Multi-objective geospatial optimization framework to eliminate charging deserts and ensure socioeconomic parity.
Berlin's transition to electric mobility faces a critical 'Last Mile' problem. Central districts like Mitte benefit from over-supply, while residential zones in Treptow-Köpenick and Pankow face ratios exceeding 30:1.
This study utilizes a Non-dominated Sorting Genetic Algorithm (NSGA-II) combined with Fuzzy-Robust logic to solve for the optimal placement of 500 new charging points. We prove that a 13% reduction in inequality is possible with minimal impact on total network utility.
Genetic sorting ensures we discover the 'Pareto Front' of solutions that maximize coverage while minimizing the Gini coefficient.
Accommodates the inherent uncertainty in EV adoption rates, ensuring the network is resilient to future demand shifts.
// Objective 1: Maximize Coverage (Utility)
Maximize: f₁(x) = Σ (Pᵢ * Cᵢ) / Σ Pᵢ
// Objective 2: Minimize Gini Coefficient (Equity)
Minimize: f₂(x) = 1/2n²μ * ΣΣ |rᵢ - rⱼ|
rᵢ = chargers / registered BEVs per district i
The solver iterates through generations to identify the Pareto Front — the set of all non-dominated solutions.
IEEE ETECOM 2025
10.1109/ETECOM66111.2025.11318989
Clifford O. Ondieki, T. Lu
Spatial Inequality reduced by 13.2%
The methodology is currently being adapted for other major European capitals.
Refining the model from district level to 100m x 100m geospatial grids.
Integrating bidirectional energy flow constraints into the optimizer.
Adding household income layers to further refine the equity metric.
A comprehensive 5-page technical roadmap for urban planners implementing equitable charging infrastructure. Learn how to apply multi-objective optimization to your city.
NSGA-II algorithm implementation guide with pseudocode
Geospatial data preprocessing workflow for GIS integration
VDE-AR-N 4110 compliance checklist for German grid operators
Fuzzy-robust optimization methodology with real-world validation
District-level equity metrics calculation framework
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