Hybrid Electric Bus Research Articles

Bas KiTE PHEB Analysis Performance using MATLAB/SIMULINK

Abstract The performance analysis of Bas KiTE Plug-in Hybrid Electric Bus (PHEB) is crucial for assessing its effectiveness in power system applications to reduce fuel consumption and emission. In this study, MATLAB/SIMULINK is employed as a tool of modelling and simulation to evaluate the performance of Bas KiTE PHEB model. The research focuses on sizing of Bas KiTE PHEB and performance of Bas KiTE PHEB electric machine model, internal combustion engine model, and energy system storage model using MATLAB/SIMULINK in term power, fuel consumption and emission. Kuala Terengganu driving cycle were use as input to see the performance. Through this study, the preliminary of Bas KiTE PHEB model use MATLAB/SIMULINK can be used as reference to build the prototype of PHEB in Kuala Terengganu.

Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Environmental characteristics, particularly in mountainous regions with significant slopes, substantially impact vehicle power demand and fuel consumption, thereby influencing both fuel economy and vehicle lifespan. However, existing research lacks energy management strategies specifically designed for these challenging terrains. This study presents an innovative energy management strategy (EMS) for fuel cell hybrid electric buses (FCHEB) utilizing a deep reinforcement learning algorithm considering terrain characteristics. A comprehensive model that incorporates road fluctuations and steep slopes was developed to accurately represent the terrain features of mountainous regions. Driving cycle and road condition data specific to hydrogen fuel cell buses in these areas were collected and processed as for the training sets of EMSs. Subsequently, an EMS based on the Proximal Policy Optimization (PPO) algorithm was devised to address the unique characteristics of these mountainous regions. Simulation results indicate a 26.16 % improvement in fuel economy and a 44.98 % enhancement in convergence efficiency compared to traditional methods that do not consider road slopes. Further validation through new bus route trials and standard driving cycle tests confirms the strategy's robustness and adaptability.

Smart energy management for hybrid electric bus via improved soft actor-critic algorithm in a heuristic learning framework

Deep reinforcement learning (DRL) is currently the cutting-edge artificial intelligence approach in the field of energy management for hybrid electric vehicles. However, inefficient offline training limits the energy-saving efficacy of DRL-based energy management strategies (EMSs). Motivated by this, this article proposes a smart DRL-based EMS in a heuristic learning framework for an urban hybrid electric bus. In order to enhance the sampling efficiency, the prioritized experience replay technique is introduced into soft actor-critic (SAC) for the innovative formulation of an improved SAC algorithm. Additionally, to strengthen the generalizability of the improved SAC agent to real driving scenarios, a stochastic training environment is constructed. Afterward, curriculum learning is employed to develop a heuristic learning framework that expedites convergence. Experimental simulations reveal that the designed EMS expedites convergence by 85.58 % and saves fuel by 6.43 % compared with the cutting-edge baseline EMS. Moreover, the computation complexity test demonstrates that the designed EMS holds significant promise for real-time implementation. These findings highlight the contribution of this article in facilitating fuel conservation for urban hybrid electric buses through the application of emerging artificial intelligence technologies.

Data-driven energy utilization for plug-in hybrid electric bus with driving patten application and battery health considerations

The efficient utilization of energy is a crucial consideration for plug-in hybrid electric buses (PHEB). However, achieving the optimal energy management strategy (EMS) for PHEB necessitates the harmonious optimization of both driving conditions and battery status. Consequently, an innovative EMS that integrates the data-driven prediction of the driving cycles and battery health status. Explicitly, the bi-directional long short-term memory algorithm is employed to reconstruct and train the health status data of power batteries, enabling the accurate estimation of their State of Health (SOH) values. Moreover, a database of historical driving behavior is meticulously collected through a real-world PHEB platform, and the driving cycles for a certain while are predicted with the clustering algorithm. Then, this invaluable information on the battery status and driving cycles is seamlessly introduced into an adaptive-network-based fuzzy inference system (ANFIS) to regulate of the energy conversion factor in the following equivalent consumption minimization strategy (ECMS). Besides, the offline global optimization of equivalent fuel consumption with the Pontryagin's minimum principle (PMP) is preliminarily conducted to generate the pre-established energy conversion factors, facilitating convenient access to the engine and motor's reasonable power distribution range. Finally, simulations and experiments have validated the effectiveness of the proposed EMS, demonstrating a 23.57 % reduction in battery SOC depletion and a 10.53 % decrease in fuel consumption compared to traditional ECMS methods. More importantly, the proposed method has been validated for its executability in embedded devices through real-world bench experiments. This research holds significant reference value for energy utilization and practical implementation in the PHEB, thereby contributing to the advancement of knowledge in this field.

Multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric bus based on stein soft actor-3-critic

Eco-driving strategies increasingly emphasize enhancing multi-performance including safety, comfort, durability, and traffic efficiency under optimal energy-saving. This paper introduces a multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric buses (FCHEBs) based on the stein soft actor-3-critic (SSA3C) algorithm. By leveraging the correlation between the international roughness index (IRI) and weighted root mean square acceleration (WRMSA), energy management system (EMS) and adaptive cruise control (ACC) are integrated the to enhancing multi-performance including vertical comfort and energy efficiency. To address the insufficient action representation and overestimation of Q-values of traditional soft actor-critic (SAC) algorithm, improvements are made to the actor and critic networks using stein variational gradient descent (SVGD) and clipped triple Q-learning (3C), respectively. Composite cycles integrating the standard cycle and IRI are developed to enhance the strategy's generalization. Experimental results indicate that the proposed eco-driving strategy, while maintaining dynamics, safety, durability, and efficiency, efficiently achieves 98.1 % energy savings and equivalent comfort level as dynamic programming (DP). Ablation experiments on vertical comfort demonstrate improvements of 1.24 % in energy efficiency and 2.94 % in WRMSA. Finally, the effectiveness of the network improvements and the adaptability of the proposed strategy are demonstrated through comparative experiments.

A multi-objective energy management optimization for a hybrid electric bus covering an urban route

The development of electrified vehicles is a promising step toward energy savings, emissions reduction, environmental protection, and more sustainable economic growth. In the case of hybrid electric vehicles (HEVs), the energy management strategy (EMS) is essential for their efficiency and energy consumption. Typically, EMS employs rule-based strategies calibrated to general driving conditions. So, this paper proposes to calibrate the EMS of an urban hybrid electric bus that covers a particular route by taking advantage of past driving information. The EMS computes the percentage of the vehicle power demand that must be supplied by each of the sources (fuel and battery) and also controls the heating, ventilating and air conditioning (HVAC) system to achieve cabin thermal comfort. The proposed approach is based on employing an optimal solution by dynamic programing in a previous loop covered by the bus in the considered route. Then, the cost-to-go matrix is stored and used in the following trips by applying the one-step look-ahead rollout, taking profit from the similarities of the loops in the route. To compare and evaluate the performance of the proposed algorithm, a benchmark was carried out by employing the widespread equivalent consumption minimization strategy (ECMS) approach, combined with rule-based strategies in the HVAC control system. Finally, the pareto front presents the trade-off between cabin temperature control performance and total fuel consumption, allowing to compare and evaluate the different EMS calibrations.

Enabling cross-type full-knowledge transferable energy management for hybrid electric vehicles via deep transfer reinforcement learning

Deep reinforcement learning (DRL) now represents an emerging artificial intelligence technology to develop energy management strategies (EMSs) for hybrid electric vehicles (HEVs). However, developing specific DRL-based EMSs for different HEVs is currently a laborious task. Therefore, we design a transferable optimization framework crossing types of HEVs to expedite the development of DRL-based EMSs. In this framework, a novel enhanced twin delayed deep deterministic policy gradient (E-TD3) algorithm is first formulated, and then a deep transfer reinforcement learning (DTRL) method is designed by incorporating transfer learning into DRL. After that, a full-knowledge transfer method based on E-TD3 and DTRL is innovatively proposed. To assess the efficacy of the designed method, an E-TD3-based EMS of a light HEV is pre-trained to be the existing source EMS whose all learned knowledge is then transferred to be reused for a hybrid electric bus (HEB) to facilitate the acquisition of the target new EMS. Simulation results demonstrate that, in the designed transferable framework, the development cycle of the HEB's EMS can be shortened by 90.38 % and the fuel consumption can be saved by 6.07 %. This article provides a practical method to reuse existing DRL-based EMSs for the rapid development of new EMSs across HEV types.

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Towards a fossil-free urban transport system: An intelligent cross-type transferable energy management framework based on deep transfer reinforcement learning

Deep reinforcement learning (DRL) is now a research focus for the energy management of fuel cell vehicles (FCVs) to improve hydrogen utilization efficiency. However, since DRL-based energy management strategies (EMSs) need to be retrained when the types of FCVs are changed, it is a laborious task to develop DRL-based EMSs for different FCVs. Given that, this article introduces transfer learning (TL) into DRL to design a novel deep transfer reinforcement learning (DTRL) method and then innovatively proposes an intelligent transferable energy management framework between two different urban FCVs based on the designed DTRL method to achieve the reuse of well-trained EMSs. To begin, an enhanced soft actor-critic (SAC) algorithm integrating prioritized experience replay (PER) is formulated to be the studied DRL algorithm in this article. Then, an enhanced-SAC based EMS of a light fuel cell hybrid electric vehicle (FCHEV) is pre-trained by using massive real-world driving data. After that, the learned knowledge stored in the FCHEV's well-trained EMS is captured and then transferred into the EMS of a heavy-duty fuel cell hybrid electric bus (FCHEB). Finally, the FCHEB's EMS is fine-tuned in a stochastic environment to ensure adaptability to real driving conditions. Simulation results indicate that, compared to the state-of-the-art baseline EMS, the proposed DTRL-based EMS accelerates the convergence speed by 91.55% and improves the fuel economy by 6.78%. This article contributes to shortening the development cycle of DRL-based EMSs and improving the utilization efficiency of hydrogen energy in the urban transport sector.

