CAR-Hybrid Electric Vehicle Performance Optimization Study (2)

Figure 2 shows the iterative optimization process for the plans. From Figure 2 we can see that the initial value calculated from the beginning of the process of change discrete optimization parameters of value, and every point in the calculation of the current constraints, determine whether the conditions to meet the end of iteration, and then through the quadratic programming algorithm for calculating the direction and the next iteration step until the entire optimization process converges to a set of numerical values . Of course, when the value is not bound by the conditions at that time, optimizing the value of the convergence process will not be terminated and overtime. Table 7 through optimized results. Through the optimization process, the result will be optimized for simulation, simulation results obtained as shown in Table 8. From the results we can see that the optimized simulation models to meet the initial set of dynamic premise, to minimize the power system optimization purposes, and this may reduce costs, to some extent, improved vehicle performance. (B) control strategy optimization results EQ6110HEV hybrid electric vehicle is used widely adopted in parallel HEV power-assisted control strategy, the strategy will be electric drive system as a supplemental source, the main engine of a vehicle driven source, using various modes of motor engine optimization work interval, traveling to meet the requirements of car under the conditions to ensure the work of the engine as far as possible, low fuel consumption and low emissions of the ideal region, the electrical output torque of the engine from the "peak", while noting that the battery SOC value will be maintained at a reasonable range. The control strategy in the control parameters in the process of iterative optimization of the situation shown in Figure 3, control strategy optimization results as shown in Table 9, Figure 4 shows the various control parameters and the changes in relations between the target. Figure 5 is the optimal control strategy and the initial control strategy economy comparison. Figure 5 can be seen from the control strategy optimization of the fuel economy of a substantial improvement, 100 km fuel consumption decreased by about 18%. Optimal design achieved the expected goals. V. Conclusion and Outlook This paper EQ6110HEV targeted, systematic analysis of a hybrid electric vehicle design optimization process, including the objective function settings, optimizing the selection of variables, constraints and the selection of optimization algorithm choice, and the final calculation is given optimize the process and results. In addition, the study found that in the simulation, the HEV and fuel economy not only vehicle configuration and control strategy, but also the driving cycle with HEV greater relevance. For EQ6110 such as hybrid bus, traveling because of its relatively fixed routes and road conditions, the paper optimization study conducted by the economic performance of the vehicle upgrade very practical help, but no fixed cycle for driving other types HEV, applicable to the status of the multi-control strategy also to be considered. References: 1. Valerie H. Johnson. Keith B. Wipke and David J. Rausen. HEV ControlStrategy for Realtime Optimization of Fuel Economy and Emissions [J]. SAE 2000 - 01 - 1543 2. Xue Yi. Principle and optimization methods. Beijing: Beijing Industrial University Press, 2001 3. Zhang Xiang, Zhao Han, Qian Li-jun, and so on. Electric Vehicle Design Optimization technology research. Shanghai Automotive, 2004 (6) 4. Long Liang, Zhang Xin, Li Xiu, and so on. Parallel Hybrid Electric Vehicle Powertrain System Simulation Research. North Jiaotong University Journal, 2002, 26 (4)