Hybrid Electric Bus Research Articles

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition

The rapid development of cloud techniques like Vehicle-to-Cloud (V2C) makes it possible to gather more information and develop computationally efficient energy management systems (EMS) for electric vehicles. This paper proposes a novel EMS with low computational cost targeting hybrid battery/ultracapacitor electric buses to reduce energy consumption and battery life degradation. In the offline training process, by applying the K-means clustering method with 10 selected features, 16 typical driving conditions are classified. For each driving condition, dynamic programming is employed offline to generate global optimal results, which are then used in control rule extraction for online operation. During the online operation, the proposed EMS executes the designed driving pattern recognition algorithm with V2C assistance to select optimal control rules. The simulation results indicate that the proposed EMS effectively decreases the battery degradation and energy consumption cost by 13.89%, compared with the conventional rule-based strategy. In addition, it is shown that V2C assistance leads to a 6.81% lower cost. Besides, the robustness of the proposed EMS is validated by testing the EMS with highly randomized input with uncertainties up to 15% and long duration of V2C data packet loss up to 10 s.

Experimental Research on Braking Feedback and Taxiing Feedback System of New Energy Vehicles

This paper took a new energy vehicle feedback system as the research object, aiming to study the energy recovery law of the new energy vehicle under braking feedback and taxiing feedback conditions. Firstly, the braking energy feedback control strategy and different forms of taxiing energy feedback were studied. Then the integration and application of braking energy recovery system were carried out on a pure electric bus and a hybrid electric bus, with each vehicle model corresponding to different integration and test schemes, which provided a guarantee for the relevant test of real vehicle environments. Finally, relevant vehicle experiments were carried out to test the impact of superposition and coordination strategies on the contribution rate of braking energy recovery under a typical Chinese city bus circle and compared the difference in vehicle energy consumption with and without taxi feedback strategy. The test results showed that the coordinated braking energy recovery control strategy can make more effective use of the maximum torque that can be fed back by the motor, and the fuel consumption of the taxiing feedback mode was lower than that of the no taxiing feedback mode under different driving conditions.

Fuel economy‐oriented trajectory tracking control strategy for plug‐in hybrid electric buses

AbstractMost of the existing trajectory tracking control strategies for vehicles aim at achieving high tracking accuracy but ignore energy consumption and road geometry. This paper proposes a fuel economy‐oriented trajectory tracking control strategy for plug‐in hybrid electric buses to improve fuel economy while ensuring tracking accuracy under different road geometries in the real world. First, a reference trajectory is reconstructed for trajectory tracking control to simulate the road geometry in real world. Next, a tracking controller based on model predictive control and proportional–integral control is developed to track the reference trajectory. Finally, an optimal control strategy with target preview method based on Pontryagin's minimum principle is developed to achieve the optimal fuel economy while tracking the trajectory. Simulation results illustrate that the proposed control strategy can realize not only excellent trajectory tracking performance but also optimal fuel economy.

Determinants of Electric Vehicle Diffusion in China

We analyze the effect of four determinants of electric vehicle diffusion in China for a panel of 31 regions for the period 2010–2016. We analyze diffusion of four different electric vehicle types, namely battery electric cars and buses as well as plug-in hybrid electric cars and buses. System GMM panel estimation results show that total monetary subsidies have a positive effect only on the diffusion of battery electric cars. A closer look reveals that subsidies provided by regional governments are decisive for all types of vehicles but the subsidy provided by the central government and its degression over time dilute the overall effect of subsidies and is partly detrimental. Non-monetary ownership policies, such as license-plate lotteries, show a positive effect only for battery electric cars. Availability of public charging infrastructure increases diffusion of all vehicle types. Charging points are relevant for cars, while charging stations are especially decisive for the diffusion of electric buses. Using local environmental conditions as a novel determinant for the diffusion of electric vehicles reveals that the local air pollution influences the diffusion of buses, but not of cars.

On the estimation of optimal state-of-charge trajectory for plug-in hybrid electric buses using trip information

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

Emission Characteristics of Hybrid Electric Bus During Peak Period Based On Remote Monitoring

Based on the OBD remote monitoring system, the real-time data of Tianjin 607 bus is collected, and two representative groups of data are selected for analysis. The results show that the vehicles start and stop frequently during peak period, the frequency of electric motor use increases from 27.72% in off-peak period to 53.72% in peak period, the average driving speed is faster than usual, and the drivers tend to drive more intensely for catching up time. Under the same working conditions, the frequency of electric motor use is positively correlated with the emission reduction benefits brought by the use of hybrid electric vehicles. Under different road conditions, especially in the peak operation, the exhaust temperature is low, which has a greater impact on the actual emission reduction benefits of the electric motor. The key to fundamentally alleviate the emission problem of bus in peak period is to improve the vehicle operation conditions and the conversion rate of SCR system.

