⚡ electrical engineering

Scenario-Based Assessment of Thermal Performance, Battery Degradation, and Total Cost of Ownership for EV Battery Thermal Management in Tropical Logistics Operations

This study demonstrates that in tropical logistics operations, implementing predictive pre-cooling for electric vehicle batteries significantly outperforms passive air and reactive liquid cooling by maintaining lower peak temperatures, extending battery service life to over ten years, and reducing the eight-year total cost of ownership by 20.21% compared to passive systems.

Original authors: Praphat Phuangchuen, Thunyaput Kittiphotiklang, Siriwat Ninlawat, Wirasak Chotikandun, Prakasit Prabpal, Somchat Sonasang

Published 2026-10-30

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CC BY 4.0

Original authors: Praphat Phuangchuen, Thunyaput Kittiphotiklang, Siriwat Ninlawat, Wirasak Chotikandun, Prakasit Prabpal, Somchat Sonasang

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Every electric vehicle carries a large battery pack that powers its motor, but these batteries are sensitive to heat. Just as a human body functions best within a comfortable temperature range, a battery performs efficiently and lasts longer when kept cool. When the weather is hot, the battery generates its own heat while driving and charging, and if it gets too warm, the chemical reactions inside speed up. This causes the battery to lose its ability to hold a charge over time, a process known as degradation. For companies that run fleets of delivery trucks in tropical climates, where temperatures stay high year-round, this heat is a constant enemy. It forces them to replace expensive batteries sooner than planned, which costs money and creates a steady stream of old batteries that must be recycled or disposed of. The core question for engineers is how to manage this heat: should they use simple, cheap methods that let the battery get hot, or invest in more complex systems that keep it cool but cost more to run?

A team of researchers in Thailand set out to answer this question by simulating the life of an electric delivery truck over eight years in a hot, tropical environment. They did not build a new truck or run a physical test on a real road; instead, they created a detailed computer model that acted as a virtual laboratory. This model allowed them to compare three different ways of managing battery temperature under the same harsh conditions. The first method was passive air cooling, which relies on natural airflow and is the simplest and cheapest option. The second was reactive liquid cooling, a system that uses a pump to circulate coolant only when the battery gets hot. The third was predictive pre-cooling, a smarter approach that uses information about the day's schedule to cool the battery down before it even starts to heat up. The researchers fed these three scenarios into their model to see how the temperature of the battery changed hour by hour, how quickly the battery would wear out, and how much it would cost to own the vehicle over the long term.

The results of the simulation showed a clear difference in how the batteries fared. With the simple passive air cooling, the battery temperature soared to a peak of 51.07 degrees Celsius during a hot day. This intense heat caused the battery to degrade quickly, reaching the point where it was considered too worn out for use after just 3.43 years. Because the study looked at an eight-year period, this meant the fleet would have to buy and install a brand-new battery pack twice. The reactive liquid cooling system kept the battery much cooler, peaking at 33.41 degrees Celsius, which extended its life to 5.87 years. This strategy required only one battery replacement during the eight years. The predictive pre-cooling strategy performed the best, keeping the battery at a gentle 31.00 degrees Celsius. In this scenario, the battery remained healthy enough to last the entire eight years without needing a single replacement.

These differences in battery life translated directly into the total cost of owning the vehicle. While the predictive pre-cooling system required the most money upfront to buy and install, and used a bit more electricity to run its cooling pumps, it saved the most money in the long run. The researchers calculated that over eight years, a vehicle using passive air cooling would cost 77,700 US dollars to own, largely due to the expense of buying two new batteries and the lost time while the truck was being repaired. The reactive liquid cooling system brought that cost down to 72,500 dollars. The predictive pre-cooling system was the most economical, costing only 62,000 dollars. This represents a saving of 20.21 percent compared to the simplest cooling method. The money was saved because the truck never had to stop for a battery replacement, and the vehicle remained on the road working for the full eight years.

Beyond the financial savings, the study highlighted a significant environmental benefit related to the timing of waste. Every time a battery is replaced, a new one enters the vehicle and the old one moves toward the recycling system. By keeping the battery cool and extending its life, the predictive strategy delayed the arrival of old batteries at the recycling center. In the eight-year simulation, the simple cooling method resulted in two old batteries per truck entering the end-of-life system, while the predictive method resulted in zero. This does not mean the batteries were saved forever; they will eventually need to be recycled. However, delaying this process means that the materials inside the battery are used for a longer time before they become waste. For a large fleet of trucks, this delay adds up to hundreds of fewer battery packs entering the recycling stream during the study period, slowing down the turnover of raw materials.

The researchers also tested how sensitive these results were to changes in the environment. They found that as the outside temperature got even hotter, the advantage of the smart cooling system grew larger. In extremely hot conditions, the simple cooling system became much more expensive to run because the batteries degraded so fast. They also looked at the cost of electricity, noting that while the smart system used more power to cool the battery, the savings from not buying new batteries were so large that the higher electricity cost did not matter. The study concluded that for electric vehicles used in hot climates, the best choice is not necessarily the cheapest system to buy, but the one that keeps the battery healthy enough to avoid expensive replacements. The findings suggest that investing in smarter thermal management is a practical way to save money and reduce the frequency of battery waste, provided the system is designed to handle the specific challenges of tropical logistics.

