A Consensus Algorithm for Multi-Objective Battery Balancing

Abstract

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

Meta-analysis of experimental results for heat capacity and thermal conductivity in lithium-ion batteries: A critical review

“…This subsection gives an overview of experiments measuring the specific heat capacity of the entire stack 𝑐 𝑠𝑡𝑎𝑐𝑘 and compares the results with the two previous subsections. According to Equation (7), 𝑐 𝑠𝑡𝑎𝑐𝑘 depends heavily on the mass fractions of the stack components, which are listed in Table 4 for some lithium-ion cells based on the total cell mass 𝑚 𝑐𝑒𝑙𝑙 .…”

“…For example, Kraft et al [6] observed that cells with a high-capacity cathode active material (lithium and manganese-rich) released up to four times as much heat during discharges compared to cells with a commonly used cathode active material (nickel-rich). On the other hand, Barreras et al [7] showed that the problem of non-uniform temperatures during EV fast charging can be solved with innovative balancing concepts, but at the expense of additional cost and complexity, and the cooling needs are still significant. Indeed, temperature is an important battery state that affects energy efficiency [8],…”

Optimal Control of Active Cell Balancing: Extending the Range and Useful Lifetime of a Battery Pack

“…Proof: First, let us denote the set of feasible points for the relaxed problems as F = ({x, y, u, w} | (7b) − (7h) hold) (11) and let us denote the set of feasible points for the original problem as…”

“…Although prolonging range is seemingly one of the key abilities of active cell balancing [10], this benefit is only actively aimed for in [8] and [7]. Moreover, having acknowledged violation of the voltage limit as the key indicator for stopping current flow, it is important to see that literature often applies inaccurate battery models [3], i.e., without dynamical overpotential dynamics and with linear open-circuit voltage relations [11]. In previous work of Hoekstra et al [12], [13], range maximization has been posed as the sole objective of active cell balancing.…”

Electric Vehicle Efficient Power and Propulsion Systems

“…First, Barreras, J.V. et al [1] present novel research on the distributed multi-objective control algorithm, which is based on a multi-agent consensus algorithm. The authors implemented and validated the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data.…”