A state-of-charge(SOC) balancing method which accounts for state-of-health(SOH) status of battery cells is presented in this paper. The data collected from ageing experiments indicates that there is correlation between the minimum impedance of the battery and capacity fading which can be used to predict capacity capability of the battery. However, this relationship is complex and nonlinear. In this paper, artificial neural network(ANN) is utilized to learn this relationship and an ANN based capacity estimator is developed to predict available capacity of battery cells. An online impedance measurement method with improved measurement resolution is presented to obtain a more accurate minimum impedance for the ANN based capacity estimator. The ANN estimated capacity is fed to an SOC balancing controller to calculate SOC values for the battery cells. A battery cell with worse health has lower available capacity than a battery cell with better health, and therefore its SOC value is adjusted to a smaller value when SOH or capacity estimation functionality is activated. Because of this mechanism and the control principle of the presented SOC balancing controller, the system draws energy at a slower rate from the battery cell(s) with lower SOH and draws energy at a faster rate from the battery cell(s) with higher SOH such that all battery cells reach their end of discharge at the same time. This results in extending operation time of the system, makes best use of energy from every battery cell, and avoids over-discharging battery cells with lower SOH.

This article presents a hierarchical state-of-charge (SOC) balancing control method for a battery energy storage system. In the presented system, multiple battery cells are connected in-parallel at the inputs of a single-inductor multiinput single output (SI-MISO) power converter to form a battery module and multiple battery modules are connected in series at the output to form the complete battery system or pack. The presented hierarchical controller is able to control the SOC (achieve SOC balancing) at both the cell level and the module level (between all cells that are effectively connected in series and in-parallel) while simultaneously maintaining bus voltage regulation at the battery system or pack output. This is achieved by using the presented modulation scheme for the duty cycles of the power converter switches. Theoretical analysis and results from a proof-of-concept experimental prototype are presented and discussed in this article in order to evaluate and validate the operation of hierarchical SOC balancing controller and battery system.

This paper presents a study on Lithium-ion battery aging behaviors/patterns and related State-of-Health (SOH) indicators before presenting the development of data-driven based SOH estimators. Battery charge/discharge cycling experiments are conducted in order to obtain needed data for this work. The battery ageing behavior patterns until the battery cell reaches highly deteriorated health conditions are investigated and characterized in this paper by analyzing the aggregated battery ageing data. The observed battery ageing behavior patterns include: (1) the rate at which the battery voltage decreases during discharging increases as the battery ages, (2) the speed at which the battery terminal voltage increases during Constant Current (CC) charging increases as the battery's health deteriorates, (3) the time period for CC charging operation decreases as the battery ages, (4) the rate at which the battery current decreases during Constant Voltage (CV) charging increases as the battery ages, and (5) the speed at which the battery temperature drops during CV charging increases as the battery ages. Corresponding SOH indicators are developed to quantify these battery ageing behavior patterns for the development of SOH estimators. Deep Neural Network (DNN) is utilized to extract and model the nonlinear and complex correlation between the defined SOH indicators and SOH values of the Lithium-ion battery. Multiple DNN-based SOH estimators are developed in this paper. The SOH estimation results from different DNN-based SOH estimators indicate that the diversity of SOH indicators used for the development of SOH estimator can substantially improve the estimation performance.

This study presents a distributed photovoltaic (PV) solar system architecture with a single‐power inductor, single‐power converter and single maximum power point tracking (MPPT) controller that only requires one sensor. This PV architecture is able to perform MPPT for a multichannel distributed PV system at panel level, cell group level and/or single cell level under mismatching and partial shading conditions. The single power stage is a multiple‐input single‐inductor boost power converter which operates in continuous conduction mode. This work targets the high‐cost and high‐complexity issues of module integrated converter (MIC) and sub‐MIC PV architectures, which affect their practicality in many applications. The concept and operation of the single‐power‐converter single‐controller single‐sensor MPPT architecture is presented, analysed and verified by results obtained from a proof‐of‐concept experimental prototype.

This study presents a bi‐directional single‐inductor multi‐input single‐output battery system with state‐of‐charge (SOC) balancing controller. In the discharging operation mode of the battery system, the SOC balancing between multiple batteries is achieved by adaptively modulating the multipliers of the discharging rate of each battery cell, while maintaining the output voltage regulated at the desired value by modulating the average value of duty cycle of the power converter. The SOC balancing is achieved naturally during the operation of the system without extra energy dissipation or charge transfer between the batteries. In the charging operation mode, each battery can be charged independently by using the same bi‐directional power stage. The convergence ability of the SOC balancing controller is discussed and results obtained from a proof of concept experimental prototype are presented and discussed in order to evaluate and validate the operation of the controller and system.

Single-inductor multiple-output (SIMO) switching DC-DC power converter architecture is a cost-effective alternative to multiple individual switching power converters solution in many power distribution system applications where multiple voltage rails are required at reduced cost. However, with multiple output voltage rails coupled to the same switching node, the SIMO power converters suffer from cross regulation between the multiple outputs, which complicates the closed-loop controller design of the SIMO converter and degrades regulation performance. In this paper, a power-multiplexed control (PM control) scheme is proposed aiming to completely decouple the operations of multiple outputs from one another. The proposed PM control scheme results in eliminating the cross regulation among the multiple outputs while maintaining desired voltage regulation performance for each output under both steady-state and dynamic operations. Low-cost microcontroller or analog circuitries can be used to implement the proposed controller. Experimental proof of concept prototype results verify the feasibility and advantages of the proposed controller.