Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand

arXiv:2412.09978 · math.OC, cs.SY, eess.SY · Submitted 2024-12-13 · Read on arXiv

Listen

Transcript

Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand".

Dev: As a fastidious and diligent AI researcher, I have meticulously analyzed both provided texts from the arXiv paper,

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So, we're looking at this paper from Tai-Yu Ma et al., titled "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand." It seems like the main idea here is using a dynamic scheduling method to coordinate when vehicles charge with real-time energy prices and vehicle needs to avoid long waits and boost profit.

Dev: Right, so it tackles that whole problem of making sure vehicles charge efficiently when demand is unpredictable and the charging spots are busy. The authors propose this sequential mixed integer linear programming model to figure out the day-ahead plans based on what they expect from the vehicles' energy needs and their waiting times.

Taro: I'm interested in how they handle that uncertainty, because in real life, demand is never perfectly predictable, and that makes any planning pretty shaky.

Rosa: Exactly, Taro. The paper lays out a two-stage sequential optimization approach where you have this long-time horizon planning model first to set the initial charging time and target state of charge for each vehicle based on anticipated energy demands.

Dev: Then it moves to a second stage, which is the vehicle dispatching part, assigning idled vehicles to service those ride requests that come in.

Taro: And then the third stage comes in online, optimizing where the vehicles actually charge right now in response to what's happening at that moment.

Rosa: That’s the core flow—P1 for planning, P2 for dispatching, and P3 for online assignment based on realized system states at each decision epoch i. This whole process aims to maximize the total profit of the fleet over a twenty-four-hour period.

Dev: The paper points out that previous work often had three drawbacks: first, when vehicles wait to charge at charging stations, there are no maximum waiting time limits, which leads to unrealistically long waits there.

Taro: And second, they noted that there's no minimum charging duration constraint per electric vehicle. That means the system doesn't force a vehicle to stay charged for a certain amount of time even if it could leave sooner.

Rosa: That’s one of the key contributions they make here—they explicitly model charger-level charging congestion through a reservation-based charging request scheduler and enforce a maximum waiting time constraint.

Title and authors: Dev: They also incorporate more realistic operational constraints, like minimum required charging durations and demand-driven partial recharging decisions, which moves them past simpler models.

Taro: It’s interesting how they handle the uncertainty by having that day-ahead plan serve as a guidance for coordination—it distributes the charging demand over time while integrating overnight costs to get vehicles to a full state of charge at the start of the next day.

Rosa: They also focus on stochastic robustness, meaning even if that initial day-ahead plan P1 is built on average energy consumption rates, they re-optimize each vehicle's target state of charge against the realized system state at every epoch.

Dev: When we look at their experimental setup, they ran simulations on a Manhattan-like study area using synthetic data drawn from NYC yellow taxi records for a fleet size of one hundred electric vehicles <ref:2412.09978#pg1,on a Manhattan-like study area using synthetic data drawn from NYC>.

Taro: They tested it under two customer demand scenarios: three thousand customers per day and four thousand customers per day, which gives us a good idea of how the system handles different levels of traffic <ref:2412.09978#pg1>.

Rosa: The results show that the Congestion-Aware policy significantly outperforms all benchmark policies, especially when charging congestion is really significant in the system.

Dev: For the three thousand customers per day scenario, it increases total profit by four point six six percent to fourteen point eight two percent, and for the four thousand customers per day scenario, it boosts that profit by between three point nine one percent and nineteen point three two percent.

Taro: And what's more than just the profit numbers is how much better they are at service rate improvement, showing gains ranging from plus twenty point zero three percent to plus four point six percent relative to the weakest benchmark policy.

Rosa: They also show that the Congestion-Aware policy is specifically effective in managing congestion by activating vehicle charging operations earlier during peak demand hours, like the fourth through eighth hours, which results in fewer vehicles charging simultaneously compared to other benchmarks.

Dev: This leads to significantly reduced total charging waiting time and higher occupancy rates on fast chargers during those critical periods, plus they get a significantly lower total charging duration and charged energy per session than policies like OptChg.

Taro: The stability aspect is also mentioned, showing that the standard deviations of the key performance indicators for both the Congestion-Aware policy and the OptChg policy are generally lower across all metrics, which means it’s more stable when demand varies.

Title and authors: Rosa: So, to wrap up this paper on "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand," what we have here is a sophisticated dynamic charging approach that coordinates dispatch and charging operations under stochastic demand and variable energy prices.

