Implementable Strategy Research of Brake Energy Recovery Based on Dynamic Programming Algorithm for a Parallel Hydraulic Hybrid Bus

The purpose of this paper is to develop an implementable strategy of brake energy recovery for a parallel hydraulic hybrid bus. Based on brake process analysis, a dynamic programming algorithm of brake energy recovery is established. And then an implementable strategy of brake energy recovery is proposed by the constraint variable trajectories analysis of the dynamic programming algorithm in the typical urban bus cycle. The simulation results indicate the brake energy recovery efficiency of the accumulator can reach 60% in the dynamic programming algorithm. And the hydraulic hybrid system can output braking torque as much as possible.Moreover, the accumulator has almost equal efficiency of brake energy recovery between the implementable strategy and the dynamic programming algorithm. Therefore, the implementable strategy is very effective in improving the efficiency of brake energy recovery.The road tests show the fuel economy of the hydraulic hybrid bus improves by 22.6% compared with the conventional bus.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

MPC‑Based Coordinated Control of Gear Shifting Process for a Power‑split Hybrid Electric Bus with a Clutchless AMT

Two-speed clutchless automated manual transmission(AMT)has been widely implemented in electric vehicles for its simple structure and low cost.In contrast,due to the complex response characteristics of powertrain,utilizing clutchless AMT in a hybrid power system comes with complex coordination control problems.In order to address these issues,a power-split hybrid electric bus with two-speed clutchless AMT is studied in this paper,and a coordinated control method based on model predictive control(MPC)is used in gear shifting control strategy(GSCS)to improve gear shifting quality and reduce system jerk.First,the dynamic model of power sources and other main powertrain components including a single planetary gear set and AMT are established on the basis of data-driven and mechanism modeling methods.Second,the GSCS is put forward using the segmented control idea,and the shifting process is divided into five phases,including(I)unloading of drive motor,(II)shifting to neutral gear,(III)active speed synchronization by drive motor,(IV)engaging to new gear,and(V)resuming the drive motor’s power,among which the phases I and V have evident impact on the system jerk.Then,the MPC-based control method is adopted for these phases,and the fast compensation of driving torque is realized by combining the prediction model and quadratic programming method.The simulation results show that the proposed GSCS can effectively reduce shift jerk and improve driving comfort.This research proposes a coordinated control strategy of two-speed clutchless AMT,which can effectively improve the smoothness of gear shifting and provides a reference for the application of two speed clutchless AMT in power-split hybrid powertrains.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Control system design of hybrid fuel cell city bus

This paper introduced the design of the hybrid powertrain of the Fuel Cell City Bus demonstrated in 2008 Beijing Olympic Games. The configuration of the hybrid fuel cell powertrain was introduced. The safety of hydrogen storage and delivery system, the hydrogen leakage alarm system were developed. The real-time distributed control and diagnosis system based on the Time Trigger Controller Area Network （TTCAN） with 10 ms basic control period was developed. The concept and implementation of processor （or controller） monitor and process （or task） monitor technique based on the TYCAN were applied in this paper. The fault tolerant control algorithm of the fuel cell engine and the battery man- agement system were considered. The demonstration experience verified that the fault tolerant control was very important for the fuel cell city bus.

A high-fidelity memory scheme for quantum data buses

A novel quantum memory scheme is proposed for quantum data buses in scalable quantum computers by using adjustable interaction. Our investigation focuses on a hybrid quantum system including coupled flux qubits and a nitrogen–vacancy center ensemble. In our scheme, the transmission and storage（retrieval） of quantum state are performed in two separated steps, which can be controlled by adjusting the coupling strength between the computing unit and the quantum memory. The scheme can be used not only to reduce the time of quantum state transmission, but also to increase the robustness of the system with respect to detuning caused by magnetic noises. In comparison with the previous memory scheme, about 80% of the transmission time is saved. Moreover, it is exemplified that in our scheme the fidelity could achieve 0.99 even when there exists detuning, while the one in the previous scheme is 0.75.

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 carded on the transit bus city driving cycle is described and analyzed. The experimental results demonstrate the technical feasibility and fuel economy of this approach.

Korea＇s Future Policy Recommendation for the Promotion of Eco-Friendly Vehicles in Metropolitan Area

This paper introduces and analyzes Korea＇s NGV （natural gas vehicles） policy for soot-free bus fleet which intends to promote CNG （compressed natural gas） bus in metropolitan area for the reduction of air pollution from road sector. At the early stage, Korean goverrmaent established various supporting policy systems to encourage public transportation companies to purchase CNG buses as a means to replace diesel buses. It was evaluated as very successful with making net economic benefit of CNG bus promotion policy. During the 2nd stage, Korean government implemented CNG hybrid bus promotion policy to further reduce both air pollution and greenhouse gas. Now, a new social demand for the vehicles is zero-emission vehicles. The author asserts that current FCEV （fuel cell electric vehicle） should be considered as an alternative to zero-emission vehicles in Korea and suggests policy recommendation for the promotion of FCEV by referring the current CNG bus promotion policy in public transportation sector.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus(PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based(RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based(DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge(SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming(DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm(GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

Multi-objective parameter optimization for a single-shaft series-parallel plug-in hybrid electric bus using genetic algorithm

Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.

Timetable optimization for single bus line based on hybrid vehicle size model

This study proposes a flexible timetable optimization method based on hybrid vehicle size model to tackle the bus demand fluctuations in transit operation. Three different models for hybrid vehicle, large vehicle and small vehicle are built in this study, respectively. With the operation data of Shanghai Transit Route 55 at peak and off-peak hours, a heuristic algorithm was proposed to solve the problem. The results indicate that the hybrid vehicle size model excels the other two modes both in the total time and total cost. The study verifies the rationality of the strategy of hybrid vehicle size model and highlights the importance of the adaptive vehicle size in dealing with the bus demand fluctuation. The main innovation of the study is that unlike traditional timetables, the arrangement of the scheduling interval and the corresponding bus type or size are both involved in the timetable of hybrid vehicle size bus mode, which will be more effective to solve the problem of passenger demand fluctuation. Findings from this research would provide a new perspective to improve the level of regular bus service.

A novel downshifting strategy based on medium-time-distance information for hybrid electric bus

Vehicle downshifting during braking for the hybrid electric vehicle(HEV) equipped with the automatic mechanical transmission(AMT) could adjust work points of the motor. Thus, downshifting has great potential to effectively improve the efficiency of braking energy recovery. However, the power interruption during shifting could cause some loss of regenerative energy meanwhile.Hence, the choice of the downshifting point during vehicle braking which has crucial effect on energy recovery efficiency needs to be intensively studied. Moreover, the real-time application of the high-efficiency braking energy recovery strategy is a challenging problem to be tackled. Therefore, this paper proposes a dynamic-programming-rule-based(DPRB) downshifting strategy for a specific hybrid electric bus(HEB) driving condition. Firstly, the braking characteristic of the HEB during the process of pulling in is analyzed. Secondly, the medium-time-distance(MTD) demonstrating the dimension of time and space is proposed to define the boundary condition of the running bus. Then, look-up tables are established based on a dynamic programming algorithm offline using multiple sets of historical data. Thus, Based on the real-time driving data, whether to enter the optimal gear selection process can be decided online. Finally, simulations and hardware-in-the-loop(HIL) tests are carried out, and the results show that the proposed method can be indeed effective for braking energy recovery.