Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature

TL;DR

The paper addresses slow LIB preheating at low ambient temperatures and risks of lithium plating by proposing a hybrid heating strategy that combines external resistive PTC film heating with bidirectional pulse heating. It models a pouch LIB using the DFN electrochemical framework coupled with a 1D thermal model, and optimizes the joint control of $v_{PTC}$ and pulse amplitudes via Maximum A Posteriori Policy Optimization (MPO), with state $s=ig\{SOC, T_m, T_{out}, v_t, T_{des}\big\}$ and actions $a=ig\{v_{PTC}, i_c, i_d\big\}$. The results reveal a policy where film heating rapidly raises temperature to enable effective high-amplitude pulse heating later, achieving fast warming while limiting $T_{range}$, and distributing heating load to reduce the auxiliary power requirement. This approach offers a practical route to faster, more uniform preheating in cold conditions and suggests simple, implementable control rules, with experimental validation and Pareto analyses proposed for future work.$

Abstract

Low ambient temperatures significantly reduce Lithium ion batteries' (LIBs') charge/discharge power and energy capacity, and cause rapid degradation through lithium plating. These limitations can be addressed by preheating the LIB with an external heat source or by exploiting the internal heat generation through the LIB's internal impedance. Fast external heating generates large temperature gradients across the LIB due to the low thermal conductivity of the cell, while internal impedance heating (usually through AC or pulse charge/discharging) tends to be relatively slow, although it can achieve more uniform temperature distribution. This paper investigates the potential of combining externally sourced resistive film heating with bidirectional pulse heating to achieve fast preheating without causing steep temperature gradients. The LIB is modeled with the Doyle Fuller Newman (DFN) electrochemical model and 1D thermal model, and reinforcement learning (RL) is used to optimize the pulse current amplitude and film voltage concurrently. The results indicate that the optimal policy for maximizing the rate of temperature rise while limiting temperature gradients has the film heating dominate the initial phases and create the ideal conditions for pulse heating to take over. In addition, the pulse component shares the heating load and reduces the energy rating of the auxiliary power source.

Figures (5)

• Figure 1: LIB unit and cell used for electrochemical and thermal modeling
• Figure 2: Actor-Critic RL for optimal preheating
• Figure 3: Optimization of pulse and PTC heating (with $v_{max} = 10V$) for LIB cell heating
• Figure 4: Comparison of different preheating methods
• Figure 5: PTC and pulse heating for different maximum PTC voltages