On Dynamic Programming Technique Applied to a Parallel Hybrid Electric Vehicle
Examensarbete för masterexamen
Lack of fossil fuel supplies as well as greenhouse gases e®ect on the environment, have motivated car manufacturers to introduce new generations of cars in order to cope with fuel consumption and emissions issues. One of the most interesting structures that is introduced recently to the production lines belongs to hybrid electric vehicles. A hybrid electric vehicle powertrain, generally, contains an electric energy bu®er and an electric machine as well as the conventional internal combustion engine that can work together in several di®erent architectures known as series, parallel and series-parallel de- pending on how the electric machine is coupled with the internal combustion engine. This extra degree of freedom in the powertrain has raised several di®erent research routes on how to optimize the power split between the electric machine and the conventional internal combustion engine. The present work presents a Dynamic Programming approach that solves the optimal power split between the internal combustion engine and the electric machine in parallel hybrid electric vehicles in an e±cient way, taking minimal fuel consumption considerations into account. The power split must be carried out in such a way that in every moment the demanding power on the ¯nal drive is ful¯lled by either the internal combustion engine alone, the electric machine alone or both together. Another important characteristic of hybrid electric vehicles is the possibility to regenerate breaking energy that is dissipated in conventional vehicles, by e±ciently using the electric machine in generator mode while braking, and storing this energy into the electric bu®er for further use. This is also taken into account while designing the optimal controller in the presented work. This optimal control problem is complicated in the sense that the future driving demands are not known a priori to the controller and hence the decision making is impossible if we treat the problem in a simple way. What can be done to cope with this issue is to divide the problem into two di®erent cases. The ¯rst and the most straightforward case is the deterministic case in which the whole driving cycle is known to the controller beforehand, as is considered through the whole thesis. This can be applied e±ciently to vehicles that are driven through a speci¯c route many times and have stop-and-go driving cycles such as commuter busses or refuse vehicles. The second case that is much more complicated and can be applied to any vehicle is the stochastic case that contains no prede¯ned driving cycle and instead uses di®erent methods to predict or to estimate the future driving demands depending on the speci¯c technique that is applied. This can easily be realized having the new GPS and GIS equipments in hand, though it is not covered in the presented work. A Dynamic Programming-based (DP-based) algorithm has been developed as the con- trol system design and applied to the derived vehicle model based on fully deterministic driving pro¯les. The employed controller shows highly satisfactory reductions in the fuel consumption compared to a simple non-hybrid model in simulations. This algorithm has then been used as the heart of a newly developed toolbox to be working together with QSS Toolbox under Matlab and Simulink environments for much easier further case studies.
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