Search Results (511)

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

The design of hybrid power units for compact racing vehicles demands optimal use of limited space while ensuring performance and regulatory compliance. This work presents a methodology for integrating a parallel P0 hybrid system into a Formula Student single-seater, combining a 480 cc single-cylinder ICE and a 30 kW PMSM within the existing chassis envelope. The design process included volume analysis, mechanical and cooling system integration, and a modular Li-ion battery pack. An energy management strategy, optimized via Dynamic Programming, improved torque utilization and energy recovery, considering a race track lap simulation. Full article

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

Parallel electric–hydraulic hybrid (PEHH) powertrains offer benefits of lower energy consumption and increased battery lifetime compared to pure electric ones. These merits can be extended with advanced control methods that optimally deploy on-board energy sources. This paper proposes a nonlinear model predictive control (NMPC) energy management strategy (EMS) for a PEHH wheel loader. The optimization minimizes energy usage and battery degradation by selecting the optimal power ratio between the electric and hydraulic subsystems. The state prediction is based on a discrete nonlinear dynamic model and an estimate of the future exogenous inputs developed from a high-fidelity digital-twin model of a wheel loader. The NMPC formulation is compared to a baseline rule-based EMS inspired by offline optimal control. The proposed NMPC results in 31.7% less battery degradation and 9.14% energy consumption reduction even with a 20% error in the preview information. Hardware-in-the-loop (HiL) experiments validate our results and show that the NMPC EMS can be implemented in real time even with higher prediction error increasing the maximum computational time. Full article

Drones have long been explored by commercial and military users for supply. While several systems offering small payloads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, we evaluate these challenges from a conceptual modelling perspective and forecast their applicability once these barriers are overcome. This study uses technology trend modelling and bibliometric activity mapping methodologies to predict the applicability of specific technologies that are currently identified as operational challenges. Specifically for supply drones, we model trends in technological improvements of battery technology and aircraft control, and project its focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and constraints of larger supply drones. A case study involving the conceptual design of a supply drone large enough to move a shipping container is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance. Full article

To address the stringent emission regulations of the International Maritime Organization (IMO) and the growing demand for green port operations, this study proposes an innovative range-extended series hybrid powertrain system featuring a dedicated methanol engine as an Auxiliary Power Unit (APU) for harbor tugs. Based on an analysis of actual ship operational data, a core design paradigm of “battery-dominant, engine-as-range-extender” is established. A robust capacity parameter matching method is proposed, yielding a configuration comprising a 200 kW∙h/600 kW Lithium Iron Phosphate Battery Pack (LFPBP), a 250 kW methanol APU, and a 400/600 kW Permanent Magnet Synchronous Propulsion Motor (PMSM). A hierarchical intelligent energy management strategy (EMS), integrating state-machine coordination and real-time power allocation, is designed. High-fidelity simulations under a typical duty cycle demonstrate that the proposed system achieves an equivalent fuel-saving rate of 50.8% compared with a conventional diesel system, with the engine operating exclusively in its high-efficiency zone (>42% efficiency) for only 35% of the operational time. A full life-cycle techno-economic analysis reveals an incremental investment payback period (PBP) of approximately 3 months and a net present value (NPV) exceeding USD 9.69 million over a 10-year period. Quantitative environmental analysis shows an annual reduction of approximately 94.8% in CO₂ emissions (assuming the use of green methanol produced from renewable sources and captured CO₂), 95% in NO_x emissions, and the near-elimination of SO_x and particulate matter (PM). This study provides a systematic and economically attractive solution with promising engineering feasibility verified by simulation, which paves the way for further experimental validation and practical engineering implementation. Full article

Multi-motor electric heavy-duty vehicles face significant energy efficiency challenges due to the complex coordination of discrete gear selection and continuous energy flow allocation in the powertrains. This study aims to address this coordination dilemma by proposing a multi-agent deep reinforcement learning energy management strategy (EMS). The framework employs collaborative control across three agents to simultaneously optimize middle axle/rear axle gear shifts (DQN) and power distribution (DDPG), effectively handling the hybrid action space. A specialized rule is integrated to accelerate convergence and enhance real-cycle adaptability. Simulation results on CHTC-TT and CHTC-HT cycles show the proposed strategy achieves only 3.14% and 4.65% higher energy consumption, respectively, compared to a rule-optimized benchmark. This validates its practicality and robustness for real-world electric heavy-duty transportation applications. Full article

When looking towards the goal of reducing greenhouse gas (GHG) emissions, automotive manufacturers face several challenges when planning future vehicle offerings in different markets. The planned vehicle offerings must cope with uncertainties in the supply chains of critical materials and adhere to regulatory requirements in different regions, all while appealing to customer preferences and maintaining low cost. Regulatory requirements, which are often based on tailpipe GHG emissions, do not necessarily align with Lifecycle Analysis (LCA) of GHG emissions, which becomes yet another challenge towards attaining sustainability goals. Planning the future mix of vehicles to be manufactured under all such considerations can be a complex task, often relying on methods with poor transparency, unguaranteed optimality, or requiring difficult-to-predict a priori knowledge. This paper considers the special case of a short time window (one future model–year), which allows for modelling the future planning decisions as a linear programming (LP) problem, which in turn, can be solved to global optimality via well-established algorithms, such as Dual-Simplex. The proposed formulation is demonstrated via one simple example, as well as a scaled-up study with two regions, two vehicle size categories, and four powertrain configurations. A key insight that the proposed formulation is able to demonstrate in the scaled-up study is how the optimum (lowest) LCA GHG solution depends on the availability of battery materials, ranging from an increased share of hybrids under low battery supply to an increased share of electric vehicles for abundant battery supply. Full article

