Benchmark fuel economy for a parallel hybrid electric three-wheeler vehicle (rickshaw)

Abstract

The core contribution to this work is the development of benchmark fuel economy for a three-wheeler hybrid electric rickshaw and its comparison with heuristics controllers designed with optimal and non-optimal rules. Dynamic programming is used as a feasible technique for powertrain benchmark analysis. A parallel hybrid electric three-wheeler vehicle is modeled in MATLAB/Simulink through forward facing simulator. The dynamic programming technique is employed through the backward facing simulator, ensuring optimal power sharing between two energy sources (engine and motor) while keeping the battery state of charge in the charge-sustaining mode. The extracted rules from dynamic programming forming near-optimal control strategies are playing a vital role in deciding overall fuel consumption. Unlike the dynamic programming control actions, these extracted rules are implementable through the forward facing simulator. From the simulation results, it can be concluded that a substantial improvement of fuel economy is achieved through the application of dynamic programming. Rule-based (near-optimal) strategy using dynamic programming results shows about 9% more fuel consumption as compared with the dynamic programming (benchmark solution), which is then compared with non-optimal rule-based heuristics controller. It is shown that non-optimal rule-based controller has 18% more fuel consumption than dynamic programming results.

Power Management Strategy of a Parallel Hybrid Three-Wheeler for Fuel and Emission Reduction

“…In this section, mathematical models for main powertrain components of a parallel hybrid electric vehicle are modelled and based on the powertrain models two hybrid electric vehicle models are developed, i.e., forward-facing model and backward-facing model. [18,22,25,36,37] used a similar methodology.…”

“…The battery model is generated as multiple modules with a combination of parallel and series cells to represent the 6 Ah Saft Li-Ion battery from the ADVISOR library. The battery input or output power P batt , is expressed by, P batt = P elec_mot + P aux (25) where P aux is a constant auxiliary power demand that represents the power consumption of all the secondary systems of the vehicle. The battery pack is modelled as an equivalent circuit comprising an open circuit voltage V oc in series with an internal resistance R int as illustrated in Figure 5.…”

“…This approach, already known in the literature, is used to design a computationally effective rule-based supervisory power management strategy to operate a hybrid electric three-wheeler efficiently during real vehicle driving conditions. [15,19,24,25] used a similar methodology.…”

“…Thus, the use of DP to design a rule-based controller for hybrid electric three-wheelers should be carried out carefully to avoid charge-hoarding. A recent study from Asghar et al [25] has attempted to implement a sub-optimal rule-based power management strategy based on DP results for a parallel hybrid electric three-wheeler. Despite the extensive methodology followed in extracting rules from the DP results; report has not thoroughly dealt with transmission gear strategy, numerical issues of DP, exhaust emissions and powertrain component sizing techniques in designing the control strategy of the hybrid electric three-wheeler.…”

“…Striking a good balance to optimize all the objectives is difficult in a high dimensional objective space with more than three objectives using rule-based control law. Usually, three or less multi-objective are considered in developing DP based heuristic control laws [18,19,21,23,25]. The present study considers a multi-objective optimization problem with more than three objectives; which are defined as many objective optimization problems [33].…”