Engine Start-stop Research Articles

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car With Engine Start-Stop System and Dynamic Coordination

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car With Engine Start-Stop System and Dynamic Coordination

Research on Plug-in Hybrid Electric Vehicle (PHEV) Energy Management Strategy with Dynamic Planning Considering Engine Start/Stop

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.

A unified benchmark for deep reinforcement learning-based energy management: Novel training ideas with the unweighted reward

Deep reinforcement learning stands as a powerful force in the realm of intelligent control for hybrid power systems, yet some imperfections persist in the positive progression of learning-based strategies, necessitating the proposal of essential solutions to address these flaws. Firstly, a public and reliable benchmark model for hybrid powertrains and the optimization results of energy management strategies are essential. Hence, two Python-based standard deep reinforcement learning agents and four Simulink-based hybrid powertrains are employed, forming a co-simulation training approach as the reliable solution. Secondly, a detailed analysis from the perspectives of range, magnitude, and importance reveals that the optimization terms in traditional reward functions can mislead the agent during the training process and require cumbersome weight tuning. Accordingly, this paper proposes a novel training idea that combines the rule-based engine start-stop with an unweighted reward tailored for optimizing engine efficiency and facilitating training progress. Finally, a hardware-in-the-loop test is performed, treating the P2 hybrid electric vehicle as the target. The results show that two deep reinforcement learning-based energy management strategies achieved fuel economies of 6.537 L/100 km and 6.330 L/100 km, respectively, and more efficient and reasonable control sequences ensure the working state of the engine as well as the state of charge of batteries.

A new accelerated thermal fatigue experiment method of pistons and its application

This study proposed a new test method to take the piston instead of the internal combustion engine as the experimental object and use electromagnetic induction heating and auxiliary cooling to simulate the actual operating conditions of the piston during engine start-stop, to achieve accurate control of the temperature of the heating and cooling cycles. The experimental process can be accelerated by worsening the service conditions. In order to prove the feasibility of this scheme, the temperature field distribution, fatigue life, and failure mode of the piston are compared and analyzed using finite element simulation, theoretical calculation, and surface topography observation. The results show that the error between the simulated temperature value of the key point of the piston and the experimental value of the actual operating condition is less than 5%, the position and value of the maximum temperature of the piston under the thermal fatigue test condition are consistent with the actual engine operating condition, the error between the fatigue life test results and the theoretical calculation results is less than 10%, and the location and cracking mode of the main crack is consistent with the actual working condition. It is proved that the test method is reasonable and feasible, which can provide an important guarantee for the fast and efficient design of high-performance pistons.

Pre-optimization-assisted deep reinforcement learning-based energy management strategy for a series–parallel hybrid electric truck

P2-P3 series-parallel hybrid electric vehicles (HEVs) feature intricate topologies with multiple power sources and multiple working modes, posing a challenge for developing effective energy management strategies (EMSs). This paper introduces a framework that combines deep reinforcement learning (DRL) with pre-optimized energy management to address this challenge. Considering the characteristics of HEVs, the framework incorporates the pre-optimized equivalent motor and pre-optimized mode selection rule into a deep deterministic policy gradient (DDPG) loop. The pre-optimization of the equivalent motor aims to reduce the energy management task’s dimensionality by equating two motors as the equivalent motor, while the pre-optimized mode selection rule systematically integrates mode selection and energy allocation into the EMS. The study assumes HEVs operate in connected urban environments and incorporates middle-horizon traffic information to enhance EMS performance. Additionally, the study integrates the frequent engine start-stop characteristics of HEVs into EMS design. Simulation results demonstrate that the fuel economy of the proposed EMSs in this study ranges from 87.2% to 90.7% of the DP-based EMS. The inclusion of an engine start-stop penalty significantly reduces the number of engine start-stops without compromising fuel economy.

Remote Vehicle Monitoring System Starting and Tracking Using IoT System

Safety in the vehicle is one of the most important factors in the daily life of every vehicle user. This project is more focused on improving vehicle systems to increase vehicle safety systems. The main objective of this project is to study IoT (Internet of Things) safety systems in automotive applications. Next is to design engine start-stop and tracking for motorcycles. In addition, to analyze battery voltage, GPS location data, and engine start-stop system from the motorcycles with safety features. The vehicle used for this project is a Yamaha 135LC version 2 motorcycle, the Blynk app, and a two NodeMCU ESP8266 microcontroller with a Wi-Fi module, a relay, and a GPS module. As a result of the start-stop engine, PicoScope was used to analyze the voltage of the battery after the system had fully functioned when the signal was sent to the relay by Blynk apps. The experiment was carried out from five different locations for the engine start-stop system and seven different locations for the GPS, with the engine start-stop system obtaining motor battery voltage readings using PicoScope and the GPS obtaining vehicle coordinate results by reading the latitude and longitude from the smartphone. The GPS data was then compared between the location of the GPS module and the location of the smartphone's GPS to determine the accuracy of the location.