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

A Predictive PMP Strategy for Plug-In Hybrid Electric Buses Considering Motor Thermal Characteristics and Loss Distribution

Energy management strategy (EMS) is the key to improving the fuel economy of the plug-in hybrid electric bus (PHEB). Some of the current researches mainly concentrate upon reducing fuel consumption, but do not fully consider the influence of thermal dynamics of the vehicle motor. In order to solve the shortage of this research, a predictive EMS considering electromagnetic thermal control is proposed in this paper. First, integrated with the actual working conditions and environment, the distribution of the loss of the main part of the motor under two extreme working conditions, namely, low-speed overload and high speed, is studied. Then the motor thermal model is built to study the maximum temperature variation rule in the actual operating condition. Finally, the motor thermal model is combined with the model prediction control (MPC) strategy based on Pontryagin’s minimum principle (PMP) strategy to ensure fuel economy and avoid motor overheating. The research results have guiding significance for the improvement and design of subsequent EMSs considering the influencing factors of motor temperature rise.

Multi-criteria decision analysis of alternative fuel buses: methodology and case-study

ABSTRACT In this paper, a Multi-Criteria Decision Analysis methodological framework is developed to rank alternative fuel buses. It is applied to the case-study of the public transportation system of the island of Gotland in Sweden. Seven alternative fuel buses are identified: bioethanol, biodiesel, biogas, plug-in hybrid electric biodiesel, plug-in hybrid electric biogas, battery electric and hybrid electric hydrogen fuel cell buses. These alternatives are evaluated under 11 criteria that are categorized under four dimensions and measured under a Life Cycle Sustainability Analysis approach where possible. The ranking is accomplished by means of the weighted summation method; weights are determined through the Analytic Hierarchy Process. Explicit weights on these criteria provided by local experts and stakeholders are used to evaluate the alternatives under various island-specific scenarios. Results show that biogas and plug-in hybrid electric biogas buses are the preferred alternatives as long as biogas is produced from waste residuals; otherwise, the import of biodiesel could be the preferred short-term alternative. Bioethanol appears to be the least suitable fuel. The developed methodological framework proved helpful to the local authorities of Gotland when they sought to identify the best compromise fuel bus solution and has shown that it can be applied in similar case-studies.

Power Requirement Study of Bus KITe PHEB in Kuala Terengganu

Air pollution and the depletion of fossil fuel are both made worse by the increasing number of vehicles on the road. Conventional vehicles never fulfil future regulations for emission and fuel usage. In addition to this, diesel-powered vehicles are quickly becoming one of the most significant sources of particulate matter (PM) and nitrogen oxides (NOx) in the atmosphere. Electric vehicles (EV), hybrid electric vehicles (HEV), and plug-in hybrid electric vehicles (PHEV) are only a few examples of the innovative vehicle topologies that have emerged in recent years. The battery pack in a PHEV is more substantial than that of a HEV. Plus, it gets the same mileage as regular cars while using a combination of fuel and electricity as its propulsion source. The power requirement for the BUS KITe plug-in hybrid electric bus (PHEB) powertrain has been determined in this work by employing a steady-state velocity and the Kuala Terengganu driving cycle in accordance with the bus parameter, specification, and performance requirement. The power requirements of the bus can be used to determine the appropriate dimensions for the electric machine, internal combustion engine, and energy storage system that make up the BUS KITe PHEB model in MATLAB/SIMULINK environment.