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

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

Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus

Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi–Sugeno approach and combine this model with an H_{\\infty } state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures.

Data-driven predictive energy management and emission optimization for hybrid electric buses considering speed and passengers prediction

The energy-saving and emission reduction potential of hybrid electric vehicles are of great significance to the environment’s sustainable development. The trade-off between energy consumption economy and environmental friendliness is essential. To promote efficiency while reducing the emission of hybrid electric buses (HEB), we propose a novel predictive energy management strategy with passenger number prediction and exhaust emission optimization. An integrated prediction of vehicle speed and passenger number based on deep learning is proposed to predict future power demand accurately. An emission penalty is introduced into the objective function. Then, the impacts of passenger number prediction and the penalty on energy consumption and exhaust emissions are discussed. Simulation results show that the deep neural network predictor performs better in predicting speed and passenger number than Markov chain and radial basis function neural network predictors. The proposed energy management’s energy efficiency reaches 97.02% of global dynamic programming and 2.49% higher than that of instantaneous optimal control. With the exhaust emission optimization, CO2, CO, NOx, and HC emissions are reduced by 6.22%, 10.51%, 6.3%, and 4.83%, respectively, while the energy consumption cost is only increased by 1.34%. The proposed approach is verified to be environmentally friendly and energy-saving.

Robustification Through Minimax Dynamic Programing and Its Implication for Hybrid Vehicle Energy Management Strategies

Abstract Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programing (SDP) controller with minimax dynamic programing (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a tradeoff of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning

In this paper, a novel fuel cell engines friendly real-time energy management is proposed, which considers the intersection speed planning to reduce frequent load change conditions in the process of driving. The main advantage of this energy management is that it can improve road transportation efficiency, promote the goal of minimum hydrogen consumption and extend the service life of the fuel cell engines. For the intersection speed planning method, the information of the vehicle in front of the driving route and the traffic signal light states are considered based on dynamic programming (DP). With the intersection speed planning, the corresponding control variable is applied for the model predictive control (MPC) based energy management when the bus is 100m away from the traffic light. The simulation results show that the equivalent hydrogen saving rate can improve by approximately 3.04% and reduce 3.4% idle working conditions compared with the MPC based without intersection speed planning energy management. The hardware in the loop test show that the vehicle speed can follow the target speed, and the equivalent hydrogen consumption error is within 2.5%, which meets the allowable error range.

Battery sizing for plug-in hybrid electric buses considering variable route lengths

For plug-in hybrid electric buses, optimizing the battery size is beneficial for lowering the capital cost and improving the efficiency of energy split between multiple power sources. In addition to the energy management strategy, the optimization of battery size is highly dependent on expected trip mileage. For a fleet of urban buses, the travel distance varies between different routes or even within the same route due to random events. The strategy proposed in this paper addresses uncertainty in route length by a method that coordinates the energy management strategy and battery aging to determine optimal battery size. The route length is assumed to follow a normal distribution, and the overall speed profile along the route is composed of basic driving cycles established using a Markov chain model. The co-optimization of battery size, energy management, and battery aging is formulated as a convex programming problem. The optimal battery size for a given expectation value of route length is identified by minimizing the total weighted cost of energy consumption and battery aging over the whole route length distribution. The results, when assuming a variable route length and calculating a weighted cost, are significantly different compared to the case when a fixed route length is used. When optimal depth-of-discharge is not considered, the weighted cost versus route length has a shallow parabolic shape, and an optimal battery size corresponding to minimum weighted cost can be identified. If the optimal depth-of-discharge is taken into account, the optimal battery size is roughly constant versus route length.

