Heavy-duty Hybrid Electric Vehicles Research Articles

An Efficient Power Control Scheme for Heavy-Duty Hybrid Electric Vehicle With Online Optimized Variable Universe Fuzzy System

An Efficient Power Control Scheme for Heavy-Duty Hybrid Electric Vehicle With Online Optimized Variable Universe Fuzzy System

Hierarchical Rewarding Deep Deterministic Policy Gradient Strategy for Energy Management of Hybrid Electric Vehicles

Electrified vehicles are important for reducing emissions and will dominate road transportation in the future. An energy management strategy is critical for hybrid electric vehicles to manage the use of on-board energy resources. This paper proposes a novel double-layer energy management strategy based on hierarchical rewarding DDPG (HR-DDPG) for a heavy-duty hybrid electric vehicle (HEV) to improve energy efficiency adaptively to various scenarios. The proposed hierarchical rewarding structure with two reward functions can guide the DDPG agent to explore the optimal policy more efficiently and deeply and is well-suited to achieve targeted adjustment according to the vehicle operating modes to cope with rapidly changing scenarios. The optimality and adaptability of the new strategy are evaluated by standard and real-world driving data. The results show that the proposed strategy leads to an improvement of 8.11% in energy efficiency compared with the conventional DDPG strategy, and its energy-saving performance can reach up to 93.87% of the capacity of a dynamic programming (DP) based energy management strategy on the comprehensive driving condition II.

Experimental investigations on emission characteristics of heavy-duty hybrid electric vehicles

The emission characteristics under different operating modes (engine mode and hybrid mode) and different test cycles (C-WTVC and CHTC) of a heavy-duty hybrid electric dump truck was investigated on the chassis dynamometer. The emission performance was recorded using Portable Emissions Measurement System (PEMS) and analyzed combined with the characteristic parameters of the test conditions. It is found that the NOx emission under hybrid mode is higher than that under engine mode, while the CO emission under hybrid mode is lower than engine mode. Under engine mode, the NOx emission of CHTC is higher than that of C-WTVC. However, under hybrid mode, the NOx emission of CHTC is lower than C-WTVC. Analysis of CO emission characteristics shows that under engine mode, CO emission is concentrated at low speed and small load condition, while under hybrid mode, CO emission is concentrated at high speed and large load condition.

A short-term prediction-based efficient optimization power control strategy for heavy-duty hybrid electric vehicle

This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). In this strategy, a power preconditioning method is established on the basis of demand power prediction which guarantees the stable power output under transient high-power condition. Through the prediction sequence, MISCQP algorithm is proposed to solve receding horizon problem and achieve real-time control by improving the iteration efficiency. For this purpose, the clustering algorithm is designed to skip the unnecessary short step in the iteration which is too few to obtain sufficient descent. The iteration points in various iteration domains are clustered and the corresponding cluster centers are obtained. Next, the aforementioned clustering results are introduced to improve the termination criterion. The updated criterion turns to skip the short step when the distance of cluster centers of various iteration domains varies within the set threshold. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy achieves 5.00 %, 5.86 %, 6.27 % less fuel consumption while maintaining stable power output under all the driving cycles. And the average iteration number of proposed strategy is decreased by 10.36 %, 8.47 %, 9.21 %, respectively.

Investigation of methods for measuring fuel economy and emissions of heavy-duty hybrid-electric vehicles (HEVs)

Hybrid-electric vehicles can achieve low fuel consumption and emission by optimal combination of electrical energy and internal combustion engine power. There are four main test method of energy consumption and emission for heavy-duty HEVs, including traditional engine bench test, powertrain test, HILS and chassis dynamometer test. The tradition engine operating conditions are distinguished from that in HEVs, which can’t reflect the performance of HEVs. Powertrain test can achieve braking energy regeneration, but need bulky installation on testbed. HILS test solves the problem that the engine test cycle is different from the actual operating condition on road and is cost-effective. However, it requires a lot of effort to monitor the HILS simulation input parameters and carry out vehicle validation. Chassis dynamometer test method can better reflect the real-road driving condition. But it is nearly impossible to test all different HEVs types on chassis dynamometer and the test cost is very expensive. By comparing different heavy-duty HEVs energy consumption and emission test methods, the advantages and disadvantages are identified, which provides guidance for the formulation of the next-stage standards of heavy-duty HEV, and promotes the healthy and orderly development of the HEV industry.

Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming

In order to enhance the power and bring additional energy-saving possibilities of heavy-duty vehicles, a novel dual-engine system is utilized in a series hybrid electric vehicle. However, the minimum fuel consumption of this configuration is still unclear, and the effective online energy management strategy for this system is absent. The studied vehicle adopts the dual-engine configuration, which is powered by Li-ion batteries and two auxiliary power units. This paper presents a two-step framework to address the energy management problem. A map-based strategy is adopted to distribute power between engines. The dynamic programming (DP) algorithm is incorporated to distribute power between engines and batteries and find the minimum fuel consumption. Simulation results show that the DP algorithm can save 7.3% fuel compared to the charge depleting and charge sustaining strategy. The dual-engine system achieves a 1.9% lower fuel consumption compared with the conventional hybrid system with one engine. The operation points of engines and the power profiles of batteries derived from the DP are analyzed, and a rule-based strategy is designed correspondingly. The proposed rule-based algorithm can reduce 2.2%–6.0% fuel consumption and is not sensitive to the variations of battery size, parameters of the rule-based strategy, and driving cycles.

Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems

Heavy-duty hybrid electric vehicles and marine vessels need a sizeable electric energy storage system (ESS). The size and energy management strategy (EMS) of the ESS affects the system performance, cost, emissions, and safety. Traditional power-demand-based and fuel-economy-driven ESS sizing and energy management has often led to shortened battery cycle life and higher replacement costs. To consider minimizing the total lifecycle cost (LCC) of hybrid electric propulsion systems, the battery performance degradation and the life prediction model is a critical element in the optimal design process. In this work, a new Li-ion battery (LIB) performance degradation model is introduced based on a large set of cycling experiment data on LiFePO4 (LFP) batteries to predict their capacity decay, resistance increase and the remaining cycle life under various use patterns. Critical parameters of the semi-empirical, amended equivalent circuit model were identified using least-square fitting. The model is used to calculate the investment, operation, replacement and recycling costs of the battery ESS over its lifetime. Validation of the model is made using battery cycling experimental data. The new LFP battery performance degradation model is used in optimizing the sizes of the key hybrid electric powertrain component of an electrified ferry ship with the minimum overall LCC. The optimization result presents a 12 percent improvement over the traditional power demand-driven hybrid powertrain design method. The research supports optimal sizing and EMS development of hybrid electric vehicles and vessels to achieve minimum lifecycle costs.

Engine in the Loop を適用したハイブリッド重量車の燃料消費率および排出ガスの評価手法に関する検討

Engine in the Loop を適用したハイブリッド重量車の燃料消費率および排出ガスの評価手法に関する検討

A Split-Field-Windings Doubly Salient Brushless DC Generator With Reduced Excitation Capacity for Hybrid Electric Vehicles

This paper proposes a split-field-windings doubly salient brushless dc generator (SDSBLDCG) to reduce excitation capacity. The series field winding connected at the dc side of the associated rectifier provides part of the field magnetomotive force. A split factor is introduced to indicate different combinations of the field windings based on the same slot fill and current density. Then, the influence of split factor on the generator performance is investigated by finite-element analysis. The dynamic characteristics of the SDSBLDCG system are also analyzed to evaluate the adoptable split factor. A 43 kW prototype SDSBLDCG with 24 stator poles and 16 rotor poles having optimized split factor, as well as its generator control unit, is designed and developed for the medium-duty hybrid electric bus. The capacity of the field current regulator is reduced by 45% compared with that of the conventional doubly salient brushless dc generator. The utility of the controller and the dynamic performance of the system are also verified. Both simulated and measured data show that the proposed SDSBLDCG is a promising candidate for the on-board generator system, particularly in medium- and heavy-duty hybrid electric vehicles.

Design-space exploration of series plug-in hybrid electric vehicles for medium-duty truck applications in a total cost-of-ownership framework

The light-duty vehicle market has seen some adoption of hybrid electric vehicles that is not reflected in the heavy-duty market. The major challenges associated with the heavy-duty segment are: (i) greater emphasis on economic viability, (ii) reluctance to take on risk associated with new technologies, and (iii) numerous diverse applications that preclude a one-size-fits-all approach to hybrid-electric powertrain design. To overcome these challenges, a model-based framework is required that enables the exploration and optimal design of powertrain architectures for diverse applications while capturing the impact of hybridization on the economics of ownership under different economic scenarios. This paper demonstrates such a framework that incorporates powertrain simulation and battery degradation models to predict fuel consumption, electrical energy consumption, and battery replacements. These results are combined with economic assumptions to enable the exploration of a large design space (which spans powertrain design & control variables, noise variables, and economic scenarios) from a total cost-of-ownership perspective to provide better insights to vehicle integrators, component manufacturers, and buyers of heavy-duty hybrid electric vehicles. The methodology is applied to series plug-in hybrid electric and extended-range electric powertrain architectures for medium duty truck applications. The results show that under the assumptions made, economically favorable solutions for series plug-in hybrid electric medium-duty trucks exist in the 2020 time-frame for the NY Composite Truck drive cycle, while for the HTUF Refuse Truck and HTUF Class 6 P&D Truck drive cycles, feasible solutions are not obtained until 2025 and 2030 time-frames respectively.

Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric Vehicles

In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start–stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.

