Sanjarbek Ruzimov

Transient engine operation in real driving conditions generally leads to higher fuel consumption than that predicted by steady-state maps. For this reason, this work investigates the impact of using steady-state and transient engine representations within Equivalent Consumption Minimization Strategy (ECMS) and Dynamic Programming (DP)-based energy management strategies for a P2 mild hybrid electric vehicle. ECMS makes decisions based on the current operating state, hence providing local optimal solution, whereas DP yields a globally optimal solution by considering the entire driving cycle. This work quantifies the maximum achievable fuel savings using these control strategies considering engine transient operation. In this work, an ECMS controller is proposed that calculates the equivalent fuel consumption precisely by utilizing engine and electric machine efficiencies derived from their current operating points on their operating characteristic maps. In the cost function the term for engine fuel consumption is evaluated in two separate cases, using a steady-state fuel model in one case and a transient engine model in the other. In parallel, a DP benchmark based on Bellman’s backward recursion is formulated using both engine modelling approaches. The findings indicate that the impact extends beyond total fuel consumption to the dynamic behavior of the powertrain as well. Specifically, the root mean square (RMS) and total variation (TV) of engine and motor torques show that including transient model in the cost function leads to different optimal control actions and significantly influences torque smoothness and overall drivetrain stability.

Mild hybrid electric vehicles (MHEVs) are a cost-effective solution for reducing fuel consumption and emissions in the automotive sector, offering a low-level electrification alternative to battery electric and plug-in hybrid vehicles. This study uses the Equivalent Consumption Minimisation Strategy (ECMS) to investigate the optimal sizing of P2 MHEV powertrain components and the individual contributions of hybridisation features such as regenerative braking, idling fuel cut-off, load shifting and electric torque assist. Parametric simulations were performed by varying the power of the electric motor and the capacity of the battery for standard driving cycles. The results show that total fuel consumption for the NEDC driving cycle can be reduced by up to 29%, with regenerative braking providing the largest contribution. The optimal electric motor power for mild hybrid applications was found to be in the 20–30 kW range, depending on the driving cycle.

This study introduces and compares online rule-based and optimization-based energy management strategies for a mild hybrid electric vehicle, with their performance evaluated against an offline Dynamic Programming benchmark. A structured rule-based strategy is proposed to enforce engine operation along its optimal efficiency line, while the remaining power demand is balanced by the electric motor. To achieve charge-sustaining battery operation, a soft state of charge regulation mechanism is incorporated. An Equivalent Consumption Minimization Strategy (ECMS) is also developed using a precise formulation of battery equivalent fuel consumption computed from instantaneous engine and electric path efficiencies, instead of constant efficiencies used in the literature. DP, which provides a globally optimal solution over the entire driving cycle, is employed as a benchmark for assessing the rule-based and ECMS strategies. The control strategies are compared under charge-sustaining conditions, considering engine and motor operation characteristics, overall fuel consumption, and battery usage intensity. Furthermore, the influence of load shifting between the internal combustion engine and the electric motor on overall vehicle performance is analyzed. Fuel consumption decreases by 13.5% relative to the engine-only baseline with the proposed ECMS with precise equivalent fuel consumption, and DP yields an additional 1.6% benefit. Compared with the developed rule-based controller, ECMS nearly halves the battery usage intensity, and DP provides 3.1% further reduction relative to ECMS.

This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively adjusted according to driving conditions in adaptive ECMS. In REEVs, however, the battery efficiency exhibits nonlinear behavior in the low SOC range, which directly leads to variability in the equivalence factor within conventional ECMS. This study investigates the influence of the variable equivalence factor on the overall fuel economy. The equivalence factors are usually considered constant or vary adaptively depending on driving cycles. However, the variation in battery efficiency is often neglected. The present study compares the results obtained for both constant and variable battery efficiencies in deriving the equivalence factors. The simulation results show that an improvement of approximately 3% in fuel economy was obtained for UDDS, NEDC, and WLTC driving cycles as a result of applying the variable equivalence factor. Additionally, through an analysis of various penalty function designs, the study highlights their crucial role in optimizing fuel consumption across different driving cycles.

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.

This research work aims to examine the effects of road pavement roughness on critical vehicle operating characteristics such as ride comfort and fuel consumption. The proposed approach is based upon onboard sensors like Inertial Measurement Units (IMUs) or smartphone sensors to measure vertical accelerations and pitching angles of the vehicle for accurate estimation of the road profile state. To ensure that the road profile data is being constantly updated and improved, a cloud-based crowdsourcing platform is integrated to provide real-time and dynamically updated road condition information. This paper proposes a predictive speed-planning algorithm as a solution to the problem of integrating this road profile information for the purpose of simultaneously achieving good ride comfort and fuel efficiency. The algorithm formulates objective functions that reduce passenger discomfort by minimizing vertical vibrations and pitching of the vehicle and minimizes fuel consumption by optimizing the driving speed profiles. The key constraints that are incorporated into the algorithm are maximum and minimum allowable speeds, safety margins, vehicle dynamic performance, traffic rules, and road geometry. The developed methodology has the potential to be used as a control algorithm for autonomous vehicle speed control and as an Advanced Driver Assistance System (ADAS) that advises human drivers about the most efficient speeds to maintain. Safe, comfortable, and energy-efficient vehicle operations are the outcomes of this research that enables proactive adaptation of vehicle speeds according to the anticipated road roughness.

This paper describes the modeling and experimental validation of the performance of two height adjustment suspensions with concentrically and eccentrically mounted screws. In the former solution, an anti-rotation system is required for the generation of reaction torque on the power screw–nut mechanism. The anti-rotation represents the main drawback of such mechanisms. In contrast, the eccentric solution attempts to solve this problem by placing the screw–nut mechanism eccentrically with respect to the shock absorber tube axis. In this paper, the working principle of the eccentric solution is explained. Its performance is compared to the concentric counterpart through simulations and experiments. Although the efficiencies of eccentric and concentric systems are very similar at the power screw, overall efficiencies differ substantially. During lifting, average efficiencies are around 3.4% and 6.5% for concentric and eccentric systems, respectively. When lowering, these values are 6.2% and 26%. The higher overall efficiency of the eccentric screw system is attributed to the anti-rotation system and the balancing of the bending moment due to the offset application of the load. To yield a complete perspective on the eccentric mounted screw solution, four prototypes are installed and tested on a demo vehicle.

This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB^® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.

In the last decade, a number of research works in electrified vehicles have been devoted to the analysis of the electric consumption of battery electric vehicles and the evaluation of the main influencing factors. The literature analysis reveals that the electric motor size, efficiency, and driving condition substantially affect the electric energy stored in the vehicle battery. This paper studies the degree of sensitivity of energy consumption to electric motor size and to its efficiency map characteristics. In order to accomplish this task, three electric motors whose parameters are re-scaled to fit the maximum power torque and speed with different efficiency maps are simulated by installing them on two commercially available battery electric vehicles. This allows for isolating the influence of the efficiency map on electricity consumption. The original characteristics of the motors are then used to evaluate the influence on the electricity consumption of both the size and the efficiency characteristics. The results of the simulation revealed that the influences of the efficiency map and the electric motor size can be around 8–10% and 2–11%, respectively. When both factors are taken into account, the overall difference in electricity consumption can be around 10–21%.