Mild Hybrid Electric Vehicle Research Articles

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.

Reducing Greenhouse Emissions from Light-Duty Vehicles: Supply-Chain and Cost-Effectiveness Analyses Suggest a Near-Term Role for Hybrids

<div>Policy makers generally favor all-electric vehicles over hybrid-electric vehicles because of greater unit effectiveness in reducing carbon emissions. Since both systems use lithium-ion batteries, global demand for batteries is projected to grow 10-fold by 2030. If any step in the global battery supply-chain experiences bottlenecks, shortages can occur. We use a novel cost-effectiveness metric, carbon reduction per unit of battery capacity consumed, to rank emissions reductions accomplished by, alternatively, eight plug-in hybrid-electric vehicles, 75 hybrid-electric vehicles, and 230 mild hybrid-electric vehicles, which have the same total battery capacity as one all-electric vehicle. Our main finding, although counterintuitive, is that, with limited battery supplies, larger reductions in carbon emissions can be accomplished by hybrids than by all-electric vehicles. Insofar as carbon emission reduction is the policy goal, policies need to be reconsidered to ensure that they are not tilted too far in favor of all-electric vehicles relative to hybrids.</div>

Bifunctional additive: Lead dioxide nanoparticle-doped graphene oxide composites for the preparation and performance study of positive electrodes in lead-carbon batteries

Lead-carbon batteries (LCBs) possess the dual functions of supercapacitors and lead-acid batteries (LABs), which can meet the demand for renewable energy and in mild hybrid electric vehicles (HEVs) for energy storage and short-term high-rate charging and discharging. With the cycle of high-rate partial state-of-charge (HRPSoC), irreversible sulfation of the positive electrode becomes increasingly prominent and serious, resulting in low discharge capacity and short cycle life. In this study, to reduce the sulfation of the positive plate and improve the battery capacity and cycle life, a lead dioxide nanoparticle-doped graphene oxide (nano-PbO2/GO) composite material with dual functions of conducting and nucleating was prepared by a hydrothermal method as a positive electrode additive. The results show that the addition of nano-PbO2/GO promotes the formation of a three-dimensional short rod-like structure of PbO2 nanoparticles in a dendritic arrangement, accelerates the generation of new phases and inhibits the growth of bulk PbSO4. Moreover, the positive plate with nano-PbO2/GO exhibits good electrochemical activity to maintain the stability and reversibility of the electrode redox reaction. Finally, the initial discharge specific capacity of the LCBs with nano-PbO2/GO is increased to 153.85 mAh/g, and the HRPSoC cycle life is up to 15658 cycles at a 2 C discharge rate. Overall, the dual functions of nano-PbO2/GO greatly alleviate battery sulfation and provide a reference for improving the performance of LCBs by modifying the electrode structure.

Energy conversion analysis for mild hybrid electric vehicles equipped with an electric supercharged SI engine via multi-domain acausal modeling

Mild hybrid electric systems, which are highly dependent on engine performance and use less scarce resources, are powertrain systems that can promote immediate vehicle electrification worldwide while taking advantage of existing infrastructure and facilities. In this study, a 12-V mild hybrid electric vehicle (MHEV) with an electric supercharged spark ignition (SI) engine, which is a complex system that integrates an engine, supercharger, electric motor, and battery, was physically modeled using Modelica, a multi-domain acausal modeling language, for a small and light-weight vehicle (vehicle mass: <1000 kg) that aims to make effective use of regenerated power. Additionally, the developed model was used to simulate driving cycle in realistic time and to quantitatively analyze and understand the regenerative power energy conversion process across multiple physical domains and the fuel consumption improvement mechanism in detail and dynamic performance improvement of the electric supercharger. As a result, in the target compact 12-V MHEV with an electric supercharged SI engine, although the intake enthalpy increase due to the intake air compression by the electric supercharger is negligible, the increase in engine speed can be suppressed as the engine torque increases due to supercharging, reducing the engine friction work, in turn resulting in a fuel consumption improvement of approximately 2.5 % under the WLTC mode driving cycle. Furthermore, the electric supercharged SI engine dynamic performance improved by more than 20 %.

