Theo Hofman

Rule-based energy management strategies for hybrid vehicles

The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced, based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one decision variable and requires no tuning of many threshold control values and parameters. This decision variable represents the maximum propulsion power of the secondary power source (i.e. electric machine/battery) during pure electric driving. The RB-ECMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB-ECMS proposed in this paper requires significantly less computation time with a similar result to DP (within 1% accuracy).

Optimal Control of the Gearshift Command for Hybrid Electric Vehicles

This paper proposes a design method for the energy management strategy to explore the potential fuel saving of a hybrid electric vehicle (HEV) equipped with an automated manual transmission. The control algorithm is developed based on the combination of dynamic programming (DP) and Pontryagins minimum principle (PMP) to optimally control the discrete gearshift command, in addition to the continuous power split between the internal combustion engine and the electric machine. The proposed method outperforms DP in terms of computational efficiency, being 171 times faster, without loss of accuracy. Simulation results for a middle-sized HEV on the New European Drive Cycle show that, to further optimize the gearshift strategy, an additional fuel saving of 20.3% can be reached. Furthermore, with the start-stop functionality available, it is shown that the two-point boundary-value problem following from PMP cannot be solved with sufficient accuracy without loss of optimality. This means that the finding of a constant value for the Lagrange multiplier while satisfying the battery state-of-energy (SOE) at the terminal time is not always guaranteed. Therefore, an alternative approach of SOE feedback control to adapt the Lagrange multiplier is adopted. The obtained results are very close to the globally optimal solution from DP. Simulation results, including the start-stop functionality, show that the relative fuel saving can be up to 26.8% compared with the case of a standard gearshift strategy.

Optimal Control of a Mechanical Hybrid Powertrain

This paper presents the design of an optimal energy management strategy (EMS) for a low-cost mechanical hybrid powertrain. It uses mechanical components only-a flywheel, clutches, gears, and a continuously variable transmission-for its hybrid functionalities of brake energy recuperation, reduction of inefficient part-load operation of the engine, and engine shutoff during vehicle standstill. This powertrain has mechanical characteristics, such as a relatively small energy storage capacity in the form of the compact flywheel and multiple driving modes to operate the powertrain because of the use of clutches. The optimization problem is complex because it is two fold: 1) to find the optimal sequence of driving modes and 2) to find the optimal power distribution between the engine, the flywheel, and the vehicle. Dynamic programming is used to compute the globally optimal EMS for six representative driving cycles. The main design criterion is the minimization of the overall fuel consumption, subject to the systems kinematics, dynamics, and constraints. The results provide a benchmark of the fuel-saving potential of this powertrain design and give insight into the optimal utilization of the flywheel system. In addition, the complexity (and computation time) of the problem is reduced by a priori (static) optimization of the power distribution for each driving mode. Static optimization of a dynamic optimization problem yields a suboptimal solution; however, the results show that the consequences on the fuel saving are small with respect to the optimal one (the difference is <; 0.8%).

Topology Optimization for Hybrid Electric Vehicles With Automated Transmissions

Currently, many different topologies are designed with different transmission technologies such as automated manual transmission (AMT) and continuously variable transmission (CVT). The choice of topology determines the energy-flow efficiency between the hybrid system, the engine, and the vehicle wheels. The optimal topology minimizing fuel consumption is influenced by the transmission technology. Therefore, an AMT (high efficiency) and a push-belt CVT (moderate efficiency), are used in this research for comparison. In addition, a controlled switching topology is introduced as a benchmark, where controlled coupling with additional clutches of the electric machine before or after the transmission minimizing transmission losses and improving hybrid performance is investigated. The results showed that a switching topology can significantly improve CO2 emission reduction (average relative improvements between 2% and 7%), particularly for CVT-based hybrid vehicles. Moreover, in case of an AMT, a precoupled topology is preferable, and in the case of a CVT, a postcoupled is preferable for full-hybrid vehicles. For these cases, selecting the optimal fixed topology can improve the relative CO2 emission reduction between 2% and 8%.

