Investigation Of Power-Balancing Energy Management Control Strategy For A Series-parallel Hybrid Electric Bus

With the energy becoming less and less, the fuel comsuption of urban transportation is required more and more, therefore this contradiction is turn into the internal drives and exterior pressures to investigate and develop the new energy sources vehicles. The various elements should be taken into the comprehensive consideration, such as the industrialization maturity and feasibility of all kind muti-energy vehicle technology, so far the hybrid electric vehicle provide enough efficiency improvement and keep the character inherit from the traditional vichle, and not only has gradually been the research focus of science and industry but also with great potential and promising prospect. The series hybrid electric vehicles can be prodived with certain adantages to improve the fuel efficiency of engine when operating on low speed condition, and while the parallel hybrid electric vehicle with the excellence to drive powerfully on high speed or laod condition. Logically, the series-parallel electric powertrain with both merit of the series and parallel, so it is adaptive to various driving cycle for a series-parallel HEV, especially, it is suitable no more than as an urban transportation vehicle. The reason to select the series-parallel for the optimal choice is its best fuel economy and high fuel efficiency, and it is very important to to acheave this purpose by coordinated controlling between the muti-energy components. Muti-energy management control strategy is a time varying,nonlinear and multidimensional model involving decision making of complex problem, thery are derived from the complexity architecture of the hybrid powertrain itself and the synergetic operation of different components, it is difficult to construct an accurate mathematical model of the hybrid powertrain; Another reason is the unpredictability of driving conditions and driver's operation and the difficulty of driving intention judgment resulted from the diversity of driving style enhance the difficulty of the conresponding control strategy for the engineer. In a word, energy management control strategy, as one of the key techiques of HEV, is the algorithm to realize vehicle energy management and power distribution control for the powertrain; it is unique for the corresponding configuration.For the sake of improving fuel economy of a series-parallel hybrid electric bus (SPHEB), this dissertation addresses the vehicle optimal energy managenment and design of the control strategy, and the main contents may be briefly summed up as follows: according to the characteristic of the novel series-parallel architecture, based on the power matching,the theoretical model and data model of key components and kinetic model, an energy management is proposed to expect applied to the engineering practice, which is focus on power balancing distribution control strategy between engine and battery not only advance the engine operating efficiency but also take the battery efficiency into consideration. Aim to the purpose, the equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function that is to minimize the fuel consumption at each sampled time, an optimal control law is proposed based on Principle of Pontryagin, and then proved to be the theoretic model of equivalent consumption minimization strategy, then a global optimization control strategy based on dynamic programming is proposed. Since there are many factors are great influence on the fuel economy of vehicle in real world, such as unpredictability of driving conditions and diversity of driving style. A Markov model for driver's power requirements based on statistical data from several driving cycle, an optimal power management control strategy based on stochastic dynamic programming (SPD) is proposed, and then an adaptive supervisory control strategy for the battery management is developed. At last, an integrated control strategy that combined the design rules are derived from the results of DP with the driving pattern recognition approach. To validate the proposed strategy effective and reasonable, simulated test based on a forward model, hardware-in-the-loop test and real-world test are carried out. Power matching and parametric design are the fundament of energy management of HEV to explore, an approach of parameter matching of HEV based on Chinese transit bus city driving cycle is developed, so the major power-train components, such as engine, integrated starter generator, traction motor, battery and final drive gear ratio, are selected to meet the requirements on dynamic performance and economic performance from the results of parameter matching method. A forward simulation model of the series-paralle hybrid electric bus is construsted based on Matlab/Simulink software make use of empirical modeling approach and combine with the aid of theoretical modeling. It provides the essential simulated platform for the exploration of energy management control stragety.Analysis the operation mode of the series- parallel hybrid electric city bus power-train system, and according to the character of its configuration, control strategy model of power balancing distribution between engine and battery not only advance the engine operating efficiency but also take the battery efficiency into consideration is proposed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function that is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. There are three algorithms applied to seek for the optimal fuel economy, they are Principle of Pontryagin, quivalent consumption minimization strategy and dynamic programming. The simulation results indicate that the fuel economy of proposed global optimization control strategy is higher than the instantaneous optimization control strategy, and also advance 34% than the prototype city bus.In order to satify the adaptability of diverse driving style, a markov model for driver's power requirements based on statistical data from several driving cycle is construsted, and an optimal power management control strategy based on stochastic dynamic programming (SPD), this algorithm consists of two successive steps, namely, policy evaluation and policy improvement, repeated iteratively until convergence. For each possible SOC and velocity state, the policy iteration be intuitively interpreted as the expected cost function value averaged over a stochastic distribution of drive cycles starting at that state. The obtained control law is in the form of a stationary full-state feedback and can be directly implemented.Although overcome the limitation of DP algorithm and SDP method will still suffer from"curse of dimensionality"in EMS design.That is,the computation and memory needed by value iteration and policy iteration will increase exponentially with the number of states.This will limit its application in engineering.To deal with this problem, the method ofλestimation is introduced. The method is the Equivalent fuel Consumption Minimization Strategy based on driving pattern recognition in essence. The main idea of adaptive real time control strategy is periodically updating the equivalence factor dependence on the corresponding driving condition. It is assumed that information about the route is available in advance. Using this knowledge, global optimization methods can be used in real-time control to approach optimal fuel consumption while keeping the state of charge (Soc) of the batteries at a desired level. The measured fuel consumption and the obtained battery Soc trajectory demonstrate good performance of the proposed adaptive control.An approach of designing SPHEB real-time energy management strategy was derive from the offline global optimization results. The Macro-distribution rules of SPHEB powertrain energy flow under various optimal control and the powertrain operating mode switching rules and power distribution rules were designed, throuth the method of statistical analysis and multivariate nonlinear regression. From the correlation analysis of regression formula, there is a conclution that: the formula regressed by the Levenberg-Marquardt algorithm, whose calculated values was highly relevant to the optimized values, could be used to make an engine operating map for optimizing the distribution of powertrain energy flow. Under the guidance of the concept of"driver-vehicle-road", the integrated control strategy which is constitued by the rules of driving pattern recognition and driver intention combined with the energy management contrl strategy designed above. The simulation results show that the driving pattern recognition based control strategy can be adaptive to the various driving cycle and indicate a good application prospect.Last, the dissertation carried out a series of tests to validate the proposed power-balancing control strategy for series-parallel hybrid electric bus. Both the hardware in the loop simulation and real-world test are adopted to collect the information about fuel consumption and drive performance. And some opinions for further improvement of performances of the system were given. The conclutions of the test are given as following: under the control of proposed strategy, the series-parallel hybrid electric bus not only can be satisfied the drive performance but also achieve the improvement of fuel economy by comparing with the prototype bus up to 23.73%.