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.

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.

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.

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.