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.
Highlights
Environmental challenges and reduction of global crude oil reserves gained the attention of researchers and automobile manufacturers for exploration of novel vehicle technologies
The focus of the research is on a parallel hybrid electric rickshaw where the internal combustion engine (ICE) and the electric machine are mechanically coupled.[2]
The fuel consumption through dynamic programming (DP) is about 33 km/L taken as a benchmark fuel consumption, while modified RB strategy based on rules extracted from DP has about 9% more fuel consumption than DP showing the near-optimal solution
Summary
Environmental challenges and reduction of global crude oil reserves gained the attention of researchers and automobile manufacturers for exploration of novel vehicle technologies. The uk represents the vector of control variables such as desired output power from the battery, desired output from the engine, and the desired output torque from the electric machine, and xk is the state vector of the given system.
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