Common DC-Link Capacitor Harmonic Current Minimization for Cascaded Converters by Optimized Phase-Shift Modulation

This paper investigates the influence of a constant carrier phase shift on the DC-link capacitor harmonic current of cascaded converters used in fuel-cell and mild-hybrid electric vehicles. In these applications, a DC-DC converter can be adopted between the battery and the motor drive inverter in a cascaded structure, where the two converters share the same DC-link. Since the DC-link capacitor of such a system represents a critical component, the optimization of the converter operation to limit the current stress and extend the lifetime of the capacitor is an primary objective. This paper proposes the use of a carrier phase shift between the modulations of the two converters in order to minimize the harmonic current of the DC-link capacitor. By harmonic analysis, an optimal carrier phase shift can be derived depending on the converter configuration. Analytical results are presented and validated by hardware-in-the-loop experiments. The findings show that the pulse width modulation carrier phase shift between the interleaved boost converter and the voltage source motor drive inverter has a significant influence on the DC-link capacitor current and thus on its lifetime. A case study with two-cell and three-cell interleaved boost converters shows a possible DC-link capacitor lifetime extension of up to 390%.

Concept evaluation of a P2 MHEV SUV: application for possible EU7 boundaries

In this work, the experimental results that appeared in the recent published article “Current experimental developments in 48 V-based CI-driven SUVs in response to expected future EU7 legislation” are used to create a proper system simulation model with the simulation platform AVL CRUISE^text {TM} M. This simulation model is then used to perform a system validation in order to evaluate the configuration with a straight-four compression ignition (CI) engine and the selected exhaust aftertreatment system (EAS). The mild hybrid electric vehicle (MHEV) has an 48 V P2 architecture and an 8-gear dual-clutch transmission (DCT) as a powertrain configuration. In addition to evaluating the 48 V potential, the simulation is performed with a conventional 12 V configuration, but also including an electrically heated catalyst (EHC). As boundary conditions for the simulation, we use the different engine operating mode (EOM) calibrations from the test bed to trigger the dedicated operation modes of the internal combustion engine (ICE). For the exhaust aftertreatment system (EAS), an optimization loop is performed to obtain a layout which will be near a serial production. This includes optimizing the heat losses and reducing the thermal mass of the canning. Beside the plant models, a hybrid control unit (HCU) is used, which includes an exhaust aftertreatment system coordinator (EASC). With these functionalities, the EOMs, electrically heated catalyst (EHC), electric machine (EM) and dosing control unit (DCU) are optimized to obtain the lowest possible nitrogen oxides (NOx) with an carbon dioxide (CO_{2}) reduction potential. The targets for the emission limits are defined on the basis of the available information from the Consortium for ultra-Low Vehicle Emissions (CLOVE) and International Council on Clean Transportation (ICCT) proposals.

Reducing Air Pollutant Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst warm-up in mild HEV via Cascaded MPC

In this paper, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.

Reducing NOx Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst Warm-up in Mild HEVs

In this paper, hierarchical model predictive control is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between the temperature control (high-level) and the torque control (low-level), which are connected via the battery. In high-level of the hierarchical control, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At the low-level of the hierarchical control, the optimal torque distribution for the engine and motor is determined. The controller at both levels then applies model predictive control to consider the future behavior of the HEV's components. Fuel consumption, battery SoC and NOx emissions from the engine are taken into account. Finally, the effectiveness of our proposed method is demonstrated by simulations.

Energy Management Strategy of Mild Hybrid Electric Vehicle Considering Motor Power Compensation

An energy management control strategy based on the instantaneous optimization method of equivalent consumption minimization strategy (ECMS) under motor power compensation for mild hybrid vehicles is proposed in this study to improve fuel economy and ensure the dynamic performance of cars. A mild hybrid platform is built, and the future supplementary model of electric energy and the future consumption model of electric energy are established according to different power flow directions. It determines the equivalent fuel consumption rate of powertrain as the objective function by defining the equivalent factor and corresponding derivation, carries out optimization calculation, and obtains the energy distribution relationship between the engine and the motor. The motor power compensation strategy based on the control strategy is adopted to solve the effect of turbocharged engines’ transient response on vehicle dynamics and fuel economy. The actual results showed that vehicle power and fuel economy can be improved under the control strategy and compensation strategy design. Meanwhile, different motors allow the compensating coefficient to have different power-boosting and fuel economy effects.