Frictional vibration behaviors of a new piezo-damping composite under water-lubricated friction

Water-lubricated bearings have become an important component of marine propulsion systems due to the requirements of environmental friendliness and concealability. However, water-lubricated bearings are often subjected to bending stress, additional stress, frictional resistance and other multi-factor effects, resulting in boundary friction or even dry friction on the friction interfaces when encountering unique working conditions such as engine start-stop, steering, low-speed, and heavy-load. The severe frictional processes often induce vibration behaviors and generate frictional noise, which seriously weakens the sound concealment performance and service life of marine propulsion systems. Therefore, it is urgent to improve the vibration and sound pressure behaviors of the composite bearings under water-lubricated conditions. In this study, novel barium titanate/carbon black/polyurethane composites (BaTiO3/CB/PU) are fabricated and then explored the damping capacities and tribological properties of the new polymers. The results showed that the BaTiO3 concentrated on transforming the mechanical energy generated by frictional vibration into electrical energy which generates the thermal energy. The water cooled the frictional heat and the piezo-damping heat. This energy dissipation method increased the damping capacity to reduce the vibration and noise behaviors induced by friction. The composite with 3.0 wt% BaTiO3 performed the highest loss factor under the operating temperatures and presented the best frictional vibration reduction properties. The knowledge obtained in this study is useful not only to explore a new piezo-damping composite, but also to provide a theoretical basis for developing higher performance polymer-based water-lubricated bearing.

Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start–Stop System

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.

Effects of a start-stop system for gasoline direct injection vehicles on fuel consumption and particulate emissions in hot and cold environments

Engine start-stop (S&S) technology has been substantially incorporated into modern vehicles to save fuel during idling in congested urban areas because fuel economy regulations have become more stringent. However, the potential for increasing particle emissions after engine restarts, especially in cold environments, is of great concern. To investigate the effects of S&S systems on fuel consumption and tailpipe emissions, a chassis dynamometer was employed to measure the fuel consumption, particulate matter (PM), solid particle number (PN), particle number size distribution and black carbon (BC) for a typical gasoline direct injection vehicle when the S&S was on (S&S-on) and when the S&S was off (S&S-off) according to the worldwide harmonized light-duty test cycle in both hot (28 °C) and cold (5 °C) environments. S&S operation resulted in 3.1–4.3% fuel-savings at 28 °C but had a tendency to increase particulate emissions, especially of BC (21.8–31.8%) and PM (19.2–32.8%). Although PN emissions with S&S-on over the entire cycle were slightly lower than those with S&S-off, more particles were emitted during the engine restart moments. In a cold environment, the fuel-savings advantage of the S&S system was weakened, and the negative impacts on the particle emissions during the restart moment worsened. The S&S system resulted in higher abundances of accumulation mode particles, especially under cold ambient conditions. The relationship between the PN reduction rates and idling segments was determining to be exponential. Our results indicate that the S&S system, which may increase particle emissions during restarts, does save fuel, and that a comprehensive evaluation of the system in cold environments is needed to determine the serviceability of new engine technologies and after-treatments.

Double Deep Reinforcement Learning-Based Energy Management for a Parallel Hybrid Electric Vehicle With Engine Start–Stop Strategy

Committed to optimizing the fuel economy of hybrid electric vehicles (HEVs), improving the working conditions of the engine, and promoting research on deep reinforcement learning (DRL) in the field of energy management strategies (EMSs), this article first proposed a DRL-based EMS combined with a rule-based engine start–stop strategy. Moreover, considering that both the engine and the transmission are controlled components, this article developed a novel double DRL (DDRL)-based EMS, which uses a deep Q-network (DQN) to learning the gear-shifting strategy and uses a deep deterministic policy gradient (DDPG) to control the engine throttle opening, and the DDRL-based EMS realizes the multiobjective synchronization control by different types of learning algorithms. After off-line training, the simulation result of the online test shows that the fuel consumption gaps of the proposed DRL- and DDRL-based EMSs are −0.55% and 2.33% compared to that of the deterministic dynamic programming (DDP)-based EMS by overcoming some inherent flaws of DDP, respectively. The computational efficiency has been significantly improved, and the average output time per action is 0.91 ms. Therefore, the control strategy that combines learning- and rule-based controls and the multiobjective control strategies both have the potential to ensure optimization and real-time efficiency.