The complexity of modern vehicle control systems, the increasing diversity of powertrain and exhaust aftertreatment applications, and the need for shortened development times require innovative approaches towards calibration. This paper presents an experimental, analytical, and modeling study of particulate filter (commonly called DPF—diesel particulate filter) in a diesel hybrid vehicle where models have been developed to simulate test data, replacing the requirement of numerous tests on testbed or on the road with system simulations and offline parameter optimisation techniques. A soot estimation model has been developed based on the operation of the engine including its transient response, and the thermal–chemical behaviour of the DPF. A methodology has been developed to optimize the calibratable maps and parameters within this model. The results show that the proposed method improves the accuracy of soot estimation in the engine transient operation and avoids a large number of experimental tests required in traditional calibration methods. Modern automotive manufacturers face regulatory compliance requirements ensuring emission standards across diverse real driving emission (RDE) boundary conditions encompassing route characteristics, driving dynamics, and ambient environmental variables throughout vehicles’ operational lifetime. The soot load in the DPF and the DPF regeneration frequency can massively impact the tailpipe NO_x emissions and overall fuel consumption, so it is key to accurately estimate the soot accumulation in all operating conditions. This means testing and validating calibration in each possible scenario and so needs an enormous number of tests on testbed and on the road. These tests, however, can be replaced with system simulations and offline calibration if we have a robust model for the system, as described in the following parts of this paper. Full article

Laboratory test results for vehicle emissions, fuel economy, and driving range often fail to reflect real-world performance, undermining the effectiveness of sustainability policies and consumer guidance. This study provides the first integrated national assessment of real-world emissions and range outcomes for passenger vehicles in Australia. Using Portable Emissions Measurement Systems (PEMS) data from 114 petrol, diesel, hybrid, and battery-electric vehicles (BEVs) tested by the Australian Automobile Association (AAA), the analysis compares laboratory-certified values against on-road results and benchmarks them with international datasets from Europe and China. Real-world CO₂ emissions were, on average, 6.9% higher than laboratory ratings for petrol vehicles and 3.2% higher for diesel vehicles. Many diesel models exceeded Euro 6 NO_x limits by several multiples, while hybrids exhibited inconsistent CO₂ reductions under urban conditions. BEVs also displayed measurable divergence: real-world energy consumption was 1–20% higher than laboratory ratings, resulting in an average 16% reduction in effective driving range relative to WLTP values. These outcomes reveal a consistent tendency toward overstated laboratory performance across powertrains, highlighting systemic shortcomings in certification test cycles. The findings have direct implications for greenhouse gas mitigation, urban air quality, and consumer energy efficiency and support Australia’s active transition to WLTP and Euro 6 standards, institutionalisation of real-world testing, and inclusion of verified real-world energy use and range data in consumer labelling to enhance transparency and policy effectiveness. Full article

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H₂), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H₂ and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H₂, and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H₂ and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

Decarbonisation of the aviation sector is essential for achieving global-climate targets, with hydrogen propulsion emerging as a viable alternative to battery–electric systems for vertical flight. Unlike previous studies focusing on clean-sheet eVTOL concepts or fixed-wing platforms, this work provides a comprehensive retrofit evaluation of a two-seat light helicopter (Cabri G2/Robinson R22 class) to a hydrogen–electric hybrid powertrain built around a Toyota TFCM2-B PEM fuel cell (85 kW net), a 30 kg lithium-ion buffer battery, and 700 bar Type-IV hydrogen storage totalling 5 kg, aligned with the Vertical Flight Society (VFS) mission profile. The mass breakdown, mission energy equations, and segment-wise hydrogen use for a 100 km sortie are documented using a single main rotor with a radius of R = 3.39 m, with power-by-segment calculations taken from the team’s final proposal. Screening-level simulations are used solely for architectural assessment; no experimental validation is performed. Mission analysis indicates a 100 km operational range with only 3.06 kg of hydrogen consumption (39% fuel reserve). The main contribution is a quantified demonstration of a practical retrofit pathway for light rotorcraft, showing approximately 1.8–2.2 times greater range (100 km vs. 45–55 km battery-only baseline, including respective safety reserves). The Hawk demonstrates a 28% reduction in total propulsion system mass (199 kg including PEMFC stack and balance-of-plant 109 kg, H₂ storage 20 kg, battery 30 kg, and motor with gearbox 40 kg) compared to a battery-only configuration (254.5 kg battery pack, plus equivalent 40 kg motor and gearbox), representing approximately 32% system-level mass savings when thermal-management subsystems (15 kg) are included for both configurations. Full article