Fleet Agility: Managing Heterogeneous (EV & ICE) Networks via a Unified TMS

The logistics industry stands at a defining inflection point. As sustainability mandates tighten, fuel costs climb, and urban delivery windows shrink, fleet operators are navigating one of the most complex transitions in the history of supply chain management: the coexistence and eventual convergence of electric vehicles (EVs) and internal combustion engine (ICE) vehicles within a single operational network.

This isn't a future scenario. It's today's reality for any serious logistics and supply chain management operation. And the central question is no longer whether to integrate EVs but how to manage a heterogeneous fleet without sacrificing efficiency, service levels, or cost control.

The answer, increasingly, lies in a purpose-built, AI-powered Transportation Management System (TMS) , one capable of treating EVs and ICE vehicles not as separate fleets but as a unified, intelligently orchestrated network.

The Complexity of Mixed-Fleet Operations

Running a heterogeneous fleet introduces operational variables that traditional transportation management system software was never designed to handle. ICE vehicles are well understood: known fuel ranges, predictable refuelling times, and decades of routing logic built around them. EVs, by contrast, introduce a new set of constraints:

• Range anxiety at scale: EV range varies significantly with payload weight, terrain, ambient temperature, and driving patterns , all of which fluctuate daily in last-mile delivery environments.
  
  
• Charging infrastructure dependencies: Unlike fuel stations, charging points are fewer, slower, and geographically concentrated. A vehicle stuck in a charging queue is effectively dead capacity.
  
  
• Dynamic battery state management: Unlike a fuel gauge, battery state of charge (SoC) degrades non-linearly. Predictive logistics models must account for energy consumption curves, not linear burn rates.
  
  
• Regulatory and zone compliance: Many urban cores are rolling out Low Emission Zones (LEZs) where only EVs or compliant ICE vehicles are permitted , adding a compliance dimension to every route assignment decision.

For a logistics company software platform to truly serve this environment, it must do more than schedule vehicles. It must think in energy, time, compliance, and cost simultaneously.

Why a Unified TMS Is the Only Viable Answer

A common early response to fleet electrification is to manage EVs and ICE vehicles through separate systems , one optimized for fuel logistics and another for charge management. This bifurcated approach is operationally unsustainable. It creates data silos, duplicates supervisory overhead, and eliminates the cross-fleet optimization opportunities that generate real cost savings.

A unified logistics management system that treats all vehicle types through a single operational lens offers fundamentally different capabilities:

1. Vehicle-Agnostic Route Planning

Advanced route planning software within a modern TMS assigns trips based on a composite scoring model , not just distance and time, but energy availability, charging slot pre-booking, payload compatibility, and zone restrictions. An EV with 60% SoC can be confidently assigned to a 45-km urban cluster run; an ICE vehicle with greater range flexibility handles the suburban long-haul. This is logistics optimization at the fleet composition level, not just the route level.

2. Real-Time Tracking and Dynamic Reallocation

Real-time tracking of vehicle location, speed, and , critically for EVs , battery state enables a supply chain control tower to intervene before problems escalate. If an EV is consuming charge faster than predicted due to a detour or heavy traffic, the system can proactively reassign its remaining stops to a nearby ICE vehicle or a higher-SoC EV, preventing a failed delivery without human intervention. This is the essence of supply chain automation: the system acts on data faster than any dispatcher can.

3. Predictive Charging Schedule Integration

Charging isn't a passive activity: it needs to be planned as actively as any delivery stop. Sophisticated predictive logistics engines within a TMS can model charging needs 12 to 24 hours in advance, pre-booking slots at charging hubs, sequencing arrivals to prevent queue congestion, and ensuring that no EV departs for a shift with insufficient charge for its assigned route. This transforms charging from a reactive emergency into a planned operational activity , a cornerstone of smart logistics.

Balancing Urban Delivery Frequency with EV Constraints

High-frequency urban delivery, the bread and butter of last-mile delivery , is both the best and worst use case for EVs. It's the best because short, repeated urban cycles are well within EV range envelopes, and EVs excel at stop-start urban driving. It's the worst because high delivery density combined with tight delivery windows leaves no margin for unplanned charging stops.

This is where AI in logistics delivers measurable value. Machine learning models trained on historical delivery data can predict, with high accuracy, which urban delivery clusters will see volume spikes, traffic surges, or extended dwell times, all of which affect EV energy consumption. By feeding these predictions into the TMS's dispatch engine, operators can pre-position charged EVs in high-density zones and ensure ICE backup capacity is available when energy uncertainty is highest.

