In electric vehicles (EVs), battery management systems (BMS) carry out various functions for effective utilization of stored energy in lithium-ion batteries (LIBs). Among numerous functions performed by the BMS, estimating the state of health (SOH) is an essential and challenging task to be accomplished at regular intervals. Accurate estimation of SOH ensures battery reliability by computing remaining lifetime and forecasting its failure conditions to avoid battery risk. Accurate estimation of SOH is challenging, due to uncertain operating conditions of EVs and complex non-linear electrochemical characteristics demonstrated by LIBs. In most of the existing studies, standard charge/discharge patterns with numerous assumptions are considered to accelerate the battery ageing process. However, such patterns and assumptions fail to reflect the real world operating condition of EV batteries, which is not appropriate for BMS of EVs. In contrast, this research work proposes a unique SOH estimation approach, using an independently recurrent neural network (IndRNN) in a more realistic manner by adopting the dynamic load profile condition of EVs. This research work illustrates a deep learning-based data-driven approach to estimate SOH by analyzing their historical data collected from LIBs. The IndRNN is adapted due to its ability to capture complex non-linear characteristics of batteries by eliminating the gradient problem and allowing the neural network to learn long-term dependencies among the capacity degradations. Experimental results indicate that the IndRNN based model is able to predict a battery’s SOH accurately with root mean square error (RMSE) reduced to 1.33% and mean absolute error (MAE) reduced to 1.14%. The maximum error (MAX) produced by IndRNN throughout the testing process is 2.5943% which is well below the acceptable SOH error range of ±5% for EVs. In addition, to demonstrate effectiveness of the IndRNN attained results are compared with other well-known recurrent neural network (RNN) architectures such as long short-term memory (LSTM) and gated recurrent unit (GRU). From the comparison of results, it is clearly evident that IndRNN outperformed other RNN architectures with the highest SOH accuracy rate.

Among the renewable energy applications, the most popular inverters are cascaded multilevel inverters. Irrespective of numerous benefits these inverters face reliability issues due to the presence of more circuit components in the design. This has been a critical challenge for researchers in designing inverters with enhanced reliability by reducing the total harmonic distortion (THD). This paper proposes a 31-level asymmetric cascaded multilevel inverter for renewable energy applications. The proposed topology produces waveforms consisting of the staircase with a high number of output levels with lesser components with low THD. The investigations on the feasibility and performance of MLI under steady-state, transient, and dynamic load disturbances. The results are validated from a 1.6kW system which provides the proposed inverter.INDEX TERMS Multilevel inverter (MLI), total harmonic distraction (THD), staircase modulation technique.

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Demand response modelling have paved an important role in smart grid at a greater perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal strategy at the user's side with an effective pricing scheme. In this proposed work, the entire model is done in three different steps. The first step develops strategy patterns for the users considering integration of renewable energy and effective demand response analysis is done. The second step in the process exhibits the learning process of the consumers using Robust Adversarial Reinforcement Learning for privacy process among the users. The third step develops optimal strategy plan for the users for maintaining privacy among the users. Considering the uncertainties of the user's behavioral patterns, typical pricing schemes are involved with integration of renewable energy at the user' side so that an optimal strategy is obtained. The optimal strategy for scheduling the appliances solving privacy issues and considering renewable energy at user' side is done using Robust Adversarial Reinforcement learning and Gradient Based Nikaido-Isoda Function which gives an optimal accuracy. The results of the proposed work exhibit optimal strategy plan for the users developing proper learning paradigm. The effectiveness of the proposed work with mathematical modelling are validated using real time data and shows the demand response strategy plan with proper learning access model. The results obtained among the set of strategy develops 80 % of the patterns created with the learning paradigm moves with optimal DR scheduling patterns. This work embarks the best learning DR pattern created for the future set of consumers following the strategy so privacy among the users can be maintained effectively.INDEX TERMS Demand response, best strategy, robust adversarial reinforcement learning, renewable energy. NOMENCLATURE n i S -Strategy for set of users In -Incentives announced () n  -cost function for the model and   -Best policy parameters for learning , tt hl -reward and incentive function in learning i   -Policy strategy ( , ) n v  -Optimization function for pricing and cost analysis RL-Reinforcement Learning ADP -Approximate Dynamic Programming RARL -Robust Adversarial Reinforcement learning GNI -Gradient Based Nikaido -Isoda Function

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.