Response of centrifugal pendulum vibration absorber considering gravity at low engine speed

This study examined the performance of the centrifugal pendulum vibration absorber’s (CPVA) under the influence of centrifugal force and gravity fields. The focus was on identifying the factor that influenced noise, vibration, and harshness (NVH) problems that originate owing to the centrifugal pendulum absorber being at low engine speed such as engine start-stop or idle. Consequently, mathematical models were established employing Lagrange equation of the second kind, analytical solutions were obtained using the method of multiple scales, and several simple elementary mathematical equations describing the movement of the absorber were derived. Further, the analytical solutions were compared with the numerical simulation results of the complete non-linear equation of motion, which indicated that the former were suitable for studying the performance of CPVA. The theoretical analysis of the application with four absorbers and two absorbers under gravity field was conducted. The absorbers were found to be not always synchronized in the gravity field in certain special conditions such as lower engine speed range or small viscous damping. It was concluded that larger viscous damping coefficient can result in an improvement in the synchronization or reduce the range of nonlinear jumps caused by changes in excitation torque. Further, super-harmonic resonance of CPVA under the gravity field was determined and exact response was calculated, wherein the term “2nd order response gravity impact factor” was defined. In addition, the effects of CPVA installation angle relative to engine ignition angle were analyzed, and a set of tautochronic epicycloid CPVA was tested to verify the findings. The analytical solutions were consistent with the test results, thereby proving the applicability of the research method and results in a vehicle.

The intelligent engine start-stop trigger system based on the actual road running status.

With the rapid development of urbanization and the popularization of the vehicle, the frequent occurrence of traffic jams results in idling fuel waste, environmental pollution, and other issues. In order to alleviate these problems, engine start-stop technology has been widely used in different types of vehicles in recent years. However, current start-stop trigger technology has many deficiencies, such as mistaken triggering and frequent engine start-stop, which greatly reduces user driving experience, causing most of them to deactivate this system. The intelligent engine start-stop trigger (IEST) system based on the actual road running status was established by building the image recognition model and the digital traffic analysis model in order to solve this problem. A system test shows that IEST can avoid frequently engine starting and stopping. The results show that IEST could effectively improve the driving experience and reduce engine fuel consumption, and it promotes conventional engine start-stop technology.

Effect of Strain Rate on the Deformation Behaviour of A356-T7 Cast Aluminium Alloys at Elevated Temperatures

Internal combustion engine downsizing and powertrain electrification trends in recent years have led to higher thermal loads on the cylinder head materials with an increased number of engine start–stop thermal load cycles. This requires designing cylinder heads that are resilient against thermomechanical fatigue damage. To reduce the developmental costs, reliable numerical models for use in computer-aided design approaches are required. Thus, a comprehensive understanding of the material deformation behaviour under loads mimicking in-service conditions is desired to make better engineering decisions. This study examines the effect of strain rate on the cyclic deformation behaviour of the A356-T7 + 0.5% Cu aluminium alloy commonly used in modern internal combustion engine cylinder heads. Samples extracted from the valve bridge areas of the cylinder heads are tested in strain-controlled fatigue tests. Samples are tested at strain rates of 1% s−1 and 10% s−1 at room temperature, 150 and 200 °C. The material exhibits increased isotropic hardening and softening rates and an increased number of cycles to failure at 10% s−1. The strain rate has a dramatic influence on the mean stress development at room temperature. The role of silicon particles in the fracture mechanism is investigated using electron microscopy techniques.

Study on vibration suppression method of vehicle with engine start-stop and automatic start-stop

On the basis of previous research, this paper takes a more comprehensive study on the vibration of a vehicle with automatic transmission when engine start-stop and automatic start-stop. A 13 degrees of freedom (DOFs) vehicle dynamic model is established, and the dynamic response evaluation method is put forward. Three methods to reduce the vibration of powertrain and vehicle are analyzed, and the results show that increasing the damping of the mounting system can effectively reduce the vibration in these three methods. A novel semi-active hydraulic damping strut (HDS) as the fourth mount is designed and developed, the installation position and its outer channel size are optimized and calculated respectively based on its damping force calculation method and the design method of the powertrain mounting system (PMS). The calculation results of evaluation indexes with engine start-stop are analyzed in-depth. The optimized parameters of the semi-active HDS are taken into the 13 DOFs dynamic model to analyze the dynamic response of the engine automatic start-stop process, and the values of the evaluation indexes are decreased. To validate the effectiveness of the design method of the semi-active HDS, the experiment of the vehicle with and without the semi-active HDS are carried out, and the test results show that the seat rail acceleration are reduced with semi-active HDS introduced. The active side acceleration of the torque strut calculated by the 13 DOFs model is consistent well with the measured value with and without the semi-active HDS, which proves that the model is reasonable.

