https://www.zenodo.org/record/2642457/files/A%20review%20on%20various%20temperature-indication%20methods%20for%20Li-ion%20batteries.pdf

A review on various temperature-indication methods for Li-ion batteries

The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined.

http://ma.ecsdl.org/content/MA2010-02/4/226.full.pdf

Modeling and Simulation of Lithium-Ion Batteries from Systems Engineering Perspective

Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles

This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads

This paper aims at presenting new solutions for advanced Li-Ion battery management to meet the performance, cost and safety requirements of automotive applications. Emphasis is given to monitoring and controlling the battery temperature, a parameter which dramatically affects the performance, lifetime, and safety of Li-Ion batteries. In addition to this, an innovative battery management architecture is introduced to facilitate the development and integration of advanced battery control algorithms. It exploits the concept of smart cells combined with an FPGA-based centralized unit. The effectiveness of the proposed solutions is shown through hardware-in-the-loop simulations and experimental results.

/pdf/advances-in-li-ion-battery-management-for-electric-vehicles-3rcvievo23.pdf

Advances in Li-Ion Battery Management for Electric Vehicles

This paper describes a hardware-in-the-loop (HiL) simulation platform specifically designed to test state estimators for Li-ion batteries in electric vehicle applications. Two promising estimators, the Mix algorithm combined with the moving window least squares and the dual extended Kalman filter, are implemented in hardware on a field-programmable gate array (FPGA) and evaluated using the developed HiL platform. The simulation results show the effectiveness of using FPGAs for hardware acceleration of battery state estimators and the importance of their assessment under different operating conditions, i.e., driving schedules, which can be simulated by the HiL platform.

/pdf/hardware-in-the-loop-simulation-of-fpga-based-state-49gf4r31s5.pdf

Hardware-in-the-loop simulation of FPGA-based state estimators for electric vehicle batteries

A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application.

/pdf/comparison-of-state-and-parameter-estimators-for-electric-5gdxyqj4lm.pdf

Comparison of state and parameter estimators for electric vehicle batteries

The lead-acid battery of an electric minibus has been replaced with a smaller size lithium-ion battery system consisting of standard 12 V modules and a hierarchical battery management system. The minibus has experimentally been tested to show that the reduced battery capacity, which also cuts costs, does not affect the daily operational mission. This is assuming that the driving phases are alternated with fast charging periods. Experiments show that fast charging of 8 min guarantees up to 1 h of operation.

/pdf/experimental-analysis-of-an-electric-minibus-with-small-51rx2cjsmp.pdf

Experimental analysis of an electric minibus with small battery and fast charge policy

This paper discusses the implementation of a custom battery emulator, specifically designed for functional testing of battery management systems at the end of the production line. Particular care has been paid to make the design of the battery emulator modular and low cost. These characteristics are sought in relatively low-volume medium-power battery applications, where the adoption of conventional hardware-in-the-loop solutions is not viable. A prototype of battery emulator has been implemented, validated, and successfully used to test a battery management system for 12 series-connected cells.

/pdf/low-cost-modular-battery-emulator-for-battery-management-foyyg4pj2r.pdf

R. Di Rienzo

Papers

Advances in Li-Ion Battery Management for Electric Vehicles

Hardware-in-the-loop simulation of FPGA-based state estimators for electric vehicle batteries

Comparison of state and parameter estimators for electric vehicle batteries

Experimental analysis of an electric minibus with small battery and fast charge policy

Low-cost modular battery emulator for battery management system testing