An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications

The energy management strategy (EMS) and its real-time adjustment ability accordingly influence a lot on the fuel economy of a hybrid electric vehicle. This paper proposes an improved model predictive control (MPC) framework for the EMS of plug-in hybrid electric buses (PHEB). It aims to achieve optimal energy distribution with increased prediction accuracy and optimized speed sequences by integrating the V2V and V2I information. Firstly, when PHEB is driving between two traffic intersections, the speed prediction accuracy is improved with the Particle Swarm Optimization (PSO) method optimizing the initial value of the Extreme Learning Machine (ELM). Based on the information from V2V, the instantaneous safe speed is calculated and used as a reference to update the predicted speed and reduce speed fluctuations. Secondly, when passing through a traffic intersection, the optimal speed sequence is planned in advance by the dynamic planning algorithm, with the PHEB’s state at the traffic intersection is predicted based on the current signal state (red-yellow-green). Finally, combining speed prediction and speed planning with rolling optimization and feedback correction, MPC-based optimal energy management is achieved. The experimental results show that under the new MPC framework, fuel consumption is reduced by 13.55% relative to the rule-based strategy.

An Improved Energy Management Strategy for Hybrid Electric Vehicles Integrating Multistates of Vehicle-Traffic Information

This study aims to answer the key question for hybrid electric vehicles (HEVs) on how to manage the power flow in HEVs with recent intelligent and electrified upgrades in automotive industries. The new energy management strategy (EMS) needs to fuse both the physic and cyber systems, reflecting the dynamic vehicle system in the physical layer, as well as taking full advantage of the outside information in the cyber layer. Given that, this article proposes the cyber-physical system (CPS)-based EMS using deep reinforcement learning (DRL) in two different types of vehicles [hybrid electric bus (HEB) and Prius]. Under the proposed framework, exploratory training is carried out for the EMS which is mediated by DRL algorithms, expert prior knowledge and multistate of traffic information. Then, the prior valid knowledge trained by HEB is applied to Prius based on deep transfer learning, accelerating the new EMS convergence and ensuring the same initial parameters of the two vehicles' deep neural networks. Moreover, the cyber information is decoupled from the vehicle itself, for the first time being visualized for comparative analysis. The results show a significant improvement by considering traffic states (TS) and using dynamic programming (DP) as a benchmark with 6.94% fuel economy improvement for deep deterministic policy gradients (DDPG) test results and 8.12% for deep Q-learning (DQL), respectively. The decoupling analysis distinguished the effect of various TS for the HEB and Prius due to their different characteristics in vehicle service, driving style, and vehicle structures, which further verifies the effectiveness of the proposed EMS.

City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision-making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple-driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta-supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre-training, fine-tune-training and real-time prediction, which is tested to predict the hybrid electric city buses’ future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s−1, which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energy-saving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition.

A unified quantitative analysis of fuel economy for hybrid electric vehicles based on energy flow

Abstract Analyzing the impact of energy-saving factors on fuel consumption for hybrid electric vehicles (HEVs) is essential to revealing their inherent energy-saving mechanisms, and it is a significant reference for exploring the energy-saving potential and the direction of optimization calibration. This paper focuses on a unified quantitative exploration of the factors affecting the fuel economy. First, the coupling characteristics in energy conversion and losses are investigated based on energy flows, and a concept of average comprehensive transmission efficiency (ACTE) is defined. Second, a novel theoretical fuel consumption model is developed and the fuel-saving contribution rate (FSCR) is proposed, achieving the purpose of decoupling analysis of the fundamental fuel consumption impact factor. Finally, to verify the analysis method proposed in this paper, the results are compared between simulation and theoretical calculations on three typical hybrid electric buses, including parallel, series/parallel, and power-split HEVs. The comparison results demonstrate that the developed theoretical model has remarkable consistency with the simulation model. Furthermore, specific analyses of different impact factors are conducted, quantitatively deepening the understanding of factors affecting fuel economy and quickly facilitating the future economic performance prediction of new-generation HEVs.

Impact of replacing ICE bus fleet with electric bus fleet in Africa: A lifetime assessment

It is globally agreed that electrification of the transportation fleet will be a significant step in reducing greenhouse gas emissions. This study assesses the impact of replacing internal combustion engine bus with electric bus fleets in African countries. The assessment includes the impact of solar charging of an electric bus fleet. Emission factors based on the energy mix in the various African countries were used to determine greenhouse gas emissions from electricity generation. Data from a standard public bus fleet was used as a comparison for the internal combustion engine (ICE) fleet. Lifetime costs favor operating an electric bus by 62% and hybrid-electric bus by 50% when charging is done from the national grid. Solar charging has the potential to further reduce lifetime costs by at least 19% and at most 47%. ICE bus emits 3.46 times the emissions of an electric bus. If charging is done by solar, then ICE bus emits 329 times more. Since there is a significant tariff reduction for fuel compared to electricity in African countries, a recommendation is made to reverse this to promote the use of electric vehicles. It is recommended that government fleet transition to electric vehicles.

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.