Dev: Essentially, they use a sequential mixed integer linear programming framework that integrates long-term planning with short-term reactive optimization to maximize system profit by proactively managing vehicle energy needs.

Taro: What this means for the world is that for ride-hailing operators, it provides a robust methodology to manage charging capacity when demand is uncertain, leading to demonstrably higher profits and improved service rates compared to existing rule-based heuristics.

Rosa: And they highlight the impact of different system parameters too, showing how things like maximum waiting times at a queuing charger or battery capacity affect the performance of this Congestion-Aware policy.

Dev: But we have to keep in mind what they did not address fully: they modeled travel times and energy consumption as deterministic averages, which isolates uncertainty primarily in customer demand and station queueing rather than traffic congestion uncertainty.

Taro: And another limitation is that the benchmark policies used are rule-based heuristics, so the comparison establishes the value of coordination relative to those common heuristics rather than against other advanced optimization methods.

Rosa: The future work they point towards involves developing efficient solution approaches for system scaling, like optimizing fleet size and battery configuration, and integrating smart charging strategies to mitigate grid impact during peak hours.

Dev: They also suggest extending this approach to other systems, such as shared mobility or regular bus services, and considering dynamic pricing mechanisms to further help mitigate charging congestion.

Taro: It seems like the core value here is taking the concept of anticipating vehicle needs and coordinating it with real-time prices to avoid long waiting times at charging stations.

Rosa: That’s right, we covered a lot about this paper, "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand." It’s a solid framework for how fleets can manage their energy resources under real-world conditions.

Dev: We'll take a quick break now, and when we come back, we'll look at some other interesting research from arXiv.

The paper's summary: Rosa: So we're looking at this paper on congestion awareness for electric ride-hailing fleets under uncertain demand, and what they propose is a way to coordinate when vehicles charge with energy prices and vehicle needs to avoid long waits and boost profit.

Dev: Right, so they use this sequential mixed integer linear programming model where you have a long-term plan first based on anticipated energy needs, then dispatching idled vehicles, and finally an online part that optimizes where the vehicles actually charge right now based on what's happening at each moment i.

Rosa: What I find really compelling is how they handle those operational realism constraints. They aren't just looking at simple averages; they explicitly model charger-level congestion with a reservation-based scheduler and force a maximum waiting time constraint for vehicles.

Dev: That’s the part that makes it practical, isn't it? It moves beyond simpler models by enforcing minimum charging durations and demand-driven partial recharging decisions.

Rosa: Yeah, and they handle uncertainty by making that day-ahead plan act as guidance—it distributes the charging demand over time while integrating overnight costs so vehicles are full when they start the next day.

Dev: They also build in robustness against parameter mismatch; even if the initial long-term plan is based on average energy consumption rates, the online layer re-optimizes each vehicle's target state of charge against what’s actually happening at every decision epoch.

Rosa: The results show that this Congestion-Aware policy actually beats all the benchmark policies, especially when congestion gets significant. They see profit increases ranging from over four percent up to nearly twenty percent depending on the customer demand scenario they run.

Dev: And it's not just about the profit numbers; they show a service rate improvement, with gains sometimes reaching plus twenty point zero three percent compared to the weakest benchmark policy.

Rosa: The most interesting part for me is how it directly addresses congestion management, activating charging earlier during peak hours, like the fourth through eighth hours, which means fewer vehicles are charging at the same time.

Dev: That leads to significantly reduced total waiting time and higher occupancy rates on fast chargers during those critical periods. The stability metrics are also good; the standard deviations across KPIs for this Congestion-Aware policy and another policy called OptChg are generally lower, showing it's more stable when demand fluctuates.

Rosa: So for someone just listening to the show, what this means is that we have a robust methodology here for ride-hailing operators to manage charging capacity when demand is unpredictable, leading to better profits and improved service rates than just following old rule-based heuristics.

Dev: And they do point out what they don't cover fully; travel times and energy consumption are modeled as deterministic averages, which means the uncertainty is mostly focused on customer demand and station queueing rather than traffic congestion itself.

Rosa: Plus, they compare it against rule-based heuristics, so the comparison shows how much better this coordination is relative to those common methods instead of comparing it to other complex optimization scheduling methods.

Dev: The future work they suggest involves figuring out solution approaches for scaling up—optimizing fleet size and battery configuration—and integrating smart charging strategies to help with grid impact during peak hours.

Rosa: And they also look at extending this approach to other systems, like shared mobility or regular bus services, and considering dynamic pricing mechanisms to further reduce charging congestion.

