Analysis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicle

Škugor, Branimir; Soldo, Jure; Deur, Joško

doi:10.3390/wevj10040075

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…The reconstructed SoC trajectory shown in Figure 5c indicates linear trends in discharging and charging segments for all, quite distinctive road grade profiles. This means that the peak values of SoC gradients were effectively minimized, which minimized battery and generally electric path power losses [25].…”

Section: Optimization Resultsmentioning

confidence: 99%

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

2021

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The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) reference trajectory. The paper deals with the online synthesis of the optimal SoC reference trajectory, which inherently includes adaptive features in relation to the prediction of upcoming driving cycle features such as the trip distance, the road grade profile, the mean vehicle velocity and the mean demanded power. The method performs iteratively, starting from an offline-synthesized SoC reference trajectory obtained based on dynamic programming (DP) control variable optimization results. The overall PHEV control strategy incorporating the proposed online SoC reference trajectory synthesis method is verified against the DP benchmark and different offline synthesis methods. For this purpose, a model of a PHEV-type city bus is used and simulated over a wide range of driving cycles and conditions including varying road grade and low-emission zones (LEZ).

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Section: Optimization Resultsmentioning

confidence: 99%

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

2021

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“…Based on the observation that the optimal SoC trajectory has a linear, i.e., shortest length form, it may be hypothesized that the optimality of SoC trajectory is related to its length when expressed with respect to travelled distance. This is further analyzed in Appendix B (for more details see [28]). = 50%, = 50% (Figure 8b,d).…”

Section: Optimization Resultsmentioning

confidence: 99%

“…The battery losses have quadratic dependence with respect to battery current (i.e., E batt,loss = I batt 2 Rdt) and they are dissipated as a heat on the internal battery resistance (see Figure 3a). Due to this quadratic energy loss dependence, from the battery perspective the optimal discharging from initial to final SoC is related to constant current condition, which finally results in the SoC trajectory of minimum length (this can be easily proven by using Jensen's inequality applied to the convex function describing total battery losses; see [28] for more details). While the M/G machine losses depend on the M/G machine efficiency map (see Figure 2b), they usually dominantly relate to quadratic Ohmic losses similarly as in the battery case.…”

Section: Discussionmentioning

confidence: 99%

“…In order to further confirm the posed hypothesis on shortest-length optimality of the SoC trajectory when expressed with respect to travelled distance (Section 4), additional DP optimizations generating SoC trajectories of different length are conducted (more detailed analysis is presented in [28]). To this end, the following additional SoC-related soft constraints are added to the DP cumulative cost function (10):…”

Section: Conflicts Of Interestmentioning

confidence: 93%

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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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“…Therefore, fast charging stations and belonging transformer substations can be installed at end stations to provide bus recharging, while otherwise the available The charging process is managed by taking into account the requirements on satisfying the departure schedule, minimising battery power loss and respecting the grid power constraints. According to [26,27], the battery energy loss is minimised by demanding a linear change of SoC all over the remaining charging interval ∆T ch = t f − t k . Therefore, the SoC rate is updated in each sampling instant k according to:…”

Section: Obtaining Of Near-optimal Charging System Configurationsmentioning

confidence: 99%

Virtual Simulation of Electric Bus Fleets for City Bus Transport Electrification Planning

et al. 2020

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City bus transport electrification has a strong potential of improving city air quality, reducing noise pollution and increasing passenger satisfaction. Since the city bus operation is rather deterministic and intermittent, the driving range- and charging-related concerns may be effectively overcome by means of fast charging at end stations and/or slow charging in depot. In order to support decision making processes, a simulation tool for planning of city bus transport electrification has been developed and it is presented in this paper. The tool is designed to use real/recorded driving cycles and techno-economic data, in order to calculate the optimal type and number of e-buses and chargers, and predict the total cost of ownership including investment and exploitation cost. The paper focuses on computationally efficient e-bus fleet simulation including powertrain control and charging management aspects, which is illustrated through main results of a pilot study of bus transport electrification planning for the city of Dubrovnik.

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Analysis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicle

Cited by 5 publications

References 18 publications

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

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

Virtual Simulation of Electric Bus Fleets for City Bus Transport Electrification Planning

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