Study on the Emission Characteristics of Typical City Buses under Actual Road Conditions

Five typical in-use city buses in Tangshan with different emission standards, fuel power types, and emission control technology routes were selected as the research objects. PEMSs (Portable Emissions Measurement Systems) and OBD (On-Board Diagnostic) remote monitoring technologies were used to conduct research on actual road conditions and emission characteristics. The research aimed to analyze the driving characteristics of different power types of bus engines and after-treatment systems, as well as their impact on emissions. The results indicated that the actual road driving is mainly in the urban and suburban areas of the local region. Additionally, the VSP (Vehicle Specific Power) was found to be distributed in the low- and medium-speed range of mode Bin0~Bin24. The emission rates of each pollutant showed an increasing trend with the increase in VSP. THC and NOx emissions of CNG and LNG buses are significantly higher than those of gas–electric hybrid buses. However, the PM emissions are lower. Therefore, it is necessary to strengthen the NOx emission control of CNG and LNG buses. With the improvement in emission standards and the use of hybrid technology, CO2 and NOx emissions of China IV buses can be reduced by 13% and 53.5% compared with China III buses of the same tonnage. The CO2 and NOx emissions of China V buses using natural gas combustion and an electric mixture are 20% and 18.8% lower than those of China V buses of the same tonnage using only natural gas. In the actual operation of the diesel–electric hybrid bus equipped with the SCR (Selective Catalytic Reduction) system, the working time of the engine is about 35.5 ± 5%, and the working time of the SCR system only accounts for 65.5 ± 12%. The low working conditions of the SCR system and the low temperature of SCR system are the reasons for the high NOx concentration at the rear end of the SCR system when the hybrid electric bus is running at low speeds.

Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses

The Plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this paper proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this paper mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the HIL platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.

Robust Collaborative Optimization Design of Plug-in Hybrid Electric Bus Based on 6 Sigma Theory

Robust Collaborative Optimization Design of Plug-in Hybrid Electric Bus Based on 6 Sigma Theory

Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information

The escalating level of vehicle electrification and intelligence makes higher requirements for the energy management strategy (EMS) of fuel cell vehicles. Environmental and road conditions can significantly influence the power demand of the load, thereby affecting the lifespan and efficiency of vehicular energy systems. To ensure that the vehicle is always in optimal working condition, this study innovatively proposes a health-conscious EMS framework based on twin delayed deep deterministic policy gradient (TD3) algorithm for fuel cell hybrid electric bus (FCHEB). First, the environment and look-ahead road information obtained through vehicle sensors, GPS and Geographic Information System is used to establish the energy management problem formulation. Secondly, a TD3-based data-driven EMS is developed with the objective of optimizing hydrogen fuel economy, fuel cell durability and battery thermal health status. Finally, the strategy validation is performed in a developed validation environment that contains terrain information, ambient temperature, and real-world collected driving conditions. The validation results indicate that compared to the state-of-the-art TD3-based EMS, the proposed EMS can improve battery life by 28.02 % and overall vehicle economy by 8.92 %.

A data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty

Conventional energy management strategies (EMSs) for hybrid electric vehicles are devised assuming the vehicle mass remains constant under dynamic driving conditions. However, the EMSs cannot adapt to different load conditions due to the dynamic change of vehicle mass. Investigating the characteristics of vehicle mass change plays significant roles in energy optimization, thereby improving overall efficiency and mobility. To this end, we propose an adaptive EMS for a plug-in hybrid electric bus (PHEB) based on artificial neutral network-Pontryagin minimum principle (ANN-PMP) by considering mass distribution characteristics. Firstly, the skew-normal distribution characteristics of PHEB mass are analyzed, and the distribution characteristic spectrum of vehicle mass is obtained based on the Monte Carlo method. Secondly, the influence of mass uncertainty on the PMP is analyzed, and then the ANN-PMP is devised by updating a dynamic co-state with ANN. Finally, an enhanced ANN-PMP (so-called ANN-PMP-CS) is proposed by combining the ANN-PMP strategy obtained by mass distribution training and CS (charge sustaining strategy) to include the no-load or full-load cases. The simulation results demonstrate that the ANN-PMP can adapt to mass change while ensuring that the final state-of-charge (SOC) convergence to the target value. We also observe that the fuel economy of ANN-PMP-CS is similar to that of the widely-used dynamic programming (DP) strategy. Compared with the charge depletion-charge sustaining (CD-CS) strategy, the fuel economy can be improved by about 46.93 % on average.

A robust coordinated charging scheduling approach for hybrid electric bus charging systems

This paper proposes a coordinated electric bus (EB) charging scheduling approach for hybrid charging stations that support plug-in charging and battery-swapping simultaneously. To minimize the total charging cost under time-of-use electricity prices, both a deterministic optimization (DO) model and a robust optimization (RO) model are developed to design the EBs’ charging schedule. In the deterministic model, the remaining capacity upon arrival at the station is assumed to be known. However, in practice, the remaining capacity of the EB is typically uncertain due to unpredictable traffic conditions. Therefore, this paper proposes an RO model to address this uncertainty, utilizing a budgeted uncertainty set and developing the affinely adjustable robust counterpart. The duality theorem is employed to convert the RO model into a computationally tractable formulation. The proposed approach is illustrated with two numerical examples, including a random instance and a real-world case study of Nanjingnan Station in Nanjing, China.