Heavy-duty Hybrid Research Articles

An improved soft actor-critic-based energy management strategy of heavy-duty hybrid electric vehicles with dual-engine system

While deep reinforcement learning (DRL) based energy management strategies (EMSs) have shown potential for optimizing energy utilization in recent years, challenges such as convergence difficulties and suboptimal control still persist. In this research, a novel DRL algorithm, i.e. an improved soft actor-critic (ISAC) algorithm is applied to the EMS of a heavy-duty hybrid electric vehicles (HDHEV) with dual auxiliary power units (APU) in which the priority experience replay (PER), emphasizing recent experience (ERE) and Muchausen reinforcement learning (MRL) methods are adopted to improve the convergence performance and the HDHEV fuel economy. Simultaneously, a bus voltage calculation model suitable for dual-APUs is proposed and validated using real-world data to ensure the precision of the HDHEV model. Results indicate that the proposed EMS reduces HDHEV fuel consumption by 4.59 % and 2.50 % compared to deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3)-based EMSs respectively, narrowing the gap to dynamic programming-based EMS to 7.94 %. The proposed EMS exhibits superior training performance, with a 91.28 % increase in convergence speed compared to other DRL-based EMSs. Furthermore, the ablation experiments also validate the effectiveness of each method in the proposed EMS for the SAC algorithm, further demonstrating its superiority.

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.

Reducing CO2 Emissions of Hybrid Heavy-Duty Trucks and Buses: Paving the Transition to Low-Carbon Transport

This study investigates the CO2 reduction potential of powertrain hybridisation on heavy-duty lorries and city buses. The analysis considers modern parallel and serial hybrid architectures, assessing their efficiency and limits in CO2 emission reduction through vehicle simulation in VECTO, which is the official tool of the European Commission for calculating heavy-duty vehicle fuel and energy consumption. The results reveal distinct trends for each vehicle type and architecture. In lorries, more significant improvements are observed in urban delivery profiles, reaching up to ~16%, indicating the benefits of hybridisation in transient conditions with energy recuperation opportunities. City buses, particularly those with serial architectures, exhibit significant emission reductions that reach 36%, making them suitable for urban environments. The optimisation of electric motor size and performance plays a crucial role in achieving emission reductions, while battery capacity must be carefully considered to avoid adverse effects. For lorries in urban delivery use, further improvements of 17.5% can be achieved by utilising a 160 kW engine motor and 30 kWh battery. Buses are already quite well optimised, with serial architecture presenting the highest benefits with a 120 kW electric motor and a battery of 11 kWh. Future research should focus on supercapacitors and gearboxes to improve efficiency at higher vehicle speeds and assess hybridisation potential in interurban coach travel. The heavy-duty vehicle sector can make significant strides towards low-carbon transport by maximising hybrid powertrain efficiency and emission reductions.

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.

FMVSS 141 for Commercial Vehicles: Applicability and Limitations

Newly manufactured light-duty hybrid and electric passenger vehicles must comply with FMVSS 141 minimum sound requirements to reduce the risk of crashes with visually impaired and inattentive pedestrians. Commercial vehicles operate in a variety of noise-critical environments, from densely packed industrial yards to congested urban areas, making safe electric vehicle operation around pedestrians and bystanders vital. Though the market share of medium and heavy-duty hybrid and electric vehicles is projected to increase annually, there are currently no North American regulations specifically for minimum sound emissions of hybrid and electric vehicles heavier than 10,000 lb. GVWR. The primary intent of this paper is to investigate the efficacy and limitations of the current FMVSS 141 requirements when applied to heavy-duty electric trucks. Serving as a complementary test case, a Class 8 Freightliner eCascadia electric truck with onboard Acoustic Vehicle Alerting System (AVAS) was evaluated at Daimler Truck North America's High Desert Proving Grounds in accordance with the FMVSS 141 procedure for light-duty passenger vehicles. Analysis of the measured data found the FMVSS 141 forward gear criteria to be reasonably effective when applied to the test vehicle, but the standard's reverse gear criteria were deficient to other common industry-accepted backing alert methods. These results are used as a starting point for a discussion on various aspects of FMVSS 141, with an emphasis on how adding commercial vehicles to the scope of the standard could necessitate further changes to the defined acceptance criteria.

Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition

Hybrid heavy-duty off-road vehicles frequently experience rapid acceleration and deceleration, as well as frequent uphill and downhill motion. Consequently, the engine must withstand aggressive transients which may drastically worsen the fuel economy and even cause powertrain abnormal operation. When the engine cannot respond to the transient demand power quickly enough, the battery must compensate for the large amount of power shortage immediately, which may cause excessive battery current that adversely affects the battery safety and life span. In this paper, a nonlinear autoregressive with exogenous input neural network is used to recognize the driver’s intention and translate it into subsequent vehicle speed. Combining energy management with vehicle speed control, a co-optimization-based driver-oriented energy management strategy for manned hybrid vehicles is proposed and applied to smooth the engine power to ensure efficient operation of the engine under severe transients and, at the same time, to regulate battery current to avoid overload. Simulation and the hardware-in-the-loop test demonstrate that, compared with the filter-based energy management strategy, the proposed strategy could yield a 38.7% decrease in engine transient variation and an 8.2% decrease in fuel consumption while avoiding battery overload. Compared with a sequential-optimization-based energy management strategy, which is recognized as a better strategy than a filter-based energy management strategy, the proposed strategy can achieve a 16.2% decrease in engine transient variation and a 3.2% decrease in fuel consumption.

Semi-analytical static compliance modeling of hybrid equipment

Based on screw theory and computer-aided engineering software, the static compliance modeling of a hybrid machine through the serial and parallel assembly of components was performed to construct the mapping relationship between wrench and deformation twist in Cartesian space. For low-speed and heavy-duty hybrid equipment, the static compliance performance is extremely important, and a complete static compliance model is a prerequisite for analysis and design. The semi-analytical model considers gravity, which facilitates the rapid prediction of static compliance and static deformation, providing guidance for static compliance matching in the design process.

Online hierarchical energy management strategy for fuel cell based heavy-duty hybrid power systems aiming at collaborative performance enhancement

The fuel cell based heavy-duty hybrid power system has gained increasing attraction in high-power transportation applications. The real-time energy management strategy is essential to the collaborative performance enhancement between fuel economy and durability for this type of heavy-duty hybrid power system with inherent performance inconsistency, which still remains a promising problem. In this paper, a hierarchical energy management strategy is proposed to comprehensively consider the coupling effect between top-level power distribution among different system clusters and bottom-level power distribution within each system cluster. It is inspired by the piece-wise quadratic equivalent modeling for the energy consumption characteristics of hybrid power system. Then, the coordinated power distribution optimization can be unified for different types of system clusters and online obtained analytically, and the quantitative correlation between the fuel economy and durability is characterized. The effectiveness and practicability of the proposed strategy are verified by three assessment cases based on the hardware-in-the-loop simulation. Detailed results demonstrate that, benefited from the accurate energy consumption characteristics modeling with less than 3% residuals within most power ranges, the collaborative performance enhancement for fuel cell based heavy-duty hybrid power system can be guaranteed by the proposed strategy. Indeed, up to 4.1% hydrogen consumption conservation with state-of-charging stability as well as robustness against life-cycle duty cycle uncertainty can be achieved compared with the other two advanced online strategies.

Development of a Variable Torque Distribution for Fully Electric and Hybrid Heavy-duty Trucks based on a Modular Simulation Methodology

Development of a Variable Torque Distribution for Fully Electric and Hybrid Heavy-duty Trucks based on a Modular Simulation Methodology

Coordinated Optimization of Energy Management and Longitudinal Control for a Hybrid Heavy-Duty Military Vehicle When Considering Terrain Information

Coordinated Optimization of Energy Management and Longitudinal Control for a Hybrid Heavy-Duty Military Vehicle When Considering Terrain Information

