Abstract
Energy management strategies based on instantaneous optimization have been widely used in hybrid/plug-in hybrid electric vehicles (HEV/PHEV) in order to improve fuel economy while guaranteeing vehicle performance. In this study, an adaptive-equivalent consumption minimum strategy (A-ECMS) based on target driving cycle (TDC) generation was proposed for an extended-range electric bus (E-REB) operating on fixed routes. Firstly, a Hamilton function and a co-state equation for E-REB were determined according to the Pontryagin Minimum Principle (PMP). Then a series of TDCs were generated using Markov chain, and the optimal solutions under different initial state of charges (SOCs) were obtained using the PMP algorithm, forming the optimal initial co-state map. Thirdly, an adaptive co-state function consisting of fixed and dynamic terms was designed. The co-state map was interpolated using the initial SOC data and the vehicle driving data obtained by an Intelligent Transport System, and thereby the initial co-state values were solved and used as the fixed term. A segmented SOC reference curve was put forward according to the optimal SOC changing curves under different initial SOCs solved by using PMP. The dynamic term was determined using a PI controlling method and by real-time adjusting the co-states to follow the reference curve. Finally with the generated TDCs, the control effect of A-ECMS was compared with PMP and Constant-ECMS, which was showed A-ECMS provided the final SOC closer to the pre-set value and fully used the power of the batteries. The oil consumption solutions were close to the PMP optimized results and thereby the oil depletion was reduced.
Highlights
Literature ReviewEnvironmental deterioration and the increasing shortage of petroleum resources have greatly increased the demand for energy-saving and environmental protective vehicles
The motivation for this is explained as follows: (1) to solve the optimal co-state value, the Hamilton function and co-state equation for extended-range electric bus (E-REB) should be developed based on Pontryagin Minimum Principle (PMP); (2) the co-state is affected by driving distance and working conditions, so in order to establish the relationship between the co-state and its influencing factors, the target driving cycles are needed, there is often a lack of TDCs in practice
To ensure the sufficient use of electric energy and reduce fuel consumption while ensuring the performances of an extended-range electric bus, an adaptive-equivalent consumption minimum energy management strategy is proposed based on target driving cycles generation: (1) With the collected data and representative driving cycles, the target driving cycles are generated by a Markov chain approach and used to train the optimal initial co-state map and validate the simulations
Summary
Environmental deterioration and the increasing shortage of petroleum resources have greatly increased the demand for energy-saving and environmental protective vehicles. Dynamic programming (DP), another global algorithm, divides the whole working condition into several segments, and starting from the final state, reversely calculates the initial state and selects the controlling rule that makes the target function reach the minimum value as the optimal strategy [11,12]. The artificial intelligent algorithms for energy management include neural network control [15], particle swarm optimization [16], and genetic algorithm [17,18] These Artificial Intelligence-strategies are faced with the problem of large amount of data training and too much computation in real-time operation. Researchers have proposed adaptive-ECMS (A-ECMS) which is developed on the basis of ECMS It can adjust the co-state value of Hamilton function in real time according to the operation state of the vehicle. Because A-ECMS has the characteristics of strong adaptability, good real-time performance and excellent control effect, it is selected as the energy management strategy of this paper
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