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.

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.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

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.

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 co nstruction 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.

Design and Strategy of Series-Parallel Hybrid System Based on BSFC

In this paper,a drive control strategy is developed based on the characteristics of series-parallel plug-in hybrid system.Energy management strategies in various modes are established with the basis on the minimum brake specific fuel consumption(BSFC)curve of engine.The control strategy,which is based on rules and system efficiency,is adopted to determine the entry/exit mechanisms of various modes according to battery state of charge(SOC),required power and required speed.The vehicle test results verify that the proposed control strategy can improve vehicle economy efficiently and makes a good effect on engine control.

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.

Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

Study of Series-Parallel and Power-Split DHT for Hybrid Powertrains

Dedicated hybrid transmission(DHT)is the developing trend of hybrid transmissions.This paper studies different types of DHTs regarding the structure,function,and performance.Firstly,the structure and function of different well-known DHTs in the market are discussed and analyzed.Through the analysis,the reasonability and necessity of the different modes and the electric motor power requirements that lie behind different DHTs are derived.Secondly,the dynamics of different DHTs are further compared with the driving areas in different modes under different driving conditions.Then,the basic or minimum dynamic requirements are defined and taken into consideration before the economic comparison.To ensure the effectiveness of the comparison,the optimization of the configurations for each kind of DHT is carried out with the modular simulation model.The economic comparison is conducted under the premise of excluding the influences of the configurations in the results.Finally,the sensitivity and influence of different engine characteristics for different DHTs are studied to find out the sensibilities of the two kinds of DHTs with engine technology.Through these studies,the essential differences and features of different DHTs can be derived to help better understand the decision in choosing the technical route for the original equipment manufacturers.