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.

Modeling the Prospects of Plug-In Electric Buses to Reduce GHG Emissions and Cost While Meeting Route Demands: A Case Study of the “Unitrans” Bus Fleet Serving the Davis, California Urbanized Area

As university campuses look to decrease their greenhouse gas emissions, plug-in electric buses may provide a low carbon alternative to conventionally fossil-powered buses. This study investigates the viability for Unitrans, the bus service for the greater Davis area and the university campus, to replace current compressed natural gas buses with plug-in electric versions. This study presents an inventory of market available electric buses, their associated costs, incentives, and infrastructure concerns, and compares projected energy use, net present cost, and greenhouse gas emissions with their CNG counterparts. ADVISOR vehicle simulation software is used to estimate the energy use of a typical electric bus (New Flyer Xcelsior XE40 300 kW) and compare to the current CNG model (Orion V) along an actual Unitrans route. The model estimates that the selected bus can travel 146 miles on a single charge, with a fuel economy of 1.75 kWh per mile, which meets the service requirements. Results for bus replacement schedules between 5 and 49 in the 12-year analysis period indicate that between 1600 and 22,000 MT of carbon can be avoided. The net present cost analysis indicates that the potential savings from the replacement of a single CNG bus with an electric bus (with available incentives) ranges from $146,000 - $211,000 per bus over its lifetime, depending on infrastructure costs.

Multi-objective Collaborative Optimization for the Lightweight Design of an Electric Bus Body Frame

To analyze the rollover safety,finite element models were established for the electric bus body frame,rollover simulation platform,living space,and bus rollover.The strength and stiffness of the body frame were calculated under four typical work-ing conditions considering the main low-order elastic modal characteristics.The results indicate that the initial body frame of the electric bus satisfies the required structural strength,stiffness,modes,and rollover safety,and it has great potential for lightweight design.Sensitivity and structural contribution analyses were performed to determine the design variables for lightweight optimization of the body frame,and a mathematical model was established for multi-objective collaborative optimization design of the electric bus.Then,the radial basis function neural network was used to approximate the optimiza-tion model.Besides,the accuracy of the approximate model was verified,and the non-dominated sorting genetic algorithm II was employed to determine solutions for the lightweight optimization.Compared with the initial model,the mass of the optimized model is reduced by 240 kg(9.0%)without any changes in the materials of the body frame.

Battery electric buses charging schedule optimization considering time-of-use electricity price

Purpose–This paper aims to optimize the charging schedule for battery electric buses(BEBs)to minimize the charging cost considering the time-ofuse electricity price.Design/methodology/approach–The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model.The objective is to minimize the total charging cost of the BEB fleet.The charge decision of each BEB at the end of each trip is to be determined.Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.Findings–This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line.The results show that the total charge cost with the optimized charging schedule is 15.56%lower than the actual total charge cost under given conditions.The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent,which can provide a reference for planning the number of charging piles.Originality/value–Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Optimal charging plan for electric bus considering time-of-day electricity tariff

Purpose–The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day(TOD)electricity tariff,to reduce electricity bill.Design/methodology/approach–Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed,to minimize the electricity costs of daily operation of an electric bus.The charging time is taken as the optimization variable.The TOD electricity tariff is considered,and the energy consumption model is developed based on real operation data.An optimal charging plan provides charging times at bus idle times in operation hours during the whole day(charging time is 0 if the bus is not get charged at idle time)which ensure the regular operation of every trip served by this bus.Findings–The electricity costs of the bus route can be reduced by applying the optimal charging plans.Originality/value–This paper produces a viable option for transit agencies to reduce their operation costs.

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.

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption(TEC)estimation model for the electric bus(EB)fleet planning,operation,and life-cycle assessment.Leveraging the vast variations of temperature in Jilin Province,China,real-world data of 31 EBs operating in 14 months were collected with temperatures fluctuating from
27.0 to 35.0℃.TEC of an EB was divided into two parts,which are the energy required by the traction and battery thermal management system,and the energy required by the air conditioner(AC)system operation,respectively.The former was regressed by a logarithmic linear model with ambient temperature,curb weight,travel distance,and trip travel time as contributing factors.The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square.The latter was estimated by the operation time of the AC system in cooling mode or heating mode.Model evaluation and sensitivity analysis were conducted.The results show that:(i)the mean absolute percentage error(MAPE)of the proposed model is 12.108%;(ii)the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling;(iii)the MAPE has a 1.746% reduction if considering passengers’boarding and alighting.

Optimal operating strategy of short turning lines for the battery electric bus system

Operating short turning line is an efficient strategy to satisfy the unevenly distributed demand during peak periods while reducing operational cost.However,for the battery electric bus(BEB)system,the application of the strategy is challenging due to the disadvantages of BEBs,such as limited driving mileage and long charging time.Improper vehicle configuration and charging scheduling may dramatically increase the operational cost and cut the benefits of these strategies.In this work,we propose a general framework to design an effective short turning strategy for the BEB system at a tactical planning level.First,the trade-off relationship between the battery capacity and the average trip time is identified by modeling the BEBs operations.Second,a microeconomic model is formulated to jointly optimize the frequencies and charging schedules of the whole bus line and the short turning line,to effectively minimize passengers’waiting time and operational cost.Finally,numerical experiments have been carried out for an illustrative linear line to demonstrate the potential benefits of the sub-line operating strategy compared with the normal operation.

Reducing input/output current ripple for driving converter by multiple technology

To improve the vehicle dynamic performance and ultra-capacitor operating circumstance,this paper studied the multi-current-two-quadrant converter applied to drive high power DC motor in ultra-capacitor electric bus(UCEB).Compared with normal current-two-quadrant converter,the multi-current-two-quadrant converter can reduce the motor armature current ripple and the ultra-capacitor current ripple.Moreover,it improves power capabilities,reliability and fault tolerant capability of driving system.After analyzing the structure and working principle of the multi-current-two-quadrant converter,the expressions of armature current ripple and the quantitative relationships between the ultra-capacitor power loss and duty cycle were derived.The simulation and experimental results showed that the multi-current-two-quadrant converter has great advantages in reducing the armature current ripple and ultra-capacitor power loss,which can improve the vehicle performance and overall efficiency.

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Purpose–The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit,with explicit consideration of heterogenous charging modes.Design/methodology/approach–The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously.Specifically,at the operational level(i.e.lower level),the service schedule and recharging plan of electric buses are optimized under specific design of charging station.The objective of lower-level model is to minimize total daily operational cost.This model is solved by a tailored column generation-based heuristic algorithm.At the tactical level(i.e.upper level),the design of charging station is optimized based upon the results obtained at the lower level.A tabu search algorithm is proposed subsequently to solve the upper-level model.Findings–This study conducted numerical cases to validate the applicability of the proposed model.Some managerial insights stemmed from numerical case studies are revealed and discussed,which can help transit agencies design charging station scientifically.Originality/value–The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.