Plug-in 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.

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.

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.

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.

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

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.

Adaptive energy management strategy of plug-in hybrid electric bus

In this study, the online application of a new energy management strategy (EMS) is proposed for a power-split plug-in hybrid electric bus (PHEB) to improve its system's fuel economy and reduce power battery degradation. The new EMS employs Pontryagin's minimum principle (PMP) and the power battery state of charge (SOC) feedback method to adjust the covariates online. A model-free adaptive control (MFAC) EMS was designed and constructed according to the compact form dynamic linearization model to further obtain accurate SOC feedback; the time–domain cumulative as well as covariate upper and lower limit constraints were used to obtain a real-time MFAC-Ⅲ strategy for online applications. The average reduction in battery capacity loss on all ten routes was 8.19 %, resulting from the correlation between the MFAC-III EMS and MFAC EMS; evaluations conducted on both strategies indicated that the MFAC-III outperforms the MFAC strategy in hardware-in-the-loop.

Neuroevolved bi-directional LSTM applied to zero emission zones management in urban transport

Plugin hybrid electric buses (PHEB) are nowadays an essential technology for the decarbonization of cities, proposing a more flexible solution than fully electric buses. There are a few studies for the design of operational strategies for PHEBs (i.e., to choose the propulsion method to use along the route) to minimize tailpipe emissions and/or maximize the electric range of the bus. However, all of them offer static strategies that cannot dynamically adapt to unforeseen traffic events, which can significantly impact on the performance of the bus. In this work, we propose modeling the problem of finding optimal PHEB operational strategies as a classification problem, allowing the generation of pseudo-optimal strategies in very short computational times. The method designed, called ML-EPBO, takes decisions based on local information of the bus, as well as from the route, so it can adapt to any events causing unexpected traffic conditions. Neuroevolution is used in this work to fine-tune ML-EPBO for optimal performance. Results show how ML-EPBO instantly offer solutions highly competitive with the best existing techniques in the literature, which do not offer the possibility of dynamically adapting to unexpected traffic events because they make use of global knowledge of the problem and have high computational complexity. Indeed, the average accuracy value obtained by the model is over 99.2%, and the strategies proposed are predicted over 70% of the cases with 100% of accuracy, even outperforming the state of the art results by up to 4.7% in the level of emissions or 1.82% in the electric range.

Taguchi Robust Design-Based Component Matching and Energy Management of Plug-in Hybrid Electric Buses for City Multiple Bus Routes

Taguchi Robust Design-Based Component Matching and Energy Management of Plug-in Hybrid Electric Buses for City Multiple Bus Routes

AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging

AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging

Adaptive ECMS Based on EF Optimization by Model Predictive Control for Plug-In Hybrid Electric Buses

Adaptive ECMS Based on EF Optimization by Model Predictive Control for Plug-In Hybrid Electric Buses

Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

The fuel economy of plug-in hybrid electric bus (PHEB) is highly dependent on its energy management strategy (EMS). In practice, the fuzzy control (FC) is widely used in EMS due to its real-time performance and robustness. However, the FC with fixed parameters is difficult to obtain the optimal fuel economy under changing traffic conditions. Regarding this, the control parameters of FC need to be optimized, but this scheme needs to overcome the subsequent calculation burden and time consumption. Therefore, the design of a real-time EMS with parameter optimization is a challenging problem. Inspired by this issue, a cyber-physical optimization-based fuzzy EMS is proposed in this paper. Firstly, a cyber-physical system framework is formulated for PHEB to eliminate the conflict between parameter optimization and real-time operation of EMS. Secondly, considering the uncertainty of the vehicle environment, an IT2 FC with optimization parameters is designed for real-time torque allocation. Thirdly, an iterative modified particle swarm optimization (IMPSO) algorithm is proposed to optimize parameters to accurately and quickly converge to the optimal solution. Additionally, the optimization problem with multi-objective that takes battery life into account is introduced. Finally, simulation and hardware in loop test are used to discuss the performance of the proposed EMS. The results reveal that the IMPSO algorithm can improve the optimization effect. Compared to conventional rule-based and fuzzy-based strategies, the proposed EMS can reduce fuel consumption at least 10% and 4.5%, respectively. Meanwhile, it shows the proposed EMS could reduce the battery capacity loss by 6.42%∼9.72% with a slight increase in fuel consumption.

