Least costly energy management for series hybrid electric vehicles

Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign

Energy management and shifting stability control for a novel dual input clutchless transmission system

© 2019 Elsevier Ltd A dual input clutchless transmission system based on automated manual transmission (AMT) structure is developed for pure electric vehicles. An energy management strategy (EMS) is proposed to determine the power distribution between two motors and the optimal gear state. A mathematical model is built to minimize the energy consumption of the motors at each instant based on the motor efficiency maps. However, the proposed EMS in line with other energy-oriented strategies often result in excessive gear shifts and compromised drivability. To avoid the undesired gear shift, a shifting stabilizer is built in the EMS objective function to improve the shift quality. Accordingly, to achieve a balance between the energy consumption and the drivability, a multi-objective optimization method is adopted to reduce the unnecessary shift events while minimizing energy consumption. Two driving cycles representing typical daily driving conditions are used to demonstrate the effectiveness of the proposed system in terms of energy efficiency and shifting stability

Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Efficient Thermal Electric Skipping Strategy Applied to the Control of Series/Parallel Hybrid Powertrain

The optimal control of hybrid powertrains represents one of the most challenging tasks for the compliance with the legislation concerning CO2 and pollutant emission of vehicles. Most common off-line optimization strategies (Pontryagin minimum principle-PMP-or dynamic programming) allow to identify the optimal control along a predefined driving mission at the expense of a quite relevant computational effort. On-line strategies, suitable for on-vehicle implementation, involve a certain performance degradation depending on their degree of simplification and computational effort. In this work, a simplified control strategy is presented, where the conventional power-split logics, typical of the above-mentioned strategies, is here replaced with an alternative utilization of the thermal and electric units for the vehicle driving (Efficient Thermal Electric Skipping Strategy-ETESS). The choice between the units is realized at each time and is based on the comparison between the effective fuel rate of the thermal engine and an equivalent fuel rate related to the electrical power consumption. The equivalent fuel rate in a pure electric driving is associated to a combination of brake specific fuel consumption of the thermal engine, and electro-mechanical efficiencies along the driveline. The ETESS is applied for the simulation of segment C hybrid vehicle, equipped with a thermal engine and two electric units (motor and generator). The methodology is tested along regulatory driving cycles (WLTP, Artemis) and RDE, with different powertrain variants. Numerical results underline that the proposed approach performs very close to most common control strategies (consumed fuel per kilometer higher than PMP of about 1% on average). The main advantage is a reduced computational effort (decrease of 99% on average). The ETESS is straightforwardly adapted for an on-line implementation, through the introduction of an adaptative factor, preserving the computational effort and the fuel economy

Modeling and Optimization of a Plug-in hybrid electric vehicle

Today, the world is faced with a situation where new technologies have to be developed to decrease the dependence on natural non-renewable resources. Each day, as the demand for non-renewable resources increases, it puts great pressure on the scientific fraternity to develop new technologies that are aimed at reducing this dependence. Today\u27s road traffic plays a major part in the energy consumption worldwide. Hence it is imperative that we develop environmentally friendly solutions to this problem that arises in the transportation sector. Hybrid vehicle is one of the alternatives that can be seen as a viable solution to this energy crisis. The recent strides in the field of controls and optimization has led to the evolution of new control and optimization tools to target several simultaneous objectives in a plug-in hybrid electric vehicle. The control strategies primarily target the minimization of fuel consumption, while meeting the power demand and also enhancing the drivability. The present work deals with the backward and forward modeling of a Power Split Plug-in Hybrid electric Vehicle. The Power-split plug-in hybrid electric vehicle is a combination of both series and parallel hybrid electric vehicles. A power split hybrid derives its name from the power split device namely the planetary gear set. The planetary gear set splits the engine power, allowing for both series and parallel modes. The model developed incorporates the fuel consumption minimization principle viz. Equivalent Consumption Minimization Principle(ECMS). ECMS principle deals with assigning future fuel costs and savings to the actual usage of electrical energy. Thus, the present usage of electrical energy would mean that this energy has to be balanced by replenishment in terms of future fuel costs and the present usage of fuel for replenishment would be associated with future savings as this energy is available at a lower cost. The ECMS principle used for optimization provided the necessary minimization by maintaining the State of Charge of Renewable Electrical Storage System(RESS) within the prescribed limits. When properly designed by appropriately tuning the Charging and Discharging coefficients in the minimization strategy, we can optimize the vehicle performance over a given cycle, with the generation of power being intact and perhaps more to conform to the best emission standards in any part of the world

Equivalent Consumption Minimization Strategy With Consideration of Battery Aging for Parallel Hybrid Electric Vehicles

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. This paper introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Simulation results show that the proposed aging ECMS algorithm significantly improves battery aging with little or no fuel economy penalty compared to ordinary ECMS