Improved Rotor Position and Speed Estimators for Sensorless Control of Interior Permanent-Magnet Synchronous Machines

Model-based rotor position/speed estimators are commonly used for sensorless control of interior permanent-magnet synchronous machines (IPMSMs) operating in the medium- and high-speed regions. A rotor position/speed estimation scheme usually contains three major parts: 1) a state observer; 2) a position estimator; and 3) a speed estimator. This paper proposes a sliding-mode observer (SMO) as the state observer to estimate the position-related system states, which are the extended electromotive force components in this paper. Then, two major contributions are made to achieve improved position and speed estimation. First, the rotor speed is estimated independently using a model reference adaptive system (MRAS)-based method, which is decoupled from the position estimation. To reduce the noise contents in the estimated speed, an adaptive line enhancer is proposed to work with the SMO, leading to an improved reference model for the speed estimation. The proposed MRAS-based speed estimator has two operating modes, which are suitable for generator and motor applications, respectively. Second, the estimated rotor speed is used as a feedback input signal to mitigate the oscillating error in the estimated rotor position, leading to an integrated position and speed estimation system. The effectiveness of the proposed position and speed estimators is verified by simulation using the data logged from a real-world test vehicle. Experimental results on a test stand of an IPMSM drive system used in off-road, heavy-duty hybrid electric vehicles are also provided to further validate the proposed rotor position/speed estimation schemes.

Novel evaluation method of fuel consumption and emission for heavy-duty hybrid electric vehicles

This paper is a continuation of a previous paper titled “A novel way to calculate energy efficiency for rechargeable batteries” published on Journal of Power Sources/2012 describing a new method to calculate energy efficiency for rechargeable batteries. The present paper further describes the application of energy efficiency model on the evaluation of fuel consumption and emission for the heavy-duty hybrid electric vehicles (HD-HEVs). A more accurate calculation method of net energy change for power battery pack is proposed based on energy efficiency model of power battery pack. A more simplified and accurate correction method of fuel consumption and emission is also presented based on equivalent mileage. The fuel consumption and emission on chassis dynamometer are measured in the HD-HEVs. The experiment results show that relative errors of fuel consumption and emission between equivalent mileage correction results and linear regression correction results are less than 3%, which verifies accuracy and validates the proposed evaluation method for HD-HEVs fuel consumption and emission.

In-Use and Vehicle Dynamometer Evaluation and Comparison of Class 7 Hybrid Electric and Conventional Diesel Delivery Trucks

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen. The HEV showed 31% better fuel economy on the NYComp cycle, 25% better on the HTUF 6 cycle and 4% worse on the CARB HHDDT cycle when compared to the conventional vehicle. The in-use field data indicates that the HEVs had around 16% better fuel economy than the conventional vehicles. Dynamometer testing also showed that the HEV generally emitted higher levels of nitric oxides than the conventional vehicle over the drive cycles, up to 77% higher on the NYComp cycle (though this may at least in part be attributed to the different engine certification levels in the vehicles tested). The conventional vehicle was found to accelerate up to freeway speeds over ten seconds faster than the HEV.

Full-Scale Hardware-in-the-Loop Verification Environment for Heavy-Duty Hybrid Electric Vehicles

This paper presents an implementation of a versatile full-scale hardware-in-the-loop (HIL) verification environment for testing of heavy-duty hybrid electric vehicles and mobile work machines. The test facility includes a full-scale hybrid system that can be loaded with either an electric motor dynamometer or a programmable chassis dynamometer. Model-based software development tools and rapid control prototyping hardware are used to implement control algorithms and other vehicle controller software as well as to control other test equipment.

Energy conversion and optimal energy management in diesel–electric drivetrains of hybrid-electric vehicles

This paper develops nonlinear models for diesel–electric powertrains used in medium- and heavy-duty hybrid-electric vehicles and describes a new method for designing optimal controllers for highly coupled drivetrains. Current applications of heavily loaded, heavy-duty drives call for the use of comprehensive, innovative ways of handling a large variety of nonlinear phenomena in order to optimize performance and energy conversion, to shape steady-state characteristics and to meet other desired specifications. It is evident that energy conversion and optimal energy management should be integrated and thoroughly studied. This paper demonstrates the application of derived nonlinear models of drivetrain components (diesel, synchronous generator, traction motor) analyzes the nonlinear energy conversion and develops an innovative design method to control these highly nonlinear diesel–electric systems. Fundamental analysis and design methods, such as the Lyapunov stability theory and admissibility concept, are used to attain nonlinear analysis, design and motion control. The developed concept in nonlinear analysis and design allows one to synthesize optimal tracking controllers for highly nonlinear systems with minimal approximating simplifications and assumptions used in the model development. New promising analytical results are presented along with a supporting example involving a controlled, 40-ton vehicle with a hybrid-electric drivetrain. Experimental data is reported to validate the results.