Manish Kumar Saini

In this study, a novel regularized box particle filter (RBPF) is proposed for real-time state of health (SoH) estimation of the lithium-ion battery pack of electrical vehicles (EVs). RBPF uses the boxes instead of particles used in the conventional sequential importance re-sampling particle filter (SIR-PF). The boxes cover vast sample space as compared to the particles. Therefore, a very small number of boxes are required to cover the entire sample space as compared to SIR-PF. The next step is to remove particle impoverishment and degeneracy which is addressed by the regularization step in re-sampling of RBPF. Initially, a hardware setup is used for assessment of internal resistance of the battery which is used in establishment of linear regression model (LRM) of the battery. Initially, performance of the individual batteries in a battery pack is evaluated. Later on, algorithms are tested on different series-parallel combinations of batteries to evaluate the impact of different arrangements and numbers of batteries on the SoH estimation of proposed algorithm. The performance indices of RBPF are compared with SIR-PF and BPF algorithms in terms of root mean square error (RMSE), peak error and computational time for SoH assessment of the lithium-ion battery pack. RBPF shows better accuracy and computational time as compared to SIR-PF and box particle filter (BPF). Further, a detailed study is carried out for optimization of the size of boxes and the number of boxes for SoH estimation of batteries.

In this article, a real‐time novel adaptive deep neural network (A‐DNN) charging scheme is proposed which increases the life of the batteries by controlling the heating impact inside the battery. The input variables used in the charging algorithm are state of charge (SoC), state of health (SoH), voltage (V), current (I), and temperature (T) which makes the algorithm adaptive toward the temperature deviation and reduces the peak overshoot of the temperature at different SoH of the batteries. The parameters of the battery 1‐RC model are estimated by the forgetting factor recursive least square (FF‐RLS) method. The SoC and SoH are estimated by the dual‐particle filter (D‐PF) algorithm. Furthermore, a DNN balancing mechanism sensitive to SoC and SoH is developed to avoid the fault in the battery during the charging process. The A‐DNN charging algorithm is compared with the constant current constant voltage (CC‐CV), constant current pulse charging (CC‐PC), and deep neural network (DNN) charging algorithms at 40°C, 45°C, and 50°C. The A‐DNN outperforms in terms of peak temperature, incremental life, and charging time of the batteries at 45°C. The proposed charging methodology reduces the economic cost of the EVs by increasing the life of the battery by 34.41% at 45°C as compared to the other algorithms.

In this paper, we advocate for using PRNGs to generate long sequences of statistically random numbers, also known as pseudorandom numbers. It has several practical applications, including cryptography, weather-based stimulation, procedural environment noise generation in games and casinos, and scientific simulations of real-world problems, like city traffic systems that use it to stimulate cars. One distinctive feature of our approach is the PRNG algorithm we propose; this algorithm has successfully produced statistically sound long-term sequences. The properties of the PRNG are assessed using several statistical methods. Our work presents the Oscillating Random Number Algorithm (ORNA), a new kind of random number generator, and examines both the statistical tests proposed by NIST and the results that ORNA has generated. The following also describes the several preceding algorithms developed, including the Mersenne Twister Algorithm (MT19937) and the Permuted Congruential Algorithm (PCG128), and compares ORNA’s graphical efficiency to theirs. In this work, we compared ORNA to MT19937 and PCG128, and found that ORNA excels according to all 16 statistical tests. ORNA has three primary benefits, namely, superior statistical performance, quicker code execution, and simpler code implementation due to the algorithm’s reduced complexity.

1. Lithium-ion batteries are crucial for modern portable energy needs due to their high energy density and long life cycles (Hannan et al. 2018). Efficient BMS is essential to address challenges, c...

– Finding the reasons responsible behind PQ disturbances is as much important as detection of various inconspicuous PQ disturbances to have timely and accurate mitigation. Therefore, this paper proposes a robust solution for detection and classification of different voltage sag causes. For efficient feature extraction, a novel method is proposed for designing of wavelet using vector-quantised signal information to instil signal information into the wavelet. Multiresolution analysis of voltage signals is carried out to decompose voltage signals to multiple scales. In this way, sag-related information is more effectively captured and utilised in classification of voltage sag signals into one of the classes of sag causes. Probabilistic neural network is trained and tested using five-fold cross-validation on the data simulated in MATLAB/Simulink. Another challenge in PQ analysis, i.e. noisy data, is also addressed here by considering noise of 30dB in voltage sag signals. Quantitative evaluation of classifier performance using two measures, such as classification rate and false alarm rate, proves the proposed method efficient for voltage sag detection and classification.