Designing an intelligent control strategy for hybrid powertrains utilizing a fuzzy driving cycle identification agent

Abstract

In this paper, a new idea for designing the control strategy for the energy management of hybrid powertrains based on the driving cycle type is presented. Here, every instance of an unknown driving cycle is considered to be similar to the reference driving cycles using similarity weights. To determine the control output in the unknown driving cycle, the weights are applied to a linear combination of the optimal control decisions generated in each of the reference driving cycles. The weights which are between zero and one are determined using a fuzzy driving cycle identification agent based on the comparison of preselected driving features. The simulation studies in seven different driving cycles show that, while all driving patterns in every driving cycle are considered for the generation of energy management by online implementation of the proposed intelligent control strategy, some driving patterns would be eliminated by using a non-fuzzy identification agent. This leads to a significant reduction in the fuel consumption of the hybrid powertrain utilized with the fuzzy identification agent in some driving cycles in comparison with those without the use of non-fuzzy driving cycle identification. In addition, in some driving cycles, the intelligent control strategy has a performance close to that for the offline optimized control strategy.

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

“…One of the main concerns in the area of HEV design is to determine proper power split between the ICE and EM for a given power demand by the vehicle's supervisory controller [1,2]. The HEV supervisory control strategies can be categorized into three groups: (i) *Corresponding Author Email: saadat@sharif.edu (M. Saadat Foumani) the approaches based on global optimization techniques, for instance Dynamic Programming [3][4][5] and PSO algorithm [6][7][8], (ii) the techniques with the instantaneous optimization of an overall FC function which is calculated by converting the electric energy consumption to an equivalent FC [9][10][11], and (iii) the methods on the basis of some predefined rules, such as Electric Assist Control Strategy (EACS) [12,13] and fuzzy logic [14,15]. It is notable that the first group cannot be employed as real-time controller because it requires priori knowledge of the vehicle's driving cycle, and also, is very time consuming [16,17].…”

“…It is notable that the first group cannot be employed as real-time controller because it requires priori knowledge of the vehicle's driving cycle, and also, is very time consuming [16,17]. One of the popular supervisory control strategies which is currently used in HEVs (such as Toyota Prius [13] and Honda Insight [15]) is EACS. The EACS approach is based on some predefined rules and parameters which determine the mode of HEV operation with regard to the vehicle speed, demanded power and energy level of the battery.…”

An Optimal Control Strategy for Parallel Hybrid Electric Vehicles

“…(i) The methods based on heuristic techniques, such as rule-based [2,3], and fuzzy logic [4,5]. One of the conventional heuristic methods is Electric Assist Control Strategy (EACS), which is currently employed in Toyota Prius [2] and Honda Insight [6]; (ii) The methods on the basis of instantaneous optimizations are of another type in which the electric energy consumption is converted to fuel consumption and an overall FC function is determined [7,8]. At each moment of the driving, this function is minimized and an optimal power split between the power sources is calculated.…”

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

“…The proper design of the HEV control strategy leads the engine to operate in its optimal region and results in remarkable improvement in the vehicle FC and emission [8]. One of the main strategies that can be used to manage the power distribution is Baseline static Control Strategy (BCS), which is currently employed in di erent HEVs such as Toyota Prius [9] and Honda Insight [10]. In this strategy, some prede ned rules are used to determine the near-optimal power distribution.…”