A Comparative Analysis for Optimal Control of Power Split in a Fuel Cell Hybrid Electric Vehicle

Yazdani, Ahmad; Bidarvatan, Mehran

doi:10.4271/2016-01-1189

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The dynamic programming (DP) technique provides a globally optimal solution and has been discussed in several publications. 2,[4][5][6] This technique is not practicable online, for the reason of a priori information about the driving pattern and high computational demands. The realtime optimization controllers like stochastic DP and model predictive controllers (MPC) 7,8 also exist in literature.…”

Section: Introductionmentioning

confidence: 99%

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

Asghar

Bhatti

Izhar

2018

Advances in Mechanical Engineering

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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.

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