Development of Optimization Based Control Strategy for P2 Hybrid Electric Vehicle including Transient Characteristics of Engine

Models based on steady-state maps estimate fuel consumption to be 2–8% lower than real experimental measured values. This is due to the fact that during transient phases, the engine consumes more fuel than in steady phases. Some literature has addressed the conventional vehicle engine model that improves fuel consumption estimation’s accuracy during the transient state. However, the characteristics of the engine in the scope of hybrid electric vehicles (HEVs) with an integrated control strategy is yet to be covered. The controller is designed to minimize engine operation in the transient phase to enhance energy savings. In this paper, the correlation between fuel enrichment in transient and steady-state fuel estimation is established as transient correction factor (TCF). Its explanatory variable was the engine torque change rate. This paper describes the influence of engine transient characteristics on the fuel consumption of a mild HEV. The work attempts to improve the fuel economy of the HEV by introducing a penalty factor in the controller to optimize the use of the engine in transient regimes. A backward vehicle model was developed for a production vehicle with a conventional powertrain and validated experimentally using data available online. The corresponding hybrid vehicle model was developed by integrating the electric motor and battery components with the conventional vehicle model. A P2 off-axis configuration was chosen to this end as the HEV architecture. A conventional equivalent consumption minimization strategy (ECMS) was used to split the torque request between the engine and the electric motor. This control strategy was modified with TCF to penalize the engine torque change rate. The results of the simulation show that due to less transient operation of the engine, the fuel consumption was reduced from 923 g to 918 g under the US06 driving cycle. The fuel economy of the model has been simulated for UDDS and HW drive cycles too, and fuel consumption improved by 4.4 g and 3.2 g, respectively. It has been verified that by increasing the battery capacity twice (14s24p), the limitations imposed by the battery capacity can be minimized and the fuel usage can be reduced by 9 g in the UDDS cycle.

Real-Time Torque-Split Strategy for P0+P4 Mild Hybrid Vehicles With eAWD Capability

The 48-V P0+P4 mild hybrid electric vehicles (MHEVs) have been drawing attention for their potential in fuel economy (FE) improvement and electric all-wheel-drive capability through a dual-motor system. This P0+P4 system, enabled by the addition of an extra P4 motor to an existing P0 MHEV, poses the new control problem of optimal torque-split among three components, namely, an internal combustion engine and P0/P4 motors. This study presents an effective real-time torque-split strategy for a 48-V P0+P4 MHEV. To include realistic operational constraints, we consider the longitudinal load transfer of the vehicle, nonlinear tire effects, and constraints on braking force distribution for vehicle safety. First, an optimal torque-split problem for the considered P0+P4 MHEV is formulated and solved with dynamic programming (DP). Then, based on the DP results, we propose a real-time-implementable torque-split strategy using an approximated adaptive equivalent consumption minimization strategy and a suboptimal braking force distribution strategy. The simulation results reveal that the proposed strategy can achieve about 99.1% and 97.7% of global optimality in terms of FE under training and validation driving cycles compared to the results by the DP. As a comparison, the rule-based strategy, as a benchmark, achieves 93.6% and 94.5% under the same drive cycles.

Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of fire incidence. Hence, thermal limitations on the battery could be implemented in a supervisory controller to avoid such risks. A vast literature on the topic shows that the problem of battery thermal runaway is solved by applying active cooling or by implementing penalty factors on electric energy utilization for large capacity battery packs. However, they do not address the problem in the case of passive cooled, small capacity battery packs. In this paper, an experimentally validated electro-thermal model of the battery pack is integrated with the hybrid electric vehicle simulator. A supervisory controller using the equivalent consumption minimization strategy with, and without, consideration of thermal limitations are discussed. The results of a simulation of an MHEV with a 0.9 kWh battery pack showed that the thermal limitations of the battery pack caused a 2–3% fuel consumption increase compared to the case without such limitations; however, the limitations led to battery temperatures as high as 180 °C. The same simulation showed that the adoption of a 1.8 kWh battery pack led to a fuel consumption reduction of 8–13% without thermal implications.

