Controlling Algorithm of Reconfigurable Battery for State of Charge Balancing Using Amortized Q-Learning

Abstract

In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As an RL algorithm, Amortized Q-learning (AQL) is implemented, which enables the control of enormous numbers of possible configurations of the reconfigurable battery as well as the combination of classical controlling approaches and machine learning methods. This enhances the safety mechanisms during control. As a neural network of the AQL, a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing process compared to a conventional approach. Nevertheless, AQL shows great potential for multiobjective optimizations and can be applied as an RL algorithm for control in power electronics.

A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions

“…Knowing the voltage values of individual cells, it is able to display the current state of charge of the entire SoC package (State of Charge) to the driver. Moreover, it is able to manage the balancing process of the entire package [44]. Battery voltage balancing involves balancing the charge level of individual cells connected in series or parallel, depending on the design [45].…”

Simulation and Testing of Self-Reconfigurable Battery Advanced Functions for Automotive Application

“…As shown in many articles [1], battery-switching technologies of Self-Reconfigurable Batteries (SRBs) promise significant improvements in terms of autonomy and battery life, cell balancing [2][3][4][5][6][7], recharging capacity [8][9][10][11], improving the efficiency of vehicle drive trains [12][13][14] and even cell aging [15][16][17]. Their operating principle is as follows: switches are added to the power paths linking the cells to enable the number of active stages in series and parallel to be modulated dynamically [18], depending on the type of SRB.…”