned for minimum fuel consumption for one combination of drivetrain components.…”