From a last-mile logistics solutions perspective, this enables operators to commit to tight delivery SLAs without the hidden risk of EV range failure mid-route ,a promise that was previously impossible to make reliably with mixed fleets.

Furthermore, supply chain visibility across the entire delivery network , from warehouse departure to final doorstep scan, means that customer-facing delivery promises are backed by live operational data, not optimistic estimates. This is the foundation of modern delivery management: transparency that builds trust.

The Role of a Supply Chain Control Tower

Managing a heterogeneous fleet at scale requires more than individual route decisions. It requires a bird's-eye view of the entire network, which we call the 'Network Control Tower'. This is the operational nerve centre where supply chain analytics converts raw telemetry from hundreds of vehicles into actionable intelligence.

In a mixed-fleet environment, the control tower performs several critical functions:

• Fleet utilization scoring: Real-time dashboards show which vehicles, EV and ICE, are under-utilized, over-extended, or mismatched to their current route assignments.
  
  
• Exception management: When an EV is flagged as low-SoC mid-route, or an ICE vehicle reports a mechanical fault, the Control Tower surfaces the issue, models recovery options, and recommends , or autonomously executes a resolution.
  
  
• Cross-fleet performance benchmarking: Supply chain analytics can compare EV versus ICE performance across identical route types, informing future fleet composition decisions with empirical data rather than assumptions.
  
  

This kind of digital supply chain intelligence is what separates reactive fleet management from proactive supply chain optimization , the difference between chasing problems and preventing them.

Integration: The TMS as the Connective Tissue

A TMS operating in isolation is powerful but incomplete. The real multiplier effect comes from deep integration with the broader logistics ERP software ecosystem, specifically the logistics warehouse management system that controls dispatch sequencing and load planning.

When the WMS and TMS share data in real time, EV-specific constraints can influence decisions that begin long before a vehicle is loaded. A WMS that knows an EV has a 180 km range envelope today will sequence its load differently than for a long-haul ICE truck , prioritising compact, high-density urban loads over heavy, range-intensive suburban runs. This is supply chain management software working as an integrated system, not a collection of disconnected tools.

Similarly, integration with freight management software enables intermodal planning , identifying scenarios where EVs handle the final urban segment while longer ICE legs handle regional trunking, optimising total network cost while minimising carbon output.

The Business Case: Why Now

The feasibility argument for unified TMS management of heterogeneous fleets doesn't rest on future projections; it rests on present-day operational realities:

• Fuel cost volatility makes ICE fleet economics unpredictable. EV operating costs are more stable, but only if charging is managed efficiently.
  
  
• Regulatory pressure is accelerating. Emission standards, LEZ expansions, and corporate ESG commitments are forcing fleet electrification on timelines that don't allow for leisurely technology adoption.
  
  
• Driver productivity is directly tied to dispatch quality. A driver who receives poorly optimised routes, regardless of vehicle type, delivers fewer stops per hour and incurs higher operational costs.
  
  
• Supply chain risk management now includes fleet resilience. A network that can dynamically rebalance EV and ICE capacity is inherently more resilient to charging infrastructure outages, sudden demand spikes, or vehicle breakdowns.
  
  

Platforms like Libera, built on battle-tested technology that has orchestrated 5 million+ daily shipments across 2,400+ warehouses, are uniquely positioned to deliver this capability. The same supply chain management services infrastructure that achieved 99.96% on-time delivery at scale is the foundation upon which heterogeneous fleet management is built.

Conclusion: The Unified Fleet Is the Future-Ready Fleet

The transition to electric vehicles is not a binary switch; it is a decade-long migration during which EV and ICE vehicles will coexist, compete for capacity, and complement each other's strengths. Operators who manage this transition through siloed tools will face compounding complexity. Those who invest in a unified transportation management system , one built on AI in supply chain intelligence, real-time visibility, and predictive analytics , will emerge with leaner operations, stronger customer commitments, and a fleet that is genuinely ready for whatever regulatory and market pressures the next decade brings.

Fleet agility isn't about having the newest vehicles. It's about having the intelligence to deploy every vehicle , EV or ICE , at exactly the right moment, on exactly the right route, with exactly the right load.

That intelligence lives in your TMS.