Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentration-dependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations.

In this paper, a reduced-order electrochemical model of lithium-ion batteries is developed for control and estimation applications through analytical model order reduction based on a Galerkin projection method. The governing diffusion partial differential equations in the liquid and solid phases are approximated into low-order systems of ordinary differential equations while the physical meaning of all model parameters is preserved, allowing one to perform state and parameter estimation. The selection of basis functions for the Galerkin projection method and model order truncation is carefully determined based on analysis both in the frequency and time domains. With the reduced-order diffusion models in the liquid and solid phases, an extended single particle model incorporating the electrolyte dynamics is developed. The model is then validated against the experimental data gathered from two batteries with different chemistries (lithium nickel manganese cobalt oxide/graphite and lithium iron phosphate oxide/graphite) at different input conditions. Results show that the reduced-order model agrees very well with experimental data at various conditions. Meanwhile, it can be simulated thousands of times faster than the real time, making it suitable for long-term-life simulation, control, and estimation applications.

Lithium-ion (Li-ion) batteries suffer from substantial capacity and power degradation at low temperatures, severely deteriorating the performance of battery-based transportation electrification. To overcome this issue, different preheating techniques have been proposed to recover the performance of Li-ion batteries in cold climates. Among these, internal heating schemes are more promising than traditional conductive and convective approaches owing to their superiorities in terms of high efficiency, rapid speed, and uniform temperature distribution. This paper reviews various internal heating methodologies developed in recent years for Li-ion batteries, including mutual pulse current heating, alternating current heating, compound heating, and all-climate-battery based heating. Specifically, the effects of low temperatures on Li-ion batteries are first outlined in terms of cell performance and electrochemical characteristics. Then, the heat generation mechanism during internal heating is briefly described, based on which the internal temperature monitoring methods are also investigated considering the temperature gradient. Next, a comprehensive literature survey on different internal heating schemes with their basic principles, benefits, and drawbacks is presented. Finally, future trends of internal heating methods to benefit automotive battery performance are discussed in terms of key technologies, promising opportunities, and challenges.