On Weighing the Conflicting Cost Functions for Optimal Energy Management of Electrified Powertrain

Besides fuel economy, gear shifting and engine start-stop frequency in the optimal energy management for hybrid electric vehicles (HEVs) will also influence the overall performance and drivability. However, such drivability concerns will also impact the vehicle’s energy efficiency. To solve this conflicting optimization problem, this paper aims to find the proper weighing of the conflicting costs to achieve a right balance based on sensitivity analysis. The problem is formulated by expanding the conventional cost function with additional penalty items for gear shifting and engine start-stop, and a range extended hybrid delivery truck is modeled as a case study. Dynamic programming (DP) algorithm is applied to guarantee all the comparisons are under the same benchmark, and a split-DP solution is carried out to accelerate the searching process. Analytical fitting and trend analysis methods are used to find the proper penalty factors. Eventually, a comprehensive comparison among optimized, experiential and none penalty factors is shown, indicating that such appropriate weighing can significantly improve drivability with only 0.2% more fuel cost.

Threshold-changing control strategy for series hybrid electric vehicles

This paper proposes a new set of design principles to classify and design rule-based control strategies for the powertrain energy management of series hybrid electric vehicles. The design principles proposed consider the two most established rule-based control strategies for series hybrid electric vehicles, the Thermostat and the Power follower control strategies, and also an optimization-based control strategy, the Equivalent consumption minimization strategy, in terms of the mechanisms they employ to ensure charge sustaining operation and fuel efficient driving. Thus, the work then reflects upon the most effective design principles and derives a novel and superior rule-based control strategy for series hybrid electric vehicles that is claimed to outperform all the existing rule-based schemes in terms of fuel economy: the optimal primary source strategy (OPSS). The OPSS is implemented and then compared on a high fidelity hybrid electric vehicle model to Thermostat, Power follower and Equivalent consumption minimization strategies, as well as to a recently developed rule-based control strategy, the Exclusive operation strategy. As compared to conventional rule-based control strategies, the OPSS is found to deliver significantly improved fuel economy and which is remarkably close to that achieved by the optimization-based Equivalent consumption minimization strategy, while the design of the OPSS is simple and robust as compared to optimization-based strategies. The impressive performance is partly attributed to the recent improvements in engine start stop system technology. It is also shown that the battery is operated in a more steady manner, with a lower depth of discharge, consequently reducing battery degradation.

Influence of numerous start-ups and stops on tribological performance evolution of engine main bearings

For main bearings of internal combustion engines, most of the wear occurs during the start-ups and stops. The popularity of the start–stop system in automobile engines, which is used to save fuel consumption during idling stage, makes the working condition of main bearings severer because more frequent starts and stops will be generated. Changes in the bearing surface caused by wear will directly affect the bearing’s working performance. So in this study a transient mixed lubrication model and a wear model are coupled to analyze the influence of numerous start-ups and stops on the tribological performance evolution of engine main bearings. Starved lubrication of bearings before the oil supply is considered. The wear process is studied on the scale of surface topography and geometry. A main bearing in a four-stroke four-cylinder gasoline engine is studied under engine start–stop cycle conditions. The effects of temperature and lubricant grade on the transient tribodynamic behavior during the start-ups and stops are first investigated. Then the evolutions of surface characteristics and tribological performance of main bearings after numerous engine start-ups and stops are simulated. Results show that the temperature and lubricant grade can significantly affect the friction and wear of bearings. Hot start–stop condition leads to more serious asperity contact friction in the early stage of start-up, while cold start–stop condition generates more friction loss. The wear process of bearing surface is faster when applying oil with lower viscosity. And the impacts of engine start-ups and stops on bearing working performance are mainly seen in contact friction.

