Series-parallel Hybrid Electric Bus Research Articles

Optimal Energy Management for Series-Parallel Hybrid Electric City Bus Based on Improved Genetic Algorithm

This paper aims at proposing an efficient energy management strategy of the series-parallel hybrid electric bus (SPHEB) by using improved genetic algorithm. Firstly, the energy management strategy based on the logical threshold value is developed. The simulation model considering the vehicle dynamic performance is established by the combination of Matlab and Cruise software. Then, an improved genetic algorithm based on adaptive crossover probability and mutation probability is proposed to solve local convergence and premature convergence. Eventually, Chinese typical city bus driving cycle and the composite driving cycle are considered to show the effectiveness of the proposed energy management strategy in terms of the fuel economy. The results indicate that the fuel consumption are improved by 5.85% and 5.01% respectively, and the parameters obtained by optimizing for the composite driving cycle are more adaptable to the driving conditions and have better economic performance in all driving scenarios.

Adaptive Control Strategy Extracted from Dynamic Programming and Combined with Driving Pattern Recognition for SPHEB

An appropriate control strategy can play an important role in further improving the fuel economy performance of hybrid electric vehicle (HEV). This research developed a novel adaptive control strategy to achieve optimal power distribution for a series-parallel hybrid electric bus (SPHEB) to adapt driving pattern instantaneously. First, a methodology of extracting mode transition control and power distribution strategy from dynamic programming (DP) solution is proposed for the development of the hierarchical energy management strategy. A SPHEB energy management problem under the Chinese typical bus driving schedule at urban district (CTBDS_UD) is investigated as a case study. Second, an approach of driving pattern recognition (DPR) module is developed. For adaptive learning, four typical driving patterns are selected as the database of driving condition and using the extraction method described above to acquire optimal control strategies for four driving patterns. Third, a framework of adaptive control strategy has been proposed based on the extracted hierarchical energy management strategy from DP and combined with DPR. Finally, the simulation results demonstrate the proposed adaptive strategy can make power distribution proper adjustments in real time and be capable of improving significantly the fuel efficiency of the SPHEB.

Simulation Model and Method for Active Torsional Vibration Control of an HEV

Hybrid electric vehicles (HEV) might cause new noise vibration and harshness (NVH) problems, due to their complex powertrain systems. Therefore, in this paper, a new longitudinal dynamic simulation model of a series-parallel hybrid electric bus with an active torsional vibration control module is proposed. First, the schematic diagrams of the simulation model architecture and the active control strategy are given, and the dynamic models of the main components are introduced. Second, taking advantage of the characteristics of hybrid systems, a method of determining the key dynamic parameters by a bench test is proposed. Finally, in a typical bus-driving cycle for Chinese urban conditions, time domain and frequency domain processing methods are used to analyze vehicle body jerk, fluctuation of rotational speed, and torsional angle of the key components. The results show that the active control method can greatly improve the system’s torsional vibration performance when switching modes and at resonance.

Investigation of real-time adaptive energy management strategy for a series-parallel hybrid electric bus

To improve fuel economy of a novel series-parallel hybrid electric bus (SPHEB) further, the control rule of battery power distribution is derived from the offline optimisation algorithm based on Pontryagin's minimum principle, internal relationship between the equivalence fuel factor λ and trend of state of charge (SOC) with different driving condition is established and calculation model of SOC is formed. On the basis of the above, according to the feature of the city bus driving cycle, the algorithm of adaptive fuzzy sliding-mode control (AFSMC) is introduced to periodically update the equivalence fuel factor, and then the power-balancing adaptive control strategy is developed. To validate the proposed strategy rationality, the hardware in-the-loop integrated with bench test is applied to compare the proposed control strategy with the previous optimal control strategy. The results indicate that the efficiency of SPHEB system is improved by the proposed control strategy.

Investigation of real-time adaptive energy management strategy for a series-parallel hybrid electric bus

To improve fuel economy of a novel series-parallel hybrid electric bus (SPHEB) further, the control rule of battery power distribution is derived from the offline optimisation algorithm based on Pontryagin's minimum principle, internal relationship between the equivalence fuel factor λ and trend of state of charge (SOC) with different driving condition is established and calculation model of SOC is formed. On the basis of the above, according to the feature of the city bus driving cycle, the algorithm of adaptive fuzzy sliding-mode control (AFSMC) is introduced to periodically update the equivalence fuel factor, and then the power-balancing adaptive control strategy is developed. To validate the proposed strategy rationality, the hardware in-the-loop integrated with bench test is applied to compare the proposed control strategy with the previous optimal control strategy. The results indicate that the efficiency of SPHEB system is improved by the proposed control strategy.