Switched Optimal Control of a Heavy-Duty Hybrid Vehicle

This work investigates the fuel energy and emission reductions possible with the hybridization of a Class 8 tractor-trailer. The truck tractor has two drive axles: one powered by an internal-combustion-engine-based powertrain (CP) and the other powered by an electric powertrain (EP) consisting of an electric drive system supplied by a battery pack, resulting in a through-the-road hybrid. The EP has two modes of operation depending on the direction of power flow: motoring/battery discharging and generating/battery recharging. Switched optimal control is used to select between the two modes of EP operation, and a recently developed distributed switched optimal control is applied. The control is distributed between the CP, the EP, and the vehicle motion operation components. Control-oriented, component-specific power flow models are set forth to describe the dynamics and algebraic relationships. Four different tractor-trailers are simulated: the original CP and three hybrids with engine sizes of 15 L, 11 L, and 7 L. Simulations are performed over a short test cycle and two regulatory driving cycles to compare the fuel use, total energy, and emissions. Results show that the hybrids have reduced fuel use, total energy, and emissions compared to the original CP; the reductions and reference velocity tracking error increases as the engine size is decreased. Particularly, fuel use is reduced by at least 4.1% under a charge sustaining operation and by 9.8% when the battery energy can be restored with an off-board charger at the end of the cycle.

Effects of Hybridization on Selective Catalytic Reduction (SCR) Thermal Management of a Medium Heavy-Duty Hybrid Work Truck

Effects of Hybridization on Selective Catalytic Reduction (SCR) Thermal Management of a Medium Heavy-Duty Hybrid Work Truck

Well-to-Wheel analysis of natural gas for hybrid electric truck application

Compressed natural gas as an alternative fuel obviously has a great potential to reduce the greenhouse gas emissions. Although several studies on the life cycle are quite comprehensive for passenger vehicles, it is problematic to apply these results to heavy-duty electric hybrid trucks. This paper describes the Well-to-Wheel methodology for environmental impact from the gas production to its final application. The CO2 equivalent emissions and the methane leakage point will be identified at the end. The results indicate that compressed natural gas-based trucks have 18.7% less CO2 equivalent emissions than diesel-based ones. However, this benefit may be affected by methane leakage, particularly, in the recovery phase. Reducing methane emissions upstream could be an opportunity to optimize the pollution performance of heavy hybrid electric trucks.

Coordination control for heavy-duty dual-mode power-split hybrid electrical vehicle under dynamic condition

Coordination control for heavy-duty dual-mode power-split hybrid electrical vehicle under dynamic condition

Coordination control for heavy-duty dual-mode power-split hybrid electrical vehicle under dynamic condition

Coordination control for heavy-duty dual-mode power-split hybrid electrical vehicle under dynamic condition

An Improved Energy Management Strategy for 24t Heavy-Duty Hybrid Emergency Rescue Vehicle With Dual-Motor Torque Increasing

Emergency rescue vehicles are special equipment for fire, flood, earthquake and other disasters. They need to have high efficiency and high mobility under various terrain and road conditions. The fuel economy of emergency rescue vehicles is very poor in climbing and complex terrain due to the heavy weight of vehicle chassis and upper loading. The dual-motor torque increasing hybrid power system designed in this paper greatly improves the dynamic performance and mobility of emergency rescue vehicles, and the use of dual-motor drive in steep slopes and complex terrain can avoid the engine working in low efficiency area. In order to further improve the fuel economy of vehicles under various working conditions, a fuzzy logic control strategy based on improved particle swarm optimization (PSO) is proposed in this paper. The torques of engine and dual-motor are reasonably distributed under different working conditions. A novel fuzzy parameter optimization mode is designed, which reduces the workload of optimization calculation and ensures the optimization accuracy. The off-line simulation and hardware-in-the-loop experiments are carried out for the dual-motor torque increasing hybrid power system model and the proposed improved energy control strategy. The results show that the fuzzy logic control strategy optimized by simulated annealing particle swarm optimization algorithm (SimuAPSO-FLC) has the best effect and meets the requirements of high efficiency, high mobility and stability of the emergency rescue vehicle.

Coordinated Optimization of Energy Management and Longitudinal Control for a Hybrid Heavy-Duty Off-Road Vehicle Considering Terrain Information

Coordinated Optimization of Energy Management and Longitudinal Control for a Hybrid Heavy-Duty Off-Road Vehicle Considering Terrain Information

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.