Under partial shading conditions, multiple peaks are observed in the power-voltage (P- V) characteristic curve of a photovoltaic (PV) array, and the conventional maximum power point tracking (MPPT) algorithms may fail to track the global maximum power point (GMPP). Therefore, this paper proposes a modified incremental conductance (Inc Cond) algorithm that is able to track the GMPP under partial shading conditions and load variation. A novel algorithm is introduced to modulate the duty cycle of the dc-dc converter in order to ensure fast MPPT process. Simulation and hardware implementation are carried out to evaluate the effectiveness of the proposed algorithm under partial shading and load variation. The results show that the proposed algorithm is able to track the GMPP accurately under different types of partial shading conditions, and the response during variation of load and solar irradiation are faster than the conventional Inc Cond algorithm. Hence, the effectiveness of the proposed algorithm under partial shading condition and load variation is validated in this paper.

Photovoltaic arrays subject to partial shading conditions have more than one maximum power point (MPP), and conventional algorithms are unable to track the global maximum power point (GMPP) accurately. Thus, an improved global search space differential evolution algorithm for tracking the GMPP is introduced in this paper. The main contribution of the proposed algorithm are: a) capability in tracking GMPP and faster respond against load variation, b) optimization algorithm can search for the GMPP within a larger operating region as it is implemented by using a single-ended primary-inductor converter, c) easy tuning as less parameter has to be set in the algorithm. The proposed system is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the GMPP within 2 s with an accuracy of 99% and respond to load variation within 0.1 s.

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

With the increasing demand for Lithium-ion batteries in an electric vehicle (EV), it is always crucial to develop a highly accurate and low-cost state estimation method for the battery management system (BMS). Presently, the dual extended Kalman filter (DEKF) is extensively utilized for online SOC estimation. However, the problem of battery model parameter divergence from the true value greatly affects the estimation accuracy under realistic dynamic loading conditions. In this paper, the new dual forgetting factor-based adaptive extended Kalman filter (DFFAEKF) is proposed for SOC estimation. The proposed SOC estimation method is combined with the simple SOE estimation approach to develop the combined SOC and SOE estimation method. The quantitative relationships between SOC and SOE for all the test battery cells, which are established with the experimental data collected from different constant current discharge profiles are employed for SOE estimation. To evaluate the performance of the developed combined SOC and SOE estimation method, the three different chemistries battery cells are chosen for the testing under different dynamic loading profiles such as dynamic stress test (DST) and US06 drive cycle. For all the considered test battery cells, the experimental results indicated that the combined SOC and SOE estimation method using the proposed DFFAEKF can estimate the battery states under dynamic operating conditions with root mean square error (RMSE) less than 0.85% and 0.95% respectively. The proposed method also demonstrates fast convergence to its true value under erroneous initial conditions. Additionally, the order of worst-case big O notation complexity of the proposed DFFAEKF is equivalent to DEKF. Besides, the simplicity of the proposed method also supports to reduce the computational burden of the processor used in BMSs, and therefore it is well-suited for EV applications.