Study on Energy Management Strategies for Series-parallel Plug-in Hybrid Electric Buses

Abstract The power flow between the power sources and the performance of plug-in-hybrid electric vehicles (PHEV) significantly in fuel economy are mainly determined by energy management strategy. To research the energy management of the PHEV, the control strategy of a plug-in series-parallel hybrid electric bus (PHEB) is studied in this paper. Firstly, the simulation model of the PHEB is built with CRUISE software. Secondly, to explore the fuel-saving potential of the PHEB, the global optimal control strategy which adopts dynamic programming (DP) algorithm is studied and built with MATLAB software, although the minimum fuel consumption of certain driving cycle can be a benchmark for evaluating the energy management strategy of PHEB, the global optimal control strategy based on DP is rarely directly applied in real vehicle due to its long online computation time. At last, to ensure control timely, a rule-based control strategy is calibrated according to the computation results of global optimal control strategy. The simulation results show that, the fuel consumption per 100 km decreased by 9.11% and the electric power consumption per 100 km decreased by 6.27%.

Powertrain Simulation Research of Coaxial Series-Parallel Hybrid Electric Bus

In order to know the vehicle performance of coaxial series-parallel hybrid electric bus, on the basis of the analysis of the powertrain’s structure and operation mode, the control strategy is made, the simulation system is introduced by the secondary development of Advisor, the simulation result is analyzed and used. Through the analysis of vehicle dynamic performance, fuel economy, SOC value change of super capacitance, and the torque relations of main components, it is known that coaxial series-parallel hybrid electric bus can satisfy the requirements of the city bus’ working condition, and the operation relations between the power components can be mastered, which provides the technical support for the coaxial series-parallel hybrid electric vehicle development.

The Development and Verification of a Novel ECMS of Hybrid Electric Bus

This paper presents the system modeling, control strategy design, and hardware-in-the-loop test for a series-parallel hybrid electric bus. First, the powertrain mathematical models and the system architecture were proposed. Then an adaptive ECMS is developed for the real-time control of a hybrid electric bus, which is investigated and verified in a hardware-in-the-loop simulation system. The ECMS through driving cycle recognition results in updating the equivalent charge and discharge coefficients and extracting optimized rules for real-time control. This method not only solves the problems of mode transition frequently and improves the fuel economy, but also simplifies the complexity of control strategy design and provides new design ideas for the energy management strategy and gear-shifting rules designed. Finally, the simulation results show that the proposed real-time A-ECMS can coordinate the overall hybrid electric powertrain to optimize fuel economy and sustain the battery SOC level.

Global Optimal Energy Management Strategy Research for a Plug-In Series-Parallel Hybrid Electric Bus by Using Dynamic Programming

Energy management strategy influences the power performance and fuel economy of plug-in hybrid electric vehicles greatly. To explore the fuel-saving potential of a plug-in hybrid electric bus (PHEB), this paper searched the global optimal energy management strategy using dynamic programming (DP) algorithm. Firstly, the simplified backward model of the PHEB was built which is necessary for DP algorithm. Then the torque and speed of engine and the torque of motor were selected as the control variables, and the battery state of charge (SOC) was selected as the state variables. The DP solution procedure was listed, and the way was presented to find all possible control variables at every state of each stage in detail. Finally, the appropriate SOC increment is determined after quantizing the state variables, and then the optimal control of long driving distance of a specific driving cycle is replaced with the optimal control of one driving cycle, which reduces the computational time significantly and keeps the precision at the same time. The simulation results show that the fuel economy of the PEHB with the optimal energy management strategy is improved by 53.7% compared with that of the conventional bus, which can be a benchmark for the assessment of other control strategies.

Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus

Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

Configuration design, energy management and experimental validation of a novel series-parallel hybrid electric transit bus

This paper aims to present the configuration design approach and the energy management strategy (EMS) of a series-parallel hybrid electric transit bus (SPHEB) jointly developed by Shanghai Automotive Industry Co. Ltd. (SAIC) and Shanghai Jiao Tong University (SJTU), China. A major feature of this SPHEB is that a novel manual transmission is designed to switch the powertrain configuration between series and parallel types. To reduce the fuel consumption as well as sustain the battery state of charge, an EMS including seven energy flow modes is designed and applied to this SPHEB. Governed by this EMS, the engine is maintained to operate in high efficiency regions. The experimental test carried on the transit bus city driving cycle is described and analyzed. The experimental results demonstrate the technical feasibility and fuel economy of this approach.

Predictive energy management of a 4QT series-parallel hybrid electric bus

This paper presents a novel predictive control scheme for a series-parallel hybrid bus. The proposed scheme uses information from GPS together with a data record of the driving along the bus route to schedule the charging and discharging of the energy storage system. Switching between hybrid and pure electric mode is optimized in a receding horizon scheme based on a prediction model that reflects the uncertainty of the future driving. The benefits of the proposed predictive control scheme are shown by a simulation study on measured driving data along a bus route. The simulations show that the predictive control scheme achieves both lower fuel consumption and better control of the energy storage system than can be achieved with a non-predictive controller.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex construction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge(SOC) is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an "optimal line" typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.