Energy efficiency analysis and comparison of transmission technologies for an electric vehicle

Electric vehicles are seen as the most promising solution to convert sustainable energy into drive energy. However, there are still some major (technological) challenges, e.g., in terms of maximizing range anxiety, minimizing battery costs and charging time. A possible solution, in order to improve the relative limited range (100–200 km), battery life time and ultimate costs, is utilization, e.g., of a transmission technology between the electric machine and driven wheels. The main research question of this paper is, what are the effects of transmission types (fixed, manual, continuously variable) and parameters (final drive ratio, efficiency) on the overall (battery-to-wheel) efficiency and performance of an electric vehicle?

Design of CVT-Based Hybrid Passenger Cars

In this paper, the hybridization of a small passenger car equipped with a continuously variable transmission (CVT) is investigated. Designing a hybrid drive train is a multiobjective design problem. The main design objectives are fuel consumption, emissions, and performance. However, it is difficult to find a global optimal integral design solution due to the interdependence of design choices (parameters) regarding the drive-train topology, component sizes, component technologies, and control strategy, as well as the unknown sensitivity of the design objectives to the design parameters. In this paper, a parametric optimization procedure is presented to solve the design problem, where the main design objective is fuel consumption. The effects of parameter variation on fuel consumption have been investigated. Furthermore, a reduced hybrid drive-train model is introduced, with which the effects of design parameter variation is very quickly studied with an average error of less than 1.6%.

Predictive gear shift control for a parallel Hybrid Electric Vehicle

In this paper, Model Predictive Control (MPC) framework is exploited to synthesize a predictive controller for a parallel Hybrid Electric Vehicle (HEV) equipped with an Automated Manual Transmission. The algorithm also controls the gear shift command, together with the power split between the engine and electric machine and the engine on-off state using the route information ahead. A non-predictive controller based on a combination of Dynamic Programming (DP) and Pontryagins Minimum Principle (PMP) is described and taken as a benchmark control solution for optimizing the gear shift problem of the parallel HEV in terms of computational efficiency. This so-called DP-PMP control approach is then utilized in the MPC framework to realize the predictive controller for a gear shift problem in a receding horizon mode. Simulation results show that the non-predictive controller improves the fuel economy up to 35.9% and 43.5% on NEDC and FTP75 respectively when compared with a conventional vehicle. Even with a short horizon, fuel saving of the predictive controller is very close to that of the non-predictive controller with a relative difference of 0.3%. Moreover, the predictive controller can be seen as a suitable realtime implementable control candidate with a fast computation property.

Rule-Based Equivalent Fuel Consumption Minimization Strategies for Hybrid Vehicles

Abstract The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one main design parameter and requires no tuning of many threshold control values and parameters. This design parameter represents the maximum propulsion power of the secondary power source (i.e., electric machine/battery) during pure electric driving. The RB-ECMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB-ECMS proposed in this paper requires significantly less computation time with the similar result as DP (within ±1% accuracy).

Hybrid component specification optimisation for a medium-duty hybrid electric truck

This paper presents a modelling and simulation approach for determining the optimal degree-of-hybridisation for the drive train (engine, electric machine size) and the energy storage system (battery, ultra capacitor) for a medium-duty truck. The results show that the degree-of-hybridisation of known medium-duty hybrid electric trucks is close to the optimal degree-of-hybridisation using the methods as described in this paper. Furthermore, it is found that the Li-ion battery is from an energy and power density as well as cost point of view the most preferable energy storage system.

Optimal design of energy storage systems for hybrid vehicle drivetrains

Current hybrid drivetrain simulation packages are based on discrete (existing) system components and predefined system structures. Optimization of the performance of the hybrid drivetrain is then based on finding the most efficient control strategy of the primary and secondary power source and finally comparing the performance of the different candidate drive-trains. In this paper, the secondary power source components, part of the energy storage system (S), are modeled continuously, i.e., scalable to power and/or energy capacity needs. In this way, the size of the components of S can be added as an optimization parameter to a hybrid drivetrain design procedure.