A New System Development Framework Driven by a Model-Based Testing Approach Bridged by Information Flow

Zhu, Di; Pritchard, Ewan; Silverberg, Larry

doi:10.1109/jsyst.2016.2631142

Cited by 72 publications

(5 citation statements)

References 26 publications

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“…An overview of tools can be found at [20]. The complexity of these models is discussed in [21]. Models of hybrid powertrains typically revolve around the models of the key components: engine, motor, generator, battery etc, and the control structure follows this model structure.…”

Section: /19/2016mentioning

confidence: 99%

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles

2022

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).

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Section: /19/2016mentioning

confidence: 99%

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles

2022

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“…The vehicle properties used in this study can be found in Table 1. The motor and gen-set were modeled using the black box modeling technique [22]. For the motor model, the inputs include accelerator pedal position, brake pedal position, and vehicle velocity.…”

Section: Vehicle Modelmentioning

confidence: 99%

Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

Zhu¹,

Pritchard²,

Dadam³

et al. 2021

Combustion Engines

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Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

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“…For this study, two levels of modeling are considered. e first, called plant model (PM), shown in Figure 1, is used to simulate the vehicle over speed cycles [21]. It only represents the longitudinal behavior and is designed for the energeticconsumption simulation.…”

Section: Hybrid Vehicle Modelingmentioning

confidence: 99%

Design of Real-Time Control Based on DP and ECMS for PHEVs

Wang

Cai

Liu

2021

Mathematical Problems in Engineering

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A real-time control is proposed for plug-in-hybrid electric vehicles (PHEVs) based on dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS) in this study. Firstly, the resulting controls of mode selection and series mode are stored in tables through offline simulation of DP, and the parallel HEV mode uses ECMS-based real-time algorithm to reduce the application of maps and avoid manual adjustment of parameters. Secondly, the feedback energy management system (FMES) is built based on feedback from SoC, which takes into account the charge and discharge reaction (CDR) of the battery, and in order to make full use of the energy stored in the battery, the reference SoC is introduced. Finally, a comparative simulation on the proposed real-time controller is conducted against DP, the results show that the controller has a good performance, and the fuel consumption value of the real-time controller is close to the value using DP. The engine operating conditions are concentrated in the low fuel consumption area of the engine, and when the driving distance is known, the SoC can follow the reference SoC well to make full use of the energy stored in the battery.

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A New System Development Framework Driven by a Model-Based Testing Approach Bridged by Information Flow

Cited by 72 publications

References 26 publications

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles

Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

Design of Real-Time Control Based on DP and ECMS for PHEVs

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