[Paper Review] Optimal battery thermal management for electric vehicles with battery degradation minimization

Yue Wu, Zhiwu Huang+5 more|arXiv (Cornell University)|Aug 6, 2023

Advanced Battery Technologies Research4 citations

TL;DR

This paper proposes a near-optimal, rule-based battery thermal management strategy for electric vehicles that minimizes both battery degradation and cooling energy use by leveraging dynamic programming (DP) results. The strategy uses regenerative energy for cooling and achieves less than 3% performance difference from offline DP optimization across diverse driving conditions.

ABSTRACT

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical-thermal-aging model of the $ m LiFePO_4$ battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules "fast cooling, slow cooling, and temperature-maintaining" from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 3% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided.

Motivation & Objective

• Address the challenge of battery overheating and thermal runaway in electric vehicles under hot weather conditions.
• Simultaneously minimize battery degradation and cooling energy consumption in active battery thermal management systems (BTMS).
• Develop an online, computationally efficient control strategy that approximates the performance of offline optimal solutions.
• Enable practical implementation of optimal cooling by extracting actionable rules from dynamic programming results.
• Evaluate the strategy’s performance across urban, suburban, and highway driving scenarios under varying environmental conditions.

Proposed method

• Developed a control-oriented electrical-thermal-aging model for LiFePO4 battery packs to capture coupled electrochemical, thermal, and degradation dynamics.
• Built a detailed BTMS model in KULI software using orthogonal experimental data, incorporating compressor power, coolant flow rate, ambient temperature, and vehicle speed.
• Formulated a cost function in the DP framework combining battery aging cost and cooling-induced electricity cost for optimal compressor power selection.
• Extracted three operational rules—fast cooling, slow cooling, and temperature-maintaining—from the DP solution to create a rule-based strategy.
• Designed the rule-based strategy to prioritize the use of regenerative energy for battery cooling to enhance energy efficiency.
• Validated the strategy through simulations under multiple real-world driving profiles and environmental conditions.

Experimental results

Research questions

• RQ1 How can battery thermal management be optimized to minimize both degradation and cooling energy consumption in EVs?
• RQ2 What are the key operational phases (e.g., fast cooling, temperature maintenance) that emerge from an optimal cooling policy?
• RQ3 To what extent can a rule-based strategy approximate the performance of an offline dynamic programming solution?
• RQ4 How does the use of regenerative energy for cooling impact driving economy and battery longevity?
• RQ5 How do varying driving conditions and ambient temperatures affect the performance of the proposed cooling strategy?

Key findings

• The proposed rule-based strategy achieves less than 3% difference in battery degradation compared to the offline dynamic programming solution.
• The strategy significantly reduces battery degradation without substantially reducing driving range, especially under high-temperature and long-distance driving conditions.
• Using regenerative energy for battery cooling is a key enabler for improving driving economy and minimizing degradation.
• BTMS parameters such as coolant flow rate and compressor speed affect cooling speed but have minimal impact on overall driving economy.
• The proposed BTMS outperforms both no-cooling and existing MPC-based strategies in terms of degradation reduction and energy efficiency.
• The strategy maintains strong performance across urban, suburban, and highway driving cycles, with improved effectiveness as ambient temperature and trip length increase.