Locomotive diesel engine idle time distributions: Case studies from Poland, 2009–2013

Idle times of shunting locomotives were measured in nineteen Polish industrial sidings and analyzed. The current locomotive owner’s regulations propose 30 min as the maximum idle time for shunting locomotives. Research shows that the size of a single idle time gap ranges from seconds (most frequent) to 4 h (during winter), but 95% of observed idle times are shorter than 4–11 min (for different types of locomotives). 75% of the idle times cases are less than 1.5 min (47 s if counted only for SM31 locomotive). We fit the distribution of the measured idle times to theoretical distributions, obtaining the best fit for the Pareto type II. If the diesel engine idle time lasts for 9 min then the probability that the given engine will stay in the idle mode for the subsequent 8 min is large, namely, greater than 0.6. These results go along with Weaver (2006). He concludes that if the locomotive idling lasts longer than 8 min then it is more energy efficient to turn off and restart a modern locomotive. Letting it idle for the whole time produces more emissions. If the frequent turning on and off does not reduce the engine life we propose to turn off the locomotive engine after 10 min of unnecessary idling. Automatic Engine Start Stop system (AESS) could (in addition to its main tasks) send reports to the locomotive owner if the engineer deactivates this system too many times.

Understanding and Optimizing Aqueous Viologen Bromide Redox-Enhanced Electrochemical Capacitors

Research in electric double layer capacitors (EDLCs) and rechargeable batteries is converging, producing devices known as pseudocapacitors. Pseudocapacitors store energy electrochemically, utilizing fast and reversible faradaic redox reactions at the interface between high surface area electrodes and an electrolyte. The result is a technology that delivers a long cycle life, high specific power, and moderate specific energy. One frontier in pseudocapacitor research is redox-active electrolytes, which replace the traditional solid redox-active materials with soluble redox couples. Here we have identified viologen bromide salts as promising aqueous redox-active electrolytes for redox-EDLCs.[i] During charging, Br- is oxidized to Br3 - at the positive electrode and the viologen dication (V2+) is reduced to the stable monocation radical (V+•) at the negative electrode (Figure 1). The system shows unusually high coulombic efficiency and low self-discharge rates for an aqueous redox-supercapacitor. This was initially attributed to strong adsorption of the Br3 - and V+• to the activated carbon electrodes, but we now present a more detailed analysis that confirms the behavior is attributable to two electroprecipitation mechanisms. In these mechanisms, each ion acts as a charge-storing redox couple at one electrode an as a complexing agent at the other electrode. The processes are highly reversible and cells show negligible capacity fade even after 20,000 cycles. The devices use conventional activated carbon electrodes, and because crossover is not a concern and self-discharge is suppressed, a simple cellulose separator is sufficient and ion-selective membranes are not required. Heptyl viologen devices achieve specific energy densities of 11 Wh/kg (normalized to the mass of both electrodes and electrolyte), significantly higher than commercially available EDLCs (Figure 2). Using methyl viologen increased specific energy to 14 Wh/kg, but at the expense of cycling stability. By studying the operating mechanisms and synthesizing a large series of viologens with different alky substituents we have now identified viologens that deliver both the stability of heptyl viologen and the specific energy of methyl viologen. Finally, a simple electrochemical device model was developed to understand the behavior of the system and predict the maximum theoretically achievable performance (Figure 3). The results model experimental data well and suggest that significant performance improvement is possible, making this technology an exciting candidate for applications like automotive engine start-stop and grid-scale energy storage. [i] S.-E. Chun, B. Evanko, X. Wang, D. Vonlanthen, X. Ji, G. D. Stucky, and S. W. Boettcher, “Design of aqueous redox-enhanced electrochemical capacitors with high specific energies and slow self-discharge,” Nat. Commun., vol. 6, p. 7818, 2015. Figure 1

Design of a power-split hybrid electric vehicle control system utilizing a rule-based controller and an equivalent consumption minimization strategy

The paper proposes a power-split hybrid electric vehicle control strategy that combines a rule-based controller, including a state-of-charge controller and engine start–stop logic, with a precisely formulated equivalent consumption minimization strategy. A one-dimensional directional search-based instantaneous equivalent consumption minimization strategy optimization and a two-dimensional directional search-based instantaneous equivalent consumption minimization strategy optimization are used to find optimal powertrain points in high-efficiency engine map regions that are previously determined by an offline optimization approach. The simulation results confirm that the rule-based controller performance can be significantly improved when extended with the proposed equivalent consumption minimization strategy formulation, without requiring an adaptive empirical penalty factor to satisfy the state-of-charge sustainability condition. The proposed rule-based + equivalent consumption minimization strategy control strategies were verified against a dynamic-programming-based optimization benchmark for different certification driving cycles.