# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 1 Introduction  
The world has made great efforts to protect the environment [1, 2]. Many countries have signed agreements, such as the Kyoto Protocol and the Paris Climate Agreement, to reduce carbon dioxide emissions, which account for 77% of greenhouse gases [3]. The Paris climate agreement aims to reduce carbon emissions by 37% by 2030 [4]. Transport currently accounts for nearly a quarter of global direct CO\({}_{2}\) emissions from fuel combustion, with nearly three-quarters of those emissions coming from road vehicles using the internal combustion engine (ICE) [5]. To reduce this share, the most promising way is to replace conventional cars with electric vehicles (EVs) [6]. However, greenhouse gas emissions are produced by EVs in the process of supplying electricity. This will effectively be transferred from where they are used to where the electricity is produced, namely power plants. This has the benefit that electric vehicles will not emit any harmful substances in crowded urban centers; thus, renewable energy-driven EVs are much more energy efficient and clean. Therefore, solar and wind power have recently become the world's most popular power sources [7, 8]. To generate electricity from the wind, expensive and large-scale infrastructure in the form of wind turbines is required. As a result, charging electric vehicles to generate energy through specific solar photovoltaic (PV) systems is becoming a more general solution [9]. Specifically, PV panels can be installed on the roof of a vehicle [8], or PV exports can be used to charge electric vehicle batteries [6]. Moreover, the experimental results indicate that sufficient PV panels are able to meet the power demand of the complete battery charging process of EVs [10]. Based on this, the geometric optimization of the overall space, energy evaluation, and component lightweight of the prototype PV system suitable for EVs have been studied by some scholars [11, 12]. Solar PV-powered electric vehicles are currently a technicallyfeasible and increasingly economically attractive option in the transport sector in most countries [5]. Specifically, the use of dedicated solar PV charging systems will further reduce CO2 emissions from road transport. For instance, PV roofs can save up to 14 g CO2/km, and it has also been highlighted that the wide implementation of this technology could bring an annual reduction of 250,000 tons of CO2 emissions in the EU-28 [13]. Furthermore, additional benefits may include reduced local grid overloads and increased grid flexibility [5]. However, everything has two sides. PV systems have significant advantages, such as abundant fuel, free selection of materials, independent process, environmental protection, quietness, long life, and low maintenance costs [14; 15; 16; 17]. However, the widely used PV systems have a fatal shortcoming of low efficiency. The effective way to solve this problem is to track the maximum power point (MPPT) during the power generation process. Therefore, an MPP controller is needed to improve the efficiency of PV systems. [15; 16; 17]. To date, existing studies have, accordingly, explored the optimal artificial neural networks (ANNs) and fuzzy and heuristics controls, e.g., perturb and observe (P&O) [18], backpropagation (BP) [19], fuzzy logic (FL) [20], and genetic algorithms (GAs) [21], etc., methods. These studies, collectively, outline a critical role for MPPT, namely controlling the MPP online via training the model with an artificially designed structure. Nevertheless, since the control variables and model parameters are required to adjust frequently, three main challenges still exist in these experiments in terms of apparent oscillation at the MPP, insufficient accuracy, and slow convergence. In detail, these three challenges can hardly reach the maximum output power and commonly generate transient jumps during long-period control. Hence, to address these issues, this paper proposes a wavelet-improved fuzzy logic MPP control model, namely W-IFL.