Destination-Oriented Cost-Optimized Electric Vehicle Charging Navigation Strategy

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Destination-Oriented Cost-Optimized Electric Vehicle Charging Navigation Strategy

Introduction

The rapid development of electric vehicles (EVs) has brought significant environmental and economic benefits, yet challenges such as range anxiety, long charging times, and inefficient charging infrastructure persist. Traditional fuel vehicles can refuel quickly, but EVs require careful planning to ensure sufficient charging opportunities during long-distance travel. Existing charging navigation strategies often fail to account for the distinct characteristics of urban and highway networks, leading to suboptimal charging decisions. This paper proposes a destination-oriented, cost-optimized EV charging navigation strategy that addresses these challenges by implementing a hierarchical road network approach and optimizing charging decisions based on real-time conditions.

Background and Challenges

The adoption of EVs has surged globally, with China leading in production and sales. However, despite advancements in battery technology, range limitations remain a concern due to factors like weather conditions, driving habits, and battery degradation. Charging infrastructure, though expanding, still faces issues such as uneven distribution, high charging costs during peak hours, and long waiting times.

Current research on EV charging strategies can be categorized into two main approaches:

  1. Charging Station Selection – Many studies focus on selecting charging stations based on factors like distance, charging fees, and waiting times. However, these methods often ignore the destination, potentially leading to inefficient detours.
  2. Path Planning with Charging Integration – Some studies combine traffic information with EV energy consumption to optimize routes and charging stops. However, global search algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) suffer from high computational complexity and slow convergence, making them impractical for real-time applications.

Key challenges include:
• Highway vs. Urban Charging Dynamics – Highway travel restricts charging options to forward-only service areas, requiring proactive charging decisions to avoid being stranded.

• Real-Time Adjustments – Most existing methods assume static conditions, ignoring dynamic factors like traffic congestion and charging station availability.

• Balancing Multiple Costs – Users must consider travel distance, time, and charging expenses, requiring a multi-objective optimization approach.

Proposed Framework

To address these challenges, this paper introduces a hierarchical charging navigation strategy that differentiates between urban (low-level) and highway (high-level) road networks. The framework consists of three layers:

  1. User Interaction Layer
    This layer collects user inputs such as starting point, destination, battery capacity, and current state of charge (SOC). It also provides real-time navigation feedback, including route suggestions and charging stop recommendations.

  2. Data Processing Layer
    This layer integrates static and dynamic data:
    • Static Data – Road network topology, charging station locations, and speed limits.

• Dynamic Data – Real-time traffic conditions, charging station availability, and EV battery status.

  1. Path Planning and Charging Navigation Layer
    This layer optimizes routes and charging decisions based on cost functions that consider:
    • Travel Distance – Minimizing detours while ensuring sufficient charging stops.

• Travel Time – Accounting for driving time, charging duration, and waiting periods.

• Charging Costs – Adjusting for variable electricity pricing based on time of day.

Charging Strategy for Different Road Networks

Low-Level (Urban) Road Network Strategy
In urban areas, EVs have multiple charging options, allowing for flexible route adjustments. The proposed method uses an A* algorithm for path planning, enhanced with a charging decision function that evaluates:
• Charging Cost – Electricity rates and service fees.

• Waiting Time – Estimated queue length at charging stations.

• Post-Charging Travel Efficiency – Ensuring the chosen station minimizes additional travel to the destination.

When the EV’s SOC drops below a threshold, the system identifies nearby charging stations and selects the one with the lowest combined cost of charging and subsequent travel.

High-Level (Highway) Road Network Strategy
Highway travel presents unique constraints:
• Limited charging stations (only at service areas).

• No option to backtrack if a charging station is missed.

• Higher risk of range anxiety due to sparse infrastructure.

To optimize charging decisions, an improved Ant Colony Algorithm is employed. Key modifications include:
• Dynamic Charging Thresholds – Ensuring the EV always maintains enough charge to reach the next available station.

• Smart Charging Stop Selection – Evaluating charging costs and time to avoid unnecessary stops.

• Real-Time Adjustments – Updating routes based on traffic and station availability.

The algorithm calculates the optimal charging stops to minimize total travel time and cost while preventing battery depletion.

Simulation and Results

Highway Network Simulation
A simulation was conducted on the Beijing-Shanghai Highway (1,232 km) with 27 service stations. Key findings:
• The proposed method reduced total charging time by 36.9% compared to worst-case scenarios.

• Charging costs were lowered by 40.7% by avoiding peak pricing and unnecessary stops.

• The algorithm outperformed traditional ACO and PSO methods in both efficiency and cost savings.

Urban Network Simulation
Using real-world data from Qinhuangdao City, the study compared different charging strategies:
• Random Selection (Strategy 1) – Highest costs due to inefficient station choices.

• Cost-Optimized Elastic Charging (Strategy 2) – Improved but still suffered from long detours.

• Destination-Oriented Navigation (Strategy 3 & 4) – The proposed method (Strategy 4) achieved the lowest costs and fastest travel times by prioritizing stations closer to the destination.

Conclusion

This paper presents a destination-oriented, cost-optimized EV charging navigation strategy that effectively addresses the limitations of existing methods. By distinguishing between urban and highway networks, the framework ensures efficient charging decisions while minimizing travel time and expenses. Key contributions include:
• A hierarchical road network model that adapts to different driving environments.

• An improved Ant Colony Algorithm for highway charging optimization.

• A real-time decision function for urban charging station selection.

Future research will explore charging station load balancing and integration with smart grid systems to further enhance EV charging efficiency.

doi.org/10.19734/j.issn.1001-3695.2024.08.0305

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