The paper's improvements: Rosa: So we've talked about how they use a sequential optimization model to coordinate vehicle dispatch and charging under uncertainty, and now we need to look at what they suggest should come next for this research.

Dev: Right, so the paper points out several improvements they think would make this dynamic approach even stronger in practice. One key area is spatial realism.

Rosa: They suggest that travel times and energy consumption shouldn't be just simple averages; they should try to incorporate more real-world traffic uncertainty into those models.

Dev: And that’s important because right now, they treat them as deterministic, which isolates the uncertainty mostly to customer demand and station queueing rather than the actual road conditions.

Rosa: Exactly, and another big one is the benchmark comparison. They note that since they compared this against rule-based heuristics, future work should involve comparing this AI approach against other advanced optimization-based scheduling methods.

Dev: That makes sense; it sets up a higher bar for what's considered a good system design in this field.

Rosa: Then there’s the issue of system scaling. They propose developing efficient solution approaches for when you have to scale things up, like optimizing the fleet size or how you configure the batteries and the charging infrastructure itself.

Dev: That would be a big step because right now they focus on a fixed fleet size of one hundred vehicles in their tests.

Rosa: And they also look at integrating smart charging strategies to help manage the electricity grid impact, especially during those peak hours when congestion is worst.

Dev: That ties into the idea of dynamic pricing mechanisms too; they suggest considering how changing energy prices could further help mitigate that charging congestion we talked about earlier.

Rosa: So essentially, the direction is moving toward making this coordination model not just work in a lab setting, but something scalable and integrated with the broader power grid.

Dev: It seems like the authors are pushing for more practical applications beyond just maximizing profit in a synthetic environment.

Rosa: And they also consider extending this framework to other systems entirely, like shared mobility services or even regular bus services instead of just ride-hailing fleets.

Dev: That sounds like a lot of work on system integration and scalability for the next phase.

Conclusion: Rosa: So we're wrapping up on "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand." Essentially, this paper shows how a sophisticated dynamic scheduling approach can handle the chaos of unpredictable demand and energy prices in an electric ride-hailing fleet.

Dev: Yeah, it’s about taking that long-term planning and short-term reaction and linking them together to maximize the total profit while keeping the system stable.

Rosa: What this means for us is we have a more robust way to manage charging capacity when demand isn't perfectly predictable, leading to demonstrably higher profits compared to just using old rule-based heuristics.

Dev: And they also showed better service rates; gains were seen ranging from plus twenty point zero three percent up to plus four point six percent relative to the weakest benchmark policy.

Rosa: The authors pointed out that this model is really effective in managing congestion, activating charging earlier during peak hours, which leads to lower waiting times and higher occupancy on fast chargers.

Dev: And stability matters too; the standard deviations across key performance indicators for this Congestion-Aware policy are generally lower than other policies like OptChg.

Taro: I just want to say that while it’s strong in this setting, the authors noted a limitation: they treated travel times and energy consumption as deterministic averages, which means the uncertainty is mostly on demand and station queueing rather than actual road traffic.

Rosa: True, and another thing they flagged is that since they compared it to rule-based heuristics, the comparison shows how good this coordination is relative to those common methods instead of against other advanced optimization scheduling methods.

Dev: So for someone listening who only cares about how this runs on a loop rate, the main thing is that the sequential optimization framework provides a solid structure for handling those real-time decisions effectively.

Rosa: It’s a lot of work, but it gives operators a concrete methodology to manage energy resources under complex conditions.

Dev: We'll keep an eye on how they scale this up, as that’s the next big challenge for any field roboticist or controls engineer looking to move this from simulation to the real world.

Luxembourg Institute of Socio-Economic Research (LISER) · University of Luxembourg

math.OC, cs.SY, eess.SY

Submitted: 2024-12-13

Updated: 2026-10-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 89/100

The gist: As a fastidious and diligent AI researcher, I have meticulously analyzed both provided texts from the arXiv paper, "Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under

Key concepts

Day-Ahead Planning (P1): This long-term model sets the initial charging schedule for each vehicle based on predicted energy needs. It anticipates future waiting times and overnight replenishment costs to establish a baseline plan before real-time events occur, guiding subsequent dispatch decisions.
Online Vehicle-to-Charger Assignment (P3): This reactive model continuously re-optimizes which specific charger a vehicle should use in real time. It uses a reservation-based scheduler to minimize total operational time by adapting the initial plan to the current system state, ensuring vehicles are assigned efficiently.
Congestion-Aware Policy: This is the proposed strategy that explicitly models charging station congestion. It enforces constraints like maximum waiting times and utilizes predictive scheduling to proactively manage vehicle charging sessions, leading to better profit and reduced waiting times compared to simpler scheduling methods.