Technical Summary: Scenario-Based Assessment of Thermal Performance, Battery Degradation, and Total Cost of Ownership for EV Battery Thermal Management in Tropical Logistics Operations

Problem Statement
The rapid expansion of electric vehicle (EV) logistics in tropical regions presents a critical challenge: elevated ambient temperatures accelerate lithium-ion battery degradation, leading to premature retirement, increased replacement frequency, and higher total cost of ownership (TCO). While battery thermal management systems (BTMS) are essential for maintaining battery health, selecting the optimal strategy involves complex trade-offs between initial capital investment, auxiliary energy consumption, and long-term battery service life. Current research often focuses on either high-fidelity electrochemical thermal control or downstream recycling processes, leaving a gap in transparent frameworks that link daily thermal exposure directly to long-term state-of-health (SoH), replacement timing, and lifecycle economics. This study addresses this gap by evaluating how different thermal management strategies influence battery service life and material turnover under tropical operating conditions.

Methodology
The authors developed a deterministic, scenario-based screening framework to compare three distinct BTMS strategies over an eight-year ownership horizon:

Passive Air Cooling: Represents a baseline with limited heat rejection.
Reactive Liquid Cooling: Represents a system that responds to thermal thresholds.
Predictive Pre-cooling: Represents a strategy that initiates cooling based on anticipated thermal loads before critical conditions occur.

The framework links four analytical layers:

Thermal Layer: Prescribed daily battery-temperature trajectories ( $T_s(h)$ ) were modeled using analytical surrogate functions based on a tropical peak-day ambient profile (28–38 °C). Key metrics included peak temperature ( $T_{peak}$ ), daily mean temperature ( $\bar{T}$ ), and hours spent above a 35 °C engineering reference ( $H_{35}$ ).
Degradation Layer: Battery SoH decline was modeled using strategy-specific exponential decay functions ( $SoH_s(y) = 100 \exp(-k_s y)$ ). The degradation coefficients ( $k_s$ ) were assigned to represent distinct thermal stress scenarios rather than being derived from a specific electrochemical model. An 80% SoH threshold defined the end-of-life (EOL) point for the traction battery.
Material-Cycle Layer: The model tracked the timing of battery replacements. When a battery reached the 80% threshold, it was replaced (resetting SoH to 100%), and the number of replacement events ( $N_{rep}$ ) within the eight-year horizon was recorded. This served as an upstream indicator for the timing of battery entry into EOL management systems.
Economic Layer: A TCO model aggregated capital hardware costs, traction energy, BTMS auxiliary energy, battery replacement costs, downtime losses, and maintenance.

The study utilized Python for deterministic calculations, ensuring numerical reproducibility without stochastic sampling or machine learning training.

Key Contributions

Integrated Screening Framework: The paper provides a transparent model linking prescribed thermal trajectories to long-term SoH, replacement events, and TCO, specifically tailored for tropical logistics.
Quantification of Thermal Exposure: It quantifies the differences in peak and average temperatures across the three strategies under a standardized tropical peak-day profile.
Lifecycle Economic Analysis: It demonstrates how higher initial investment in advanced thermal management can be offset by reduced replacement frequency and downtime, providing a comparative TCO analysis.
Material-Cycle Interpretation: The study introduces "deferred EOL entries" as a metric, illustrating how extending battery service life delays the rate at which battery packs enter downstream recycling or second-life management chains.

Results

Thermal Performance: Under the tropical peak-day profile, passive air cooling resulted in a peak battery temperature of 51.07 °C, reactive liquid cooling reduced this to 33.41 °C, and predictive pre-cooling achieved the lowest peak at 31.00 °C. Passive cooling remained above 35 °C for the entire 24-hour period, while the active strategies stayed below this threshold.
Battery Service Life: The time to reach the 80% SoH threshold was 3.43 years for passive air cooling, 5.87 years for reactive liquid cooling, and 10.14 years for predictive pre-cooling.
Replacement Frequency: Over the eight-year horizon, the passive strategy required two battery replacements (in years 4 and 8), the reactive strategy required one (in year 6), and the predictive strategy required zero replacements.
Total Cost of Ownership (TCO):
- Passive Air Cooling: USD 77,700 per vehicle.
- Reactive Liquid Cooling: USD 72,500 per vehicle (6.69% savings vs. passive).
- Predictive Pre-cooling: USD 62,000 per vehicle (20.21% savings vs. passive).
  The predictive strategy achieved the lowest TCO despite the highest initial hardware cost (USD 17,200) and higher auxiliary energy use, primarily due to the avoidance of two battery replacements (USD 18,000) and significant downtime reduction.
Break-Even Analysis: The cumulative cost of the predictive strategy exceeded the passive strategy initially but crossed over at approximately 3.44 years, coinciding with the first battery replacement event in the passive scenario.
Sensitivity: The economic advantage of predictive pre-cooling increased with higher ambient temperatures (due to the non-linear cost increase of passive cooling) but decreased slightly with higher electricity tariffs, though the replacement savings remained the dominant factor.

Significance and Claims
The authors assert that their findings demonstrate that improved thermal management in tropical logistics can significantly extend battery service life, reduce replacement-related costs, and slow the turnover of battery materials into end-of-life systems. The study claims that BTMS selection should not be based solely on cooling performance or acquisition cost but must be evaluated through an integrated lifecycle perspective that accounts for degradation, replacement timing, and vehicle availability.

Crucially, the paper maintains a modest scope regarding its claims. It explicitly states that the results are scenario-based and rely on analytical surrogates rather than experimentally validated electrochemical models. The authors do not claim to have reduced waste mass or avoided recycling; rather, they argue that extending service life defers the timing of battery entry into downstream management systems. The study serves as a screening tool to identify operating conditions where proactive thermal management offers the greatest lifecycle benefit, acknowledging that future work requires coupling these models with validated electrothermal data, specific battery chemistries, and measured logistics duty cycles.

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