Abstract or Introduction
Predictive power management is investigated in this research, but in order to accurately predict the best power management; some information from the future power demands of the vehicle is needed. In this research we considered a plug-in city bus working in a predefined itinerary and with a known number of rounds in one day, which means we used an average speed cycle in an entire day as a benchmark cycle, and once the benchmark cycle is described, we proposed another method to find the optimal Lagrange multiplier over this benchmark cycle; this new multiplier is called (λbenchmark_opt). The optimal multiplier (λbenchmark_opt) was used in the algorithm by simulation under MATLAB and gave near-optimal fuel saving with an improvement of about 13% in vehicle efficiency in 5 hours of bus work (which imply an improvement of around 26% in one day when using this strategy compared to CD-CS strategy), but this strategy can be applied only for cycles which have power demand similar to the benchmark cycle, this last condition is often satisfied because it is the same itinerary, the same bus and the same timing thus in most case scenarios leads to more or less the same power demand evolution over the day.
A different kind of predictive strategy for regenerative braking is also proposed, but it is only applied for vehicles which we know their road topology and the exact position of their stopping spots (stations and intersections). This strategy is based on calculating a total regenerative braking distance in real-time based on actual vehicle parameters and if the distance remaining to the next stop is equal to the calculated distance then the bus starts braking by generator at its maximum available power; thus all the braking energy will be saved in the battery, simulation results show a big improvement in fuel saving since each time there is a predefined stopping spot 100% of the braking energy will be saved to the battery, as opposed to hybrid vehicles without this strategy generally save about 30% of braking energy and the rest of energy 70% will be lost inside the mechanical brakes as heat. Using this predictive strategy a lot of advantages can be achieved, recovering 70% more braking energy than other hybrid vehicles represents the major advantage but also when using this strategy we found out that the mechanical brake is not used as in other hybrid vehicles which means that this strategy will also increase the mechanical brakes life.
- Quote paper
- Adel Boukehili (Author), 2012, Optimal and predictive power management for hybrid vehicles. Application for predefined itinerary plug-in city bus, Munich, GRIN Verlag, https://www.grin.com/document/345697
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