Terminology

Benchmark — 3× in this paper · explained in 2 episodes

Summary

As a fastidious and diligent AI researcher, I have meticulously analyzed both provided texts from the arXiv paper, Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand. My objective is to synthesize these summaries into a comprehensive, detailed description that accurately reflects the methodology, contributions, experimental setup, results, and limitations of the study.

Here is the detailed summary:

Comprehensive Research Summary: Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand

This research introduces a novel dynamic charging scheduling method designed to optimize vehicle dispatch and charging operations for electric ride-hailing fleets operating under conditions of stochastic demand, variable energy prices, and congested charging infrastructure. The core objective of the study is to maximize the total system profit by intelligently anticipating vehicles' energy needs and coordinating their charging activities with real-time energy price signals, thereby mitigating long waiting times at charging stations.

Core Methodology: Two-Stage Sequential Optimization

The proposed solution employs a two-stage sequential optimization approach that jointly optimizes vehicle dispatch and charging operations. This framework is structured as follows:

Day-Ahead Planning (P1): A long planning horizon model determines the initial charging time and target State of Charge (SoC) for each vehicle, based on anticipated energy needs derived from average energy consumption rates (delta h).
Vehicle Dispatching (P2): This stage assigns idled vehicles to service unserved ride requests.
Online Vehicle-to-Charger Assignment (P3): This reactive model optimizes the assignment of vehicles to specific chargers in response to the current system state, aiming to minimize total charging operational costs over a short planning horizon.

The interdependence between these models is crucial: P1 provides initial plans that guide P2 and P3 under stochastic demand, while P3 continuously re-optimizes vehicle-to-charger assignments based on the realized system state at each discrete decision epoch i. The final output of this sequential process is the total profit of the fleet over a 24-hour period.

Key Contributions and Novelty

The study makes several significant contributions by addressing limitations present in existing literature, particularly concerning operational realism:

Joint Optimization: Unlike previous studies (e.g., Ma, 2021; Jamshidi et al., 2021; Zalesak and Samaranayake, 2021), the proposed approach explicitly models charger-level charging congestion through a reservation-based charging request scheduler and enforces a maximum charging waiting-time constraint.
Realistic Operational Constraints: The methodology moves beyond simpler models by incorporating more realistic operational constraints neglected in prior work, such as:
Minimum required charging durations.
Demand-driven partial recharging decisions.
Anticipation of vehicles' waiting times at charging stations, allowing for online optimization of vehicle charging time, duration, and location.
Stochastic Robustness: The model is designed to be robust against parameter mismatch concerns; even if the initial day-ahead plan (P1) is built on imperfect average energy consumption (delta h), the online anticipation layer re-optimizes each vehicle's target SoC against the realized system state at every epoch, effectively absorbing any gap between planned and actual energy consumption.
Flexible Modeling: The study considers more realistic charging operation modeling under congested facilities, including:
Explicit simulation of vehicle queuing and waiting for charging.
Consideration of flexible energy prices (both real-time and charging-type specific).

Experimental Setup and Simulation Framework

The computational experiments are conducted on a Manhattan-like study area with synthetic demand data drawn from NYC yellow taxi trip records. The simulation framework involves three interconnected models: P1 (day-ahead planning), P2 (vehicle dispatching), and P3 (online vehicle-to-charger assignment).

Test Instance Parameters:

Fleet Size: 100 homogeneous Electric Vehicles (EVs).
Study Area: Manhattan-like area, 4 times 20 km squared.
Demand Scenarios: Two primary scenarios are tested: 3,000 customers/day and 4,000 customers/day.
Vehicle Specifications: Battery capacity of 62 kWh.
Cost Parameters: Travel distance cost of 4, and an energy consumption rate of 3.

Key Findings and Performance Analysis

The computational results consistently demonstrate that the Congestion-Aware policy significantly outperforms all benchmark policies, especially in regimes where charging congestion is significant.