Fuel economy improvement and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures with lithium battery cell experimental data

This paper intends to provide design selections of hybrid powertrain architectures in 48 V mild hybrid electric vehicles. Based on the location of the electric machine in the driveline, the hybrid powertrain architectures can be categorized into five groups, P0, P1, P2, P3, and P4. This paper uses simulation software to investigate the fuel economy improvements and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures. A baseline conventional and a 12 V start/stop vehicle models based on the production vehicle are built for comparison. The 48 V battery pack model is based on experimental data including open-circuit voltage and internal resistance of a 20 Ah lithium polymer battery cell. Four standard driving cycles are used to assess the fuel economy and emissions of the vehicle models. With features of engine idle elimination, electric power assist, and regenerative braking, the 48 V P0 and P1 respectively gains average 13.5% and 15.5% simulated fuel economy compared to baseline vehicle. The 48 V P2 enables feature of electric launch/driving and improves the fuel economy by average 18.5% better than baseline vehicle. The 48 V mild hybrid system seems to be one of the promising techniques to meet future fuel economy standards and emission regulations.

A Method for Battery Sizing in Parallel P4 Mild Hybrid Electric Vehicles

A Method for Battery Sizing in Parallel P₄ Mild Hybrid Electric Vehicles

Effect of synergistic engine technologies for 48 V mild hybrid electric vehicles

48 V mild hybrid electric vehicles (MHEVs) are considered to the prospective near-term solution for cost-effective vehicle electrification. In this study, the fuel consumption improvement of various synergistic engine technologies was experimentally investigated with a 48 V P0 mild hybridization. The details of application and optimization for each technology were considered and the effect of each technology was quantitatively identified. A combination of synergistic engine technologies including a high compression ratio, late intake valve closing strategy, high tumble flow, electric supercharging and exhaust gas recirculation was applied to a 1.4 L PFI engine. Engine test results confirmed that fuel consumption could be significantly improved by these technologies, and the maximum power and torque were increased as well. The vehicle test results showed that the application of synergistic engine technologies improved vehicle fuel economy by 10%. Engine optimization and EGR could enhance the fuel economy by 5.5% and the effect of electric supercharging and downspeeding resulted in a 4.5% fuel economy improvement. These improvements through the engine technologies were greater than the effect of 48 V mild hybridization which was verified to be 5.5% of the fuel economy enhancement. The result suggests that the application of synergistic engine technologies will be highly effective for improving fuel efficiency of MHEV.

Hierarchical Optimization Decision-Making Method to Comply with China’s Fuel Consumption and New Energy Vehicle Credit Regulations

The national targets of reaching carbon peak in 2030 and carbon neutrality in 2060 propose higher requirements for energy conservation and emission reduction of China’s automobile industry. As an important measure for the government, the fuel consumption and new energy vehicle (NEV) credit policy system has a significant impact on the Chinese and even the global vehicle market. Considering the lack of a systematic evaluation model for China’s fuel consumption and NEV credit regulations, this study establishes a hierarchical optimization decision-making model based on technology frontier curves and a multi-dimension database containing extensive data of technologies, products, and enterprises in the Chinese market to simulate and evaluate the technology compliance and policy impact under multiple regulations. The results show that, from the perspective of the technology frontier curve, gasoline technologies still have great cost-effectiveness advantages when the fuel-saving requirement is less than 46%, and the space for plug-in hybrid electric vehicles (PHEVs) and range-extended electric vehicles (REVs) is gradually shrinking due to the cost reduction of battery electric vehicles (BEVs). BEV400 will be better than PHEV70 and REV100 when the fuel-saving requirement is higher than 79%. Diesel vehicles are always not competitive in the passenger car market. In terms of the compliance of corporate average fuel consumption (CAFC) regulation, the start-stop technology will be gradually phased out and mild hybrid electric vehicles will be rapidly introduced due to their high cost-effectiveness in 2025. With the tightening of regulations, the penetration rate of BEVs and PHEVs will be 23.7% and 6.7%, respectively, and mild hybrid electric vehicles will be gradually replaced by strong hybrid electric vehicles in 2030. By 2035, the penetration rate of BEVs and PHEVs will be 43.6% and 6% further. For the CAFC and NEV credit regulation (widely known as the dual credit regulation), the single-vehicle credit poses a greater impact on the penetration of NEVs than corporate credit percentage limitation and is the key factor that should be focused on. The NEV credit limitation in the dual credit regulation could push ‘poor performance’ automakers to produce the required number of NEVs and meet the bottom line. However, in the long term, when compared to the CAFC regulation, the dual credit regulation is more lenient, due to NEVs being able to get double benefits both on NEV credit and CAFC credit, and NEV credit can also unidirectionally compensate CAFC credit under the dual-credit policy context. With the increased penetration and cost reduction of NEVs, the ‘averaging’ effect of dual credit regulation will inhibit the development of energy-saving and new energy vehicles. Therefore, eliminating the connection between NEV credit and CAFC credit or only leaving the CAFC and the fuel consumption limit regulations in the future will be better for the long-term development of the energy-saving and new energy vehicle industry.

Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements

This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and transient response, and different operating conditions of the DC-DC converter, including the buck and boost modes. A deep learning architecture based on a long-short-term memory neural network (LSTM-NN) is applied. The trained network is tested under a set of operating points different from those used during the training stage. The proposed method is compared with three black-box modeling techniques commonly used in power converters, proving its superior performance. Results presented in this paper indicate that the proposed model is able to replicate the behavior of the bidirectional converter without a priori knowledge of the converter circuitry. This approach can also be applied to other power devices.

Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm

Global optimization plays an important role in the energy management strategies (EMS) of the hybrid electric vehicles (HEV). The fuel consumption of HEV could be reduced significantly with an acceleration of global optimization and application of global result in real-time control. In this paper, a new algorithm called breadth first search (BFS) was first used to realize the global optimization in a parallel mild HEV, which transforms the energy management problem of HEV into optimal path searching. Through simulation and calculation, it was found that the totally identical control strategies and fuel consumption could be obtained with BFS and dynamic programming (DP) respectively, while the calculation time for BFS was just about 50%-60% of that. With BFS results as reference, particle swarm optimization was used to adjust the equivalent factor in real-time and an adaptive equivalent consumption minimization strategy (A-ECMS) based on BFS was proposed. The fuel consumption could be decreased with the proposed A-ECMS by 8–15% in different driving cycles compared with that using rule-based strategies. It is believed that BFS has great potential in the future research on EMS of the HEVs.

Energy management and gear shifting control for a hybridized vehicle to minimize gas emissions, energy consumption and battery aging

The greenhouse effect and air pollution are a world concern considering their impact on the environment, human health, and economy. In this field, a significant impact is caused by road transportation emissions. Among the possibilities of alternative transportation, hybrid electric vehicles are a reliable and easy-to-implement solution, combining the high efficiency of pure electric propulsion systems and driving range provided by internal combustion engines. Therefore, this research aims to assess a conventional vehicle converted into a mild hybrid electric vehicle with a single-shaft hybrid series powertrain architecture, where an electric motor is directly coupled to the engine drive belt. A gear shifting control and a fuzzy logic power-split are applied and optimized by an adaptive-weight genetic algorithm, considering the potential impact on battery degradation, charging time, electric powertrain size and life cycle costs. The simulation is based on a realistic driving cycle of Campinas city, one of the largest cities in Brazil. The result is an optimized single-shaft hybrid series powertrain configuration, able to enhance overall vehicle energy efficiency, decreasing the travel cost by 15.18% without compromising its battery lifetime, reducing air pollutant emissions in 16.71% CO, 22.77% HC and 44.36% NOx.

Design and Optimization of a Mild Hybrid Electric Vehicle with Energy-Efficient Longitudinal Control

Design and Optimization of a Mild Hybrid Electric Vehicle with Energy-Efficient Longitudinal Control