Optimal Energy Management Control of a Parallel Plug-In Hybrid Electric Vehicle in the Presence of Low Emission Zones

Soldo, Jure; Škugor, Branimir; Deur, Joško

doi:10.4271/2019-01-1215

Cited by 9 publications

(14 citation statements)

References 15 publications

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“…These maps have been reconstructed from the data/maps given in [17] and [18] by properly scaling the data with respect to maximum speed and power ratios of the vehicles from [17,18] and the particular PHEV-type bus. The total wheel torque τ w is calculated as [15]:…”

Section: Mathematical Model Of Phev Powertrainmentioning

confidence: 99%

“…This section first presents the basic control strategy of a PHEV powertrain, which is aimed at selecting proper powertrain operating mode and instantaneously optimizing its operating point (more details can be found in [15]). Next, the design of optimal SoC reference trajectory, fed to the basic control strategy (i.e., its SoC feedback controller), is presented for the cases of LEZ presence and varying road grade.…”

Section: Phev Powertrain Control Strategymentioning

confidence: 99%

“…m f is real fuel mass flow, while the second term ( . m batt ) relates to virtual fuel mass flow accounting for the power drawn from or stored in battery (P batt ), where η batt,d is battery efficiency during discharging, η batt,c is round-trip loss-related efficiency during charging, and A ek is the mean engine specific fuel consumption during discharging (see [24] and [15] for more details). The ECMS optimization is performed over the transmission gear ratio h and the engine torque τ e (i.e., two degrees of freedom, 2D-ECMS).…”

Section: Basic Control Strategymentioning

confidence: 99%

“…The iteration through discrete gear ratio values h directly impacts the engine speed ω e (see Equation (1) where v v is set to its current value). In order to ensure SoC sustainability, the engine torque search range is made dependent on the SoC control error e SoC = SoC R − SoC (see function w(e SoC ) in Figure 12, [15]). When the SoC error is zero, the search range is of maximum width, i.e., between the absolute lower limit P o f f /ω e and the absolute upper limit τ max e (ω e ).…”

Section: Basic Control Strategymentioning

confidence: 99%

“…This paper presents a practical method of optimal SoC reference trajectory synthesis for a general case of driving cycles (including the presence of LEZs and varying road grade), which can be applied to any control strategy that requires explicit SoC reference. For verification purposes, the synthesized SoC reference is incorporated into the previously developed PHEV control strategy based on a rule-based controller extended with an equivalent consumption minimization strategy (ECMS) [15]. First, dynamic programming (DP)-based optimizations of control trajectories are conducted for the purpose of obtaining globally optimal SoC trajectories for the given set of driving cycles, and extracting and analyzing their patterns for specific operating conditions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

2019

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The powertrain efficiency for plug-in hybrid electric vehicles (PHEV) can be maximized by gradually discharging the battery in a blended regime, where the engine is regularly used all over the driving cycle. A key step in designing an optimal PHEV control strategy for the blended regime corresponds to synthesis of battery state-of-charge (SoC) reference trajectory. The paper first demonstrates that the optimal SoC trajectory can significantly differ from a typical linear-like shape in the case of varying road grade and presence of low-emission zones (LEZ). Next, dynamic programming (DP)-based optimizations of PHEV control variables are conducted for the purpose of extracting and analyzing optimal SoC trajectory patterns. It is shown that the optimality is closely related to the minimization of SoC trajectory length with respect to travelled distance. This finding is used for SoC reference trajectory synthesis in the presence of LEZ and varying road grades. Finally, the overall PHEV control strategy is applied to a PHEV-type city bus and verified by means of computer simulations in comparison with the DP optimization benchmark.

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Section: Mathematical Model Of Phev Powertrainmentioning

confidence: 99%

Section: Phev Powertrain Control Strategymentioning

confidence: 99%

Section: Basic Control Strategymentioning

confidence: 99%

Section: Basic Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

2019

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Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios

Anselma

2022

Applied Energy

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Modeling and Control for Emission Management in Hybrid Electric Commercial Vehicles

Holmer

2022

Linköping Studies in Science and Technology. Dissertations

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Electrification of powertrains is a major trend in the vehicle industry. The reason behind this is mainly that electrification of a powertrain generally results in better fuel economy, by eliminating inefficient, low load, operation of the engine. This can be done in two ways: load shifting to shift the operation point of the engine to a more efficient one, or by turning off the engine completely. When it comes to emissions, load shifting generally have positive effect since it usually result in higher exhaust temperatures which are beneficial for the aftertreatment system. The effect from turning off the engine completely is more complicated. When the engine is turned off the aftertreatment system will start to cool down and will eventually lose its effectiveness, resulting in higher emissions when the engine is restarted. So-called green zones, zones established by legislation or demand of costumers, where the use of combustion engines is prohibited, are a good example of where this can be expected and is therefore a focus of this thesis. The applications are not limited to hybrids but also useful for all vehicles that make stops, e.g., commercial vehicles that make regulated 45 minutes breaks and loading/off-loading cargo.A model of a complete hybrid electric heavy-duty vehicle is developed and validated. The model is a compilation of several submodels of the different components in the vehicle. To correctly estimate the pollutive emissions, the components in the aftertreatment system are the most important components and emphasis is put on how the concentrations in them are calculated. It is shown that a quasi-static model for the concentrations gives the best balance in terms of accuracy and simulation time for the application. The aftertreatment system submodels are validated against data from a high-fidelity model and the complete powertrain is validated against experimental data from a powertrain in a test stand, all with satisfactory results. The model is used to create a virtual environment where the effect different control strategies have on the emissions around green zones can be studied and optimized.A control strategy based on pre-heating of the aftertreatment system is developed. The strategy heats the aftertreatment before turning off the engine in an optimal way to reduce NO x . This strategy is shown to be effective for engine-off times up to a few hours. However, for longer engine-off times, pre-heating of the aftertreatment system induces a limitation on the amount of stored ammonia, making the strategy ineffective or even bad. The strategy is extended to handle scenarios with multiple engine-off events using an algorithm that finds the engine-off events and handle them separately, but with a common equivalence factor between fuel and NO x to link them. The strategy is shown to handle scenarios with multiple engine-off events well, and the resulting distribution of fuel between the events is close to optimal. vii viii

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Optimal Energy Management Control of a Parallel Plug-In Hybrid Electric Vehicle in the Presence of Low Emission Zones

Cited by 9 publications

References 15 publications

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios

Modeling and Control for Emission Management in Hybrid Electric Commercial Vehicles

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