Fuel consumption optimization for a plug-in hybrid electric bus during the vehicle-following scenario

In this paper, a robust SOC (state of charge of battery) trajectory planning method is proposed for a plug-in hybrid electric bus (PHEB) in the vehicle-following scenario. Firstly, the trajectory areas of SOC are planned based on the Taguchi robust design (TRD) considering the noises of the vehicle mass and driving cycles. Moreover, the reliability of the result of TRD is verified based on the Monte Carlo simulation (MCS). Then, the longitudinal driving control of PHEB is transformed into a multi-objective optimal problem that incorporates driving safety, fuel economy and ride comfort in the vehicle-following scenario. Finally, based on a novel nonlinear model predictive control (NMPC), the electric motor, engine and brake system are collaboratively optimized to comprehensively adjust the dynamic and economy of PHEB. The results show that the planning trajectory areas of SOC effectively improved the fuel economy compared with the pre-planning. Furthermore, based on the novel NMPC, the fuel consumption of the proposed method is lower than the method that just adds an energy management strategy to adaptive cruise control (ACC + EMS). The hardware-in-loop test results show that the proposed method can be implemented into the vehicle controller, and the results between the HIL and offline simulations are largely coincident.

ECMS-MPC Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Motor Temperature Rise Effect

Due to working in urban conditions, the plug-in hybrid electric bus (PHEB) has a high vehicle load rate during peak hours and when climbing long slopes. Therefore, the motor of PHEB is prone to overload and overheating, which affects the power performance and fuel economy of the PHEB. Thus, designing an energy management strategy (EMS) for PHEB that considers motor temperature is a crucial research topic. In this article, a strategy combining the equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC) is proposed for PHEB to solve the problem of satisfying the fuel economy while preventing the motor temperature from being too high. First, the components of PHEB are analyzed and modeled. Second, ECMS is applied to the MPC framework, and the motor temperature is added to the cost function as an important optimization term. Third, the gray wolf optimization algorithm (GWOA) is selected to solve the minimum value of objective function offline. Finally, the accuracy of the model and the effectiveness of the proposed strategy in saving fuel are verified by MATLAB/Simulink and the hardware-in-the-loop tests. The result shows that compared with rule-based EMS, the fuel consumption of the proposed strategy is reduced by 15.81% and 15.45% under the urban dynamometer driving schedule and new combined driving cycle, respectively. Moreover, the proposed strategy can well restrain the motor temperature from being too high and keep it within a safe range.

Incorporating Driving Style Recognition Into MPC for Energy Management of Plug-In Hybrid Electric Buses

Vehicle fuel economy is greatly influenced by the driver’s driving style. To achieve remarkable promotion in the fuel economy, a novel predictive energy management strategy (EMS) with strong adaptability to driving styles is presented for plug-in hybrid electric buses (PHEBs) in this article. First, a combined unsupervised and supervised algorithm for the driving style identification is devised based on instant driving conditions. In the algorithm, a multidimensional Gaussian distribution (MGD)-based analysis on factors influencing driver’s driving style provides valuable inputs for local mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> nearest neighbor (LMKNN) algorithm, and outstanding regression ability of the LMKNN returns exact recognition result and then incorporating the driving style recognition function into the model predictive control (MPC) to formulate a driver-oriented predictive EMS, where estimate distribution and particle swarm optimization (ED-PSO) algorithm is introduced to obtain optimal control sequence over a receding horizon. Finally, simulation validations in the MATLAB/Simulink environment show that the total cost of the proposed strategy is decreased by 12.53% compared to the charge-depleting and charge-sustaining (CD-CS) method, without sacrificing the real-time applicability.

Proximal Policy Optimization Based Intelligent Energy Management for Plug-In Hybrid Electric Bus Considering Battery Thermal Characteristic

As the performances of energy management strategy (EMS) are essential for a plug-in hybrid electric bus (PHEB) to operate in an efficient way. The proximal policy optimization (PPO) based multi-objective EMS considering the battery thermal characteristic is proposed for PHEB, aiming to improve vehicle energy saving performance while ensuring the battery State of Charge (SOC) and temperature within a rational range. Since these three objectives are contradictory to each other, the optimal tradeoff between multiple objectives is realized by intelligently adjusting the weights in the training process. Compared with original PPO-based EMSs without considering battery thermal dynamics, simulation results demonstrate the effectiveness of the proposed strategies in battery thermal management. Results indicate that the proposed strategies can obtain the minimum energy consumption, fastest computing speed, and lowest battery temperature in comparison with other RL-based EMSs. Regarding dynamic programming (DP) as the benchmark, the PPO-based EMSs can achieve similar fuel economy and outstanding computation efficiency. Furthermore, the adaptability and robustness of the proposed methods are confirmed in UDDS, WVUSUB and real driving cycle.