A Game-Theoretic Framework for Intelligent EV Charging Network Optimisation in Smart Cities

Niloofar Aminikalibar; Farzaneh Farhadi; Maria Chli

cs.MAMar 23, 2026

A Game-Theoretic Framework for Intelligent EV Charging Network Optimisation in Smart Cities

Authors: Niloofar Aminikalibar, Farzaneh Farhadi, Maria Chli

Organizations: School of Computer Science and Digital Technologies, Aston University, Birmingham, UK.

Abstract

The transition to Electric Vehicles (EVs) demands intelligent, congestion-aware infrastructure planning to balance user convenience, economic viability, and traffic efficiency. We present a joint optimisation framework for EV Charging Station (CS) placement and pricing, explicitly capturing strategic driver behaviour through coupled non-atomic congestion games over road networks and charging facilities. From a Public Authority (PA) perspective, the model minimises social cost, travel times, queuing delays and charging expenses, while ensuring infrastructure profitability. To solve the resulting Mixed-Integer Nonlinear Programme, we propose a scalable two-level approximation method, Joint Placement and Pricing Optimisation under Driver Equilibrium (JPPO-DE), combining driver behaviour decomposition with integer relaxation. Experiments on the benchmark Sioux Falls Transportation Network (TN) demonstrate that our method consistently outperforms single-parameter baselines, effectively adapting to varying budgets, EV penetration levels, and station capacities. It achieves performance improvements of at least 16% over state-of-the-art approaches. A generalisation procedure further extends scalability to larger networks. By accurately modelling traffic equilibria and enabling adaptive, efficient infrastructure design, our framework advances key intelligent transportation system goals for sustainable urban mobility.

Figures & tables

Term	Description	Term	Description
$\omega$	O-D pair: $\omega\in W$	$i$	Node identifier
$r_{\omega}$	A route for $\omega$	$p_{\omega}$	An extended path for $\omega$
$R_{\omega}$	Set of routes for $\omega$	$P_{\omega}$	Set of extended paths for $\omega$
$q_{\omega,p}$	proportion of EV drivers with $\omega$ choosing extended path $p$	$\boldsymbol{q_{\omega}}$	Strategy over all extended paths for O-D $\omega$
$q_{\omega,r}^{0}$	proportion of NCDs with $\omega$ choosing route $r$	$\boldsymbol{q_{\omega}^{0}}$	Strategy over all routes for O-D $\omega$
$x_{i}$	Number of chargers at node $i$	$y_{i}$	Charging price at node $i$

TABLE I: Notation Guide

Fig. 1: Sioux Falls Network : Solving PA model using JPPO-DE framework for peak-hours results in optimised EV Charging network- Reflecting traffic flow of drivers on links in equilibrium, charging fee and number of chargers in each node and different zones in terms of site rental & maintenance costs $(T)$ . The numbers on the links and nodes correspond to their respective identifiers.

Fig. 2: Total Social Cost of EV drivers VS. Budget within 3 Optimisation Approaches: JO consistently minimises total social cost across all budget levels. At the optimal budget of $B=169$ , JO reduces costs by 16% compared to PlO and 19% compared to PrO, due to its ability to avoid over-installation in low-demand areas and overpricing in low-cost zones.

Fig. 3: Average Social Cost and Required Chargers vs. $\boldsymbol{\mu}\&\boldsymbol{\alpha}$ : Left : As the charger service rate $\mu$ increases, assuming fixed $e$ , queueing time drops, reducing the number of chargers needed and charging fees. Right : For each 1% increase in $\alpha$ , about 13 more chargers are required. Although the average social cost per EV rises gradually with greater penetration, expanding infrastructure alone may be inadequate. Additional measures, such as demand-side incentives, may be necessary to sustain system efficiency.

Fig. 4: Network Resilience Response to CS Failure: Lower social costs emerged in the network when dynamic JPPO-DE was applied to adapt prices, compared to the fixed price model.

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Strategic Infrastructure Design via Multi-Agent Congestion Games with Joint Placement and Pricing
Mar 23, 2026Niloofar Aminikalibar, Farzaneh Farhadi, Maria ChliCongestion Games Multi-Agent System Optimization
Aston University, Birmingham, UK, B4 7ET
Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
May 20, 2026Peiyan Xiao, Yuheng Li, Ayan Mukhopadhyay +3Intelligent Transportation Systems Electric Vehicle Charging
The College of William & Mary, Williamsburg, VA, USA · University of Georgia, Athens, GA, USA · University of Louisville, Louisville, KY, USA
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Department of Civil and Environmental Engineering, Monash University, Melbourne, Australia · Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia · Department of Data Science & AI, Monash University, Melbourne, Australia

Mar 23, 2026J/K move · Enter open · S save

Strategic Infrastructure Design via Multi-Agent Congestion Games with Joint Placement and Pricing

Niloofar Aminikalibar, Farzaneh Farhadi, Maria Chli

Aston University, Birmingham, UK, B4 7ET

Congestion Games Multi-Agent System Optimization Game Theory Bilevel Optimization

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Real-world infrastructure planning increasingly involves strategic interactions among autonomous agents competing over congestible, limited resources. Applications such as Electric Vehicle (EV) charging, emergency response, and intelligent transportation require coordinated resource placement and pricing decisions, while anticipating the adaptive behaviour of decentralised, self-interested agents. We propose a novel multi-agent framework for joint placement and pricing under such interactions, formalised as a bi-level optimisation model. The upper level represents a central planner, while the lower level captures agent responses via coupled non-atomic congestion games. Motivated by the EV charging domain, we study a setting where a central planner provisions chargers and road capacity under budget and profitability constraints. The agent population includes both EV drivers and non-charging drivers (NCDs), who respond to congestion, delays, and costs. To solve the resulting NP-hard problem, we introduce ABO-MPN, a double-layer approximation framework that decouples agent types, applies integer adjustment and rounding, and targets high-impact placement and pricing decisions. Experiments on benchmark networks show that our model reduces social cost by up to 40% compared to placement- or pricing-only baselines, and generalises to other MAS-relevant domains.

Abstract

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