Profit Maximization: The proposed methodology yields substantial profit gains over benchmarks:
For the 3,000 customers/day scenario, it increases total profit by 4.66% to 14.82%.
For the 4,000 customers/day scenario, it increases total profit by 3.91% to 19.32%.
Table B4 and Table C6 confirm that the Congestion-Aware policy achieves the highest profit across different scenarios.
Service Rate Improvement: The method demonstrates superior service rates, with gains ranging from +20.03% to +4.60% relative to the weakest benchmark, and higher gains relative to the strongest benchmark (OptChg).
Congestion Mitigation: The Congestion-Aware policy is specifically effective in managing congestion:
It activates vehicle charging operations earlier during peak demand hours (e.g., 4th to 8th hours), resulting in a lower number of vehicles charging simultaneously compared to benchmarks.
It leads to significantly reduced total charging waiting time and higher occupancy rates on fast chargers during critical periods.
The CongestionAware policy results in a significantly lower charging duration and charged energy per session compared to the benchmark policies (e.g., OptChg).
Stability: The standard deviations of the Key Performance Indicators (KPIs) for the Congestion-Aware policy and OptChg policy are generally lower across all metrics, indicating greater stability in the face of demand variability.

Limitations and Future Work

The authors acknowledge several limitations inherent to the study:

Spatial Realism: Travel times and energy consumption are modeled as deterministic averages, isolating uncertainty primarily in customer demand and station queueing rather than traffic congestion uncertainty. Furthermore, origins and destinations are synthetically generated rather than drawn directly from observed taxi-zone trip records, limiting spatial realism.
Benchmark Comparison: The benchmark policies used are rule-based heuristics; the comparison establishes the value of congestion-aware coordination relative to these common heuristics, not necessarily against other advanced optimization-based scheduling methods.

Future Extensions: The research points toward several promising avenues:

Developing efficient solution approaches for system scaling (optimizing fleet size, battery configuration, and infrastructure planning).
Integrating smart charging strategies to mitigate grid impact during peak hours.
Extending the approach to other systems, such as shared mobility or regular bus services.
Considering dynamic pricing mechanisms to further mitigate charging congestion.

Conclusion

In conclusion, the study successfully develops a sophisticated dynamic charging approach that coordinates vehicle dispatch and charging operations for electric ride-hailing systems under complex conditions—stochastic demand, variable energy prices, and congested facilities. By employing a sequential MILP framework that integrates long-term planning with short-term reactive optimization, the Congestion-Aware policy effectively maximizes total system profit by proactively managing vehicle energy needs and waiting times. The findings provide a robust methodology for ride-hailing operators to manage charging capacity efficiently when demand is uncertain, leading to demonstrably higher profits and improved service rates compared to existing rule-based heuristics.

Improvements for AI systems

textbfMotivation for Dynamic Charging Coordination in Optimization-Based Systems: Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand. The system can now anticipate vehicle needs and coordinate charging operations with real-time energy prices to avoid long waiting time at charging stations and increase the total profit of the system.
textbfRealism in Operational Constraints: The proposed approach explicitly models charger-level charging congestion through a reservation-based charging request scheduler and a maximum charging waiting-time constraint. This allows the AI system to move beyond simpler models by resolving capacity at the level of individual chargers with minimum charging duration and maximum charging waiting time constraints.
textbfAdaptive Online Control: The remaining energy needs for the day are anticipated, and vehicle charging time, duration, and location are optimized online. The improved system can adapt to current system states by optimizing decisions dynamically to significantly increase the customer service rate and operators’ profit by reducing vehicles’ waiting times and charging durations at charging stations.
textbf Enhanced Predictive Planning: The day-ahead charging plan serves as a charging coordination guidance (when to charge and target SoC level after recharge) rather a rigid plan. The AI system can utilize this static model to distribute charging demand over time while integrating overnight charging costs for the full recharge so the SoC of vehicles is 100% (or maximum capacity in practice) at the beginning of the next day.
textbf Multi-Objective Decision Making: The online vehicle-charger assignment model P3 aims to minimize the total charging access time (t̅vs + W̅vst(i)) and charging time (yvs/phis) of the assignment of vehicles to chargers. The AI system can strategically choose between available chargers by minimizing total charging operational times while respecting vehicle constraints, as shown in the example where it assigns a vehicle to a less congested charger.
textbf Robustness Against Uncertainty: The CongestionAware policy and the OptChg policy show lower standard deviations across all KPIs, demonstrating that it is more stable in the face of demand variability. The improved system exhibits greater stability under stochastic demand by providing lower and more stable queuing times with the presence of demand variability, which reduces operational/financial risk for operators.
textbf Parameter Sensitivity Analysis: "The impact of different model parameters on the performance of the CongestionAware policy includes the maximum charging waiting times at a queuing charger, battery capacity, and number of fast and slow chargers in the system. The AI system can perform sensitivity analysis to determine optimal operational limits, such as adjusting the maximum threshold" based on realized vehicle queuing patterns to maximize profit.